Quantitative monitoring of sucrose, reducing sugar and total sugar dynamics for phenotyping of water-deficit stress tolerance in rice through spectroscopy and chemometrics

NASA Astrophysics Data System (ADS)

Das, Bappa; Sahoo, Rabi N.; Pargal, Sourabh; Krishna, Gopal; Verma, Rakesh; Chinnusamy, Viswanathan; Sehgal, Vinay K.; Gupta, Vinod K.; Dash, Sushanta K.; Swain, Padmini

2018-03-01

In the present investigation, the changes in sucrose, reducing and total sugar content due to water-deficit stress in rice leaves were modeled using visible, near infrared (VNIR) and shortwave infrared (SWIR) spectroscopy. The objectives of the study were to identify the best vegetation indices and suitable multivariate technique based on precise analysis of hyperspectral data (350 to 2500 nm) and sucrose, reducing sugar and total sugar content measured at different stress levels from 16 different rice genotypes. Spectral data analysis was done to identify suitable spectral indices and models for sucrose estimation. Novel spectral indices in near infrared (NIR) range viz. ratio spectral index (RSI) and normalised difference spectral indices (NDSI) sensitive to sucrose, reducing sugar and total sugar content were identified which were subsequently calibrated and validated. The RSI and NDSI models had R2 values of 0.65, 0.71 and 0.67; RPD values of 1.68, 1.95 and 1.66 for sucrose, reducing sugar and total sugar, respectively for validation dataset. Different multivariate spectral models such as artificial neural network (ANN), multivariate adaptive regression splines (MARS), multiple linear regression (MLR), partial least square regression (PLSR), random forest regression (RFR) and support vector machine regression (SVMR) were also evaluated. The best performing multivariate models for sucrose, reducing sugars and total sugars were found to be, MARS, ANN and MARS, respectively with respect to RPD values of 2.08, 2.44, and 1.93. Results indicated that VNIR and SWIR spectroscopy combined with multivariate calibration can be used as a reliable alternative to conventional methods for measurement of sucrose, reducing sugars and total sugars of rice under water-deficit stress as this technique is fast, economic, and noninvasive.

A High-Dimensional, Multivariate Copula Approach to Modeling Multivariate Agricultural Price Relationships and Tail Dependencies

Treesearch

Xuan Chi; Barry Goodwin

2012-01-01

Spatial and temporal relationships among agricultural prices have been an important topic of applied research for many years. Such research is used to investigate the performance of markets and to examine linkages up and down the marketing chain. This research has empirically evaluated price linkages by using correlation and regression models and, later, linear and...

A New Predictive Model of Centerline Segregation in Continuous Cast Steel Slabs by Using Multivariate Adaptive Regression Splines Approach

PubMed Central

García Nieto, Paulino José; González Suárez, Victor Manuel; Álvarez Antón, Juan Carlos; Mayo Bayón, Ricardo; Sirgo Blanco, José Ángel; Díaz Fernández, Ana María

2015-01-01

The aim of this study was to obtain a predictive model able to perform an early detection of central segregation severity in continuous cast steel slabs. Segregation in steel cast products is an internal defect that can be very harmful when slabs are rolled in heavy plate mills. In this research work, the central segregation was studied with success using the data mining methodology based on multivariate adaptive regression splines (MARS) technique. For this purpose, the most important physical-chemical parameters are considered. The results of the present study are two-fold. In the first place, the significance of each physical-chemical variable on the segregation is presented through the model. Second, a model for forecasting segregation is obtained. Regression with optimal hyperparameters was performed and coefficients of determination equal to 0.93 for continuity factor estimation and 0.95 for average width were obtained when the MARS technique was applied to the experimental dataset, respectively. The agreement between experimental data and the model confirmed the good performance of the latter.

Dynamic Web Pages: Performance Impact on Web Servers.

ERIC Educational Resources Information Center

Kothari, Bhupesh; Claypool, Mark

2001-01-01

Discussion of Web servers and requests for dynamic pages focuses on experimentally measuring and analyzing the performance of the three dynamic Web page generation technologies: CGI, FastCGI, and Servlets. Develops a multivariate linear regression model and predicts Web server performance under some typical dynamic requests. (Author/LRW)

Multivariate research in areas of phosphorus cast-iron brake shoes manufacturing using the statistical analysis and the multiple regression equations

NASA Astrophysics Data System (ADS)

Kiss, I.; Cioată, V. G.; Alexa, V.; Raţiu, S. A.

2017-05-01

The braking system is one of the most important and complex subsystems of railway vehicles, especially when it comes for safety. Therefore, installing efficient safe brakes on the modern railway vehicles is essential. Nowadays is devoted attention to solving problems connected with using high performance brake materials and its impact on thermal and mechanical loading of railway wheels. The main factor that influences the selection of a friction material for railway applications is the performance criterion, due to the interaction between the brake block and the wheel produce complex thermos-mechanical phenomena. In this work, the investigated subjects are the cast-iron brake shoes, which are still widely used on freight wagons. Therefore, the cast-iron brake shoes - with lamellar graphite and with a high content of phosphorus (0.8-1.1%) - need a special investigation. In order to establish the optimal condition for the cast-iron brake shoes we proposed a mathematical modelling study by using the statistical analysis and multiple regression equations. Multivariate research is important in areas of cast-iron brake shoes manufacturing, because many variables interact with each other simultaneously. Multivariate visualization comes to the fore when researchers have difficulties in comprehending many dimensions at one time. Technological data (hardness and chemical composition) obtained from cast-iron brake shoes were used for this purpose. In order to settle the multiple correlation between the hardness of the cast-iron brake shoes, and the chemical compositions elements several model of regression equation types has been proposed. Because a three-dimensional surface with variables on three axes is a common way to illustrate multivariate data, in which the maximum and minimum values are easily highlighted, we plotted graphical representation of the regression equations in order to explain interaction of the variables and locate the optimal level of each variable for maximal response. For the calculation of the regression coefficients, dispersion and correlation coefficients, the software Matlab was used.

Membrane Introduction Mass Spectrometry Combined with an Orthogonal Partial-Least Squares Calibration Model for Mixture Analysis.

PubMed

Li, Min; Zhang, Lu; Yao, Xiaolong; Jiang, Xingyu

2017-01-01

The emerging membrane introduction mass spectrometry technique has been successfully used to detect benzene, toluene, ethyl benzene and xylene (BTEX), while overlapped spectra have unfortunately hindered its further application to the analysis of mixtures. Multivariate calibration, an efficient method to analyze mixtures, has been widely applied. In this paper, we compared univariate and multivariate analyses for quantification of the individual components of mixture samples. The results showed that the univariate analysis creates poor models with regression coefficients of 0.912, 0.867, 0.440 and 0.351 for BTEX, respectively. For multivariate analysis, a comparison to the partial-least squares (PLS) model shows that the orthogonal partial-least squares (OPLS) regression exhibits an optimal performance with regression coefficients of 0.995, 0.999, 0.980 and 0.976, favorable calibration parameters (RMSEC and RMSECV) and a favorable validation parameter (RMSEP). Furthermore, the OPLS exhibits a good recovery of 73.86 - 122.20% and relative standard deviation (RSD) of the repeatability of 1.14 - 4.87%. Thus, MIMS coupled with the OPLS regression provides an optimal approach for a quantitative BTEX mixture analysis in monitoring and predicting water pollution.

Multivariate decoding of brain images using ordinal regression.

PubMed

Doyle, O M; Ashburner, J; Zelaya, F O; Williams, S C R; Mehta, M A; Marquand, A F

2013-11-01

Neuroimaging data are increasingly being used to predict potential outcomes or groupings, such as clinical severity, drug dose response, and transitional illness states. In these examples, the variable (target) we want to predict is ordinal in nature. Conventional classification schemes assume that the targets are nominal and hence ignore their ranked nature, whereas parametric and/or non-parametric regression models enforce a metric notion of distance between classes. Here, we propose a novel, alternative multivariate approach that overcomes these limitations - whole brain probabilistic ordinal regression using a Gaussian process framework. We applied this technique to two data sets of pharmacological neuroimaging data from healthy volunteers. The first study was designed to investigate the effect of ketamine on brain activity and its subsequent modulation with two compounds - lamotrigine and risperidone. The second study investigates the effect of scopolamine on cerebral blood flow and its modulation using donepezil. We compared ordinal regression to multi-class classification schemes and metric regression. Considering the modulation of ketamine with lamotrigine, we found that ordinal regression significantly outperformed multi-class classification and metric regression in terms of accuracy and mean absolute error. However, for risperidone ordinal regression significantly outperformed metric regression but performed similarly to multi-class classification both in terms of accuracy and mean absolute error. For the scopolamine data set, ordinal regression was found to outperform both multi-class and metric regression techniques considering the regional cerebral blood flow in the anterior cingulate cortex. Ordinal regression was thus the only method that performed well in all cases. Our results indicate the potential of an ordinal regression approach for neuroimaging data while providing a fully probabilistic framework with elegant approaches for model selection. Copyright © 2013. Published by Elsevier Inc.

Improved estimation of PM2.5 using Lagrangian satellite-measured aerosol optical depth

NASA Astrophysics Data System (ADS)

Olivas Saunders, Rolando

Suspended particulate matter (aerosols) with aerodynamic diameters less than 2.5 mum (PM2.5) has negative effects on human health, plays an important role in climate change and also causes the corrosion of structures by acid deposition. Accurate estimates of PM2.5 concentrations are thus relevant in air quality, epidemiology, cloud microphysics and climate forcing studies. Aerosol optical depth (AOD) retrieved by the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instrument has been used as an empirical predictor to estimate ground-level concentrations of PM2.5 . These estimates usually have large uncertainties and errors. The main objective of this work is to assess the value of using upwind (Lagrangian) MODIS-AOD as predictors in empirical models of PM2.5. The upwind locations of the Lagrangian AOD were estimated using modeled backward air trajectories. Since the specification of an arrival elevation is somewhat arbitrary, trajectories were calculated to arrive at four different elevations at ten measurement sites within the continental United States. A systematic examination revealed trajectory model calculations to be sensitive to starting elevation. With a 500 m difference in starting elevation, the 48-hr mean horizontal separation of trajectory endpoints was 326 km. When the difference in starting elevation was doubled and tripled to 1000 m and 1500m, the mean horizontal separation of trajectory endpoints approximately doubled and tripled to 627 km and 886 km, respectively. A seasonal dependence of this sensitivity was also found: the smallest mean horizontal separation of trajectory endpoints was exhibited during the summer and the largest separations during the winter. A daily average AOD product was generated and coupled to the trajectory model in order to determine AOD values upwind of the measurement sites during the period 2003-2007. Empirical models that included in situ AOD and upwind AOD as predictors of PM2.5 were generated by multivariate linear regressions using the least squares method. The multivariate models showed improved performance over the single variable regression (PM2.5 and in situ AOD) models. The statistical significance of the improvement of the multivariate models over the single variable regression models was tested using the extra sum of squares principle. In many cases, even when the R-squared was high for the multivariate models, the improvement over the single models was not statistically significant. The R-squared of these multivariate models varied with respect to seasons, with the best performance occurring during the summer months. A set of seasonal categorical variables was included in the regressions to exploit this variability. The multivariate regression models that included these categorical seasonal variables performed better than the models that didn't account for seasonal variability. Furthermore, 71% of these regressions exhibited improvement over the single variable models that was statistically significant at a 95% confidence level.

The combination of ovarian volume and outline has better diagnostic accuracy than prostate-specific antigen (PSA) concentrations in women with polycystic ovarian syndrome (PCOs).

PubMed

Bili, Eleni; Bili, Authors Eleni; Dampala, Kaliopi; Iakovou, Ioannis; Tsolakidis, Dimitrios; Giannakou, Anastasia; Tarlatzis, Basil C

2014-08-01

The aim of this study was to determine the performance of prostate specific antigen (PSA) and ultrasound parameters, such as ovarian volume and outline, in the diagnosis of polycystic ovary syndrome (PCOS). This prospective, observational, case-controlled study included 43 women with PCOS, and 40 controls. Between day 3 and 5 of the menstrual cycle, fasting serum samples were collected and transvaginal ultrasound was performed. The diagnostic performance of each parameter [total PSA (tPSA), total-to-free PSA ratio (tPSA:fPSA), ovarian volume, ovarian outline] was estimated by means of receiver operating characteristic (ROC) analysis, along with area under the curve (AUC), threshold, sensitivity, specificity as well as positive (+) and negative (-) likelihood ratios (LRs). Multivariate logistical regression models, using ovarian volume and ovarian outline, were constructed. The tPSA and tPSA:fPSA ratio resulted in AUC of 0.74 and 0.70, respectively, with moderate specificity/sensitivity and insufficient LR+/- values. In the multivariate logistic regression model, the combination of ovarian volume and outline had a sensitivity of 97.7% and a specificity of 97.5% in the diagnosis of PCOS, with +LR and -LR values of 39.1 and 0.02, respectively. In women with PCOS, tPSA and tPSA:fPSA ratio have similar diagnostic performance. The use of a multivariate logistic regression model, incorporating ovarian volume and outline, offers very good diagnostic accuracy in distinguishing women with PCOS patients from controls. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Nonlinear multivariate and time series analysis by neural network methods

NASA Astrophysics Data System (ADS)

Hsieh, William W.

2004-03-01

Methods in multivariate statistical analysis are essential for working with large amounts of geophysical data, data from observational arrays, from satellites, or from numerical model output. In classical multivariate statistical analysis, there is a hierarchy of methods, starting with linear regression at the base, followed by principal component analysis (PCA) and finally canonical correlation analysis (CCA). A multivariate time series method, the singular spectrum analysis (SSA), has been a fruitful extension of the PCA technique. The common drawback of these classical methods is that only linear structures can be correctly extracted from the data. Since the late 1980s, neural network methods have become popular for performing nonlinear regression and classification. More recently, neural network methods have been extended to perform nonlinear PCA (NLPCA), nonlinear CCA (NLCCA), and nonlinear SSA (NLSSA). This paper presents a unified view of the NLPCA, NLCCA, and NLSSA techniques and their applications to various data sets of the atmosphere and the ocean (especially for the El Niño-Southern Oscillation and the stratospheric quasi-biennial oscillation). These data sets reveal that the linear methods are often too simplistic to describe real-world systems, with a tendency to scatter a single oscillatory phenomenon into numerous unphysical modes or higher harmonics, which can be largely alleviated in the new nonlinear paradigm.

Multivariate functional response regression, with application to fluorescence spectroscopy in a cervical pre-cancer study.

PubMed

Zhu, Hongxiao; Morris, Jeffrey S; Wei, Fengrong; Cox, Dennis D

2017-07-01

Many scientific studies measure different types of high-dimensional signals or images from the same subject, producing multivariate functional data. These functional measurements carry different types of information about the scientific process, and a joint analysis that integrates information across them may provide new insights into the underlying mechanism for the phenomenon under study. Motivated by fluorescence spectroscopy data in a cervical pre-cancer study, a multivariate functional response regression model is proposed, which treats multivariate functional observations as responses and a common set of covariates as predictors. This novel modeling framework simultaneously accounts for correlations between functional variables and potential multi-level structures in data that are induced by experimental design. The model is fitted by performing a two-stage linear transformation-a basis expansion to each functional variable followed by principal component analysis for the concatenated basis coefficients. This transformation effectively reduces the intra-and inter-function correlations and facilitates fast and convenient calculation. A fully Bayesian approach is adopted to sample the model parameters in the transformed space, and posterior inference is performed after inverse-transforming the regression coefficients back to the original data domain. The proposed approach produces functional tests that flag local regions on the functional effects, while controlling the overall experiment-wise error rate or false discovery rate. It also enables functional discriminant analysis through posterior predictive calculation. Analysis of the fluorescence spectroscopy data reveals local regions with differential expressions across the pre-cancer and normal samples. These regions may serve as biomarkers for prognosis and disease assessment.

The importance of extent of choroid plexus cauterization in addition to endoscopic third ventriculostomy for infantile hydrocephalus: a retrospective North American observational study using propensity score-adjusted analysis.

PubMed

Fallah, Aria; Weil, Alexander G; Juraschka, Kyle; Ibrahim, George M; Wang, Anthony C; Crevier, Louis; Tseng, Chi-Hong; Kulkarni, Abhaya V; Ragheb, John; Bhatia, Sanjiv

2017-12-01

OBJECTIVE Combined endoscopic third ventriculostomy (ETC) and choroid plexus cauterization (CPC)-ETV/CPC- is being investigated to increase the rate of shunt independence in infants with hydrocephalus. The degree of CPC necessary to achieve improved rates of shunt independence is currently unknown. METHODS Using data from a single-center, retrospective, observational cohort study involving patients who underwent ETV/CPC for treatment of infantile hydrocephalus, comparative statistical analyses were performed to detect a difference in need for subsequent CSF diversion procedure in patients undergoing partial CPC (describes unilateral CPC or bilateral CPC that only extended from the foramen of Monro [FM] to the atrium on one side) or subtotal CPC (describes CPC extending from the FM to the posterior temporal horn bilaterally) using a rigid neuroendoscope. Propensity scores for extent of CPC were calculated using age and etiology. Propensity scores were used to perform 1) case-matching comparisons and 2) Cox multivariable regression, adjusting for propensity score in the unmatched cohort. Cox multivariable regression adjusting for age and etiology, but not propensity score was also performed as a third statistical technique. RESULTS Eighty-four patients who underwent ETV/CPC had sufficient data to be included in the analysis. Subtotal CPC was performed in 58 patients (69%) and partial CPC in 26 (31%). The ETV/CPC success rates at 6 and 12 months, respectively, were 49% and 41% for patients undergoing subtotal CPC and 35% and 31% for those undergoing partial CPC. Cox multivariate regression in a 48-patient cohort case-matched by propensity score demonstrated no added effect of increased extent of CPC on ETV/CPC survival (HR 0.868, 95% CI 0.422-1.789, p = 0.702). Cox multivariate regression including all patients, with adjustment for propensity score, demonstrated no effect of extent of CPC on ETV/CPC survival (HR 0.845, 95% CI 0.462-1.548, p = 0.586). Cox multivariate regression including all patients, with adjustment for age and etiology, but not propensity score, demonstrated no effect of extent of CPC on ETV/CPC survival (HR 0.908, 95% CI 0.495-1.664, p = 0.755). CONCLUSIONS Using multiple comparative statistical analyses, no difference in need for subsequent CSF diversion procedure was detected between patients in this cohort who underwent partial versus subtotal CPC. Further investigation regarding whether there is truly no difference between partial versus subtotal extent of CPC in larger patient populations and whether further gain in CPC success can be achieved with complete CPC is warranted.

Is math anxiety in the secondary classroom limiting physics mastery? A study of math anxiety and physics performance

NASA Astrophysics Data System (ADS)

Mercer, Gary J.

This quantitative study examined the relationship between secondary students with math anxiety and physics performance in an inquiry-based constructivist classroom. The Revised Math Anxiety Rating Scale was used to evaluate math anxiety levels. The results were then compared to the performance on a physics standardized final examination. A simple correlation was performed, followed by a multivariate regression analysis to examine effects based on gender and prior math background. The correlation showed statistical significance between math anxiety and physics performance. The regression analysis showed statistical significance for math anxiety, physics performance, and prior math background, but did not show statistical significance for math anxiety, physics performance, and gender.

Bayesian Estimation of Multivariate Latent Regression Models: Gauss versus Laplace

ERIC Educational Resources Information Center

Culpepper, Steven Andrew; Park, Trevor

2017-01-01

A latent multivariate regression model is developed that employs a generalized asymmetric Laplace (GAL) prior distribution for regression coefficients. The model is designed for high-dimensional applications where an approximate sparsity condition is satisfied, such that many regression coefficients are near zero after accounting for all the model…

Effect of duration of denervation on outcomes of ansa-recurrent laryngeal nerve reinnervation.

PubMed

Li, Meng; Chen, Shicai; Wang, Wei; Chen, Donghui; Zhu, Minhui; Liu, Fei; Zhang, Caiyun; Li, Yan; Zheng, Hongliang

2014-08-01

To investigate the efficacy of laryngeal reinnervation with ansa cervicalis among unilateral vocal fold paralysis (UVFP) patients with different denervation durations. We retrospectively reviewed 349 consecutive UVFP cases of delayed ansa cervicalis to the recurrent laryngeal nerve (RLN) anastomosis. Potential influencing factors were analyzed in multivariable logistic regression analysis. Stratification analysis performed was aimed at one of the identified significant variables: denervation duration. Videostroboscopy, perceptual evaluation, acoustic analysis, maximum phonation time (MPT), and laryngeal electromyography (EMG) were performed preoperatively and postoperatively. Gender, age, preoperative EMG status and denervation duration were analyzed in multivariable logistic regression analysis. Stratification analysis was performed on denervation duration, which was divided into three groups according to the interval between RLN injury and reinnervation: group A, 6 to 12 months; group B, 12 to 24 months; and group C, > 24 months. Age, preoperative EMG, and denervation duration were identified as significant variables in multivariable logistic regression analysis. Stratification analysis on denervation duration showed significant differences between group A and C and between group B and C (P < 0.05)-but showed no significant difference between group A and B (P > 0.05) with regard to parameters overall grade, jitter, shimmer, noise-to-harmonics ratio, MPT, and postoperative EMG. In addition, videostroboscopic and laryngeal EMG data, perceptual and acoustic parameters, and MPT values were significantly improved postoperatively in each denervation duration group (P < 0.01). Although delayed laryngeal reinnervation is proved valid for UVFP, surgical outcome is better if the procedure is performed within 2 years after nerve injury than that over 2 years. © 2014 The American Laryngological, Rhinological and Otological Society, Inc.

Socioeconomic disparities in the utilization of mechanical thrombectomy for acute ischemic stroke in US hospitals.

PubMed

Brinjikji, W; Rabinstein, A A; McDonald, J S; Cloft, H J

2014-03-01

Previous studies have demonstrated that socioeconomic disparities in the treatment of cerebrovascular diseases exist. We studied a large administrative data base to study disparities in the utilization of mechanical thrombectomy for acute ischemic stroke. With the utilization of the Perspective data base, we studied disparities in mechanical thrombectomy utilization between patient race and insurance status in 1) all patients presenting with acute ischemic stroke and 2) patients presenting with acute ischemic stroke at centers that performed mechanical thrombectomy. We examined utilization rates of mechanical thrombectomy by race/ethnicity (white, black, and Hispanic) and insurance status (Medicare, Medicaid, self-pay, and private). Multivariate logistic regression analysis adjusting for potential confounding variables was performed to study the association between race/insurance status and mechanical thrombectomy utilization. The overall mechanical thrombectomy utilization rate was 0.15% (371/249,336); utilization rate at centers that performed mechanical thrombectomy was 1.0% (371/35,376). In the sample of all patients with acute ischemic stroke, multivariate logistic regression analysis demonstrated that uninsured patients had significantly lower odds of mechanical thrombectomy utilization compared with privately insured patients (OR = 0.52, 95% CI = 0.25-0.95, P = .03), as did Medicare patients (OR = 0.53, 95% CI = 0.41-0.70, P < .0001). Blacks had significantly lower odds of mechanical thrombectomy utilization compared with whites (OR = 0.35, 95% CI = 0.23-0.51, P < .0001). When considering only patients treated at centers performing mechanical thrombectomy, multivariate logistic regression analysis demonstrated that insurance was not associated with significant disparities in mechanical thrombectomy utilization; however, black patients had significantly lower odds of mechanical thrombectomy utilization compared with whites (OR = 0.41, 95% CI = 0.27-0.60, P < .0001). Significant socioeconomic disparities exist in the utilization of mechanical thrombectomy in the United States.

Multivariate Regression Analysis and Slaughter Livestock,

DTIC Science & Technology

AGRICULTURE, *ECONOMICS), (*MEAT, PRODUCTION), MULTIVARIATE ANALYSIS, REGRESSION ANALYSIS , ANIMALS, WEIGHT, COSTS, PREDICTIONS, STABILITY, MATHEMATICAL MODELS, STORAGE, BEEF, PORK, FOOD, STATISTICAL DATA, ACCURACY

Job stress models, depressive disorders and work performance of engineers in microelectronics industry.

PubMed

Chen, Sung-Wei; Wang, Po-Chuan; Hsin, Ping-Lung; Oates, Anthony; Sun, I-Wen; Liu, Shen-Ing

2011-01-01

Microelectronic engineers are considered valuable human capital contributing significantly toward economic development, but they may encounter stressful work conditions in the context of a globalized industry. The study aims at identifying risk factors of depressive disorders primarily based on job stress models, the Demand-Control-Support and Effort-Reward Imbalance models, and at evaluating whether depressive disorders impair work performance in microelectronics engineers in Taiwan. The case-control study was conducted among 678 microelectronics engineers, 452 controls and 226 cases with depressive disorders which were defined by a score 17 or more on the Beck Depression Inventory and a psychiatrist's diagnosis. The self-administered questionnaires included the Job Content Questionnaire, Effort-Reward Imbalance Questionnaire, demography, psychosocial factors, health behaviors and work performance. Hierarchical logistic regression was applied to identify risk factors of depressive disorders. Multivariate linear regressions were used to determine factors affecting work performance. By hierarchical logistic regression, risk factors of depressive disorders are high demands, low work social support, high effort/reward ratio and low frequency of physical exercise. Combining the two job stress models may have better predictive power for depressive disorders than adopting either model alone. Three multivariate linear regressions provide similar results indicating that depressive disorders are associated with impaired work performance in terms of absence, role limitation and social functioning limitation. The results may provide insight into the applicability of job stress models in a globalized high-tech industry considerably focused in non-Western countries, and the design of workplace preventive strategies for depressive disorders in Asian electronics engineering population.

Clinical management provided by board-certificated physiatrists in early rehabilitation is a significant determinant of functional improvement in acute stroke patients: a retrospective analysis of Japan rehabilitation database.

PubMed

Kinoshita, Shoji; Kakuda, Wataru; Momosaki, Ryo; Yamada, Naoki; Sugawara, Hidekazu; Watanabe, Shu; Abo, Masahiro

2015-05-01

Early rehabilitation for acute stroke patients is widely recommended. We tested the hypothesis that clinical outcome of stroke patients who receive early rehabilitation managed by board-certificated physiatrists (BCP) is generally better than that provided by other medical specialties. Data of stroke patients who underwent early rehabilitation in 19 acute hospitals between January 2005 and December 2013 were collected from the Japan Rehabilitation Database and analyzed retrospectively. Multivariate linear regression analysis using generalized estimating equations method was performed to assess the association between Functional Independence Measure (FIM) effectiveness and management provided by BCP in early rehabilitation. In addition, multivariate logistic regression analysis was also performed to assess the impact of management provided by BCP in acute phase on discharge destination. After setting the inclusion criteria, data of 3838 stroke patients were eligible for analysis. BCP provided early rehabilitation in 814 patients (21.2%). Both the duration of daily exercise time and the frequency of regular conferencing were significantly higher for patients managed by BCP than by other specialties. Although the mortality rate was not different, multivariate regression analysis showed that FIM effectiveness correlated significantly and positively with the management provided by BCP (coefficient, .35; 95% confidence interval [CI], .012-.059; P < .005). In addition, multivariate logistic analysis identified clinical management by BCP as a significant determinant of home discharge (odds ratio, 1.24; 95% CI, 1.08-1.44; P < .005). Our retrospective cohort study demonstrated that clinical management provided by BCP in early rehabilitation can lead to functional recovery of acute stroke. Copyright © 2015 National Stroke Association. Published by Elsevier Inc. All rights reserved.

Multivariate Analysis of Seismic Field Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Alam, M. Kathleen

1999-06-01

This report includes the details of the model building procedure and prediction of seismic field data. Principal Components Regression, a multivariate analysis technique, was used to model seismic data collected as two pieces of equipment were cycled on and off. Models built that included only the two pieces of equipment of interest had trouble predicting data containing signals not included in the model. Evidence for poor predictions came from the prediction curves as well as spectral F-ratio plots. Once the extraneous signals were included in the model, predictions improved dramatically. While Principal Components Regression performed well for the present datamore » sets, the present data analysis suggests further work will be needed to develop more robust modeling methods as the data become more complex.« less

Non-proportional odds multivariate logistic regression of ordinal family data.

PubMed

Zaloumis, Sophie G; Scurrah, Katrina J; Harrap, Stephen B; Ellis, Justine A; Gurrin, Lyle C

2015-03-01

Methods to examine whether genetic and/or environmental sources can account for the residual variation in ordinal family data usually assume proportional odds. However, standard software to fit the non-proportional odds model to ordinal family data is limited because the correlation structure of family data is more complex than for other types of clustered data. To perform these analyses we propose the non-proportional odds multivariate logistic regression model and take a simulation-based approach to model fitting using Markov chain Monte Carlo methods, such as partially collapsed Gibbs sampling and the Metropolis algorithm. We applied the proposed methodology to male pattern baldness data from the Victorian Family Heart Study. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Aging, not menopause, is associated with higher prevalence of hyperuricemia among older women.

PubMed

Krishnan, Eswar; Bennett, Mihoko; Chen, Linjun

2014-11-01

This work aims to study the associations, if any, of hyperuricemia, gout, and menopause status in the US population. Using multiyear data from the National Health and Nutrition Examination Survey, we performed unmatched comparisons and one to three age-matched comparisons of women aged 20 to 70 years with and without hyperuricemia (serum urate ≥6 mg/dL). Analyses were performed using survey-weighted multiple logistic regression and conditional logistic regression, respectively. Overall, there were 1,477 women with hyperuricemia. Age and serum urate were significantly correlated. In unmatched analyses (n = 9,573 controls), postmenopausal women were older, were heavier, and had higher prevalence of renal impairment, hypertension, diabetes, and hyperlipidemia. In multivariable regression, after accounting for age, body mass index, glomerular filtration rate, and diuretic use, menopause was associated with hyperuricemia (odds ratio, 1.36; 95% CI, 1.05-1.76; P = 0.002). In corresponding multivariable regression using age-matched data (n = 4,431 controls), the odds ratio for menopause was 0.94 (95% CI, 0.83-1.06). Current use of hormone therapy was not associated with prevalent hyperuricemia in both unmatched and matched analyses. Age is a better statistical explanation for the higher prevalence of hyperuricemia among older women than menopause status.

Esophageal wall dose-surface maps do not improve the predictive performance of a multivariable NTCP model for acute esophageal toxicity in advanced stage NSCLC patients treated with intensity-modulated (chemo-)radiotherapy.

PubMed

Dankers, Frank; Wijsman, Robin; Troost, Esther G C; Monshouwer, René; Bussink, Johan; Hoffmann, Aswin L

2017-05-07

In our previous work, a multivariable normal-tissue complication probability (NTCP) model for acute esophageal toxicity (AET) Grade  ⩾2 after highly conformal (chemo-)radiotherapy for non-small cell lung cancer (NSCLC) was developed using multivariable logistic regression analysis incorporating clinical parameters and mean esophageal dose (MED). Since the esophagus is a tubular organ, spatial information of the esophageal wall dose distribution may be important in predicting AET. We investigated whether the incorporation of esophageal wall dose-surface data with spatial information improves the predictive power of our established NTCP model. For 149 NSCLC patients treated with highly conformal radiation therapy esophageal wall dose-surface histograms (DSHs) and polar dose-surface maps (DSMs) were generated. DSMs were used to generate new DSHs and dose-length-histograms that incorporate spatial information of the dose-surface distribution. From these histograms dose parameters were derived and univariate logistic regression analysis showed that they correlated significantly with AET. Following our previous work, new multivariable NTCP models were developed using the most significant dose histogram parameters based on univariate analysis (19 in total). However, the 19 new models incorporating esophageal wall dose-surface data with spatial information did not show improved predictive performance (area under the curve, AUC range 0.79-0.84) over the established multivariable NTCP model based on conventional dose-volume data (AUC  =  0.84). For prediction of AET, based on the proposed multivariable statistical approach, spatial information of the esophageal wall dose distribution is of no added value and it is sufficient to only consider MED as a predictive dosimetric parameter.

Esophageal wall dose-surface maps do not improve the predictive performance of a multivariable NTCP model for acute esophageal toxicity in advanced stage NSCLC patients treated with intensity-modulated (chemo-)radiotherapy

NASA Astrophysics Data System (ADS)

Dankers, Frank; Wijsman, Robin; Troost, Esther G. C.; Monshouwer, René; Bussink, Johan; Hoffmann, Aswin L.

2017-05-01

In our previous work, a multivariable normal-tissue complication probability (NTCP) model for acute esophageal toxicity (AET) Grade  ⩾2 after highly conformal (chemo-)radiotherapy for non-small cell lung cancer (NSCLC) was developed using multivariable logistic regression analysis incorporating clinical parameters and mean esophageal dose (MED). Since the esophagus is a tubular organ, spatial information of the esophageal wall dose distribution may be important in predicting AET. We investigated whether the incorporation of esophageal wall dose-surface data with spatial information improves the predictive power of our established NTCP model. For 149 NSCLC patients treated with highly conformal radiation therapy esophageal wall dose-surface histograms (DSHs) and polar dose-surface maps (DSMs) were generated. DSMs were used to generate new DSHs and dose-length-histograms that incorporate spatial information of the dose-surface distribution. From these histograms dose parameters were derived and univariate logistic regression analysis showed that they correlated significantly with AET. Following our previous work, new multivariable NTCP models were developed using the most significant dose histogram parameters based on univariate analysis (19 in total). However, the 19 new models incorporating esophageal wall dose-surface data with spatial information did not show improved predictive performance (area under the curve, AUC range 0.79-0.84) over the established multivariable NTCP model based on conventional dose-volume data (AUC  =  0.84). For prediction of AET, based on the proposed multivariable statistical approach, spatial information of the esophageal wall dose distribution is of no added value and it is sufficient to only consider MED as a predictive dosimetric parameter.

Fast Detection of Copper Content in Rice by Laser-Induced Breakdown Spectroscopy with Uni- and Multivariate Analysis.

PubMed

Liu, Fei; Ye, Lanhan; Peng, Jiyu; Song, Kunlin; Shen, Tingting; Zhang, Chu; He, Yong

2018-02-27

Fast detection of heavy metals is very important for ensuring the quality and safety of crops. Laser-induced breakdown spectroscopy (LIBS), coupled with uni- and multivariate analysis, was applied for quantitative analysis of copper in three kinds of rice (Jiangsu rice, regular rice, and Simiao rice). For univariate analysis, three pre-processing methods were applied to reduce fluctuations, including background normalization, the internal standard method, and the standard normal variate (SNV). Linear regression models showed a strong correlation between spectral intensity and Cu content, with an R 2 more than 0.97. The limit of detection (LOD) was around 5 ppm, lower than the tolerance limit of copper in foods. For multivariate analysis, partial least squares regression (PLSR) showed its advantage in extracting effective information for prediction, and its sensitivity reached 1.95 ppm, while support vector machine regression (SVMR) performed better in both calibration and prediction sets, where R c 2 and R p 2 reached 0.9979 and 0.9879, respectively. This study showed that LIBS could be considered as a constructive tool for the quantification of copper contamination in rice.

Fast Detection of Copper Content in Rice by Laser-Induced Breakdown Spectroscopy with Uni- and Multivariate Analysis

PubMed Central

Ye, Lanhan; Song, Kunlin; Shen, Tingting

2018-01-01

Fast detection of heavy metals is very important for ensuring the quality and safety of crops. Laser-induced breakdown spectroscopy (LIBS), coupled with uni- and multivariate analysis, was applied for quantitative analysis of copper in three kinds of rice (Jiangsu rice, regular rice, and Simiao rice). For univariate analysis, three pre-processing methods were applied to reduce fluctuations, including background normalization, the internal standard method, and the standard normal variate (SNV). Linear regression models showed a strong correlation between spectral intensity and Cu content, with an R2 more than 0.97. The limit of detection (LOD) was around 5 ppm, lower than the tolerance limit of copper in foods. For multivariate analysis, partial least squares regression (PLSR) showed its advantage in extracting effective information for prediction, and its sensitivity reached 1.95 ppm, while support vector machine regression (SVMR) performed better in both calibration and prediction sets, where Rc2 and Rp2 reached 0.9979 and 0.9879, respectively. This study showed that LIBS could be considered as a constructive tool for the quantification of copper contamination in rice. PMID:29495445

Access disparities to Magnet hospitals for patients undergoing neurosurgical operations

PubMed Central

Missios, Symeon; Bekelis, Kimon

2017-01-01

Background Centers of excellence focusing on quality improvement have demonstrated superior outcomes for a variety of surgical interventions. We investigated the presence of access disparities to hospitals recognized by the Magnet Recognition Program of the American Nurses Credentialing Center (ANCC) for patients undergoing neurosurgical operations. Methods We performed a cohort study of all neurosurgery patients who were registered in the New York Statewide Planning and Research Cooperative System (SPARCS) database from 2009–2013. We examined the association of African-American race and lack of insurance with Magnet status hospitalization for neurosurgical procedures. A mixed effects propensity adjusted multivariable regression analysis was used to control for confounding. Results During the study period, 190,535 neurosurgical patients met the inclusion criteria. Using a multivariable logistic regression, we demonstrate that African-Americans had lower admission rates to Magnet institutions (OR 0.62; 95% CI, 0.58–0.67). This persisted in a mixed effects logistic regression model (OR 0.77; 95% CI, 0.70–0.83) to adjust for clustering at the patient county level, and a propensity score adjusted logistic regression model (OR 0.75; 95% CI, 0.69–0.82). Additionally, lack of insurance was associated with lower admission rates to Magnet institutions (OR 0.71; 95% CI, 0.68–0.73), in a multivariable logistic regression model. This persisted in a mixed effects logistic regression model (OR 0.72; 95% CI, 0.69–0.74), and a propensity score adjusted logistic regression model (OR 0.72; 95% CI, 0.69–0.75). Conclusions Using a comprehensive all-payer cohort of neurosurgery patients in New York State we identified an association of African-American race and lack of insurance with lower rates of admission to Magnet hospitals. PMID:28684152

Remote-sensing data processing with the multivariate regression analysis method for iron mineral resource potential mapping: a case study in the Sarvian area, central Iran

NASA Astrophysics Data System (ADS)

Mansouri, Edris; Feizi, Faranak; Jafari Rad, Alireza; Arian, Mehran

2018-03-01

This paper uses multivariate regression to create a mathematical model for iron skarn exploration in the Sarvian area, central Iran, using multivariate regression for mineral prospectivity mapping (MPM). The main target of this paper is to apply multivariate regression analysis (as an MPM method) to map iron outcrops in the northeastern part of the study area in order to discover new iron deposits in other parts of the study area. Two types of multivariate regression models using two linear equations were employed to discover new mineral deposits. This method is one of the reliable methods for processing satellite images. ASTER satellite images (14 bands) were used as unique independent variables (UIVs), and iron outcrops were mapped as dependent variables for MPM. According to the results of the probability value (p value), coefficient of determination value (R2) and adjusted determination coefficient (Radj2), the second regression model (which consistent of multiple UIVs) fitted better than other models. The accuracy of the model was confirmed by iron outcrops map and geological observation. Based on field observation, iron mineralization occurs at the contact of limestone and intrusive rocks (skarn type).

Salting-out assisted liquid-liquid extraction and partial least squares regression to assay low molecular weight polycyclic aromatic hydrocarbons leached from soils and sediments

NASA Astrophysics Data System (ADS)

Bressan, Lucas P.; do Nascimento, Paulo Cícero; Schmidt, Marcella E. P.; Faccin, Henrique; de Machado, Leandro Carvalho; Bohrer, Denise

2017-02-01

A novel method was developed to determine low molecular weight polycyclic aromatic hydrocarbons in aqueous leachates from soils and sediments using a salting-out assisted liquid-liquid extraction, synchronous fluorescence spectrometry and a multivariate calibration technique. Several experimental parameters were controlled and the optimum conditions were: sodium carbonate as the salting-out agent at concentration of 2 mol L- 1, 3 mL of acetonitrile as extraction solvent, 6 mL of aqueous leachate, vortexing for 5 min and centrifuging at 4000 rpm for 5 min. The partial least squares calibration was optimized to the lowest values of root mean squared error and five latent variables were chosen for each of the targeted compounds. The regression coefficients for the true versus predicted concentrations were higher than 0.99. Figures of merit for the multivariate method were calculated, namely sensitivity, multivariate detection limit and multivariate quantification limit. The selectivity was also evaluated and other polycyclic aromatic hydrocarbons did not interfere in the analysis. Likewise, high performance liquid chromatography was used as a comparative methodology, and the regression analysis between the methods showed no statistical difference (t-test). The proposed methodology was applied to soils and sediments of a Brazilian river and the recoveries ranged from 74.3% to 105.8%. Overall, the proposed methodology was suitable for the targeted compounds, showing that the extraction method can be applied to spectrofluorometric analysis and that the multivariate calibration is also suitable for these compounds in leachates from real samples.

Correlative and multivariate analysis of increased radon concentration in underground laboratory.

PubMed

Maletić, Dimitrije M; Udovičić, Vladimir I; Banjanac, Radomir M; Joković, Dejan R; Dragić, Aleksandar L; Veselinović, Nikola B; Filipović, Jelena

2014-11-01

The results of analysis using correlative and multivariate methods, as developed for data analysis in high-energy physics and implemented in the Toolkit for Multivariate Analysis software package, of the relations of the variation of increased radon concentration with climate variables in shallow underground laboratory is presented. Multivariate regression analysis identified a number of multivariate methods which can give a good evaluation of increased radon concentrations based on climate variables. The use of the multivariate regression methods will enable the investigation of the relations of specific climate variable with increased radon concentrations by analysis of regression methods resulting in 'mapped' underlying functional behaviour of radon concentrations depending on a wide spectrum of climate variables. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Application of near-infrared spectroscopy for the rapid quality assessment of Radix Paeoniae Rubra

NASA Astrophysics Data System (ADS)

Zhan, Hao; Fang, Jing; Tang, Liying; Yang, Hongjun; Li, Hua; Wang, Zhuju; Yang, Bin; Wu, Hongwei; Fu, Meihong

2017-08-01

Near-infrared (NIR) spectroscopy with multivariate analysis was used to quantify gallic acid, catechin, albiflorin, and paeoniflorin in Radix Paeoniae Rubra, and the feasibility to classify the samples originating from different areas was investigated. A new high-performance liquid chromatography method was developed and validated to analyze gallic acid, catechin, albiflorin, and paeoniflorin in Radix Paeoniae Rubra as the reference. Partial least squares (PLS), principal component regression (PCR), and stepwise multivariate linear regression (SMLR) were performed to calibrate the regression model. Different data pretreatments such as derivatives (1st and 2nd), multiplicative scatter correction, standard normal variate, Savitzky-Golay filter, and Norris derivative filter were applied to remove the systematic errors. The performance of the model was evaluated according to the root mean square of calibration (RMSEC), root mean square error of prediction (RMSEP), root mean square error of cross-validation (RMSECV), and correlation coefficient (r). The results show that compared to PCR and SMLR, PLS had a lower RMSEC, RMSECV, and RMSEP and higher r for all the four analytes. PLS coupled with proper pretreatments showed good performance in both the fitting and predicting results. Furthermore, the original areas of Radix Paeoniae Rubra samples were partly distinguished by principal component analysis. This study shows that NIR with PLS is a reliable, inexpensive, and rapid tool for the quality assessment of Radix Paeoniae Rubra.

Understanding the Relationship between School-Based Management, Emotional Intelligence and Performance of Religious Upper Secondary School Principals in Banten Province

ERIC Educational Resources Information Center

Muslihah, Oleh Eneng

2015-01-01

The research examines the correlation between the understanding of school-based management, emotional intelligences and headmaster performance. Data was collected, using quantitative methods. The statistical analysis used was the Pearson Correlation, and multivariate regression analysis. The results of this research suggest firstly that there is…

Using Performance Data Gathered at Several Stages of Achievement in Predicting Subsequent Performance.

ERIC Educational Resources Information Center

Owen, Steven V.; Feldhusen, John F.

This study compares the effectiveness of three models of multivariate prediction for academic success in identifying the criterion variance of achievement in nursing education. The first model involves the use of an optimum set of predictors and one equation derived from a regression analysis on first semester grade average in predicting the…

Barriers to health-care and psychological distress among mothers living with HIV in Quebec (Canada).

PubMed

Blais, Martin; Fernet, Mylène; Proulx-Boucher, Karène; Lebouché, Bertrand; Rodrigue, Carl; Lapointe, Normand; Otis, Joanne; Samson, Johanne

2015-01-01

Health-care providers play a major role in providing good quality care and in preventing psychological distress among mothers living with HIV (MLHIV). The objectives of this study are to explore the impact of health-care services and satisfaction with care providers on psychological distress in MLHIV. One hundred MLHIV were recruited from community and clinical settings in the province of Quebec (Canada). Prevalence estimation of clinical psychological distress and univariate and multivariable logistic regression models were performed to predict clinical psychological distress. Forty-five percent of the participants reported clinical psychological distress. In the multivariable regression, the following variables were significantly associated with psychological distress while controlling for sociodemographic variables: resilience, quality of communication with the care providers, resources, and HIV disclosure concerns. The multivariate results support the key role of personal, structural, and medical resources in understanding psychological distress among MLHIV. Interventions that can support the psychological health of MLHIV are discussed.

Comparative multivariate analyses of transient otoacoustic emissions and distorsion products in normal and impaired hearing.

PubMed

Stamate, Mirela Cristina; Todor, Nicolae; Cosgarea, Marcel

2015-01-01

The clinical utility of otoacoustic emissions as a noninvasive objective test of cochlear function has been long studied. Both transient otoacoustic emissions and distorsion products can be used to identify hearing loss, but to what extent they can be used as predictors for hearing loss is still debated. Most studies agree that multivariate analyses have better test performances than univariate analyses. The aim of the study was to determine transient otoacoustic emissions and distorsion products performance in identifying normal and impaired hearing loss, using the pure tone audiogram as a gold standard procedure and different multivariate statistical approaches. The study included 105 adult subjects with normal hearing and hearing loss who underwent the same test battery: pure-tone audiometry, tympanometry, otoacoustic emission tests. We chose to use the logistic regression as a multivariate statistical technique. Three logistic regression models were developed to characterize the relations between different risk factors (age, sex, tinnitus, demographic features, cochlear status defined by otoacoustic emissions) and hearing status defined by pure-tone audiometry. The multivariate analyses allow the calculation of the logistic score, which is a combination of the inputs, weighted by coefficients, calculated within the analyses. The accuracy of each model was assessed using receiver operating characteristics curve analysis. We used the logistic score to generate receivers operating curves and to estimate the areas under the curves in order to compare different multivariate analyses. We compared the performance of each otoacoustic emission (transient, distorsion product) using three different multivariate analyses for each ear, when multi-frequency gold standards were used. We demonstrated that all multivariate analyses provided high values of the area under the curve proving the performance of the otoacoustic emissions. Each otoacoustic emission test presented high values of area under the curve, suggesting that implementing a multivariate approach to evaluate the performances of each otoacoustic emission test would serve to increase the accuracy in identifying the normal and impaired ears. We encountered the highest area under the curve value for the combined multivariate analysis suggesting that both otoacoustic emission tests should be used in assessing hearing status. Our multivariate analyses revealed that age is a constant predictor factor of the auditory status for both ears, but the presence of tinnitus was the most important predictor for the hearing level, only for the left ear. Age presented similar coefficients, but tinnitus coefficients, by their high value, produced the highest variations of the logistic scores, only for the left ear group, thus increasing the risk of hearing loss. We did not find gender differences between ears for any otoacoustic emission tests, but studies still debate this question as the results are contradictory. Neither gender, nor environment origin had any predictive value for the hearing status, according to the results of our study. Like any other audiological test, using otoacoustic emissions to identify hearing loss is not without error. Even when applying multivariate analysis, perfect test performance is never achieved. Although most studies demonstrated the benefit of using the multivariate analysis, it has not been incorporated into clinical decisions maybe because of the idiosyncratic nature of multivariate solutions or because of the lack of the validation studies.

Comparative multivariate analyses of transient otoacoustic emissions and distorsion products in normal and impaired hearing

PubMed Central

STAMATE, MIRELA CRISTINA; TODOR, NICOLAE; COSGAREA, MARCEL

2015-01-01

Background and aim The clinical utility of otoacoustic emissions as a noninvasive objective test of cochlear function has been long studied. Both transient otoacoustic emissions and distorsion products can be used to identify hearing loss, but to what extent they can be used as predictors for hearing loss is still debated. Most studies agree that multivariate analyses have better test performances than univariate analyses. The aim of the study was to determine transient otoacoustic emissions and distorsion products performance in identifying normal and impaired hearing loss, using the pure tone audiogram as a gold standard procedure and different multivariate statistical approaches. Methods The study included 105 adult subjects with normal hearing and hearing loss who underwent the same test battery: pure-tone audiometry, tympanometry, otoacoustic emission tests. We chose to use the logistic regression as a multivariate statistical technique. Three logistic regression models were developed to characterize the relations between different risk factors (age, sex, tinnitus, demographic features, cochlear status defined by otoacoustic emissions) and hearing status defined by pure-tone audiometry. The multivariate analyses allow the calculation of the logistic score, which is a combination of the inputs, weighted by coefficients, calculated within the analyses. The accuracy of each model was assessed using receiver operating characteristics curve analysis. We used the logistic score to generate receivers operating curves and to estimate the areas under the curves in order to compare different multivariate analyses. Results We compared the performance of each otoacoustic emission (transient, distorsion product) using three different multivariate analyses for each ear, when multi-frequency gold standards were used. We demonstrated that all multivariate analyses provided high values of the area under the curve proving the performance of the otoacoustic emissions. Each otoacoustic emission test presented high values of area under the curve, suggesting that implementing a multivariate approach to evaluate the performances of each otoacoustic emission test would serve to increase the accuracy in identifying the normal and impaired ears. We encountered the highest area under the curve value for the combined multivariate analysis suggesting that both otoacoustic emission tests should be used in assessing hearing status. Our multivariate analyses revealed that age is a constant predictor factor of the auditory status for both ears, but the presence of tinnitus was the most important predictor for the hearing level, only for the left ear. Age presented similar coefficients, but tinnitus coefficients, by their high value, produced the highest variations of the logistic scores, only for the left ear group, thus increasing the risk of hearing loss. We did not find gender differences between ears for any otoacoustic emission tests, but studies still debate this question as the results are contradictory. Neither gender, nor environment origin had any predictive value for the hearing status, according to the results of our study. Conclusion Like any other audiological test, using otoacoustic emissions to identify hearing loss is not without error. Even when applying multivariate analysis, perfect test performance is never achieved. Although most studies demonstrated the benefit of using the multivariate analysis, it has not been incorporated into clinical decisions maybe because of the idiosyncratic nature of multivariate solutions or because of the lack of the validation studies. PMID:26733749

Comparison of Xenon-Enhanced Area-Detector CT and Krypton Ventilation SPECT/CT for Assessment of Pulmonary Functional Loss and Disease Severity in Smokers.

PubMed

Ohno, Yoshiharu; Fujisawa, Yasuko; Takenaka, Daisuke; Kaminaga, Shigeo; Seki, Shinichiro; Sugihara, Naoki; Yoshikawa, Takeshi

2018-02-01

The objective of this study was to compare the capability of xenon-enhanced area-detector CT (ADCT) performed with a subtraction technique and coregistered 81m Kr-ventilation SPECT/CT for the assessment of pulmonary functional loss and disease severity in smokers. Forty-six consecutive smokers (32 men and 14 women; mean age, 67.0 years) underwent prospective unenhanced and xenon-enhanced ADCT, 81m Kr-ventilation SPECT/CT, and pulmonary function tests. Disease severity was evaluated according to the Global Initiative for Chronic Obstructive Lung Disease (GOLD) classification. CT-based functional lung volume (FLV), the percentage of wall area to total airway area (WA%), and ventilated FLV on xenon-enhanced ADCT and SPECT/CT were calculated for each smoker. All indexes were correlated with percentage of forced expiratory volume in 1 second (%FEV 1 ) using step-wise regression analyses, and univariate and multivariate logistic regression analyses were performed. In addition, the diagnostic accuracy of the proposed model was compared with that of each radiologic index by means of McNemar analysis. Multivariate logistic regression showed that %FEV 1 was significantly affected (r = 0.77, r 2 = 0.59) by two factors: the first factor, ventilated FLV on xenon-enhanced ADCT (p < 0.0001); and the second factor, WA% (p = 0.004). Univariate logistic regression analyses indicated that all indexes significantly affected GOLD classification (p < 0.05). Multivariate logistic regression analyses revealed that ventilated FLV on xenon-enhanced ADCT and CT-based FLV significantly influenced GOLD classification (p < 0.0001). The diagnostic accuracy of the proposed model was significantly higher than that of ventilated FLV on SPECT/CT (p = 0.03) and WA% (p = 0.008). Xenon-enhanced ADCT is more effective than 81m Kr-ventilation SPECT/CT for the assessment of pulmonary functional loss and disease severity.

Watch what happens: using a web-based multimedia platform to enhance intraoperative learning and development of clinical reasoning.

PubMed

Fingeret, Abbey L; Martinez, Rebecca H; Hsieh, Christine; Downey, Peter; Nowygrod, Roman

2016-02-01

We aim to determine whether observed operations or internet-based video review predict improved performance in the surgery clerkship. A retrospective review of students' usage of surgical videos, observed operations, evaluations, and examination scores were used to construct an exploratory principal component analysis. Multivariate regression was used to determine factors predictive of clerkship performance. Case log data for 231 students revealed a median of 25 observed cases. Students accessed the web-based video platform a median of 15 times. Principal component analysis yielded 4 factors contributing 74% of the variability with a Kaiser-Meyer-Olkin coefficient of .83. Multivariate regression predicted shelf score (P < .0001), internal clinical skills examination score (P < .0001), subjective evaluations (P < .001), and video website utilization (P < .001) but not observed cases to be significantly associated with overall performance. Utilization of a web-based operative video platform during a surgical clerkship is an independently associated with improved clinical reasoning, fund of knowledge, and overall evaluation. Thus, this modality can serve as a useful adjunct to live observation. Copyright © 2016 Elsevier Inc. All rights reserved.

Application of Machine-Learning Models to Predict Tacrolimus Stable Dose in Renal Transplant Recipients

NASA Astrophysics Data System (ADS)

Tang, Jie; Liu, Rong; Zhang, Yue-Li; Liu, Mou-Ze; Hu, Yong-Fang; Shao, Ming-Jie; Zhu, Li-Jun; Xin, Hua-Wen; Feng, Gui-Wen; Shang, Wen-Jun; Meng, Xiang-Guang; Zhang, Li-Rong; Ming, Ying-Zi; Zhang, Wei

2017-02-01

Tacrolimus has a narrow therapeutic window and considerable variability in clinical use. Our goal was to compare the performance of multiple linear regression (MLR) and eight machine learning techniques in pharmacogenetic algorithm-based prediction of tacrolimus stable dose (TSD) in a large Chinese cohort. A total of 1,045 renal transplant patients were recruited, 80% of which were randomly selected as the “derivation cohort” to develop dose-prediction algorithm, while the remaining 20% constituted the “validation cohort” to test the final selected algorithm. MLR, artificial neural network (ANN), regression tree (RT), multivariate adaptive regression splines (MARS), boosted regression tree (BRT), support vector regression (SVR), random forest regression (RFR), lasso regression (LAR) and Bayesian additive regression trees (BART) were applied and their performances were compared in this work. Among all the machine learning models, RT performed best in both derivation [0.71 (0.67-0.76)] and validation cohorts [0.73 (0.63-0.82)]. In addition, the ideal rate of RT was 4% higher than that of MLR. To our knowledge, this is the first study to use machine learning models to predict TSD, which will further facilitate personalized medicine in tacrolimus administration in the future.

Cole-Cole, linear and multivariate modeling of capacitance data for on-line monitoring of biomass.

PubMed

Dabros, Michal; Dennewald, Danielle; Currie, David J; Lee, Mark H; Todd, Robert W; Marison, Ian W; von Stockar, Urs

2009-02-01

This work evaluates three techniques of calibrating capacitance (dielectric) spectrometers used for on-line monitoring of biomass: modeling of cell properties using the theoretical Cole-Cole equation, linear regression of dual-frequency capacitance measurements on biomass concentration, and multivariate (PLS) modeling of scanning dielectric spectra. The performance and robustness of each technique is assessed during a sequence of validation batches in two experimental settings of differing signal noise. In more noisy conditions, the Cole-Cole model had significantly higher biomass concentration prediction errors than the linear and multivariate models. The PLS model was the most robust in handling signal noise. In less noisy conditions, the three models performed similarly. Estimates of the mean cell size were done additionally using the Cole-Cole and PLS models, the latter technique giving more satisfactory results.

Spatially resolved regression analysis of pre-treatment FDG, FLT and Cu-ATSM PET from post-treatment FDG PET: an exploratory study

PubMed Central

Bowen, Stephen R; Chappell, Richard J; Bentzen, Søren M; Deveau, Michael A; Forrest, Lisa J; Jeraj, Robert

2012-01-01

Purpose To quantify associations between pre-radiotherapy and post-radiotherapy PET parameters via spatially resolved regression. Materials and methods Ten canine sinonasal cancer patients underwent PET/CT scans of [18F]FDG (FDGpre), [18F]FLT (FLTpre), and [61Cu]Cu-ATSM (Cu-ATSMpre). Following radiotherapy regimens of 50 Gy in 10 fractions, veterinary patients underwent FDG PET/CT scans at three months (FDGpost). Regression of standardized uptake values in baseline FDGpre, FLTpre and Cu-ATSMpre tumour voxels to those in FDGpost images was performed for linear, log-linear, generalized-linear and mixed-fit linear models. Goodness-of-fit in regression coefficients was assessed by R2. Hypothesis testing of coefficients over the patient population was performed. Results Multivariate linear model fits of FDGpre to FDGpost were significantly positive over the population (FDGpost~0.17 FDGpre, p=0.03), and classified slopes of RECIST non-responders and responders to be different (0.37 vs. 0.07, p=0.01). Generalized-linear model fits related FDGpre to FDGpost by a linear power law (FDGpost~FDGpre0.93, p<0.001). Univariate mixture model fits of FDGpre improved R2 from 0.17 to 0.52. Neither baseline FLT PET nor Cu-ATSM PET uptake contributed statistically significant multivariate regression coefficients. Conclusions Spatially resolved regression analysis indicates that pre-treatment FDG PET uptake is most strongly associated with three-month post-treatment FDG PET uptake in this patient population, though associations are histopathology-dependent. PMID:22682748

Analysis of Multivariate Experimental Data Using A Simplified Regression Model Search Algorithm

NASA Technical Reports Server (NTRS)

Ulbrich, Norbert M.

2013-01-01

A new regression model search algorithm was developed that may be applied to both general multivariate experimental data sets and wind tunnel strain-gage balance calibration data. The algorithm is a simplified version of a more complex algorithm that was originally developed for the NASA Ames Balance Calibration Laboratory. The new algorithm performs regression model term reduction to prevent overfitting of data. It has the advantage that it needs only about one tenth of the original algorithm's CPU time for the completion of a regression model search. In addition, extensive testing showed that the prediction accuracy of math models obtained from the simplified algorithm is similar to the prediction accuracy of math models obtained from the original algorithm. The simplified algorithm, however, cannot guarantee that search constraints related to a set of statistical quality requirements are always satisfied in the optimized regression model. Therefore, the simplified algorithm is not intended to replace the original algorithm. Instead, it may be used to generate an alternate optimized regression model of experimental data whenever the application of the original search algorithm fails or requires too much CPU time. Data from a machine calibration of NASA's MK40 force balance is used to illustrate the application of the new search algorithm.

[Use of multiple regression models in observational studies (1970-2013) and requirements of the STROBE guidelines in Spanish scientific journals].

PubMed

Real, J; Cleries, R; Forné, C; Roso-Llorach, A; Martínez-Sánchez, J M

In medicine and biomedical research, statistical techniques like logistic, linear, Cox and Poisson regression are widely known. The main objective is to describe the evolution of multivariate techniques used in observational studies indexed in PubMed (1970-2013), and to check the requirements of the STROBE guidelines in the author guidelines in Spanish journals indexed in PubMed. A targeted PubMed search was performed to identify papers that used logistic linear Cox and Poisson models. Furthermore, a review was also made of the author guidelines of journals published in Spain and indexed in PubMed and Web of Science. Only 6.1% of the indexed manuscripts included a term related to multivariate analysis, increasing from 0.14% in 1980 to 12.3% in 2013. In 2013, 6.7, 2.5, 3.5, and 0.31% of the manuscripts contained terms related to logistic, linear, Cox and Poisson regression, respectively. On the other hand, 12.8% of journals author guidelines explicitly recommend to follow the STROBE guidelines, and 35.9% recommend the CONSORT guideline. A low percentage of Spanish scientific journals indexed in PubMed include the STROBE statement requirement in the author guidelines. Multivariate regression models in published observational studies such as logistic regression, linear, Cox and Poisson are increasingly used both at international level, as well as in journals published in Spanish. Copyright © 2015 Sociedad Española de Médicos de Atención Primaria (SEMERGEN). Publicado por Elsevier España, S.L.U. All rights reserved.

Multivariate random-parameters zero-inflated negative binomial regression model: an application to estimate crash frequencies at intersections.

PubMed

Dong, Chunjiao; Clarke, David B; Yan, Xuedong; Khattak, Asad; Huang, Baoshan

2014-09-01

Crash data are collected through police reports and integrated with road inventory data for further analysis. Integrated police reports and inventory data yield correlated multivariate data for roadway entities (e.g., segments or intersections). Analysis of such data reveals important relationships that can help focus on high-risk situations and coming up with safety countermeasures. To understand relationships between crash frequencies and associated variables, while taking full advantage of the available data, multivariate random-parameters models are appropriate since they can simultaneously consider the correlation among the specific crash types and account for unobserved heterogeneity. However, a key issue that arises with correlated multivariate data is the number of crash-free samples increases, as crash counts have many categories. In this paper, we describe a multivariate random-parameters zero-inflated negative binomial (MRZINB) regression model for jointly modeling crash counts. The full Bayesian method is employed to estimate the model parameters. Crash frequencies at urban signalized intersections in Tennessee are analyzed. The paper investigates the performance of MZINB and MRZINB regression models in establishing the relationship between crash frequencies, pavement conditions, traffic factors, and geometric design features of roadway intersections. Compared to the MZINB model, the MRZINB model identifies additional statistically significant factors and provides better goodness of fit in developing the relationships. The empirical results show that MRZINB model possesses most of the desirable statistical properties in terms of its ability to accommodate unobserved heterogeneity and excess zero counts in correlated data. Notably, in the random-parameters MZINB model, the estimated parameters vary significantly across intersections for different crash types. Copyright © 2014 Elsevier Ltd. All rights reserved.

Calibration transfer of a Raman spectroscopic quantification method for the assessment of liquid detergent compositions from at-line laboratory to in-line industrial scale.

PubMed

Brouckaert, D; Uyttersprot, J-S; Broeckx, W; De Beer, T

2018-03-01

Calibration transfer or standardisation aims at creating a uniform spectral response on different spectroscopic instruments or under varying conditions, without requiring a full recalibration for each situation. In the current study, this strategy is applied to construct at-line multivariate calibration models and consequently employ them in-line in a continuous industrial production line, using the same spectrometer. Firstly, quantitative multivariate models are constructed at-line at laboratory scale for predicting the concentration of two main ingredients in hard surface cleaners. By regressing the Raman spectra of a set of small-scale calibration samples against their reference concentration values, partial least squares (PLS) models are developed to quantify the surfactant levels in the liquid detergent compositions under investigation. After evaluating the models performance with a set of independent validation samples, a univariate slope/bias correction is applied in view of transporting these at-line calibration models to an in-line manufacturing set-up. This standardisation technique allows a fast and easy transfer of the PLS regression models, by simply correcting the model predictions on the in-line set-up, without adjusting anything to the original multivariate calibration models. An extensive statistical analysis is performed in order to assess the predictive quality of the transferred regression models. Before and after transfer, the R 2 and RMSEP of both models is compared for evaluating if their magnitude is similar. T-tests are then performed to investigate whether the slope and intercept of the transferred regression line are not statistically different from 1 and 0, respectively. Furthermore, it is inspected whether no significant bias can be noted. F-tests are executed as well, for assessing the linearity of the transfer regression line and for investigating the statistical coincidence of the transfer and validation regression line. Finally, a paired t-test is performed to compare the original at-line model to the slope/bias corrected in-line model, using interval hypotheses. It is shown that the calibration models of Surfactant 1 and Surfactant 2 yield satisfactory in-line predictions after slope/bias correction. While Surfactant 1 passes seven out of eight statistical tests, the recommended validation parameters are 100% successful for Surfactant 2. It is hence concluded that the proposed strategy for transferring at-line calibration models to an in-line industrial environment via a univariate slope/bias correction of the predicted values offers a successful standardisation approach. Copyright © 2017 Elsevier B.V. All rights reserved.

Linear regression analysis: part 14 of a series on evaluation of scientific publications.

PubMed

Schneider, Astrid; Hommel, Gerhard; Blettner, Maria

2010-11-01

Regression analysis is an important statistical method for the analysis of medical data. It enables the identification and characterization of relationships among multiple factors. It also enables the identification of prognostically relevant risk factors and the calculation of risk scores for individual prognostication. This article is based on selected textbooks of statistics, a selective review of the literature, and our own experience. After a brief introduction of the uni- and multivariable regression models, illustrative examples are given to explain what the important considerations are before a regression analysis is performed, and how the results should be interpreted. The reader should then be able to judge whether the method has been used correctly and interpret the results appropriately. The performance and interpretation of linear regression analysis are subject to a variety of pitfalls, which are discussed here in detail. The reader is made aware of common errors of interpretation through practical examples. Both the opportunities for applying linear regression analysis and its limitations are presented.

Black Female Community College Students' Satisfaction: A National Regression Analysis

ERIC Educational Resources Information Center

Strayhorn, Terrell L.; Johnson, Royel M.

2014-01-01

Data from the Community College Student Experiences Questionnaire were analyzed for a sample of 315 Black women attending community colleges. Specifically, we conducted multivariate analyses to assess the relationship between background traits, commitments, engagement, academic performance, and satisfaction for Black women at community colleges.…

Obstetric attending physician characteristics and their impact on vacuum and forceps delivery rates: University of California at San Francisco experience from 1977 to 1999.

PubMed

Chang, Anne Lynn S; Noah, Melinda Scully; Laros, Russell K

2002-06-01

The objective of our study was to determine the impact of obstetric attending physician characteristics (eg, region of previous residency training, sex, year of graduation from residency) on the rates of vacuum and forceps delivery at our institution. The analysis was based on 19,897 vaginal deliveries that were performed by 171 attending physicians and 160 resident physicians between 1977 and 1999 at the University of California at San Francisco Medical Center. Z -tests and multivariate logistic regression were performed on a perinatal database that contained standard obstetric variables. Male attending physicians had a higher percentage of forceps deliveries compared with female attending physicians (11.1% vs 6.6%; P <.001); female attending physicians had a higher percentage of vacuum deliveries compared with male attending physicians (9.8% vs 5.1%; P <.001). However, multivariate regression analysis revealed that only the year in which the procedure was performed affected both the forceps and vacuum delivery rates (P <.041). The region of previous residency training of the attending physician affected the vacuum delivery rate (P <.0001) but not the forceps delivery rate (P >.06) in multivariate logistic regression analysis. Factors such as the sex of the obstetric attending physician, the sex of the resident, and the year of graduation from residency for the obstetric attending physician did not have a significant impact on the forceps or vacuum delivery rates (all P >.05). Our study is the first to report that the apparent gender differences in forceps and vacuum delivery rates among obstetric attending physicians was due to the year in which the procedure was performed and not due to sex per se. We also found that the region of previous residency training for the obstetric attending physician significantly influenced the vacuum delivery rate.

Fresh Biomass Estimation in Heterogeneous Grassland Using Hyperspectral Measurements and Multivariate Statistical Analysis

NASA Astrophysics Data System (ADS)

Darvishzadeh, R.; Skidmore, A. K.; Mirzaie, M.; Atzberger, C.; Schlerf, M.

2014-12-01

Accurate estimation of grassland biomass at their peak productivity can provide crucial information regarding the functioning and productivity of the rangelands. Hyperspectral remote sensing has proved to be valuable for estimation of vegetation biophysical parameters such as biomass using different statistical techniques. However, in statistical analysis of hyperspectral data, multicollinearity is a common problem due to large amount of correlated hyper-spectral reflectance measurements. The aim of this study was to examine the prospect of above ground biomass estimation in a heterogeneous Mediterranean rangeland employing multivariate calibration methods. Canopy spectral measurements were made in the field using a GER 3700 spectroradiometer, along with concomitant in situ measurements of above ground biomass for 170 sample plots. Multivariate calibrations including partial least squares regression (PLSR), principal component regression (PCR), and Least-Squared Support Vector Machine (LS-SVM) were used to estimate the above ground biomass. The prediction accuracy of the multivariate calibration methods were assessed using cross validated R2 and RMSE. The best model performance was obtained using LS_SVM and then PLSR both calibrated with first derivative reflectance dataset with R2cv = 0.88 & 0.86 and RMSEcv= 1.15 & 1.07 respectively. The weakest prediction accuracy was appeared when PCR were used (R2cv = 0.31 and RMSEcv= 2.48). The obtained results highlight the importance of multivariate calibration methods for biomass estimation when hyperspectral data are used.

Linear regression analysis and its application to multivariate chromatographic calibration for the quantitative analysis of two-component mixtures.

PubMed

Dinç, Erdal; Ozdemir, Abdil

2005-01-01

Multivariate chromatographic calibration technique was developed for the quantitative analysis of binary mixtures enalapril maleate (EA) and hydrochlorothiazide (HCT) in tablets in the presence of losartan potassium (LST). The mathematical algorithm of multivariate chromatographic calibration technique is based on the use of the linear regression equations constructed using relationship between concentration and peak area at the five-wavelength set. The algorithm of this mathematical calibration model having a simple mathematical content was briefly described. This approach is a powerful mathematical tool for an optimum chromatographic multivariate calibration and elimination of fluctuations coming from instrumental and experimental conditions. This multivariate chromatographic calibration contains reduction of multivariate linear regression functions to univariate data set. The validation of model was carried out by analyzing various synthetic binary mixtures and using the standard addition technique. Developed calibration technique was applied to the analysis of the real pharmaceutical tablets containing EA and HCT. The obtained results were compared with those obtained by classical HPLC method. It was observed that the proposed multivariate chromatographic calibration gives better results than classical HPLC.

Creativity, Bipolar Disorder Vulnerability and Psychological Well-Being: A Preliminary Study

ERIC Educational Resources Information Center

Gostoli, Sara; Cerini, Veronica; Piolanti, Antonio; Rafanelli, Chiara

2017-01-01

The aim of this research was to investigate the relationships between creativity, subclinical bipolar disorder symptomatology, and psychological well-being. The study method was of descriptive, correlational type. Significant tests were performed using multivariate regression analysis. Students of the 4th grade of 6 different Italian colleges…

Assessing the sensitivity and robustness of prediction models for apple firmness using spectral scattering technique

USDA-ARS?s Scientific Manuscript database

Spectral scattering is useful for nondestructive sensing of fruit firmness. Prediction models, however, are typically built using multivariate statistical methods such as partial least squares regression (PLSR), whose performance generally depends on the characteristics of the data. The aim of this ...

Coaches' Feedback and Changes in Children's Perceptions of Their Physical Competence.

ERIC Educational Resources Information Center

Horn, Thelma Sternberg

1985-01-01

This study examined the relationship between five softball coaches' feedback and changes in their female athletes' self-perceptions of competence; performance control; and expectancy for success. Multivariate regression analyses showed players' psychosocial growth was a function of both players' skill and the coaches' response to player…

Relationship between Job Burnout and Personal Wellness in Mental Health Professionals

ERIC Educational Resources Information Center

Puig, Ana; Baggs, Adrienne; Mixon, Kacy; Park, Yang Min; Kim, Bo Young; Lee, Sang Min

2012-01-01

This study aimed to determine the nature of the relationship between job burnout and personal wellness among mental health professionals. The authors performed intercorrelations and multivariate multiple regression analyses to identify the relationship between subscales of job burnout and personal wellness. Results showed that all subscales of job…

Evolution of the Marine Officer Fitness Report: A Multivariate Analysis

DTIC Science & Technology

This thesis explores the evaluation behavior of United States Marine Corps (USMC) Reporting Seniors (RSs) from 2010 to 2017. Using fitness report...RSs evaluate the performance of subordinate active component unrestricted officer MROs over time. I estimate logistic regression models of the...lowest. However, these correlations indicating the effects of race matching on FITREP evaluations narrow in significance when performance-based factors

A Multilevel Multivariate Analysis of Academic Performances in College Based on NCAA Student-Athletes

ERIC Educational Resources Information Center

McArdle, John J.; Paskus, Thomas S.; Boker, Steven M.

2013-01-01

This is an application of contemporary multilevel regression modeling to the prediction of academic performances of 1st-year college students. At a first level of analysis, the data come from N greater than 16,000 students who were college freshman in 1994-1995 and who were also participants in high-level college athletics. At a second level of…

Transforming RNA-Seq data to improve the performance of prognostic gene signatures.

PubMed

Zwiener, Isabella; Frisch, Barbara; Binder, Harald

2014-01-01

Gene expression measurements have successfully been used for building prognostic signatures, i.e for identifying a short list of important genes that can predict patient outcome. Mostly microarray measurements have been considered, and there is little advice available for building multivariable risk prediction models from RNA-Seq data. We specifically consider penalized regression techniques, such as the lasso and componentwise boosting, which can simultaneously consider all measurements and provide both, multivariable regression models for prediction and automated variable selection. However, they might be affected by the typical skewness, mean-variance-dependency or extreme values of RNA-Seq covariates and therefore could benefit from transformations of the latter. In an analytical part, we highlight preferential selection of covariates with large variances, which is problematic due to the mean-variance dependency of RNA-Seq data. In a simulation study, we compare different transformations of RNA-Seq data for potentially improving detection of important genes. Specifically, we consider standardization, the log transformation, a variance-stabilizing transformation, the Box-Cox transformation, and rank-based transformations. In addition, the prediction performance for real data from patients with kidney cancer and acute myeloid leukemia is considered. We show that signature size, identification performance, and prediction performance critically depend on the choice of a suitable transformation. Rank-based transformations perform well in all scenarios and can even outperform complex variance-stabilizing approaches. Generally, the results illustrate that the distribution and potential transformations of RNA-Seq data need to be considered as a critical step when building risk prediction models by penalized regression techniques.

Transforming RNA-Seq Data to Improve the Performance of Prognostic Gene Signatures

PubMed Central

Zwiener, Isabella; Frisch, Barbara; Binder, Harald

2014-01-01

Gene expression measurements have successfully been used for building prognostic signatures, i.e for identifying a short list of important genes that can predict patient outcome. Mostly microarray measurements have been considered, and there is little advice available for building multivariable risk prediction models from RNA-Seq data. We specifically consider penalized regression techniques, such as the lasso and componentwise boosting, which can simultaneously consider all measurements and provide both, multivariable regression models for prediction and automated variable selection. However, they might be affected by the typical skewness, mean-variance-dependency or extreme values of RNA-Seq covariates and therefore could benefit from transformations of the latter. In an analytical part, we highlight preferential selection of covariates with large variances, which is problematic due to the mean-variance dependency of RNA-Seq data. In a simulation study, we compare different transformations of RNA-Seq data for potentially improving detection of important genes. Specifically, we consider standardization, the log transformation, a variance-stabilizing transformation, the Box-Cox transformation, and rank-based transformations. In addition, the prediction performance for real data from patients with kidney cancer and acute myeloid leukemia is considered. We show that signature size, identification performance, and prediction performance critically depend on the choice of a suitable transformation. Rank-based transformations perform well in all scenarios and can even outperform complex variance-stabilizing approaches. Generally, the results illustrate that the distribution and potential transformations of RNA-Seq data need to be considered as a critical step when building risk prediction models by penalized regression techniques. PMID:24416353

Improved quality of care for cardiovascular disease for Latinos seen in high proportion Latino vs low proportion Latino clinics.

PubMed

López, Lenny; Cook, Nakela; Hicks, Leroi

2015-01-01

Primary care practices that concentrate linguistically and culturally appropriate services for Latinos may result in higher cardiology consultation rates and improved process measure performance for patients with coronary artery disease (CAD) and congestive heart failure (CHF). Multivariable Cox proportional-hazards regression was used to assess differences in referral at high proportion (HP) vs low proportion (LP) practices. Multivariable Poisson regression was used to assess the frequency of follow-up consultation. Among the 9,761 patients, 9,168 had CAD, 4,444 had CHF, and 3,851 had both conditions. Latinos comprised 11% of the CAD cohort and 11% of the CHF cohort. Multivariable analyses showed higher consultation rates for Latinos at HP practices for CAD and CHF. Blacks and Whites at HP practices had no significant differences in rates of consultation compared to those in LP practices. Latinos at HP practices had 25% more consultations for CAD and 23% more consultations for CHF than Latinos at LP practices. Latinos at HP clinics had higher overall mean quality performance on clinical measures for both CAD and CHF. Latinos at an LP clinic had the largest improvement in quality performance with consultation. Among Latinos with CAD or CHF receiving care within a single large academic care network, Latino patients at HP practices have higher rates of cardiologist consultation and performance on CVD process measures compared to Latino patients at LP practices. Elucidating the essential components of individual practice environments that provide higher quality of care for Latinos will allow for well designed systems to reduce health care disparities.

Voxelwise multivariate analysis of multimodality magnetic resonance imaging

PubMed Central

Naylor, Melissa G.; Cardenas, Valerie A.; Tosun, Duygu; Schuff, Norbert; Weiner, Michael; Schwartzman, Armin

2015-01-01

Most brain magnetic resonance imaging (MRI) studies concentrate on a single MRI contrast or modality, frequently structural MRI. By performing an integrated analysis of several modalities, such as structural, perfusion-weighted, and diffusion-weighted MRI, new insights may be attained to better understand the underlying processes of brain diseases. We compare two voxelwise approaches: (1) fitting multiple univariate models, one for each outcome and then adjusting for multiple comparisons among the outcomes and (2) fitting a multivariate model. In both cases, adjustment for multiple comparisons is performed over all voxels jointly to account for the search over the brain. The multivariate model is able to account for the multiple comparisons over outcomes without assuming independence because the covariance structure between modalities is estimated. Simulations show that the multivariate approach is more powerful when the outcomes are correlated and, even when the outcomes are independent, the multivariate approach is just as powerful or more powerful when at least two outcomes are dependent on predictors in the model. However, multiple univariate regressions with Bonferroni correction remains a desirable alternative in some circumstances. To illustrate the power of each approach, we analyze a case control study of Alzheimer's disease, in which data from three MRI modalities are available. PMID:23408378

Quantitative analysis of binary polymorphs mixtures of fusidic acid by diffuse reflectance FTIR spectroscopy, diffuse reflectance FT-NIR spectroscopy, Raman spectroscopy and multivariate calibration.

PubMed

Guo, Canyong; Luo, Xuefang; Zhou, Xiaohua; Shi, Beijia; Wang, Juanjuan; Zhao, Jinqi; Zhang, Xiaoxia

2017-06-05

Vibrational spectroscopic techniques such as infrared, near-infrared and Raman spectroscopy have become popular in detecting and quantifying polymorphism of pharmaceutics since they are fast and non-destructive. This study assessed the ability of three vibrational spectroscopy combined with multivariate analysis to quantify a low-content undesired polymorph within a binary polymorphic mixture. Partial least squares (PLS) regression and support vector machine (SVM) regression were employed to build quantitative models. Fusidic acid, a steroidal antibiotic, was used as the model compound. It was found that PLS regression performed slightly better than SVM regression in all the three spectroscopic techniques. Root mean square errors of prediction (RMSEP) were ranging from 0.48% to 1.17% for diffuse reflectance FTIR spectroscopy and 1.60-1.93% for diffuse reflectance FT-NIR spectroscopy and 1.62-2.31% for Raman spectroscopy. The results indicate that diffuse reflectance FTIR spectroscopy offers significant advantages in providing accurate measurement of polymorphic content in the fusidic acid binary mixtures, while Raman spectroscopy is the least accurate technique for quantitative analysis of polymorphs. Copyright © 2017 Elsevier B.V. All rights reserved.

Structural brain connectivity and cognitive ability differences: A multivariate distance matrix regression analysis.

PubMed

Ponsoda, Vicente; Martínez, Kenia; Pineda-Pardo, José A; Abad, Francisco J; Olea, Julio; Román, Francisco J; Barbey, Aron K; Colom, Roberto

2017-02-01

Neuroimaging research involves analyses of huge amounts of biological data that might or might not be related with cognition. This relationship is usually approached using univariate methods, and, therefore, correction methods are mandatory for reducing false positives. Nevertheless, the probability of false negatives is also increased. Multivariate frameworks have been proposed for helping to alleviate this balance. Here we apply multivariate distance matrix regression for the simultaneous analysis of biological and cognitive data, namely, structural connections among 82 brain regions and several latent factors estimating cognitive performance. We tested whether cognitive differences predict distances among individuals regarding their connectivity pattern. Beginning with 3,321 connections among regions, the 36 edges better predicted by the individuals' cognitive scores were selected. Cognitive scores were related to connectivity distances in both the full (3,321) and reduced (36) connectivity patterns. The selected edges connect regions distributed across the entire brain and the network defined by these edges supports high-order cognitive processes such as (a) (fluid) executive control, (b) (crystallized) recognition, learning, and language processing, and (c) visuospatial processing. This multivariate study suggests that one widespread, but limited number, of regions in the human brain, supports high-level cognitive ability differences. Hum Brain Mapp 38:803-816, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Physical Function in Older Men With Hyperkyphosis

PubMed Central

Harrison, Stephanie L.; Fink, Howard A.; Marshall, Lynn M.; Orwoll, Eric; Barrett-Connor, Elizabeth; Cawthon, Peggy M.; Kado, Deborah M.

2015-01-01

Background. Age-related hyperkyphosis has been associated with poor physical function and is a well-established predictor of adverse health outcomes in older women, but its impact on health in older men is less well understood. Methods. We conducted a cross-sectional study to evaluate the association of hyperkyphosis and physical function in 2,363 men, aged 71–98 (M = 79) from the Osteoporotic Fractures in Men Study. Kyphosis was measured using the Rancho Bernardo Study block method. Measurements of grip strength and lower extremity function, including gait speed over 6 m, narrow walk (measure of dynamic balance), repeated chair stands ability and time, and lower extremity power (Nottingham Power Rig) were included separately as primary outcomes. We investigated associations of kyphosis and each outcome in age-adjusted and multivariable linear or logistic regression models, controlling for age, clinic, education, race, bone mineral density, height, weight, diabetes, and physical activity. Results. In multivariate linear regression, we observed a dose-related response of worse scores on each lower extremity physical function test as number of blocks increased, p for trend ≤.001. Using a cutoff of ≥4 blocks, 20% (N = 469) of men were characterized with hyperkyphosis. In multivariate logistic regression, men with hyperkyphosis had increased odds (range 1.5–1.8) of being in the worst quartile of performing lower extremity physical function tasks (p < .001 for each outcome). Kyphosis was not associated with grip strength in any multivariate analysis. Conclusions. Hyperkyphosis is associated with impaired lower extremity physical function in older men. Further studies are needed to determine the direction of causality. PMID:25431353

Development and validation of prognostic models in metastatic breast cancer: a GOCS study.

PubMed

Rabinovich, M; Vallejo, C; Bianco, A; Perez, J; Machiavelli, M; Leone, B; Romero, A; Rodriguez, R; Cuevas, M; Dansky, C

1992-01-01

The significance of several prognostic factors and the magnitude of their influence on response rate and survival were assessed by means of uni- and multivariate analyses in 362 patients with stage IV (UICC) breast carcinoma receiving combination chemotherapy as first systemic treatment over an 8-year period. Univariate analyses identified performance status and prior adjuvant radiotherapy as predictors of objective regression (OR), whereas the performance status, prior chemotherapy and radiotherapy (adjuvants), white blood cells count, SGOT and SGPT levels, and metastatic pattern were significantly correlated to survival. In multivariate analyses favorable characteristics associated to OR were prior adjuvant radiotherapy, no prior chemotherapy and postmenopausal status. Regarding survival, the performance status and visceral involvement were selected by the Cox model. The predictive accuracy of the logistic and the proportional hazards models was retrospectively tested in the training sample, and prospectively in a new population of 126 patients also receiving combined chemotherapy as first treatment for metastatic breast cancer. A certain overfitting to data in the training sample was observed with the regression model for response. However, the discriminative ability of the Cox model for survival was clearly confirmed.

Alternatives for using multivariate regression to adjust prospective payment rates

PubMed Central

Sheingold, Steven H.

1990-01-01

Multivariate regression analysis has been used in structuring three of the adjustments to Medicare's prospective payment rates. Because the indirect-teaching adjustment, the disproportionate-share adjustment, and the adjustment for large cities are responsible for distributing approximately $3 billion in payments each year, the specification of regression models for these adjustments is of critical importance. In this article, the application of regression for adjusting Medicare's prospective rates is discussed, and the implications that differing specifications could have for these adjustments are demonstrated. PMID:10113271

Evaluation and comparison of statistical methods for early temporal detection of outbreaks: A simulation-based study

PubMed Central

Le Strat, Yann

2017-01-01

The objective of this paper is to evaluate a panel of statistical algorithms for temporal outbreak detection. Based on a large dataset of simulated weekly surveillance time series, we performed a systematic assessment of 21 statistical algorithms, 19 implemented in the R package surveillance and two other methods. We estimated false positive rate (FPR), probability of detection (POD), probability of detection during the first week, sensitivity, specificity, negative and positive predictive values and F1-measure for each detection method. Then, to identify the factors associated with these performance measures, we ran multivariate Poisson regression models adjusted for the characteristics of the simulated time series (trend, seasonality, dispersion, outbreak sizes, etc.). The FPR ranged from 0.7% to 59.9% and the POD from 43.3% to 88.7%. Some methods had a very high specificity, up to 99.4%, but a low sensitivity. Methods with a high sensitivity (up to 79.5%) had a low specificity. All methods had a high negative predictive value, over 94%, while positive predictive values ranged from 6.5% to 68.4%. Multivariate Poisson regression models showed that performance measures were strongly influenced by the characteristics of time series. Past or current outbreak size and duration strongly influenced detection performances. PMID:28715489

Simultaneous Force Regression and Movement Classification of Fingers via Surface EMG within a Unified Bayesian Framework.

PubMed

Baldacchino, Tara; Jacobs, William R; Anderson, Sean R; Worden, Keith; Rowson, Jennifer

2018-01-01

This contribution presents a novel methodology for myolectric-based control using surface electromyographic (sEMG) signals recorded during finger movements. A multivariate Bayesian mixture of experts (MoE) model is introduced which provides a powerful method for modeling force regression at the fingertips, while also performing finger movement classification as a by-product of the modeling algorithm. Bayesian inference of the model allows uncertainties to be naturally incorporated into the model structure. This method is tested using data from the publicly released NinaPro database which consists of sEMG recordings for 6 degree-of-freedom force activations for 40 intact subjects. The results demonstrate that the MoE model achieves similar performance compared to the benchmark set by the authors of NinaPro for finger force regression. Additionally, inherent to the Bayesian framework is the inclusion of uncertainty in the model parameters, naturally providing confidence bounds on the force regression predictions. Furthermore, the integrated clustering step allows a detailed investigation into classification of the finger movements, without incurring any extra computational effort. Subsequently, a systematic approach to assessing the importance of the number of electrodes needed for accurate control is performed via sensitivity analysis techniques. A slight degradation in regression performance is observed for a reduced number of electrodes, while classification performance is unaffected.

Factors Associated with Participation in Employment for High School Leavers with Autism

ERIC Educational Resources Information Center

Chiang, Hsu-Min; Cheung, Ying Kuen; Li, Huacheng; Tsai, Luke Y.

2013-01-01

This study aimed to identify the factors associated with participation in employment for high school leavers with autism. A secondary data analysis of the National Longitudinal Transition Study 2 (NLTS2) data was performed. Potential factors were assessed using a weighted multivariate logistic regression. This study found that annual household…

Episiotomy increases perineal laceration length in primiparous women.

PubMed

Nager, C W; Helliwell, J P

2001-08-01

The aim of this study was to determine the clinical factors that contribute to posterior perineal laceration length. A prospective observational study was performed in 80 consenting, mostly primiparous women with term pregnancies. Posterior perineal lacerations were measured immediately after delivery. Numerous maternal, fetal, and operator variables were evaluated against laceration length and degree of tear. Univariate and multivariate regression analyses were performed to evaluate laceration length and parametric clinical variables. Nonparametric clinical variables were evaluated against laceration length by the Mann-Whitney U test. A multivariate stepwise linear regression equation revealed that episiotomy adds nearly 3 cm to perineal lacerations. Tear length was highly associated with the degree of tear (R = 0.86, R(2) = 0.73) and the risk of recognized anal sphincter disruption. None of 35 patients without an episiotomy had a recognized anal sphincter disruption, but 6 of 27 patients with an episiotomy did (P <.001). Body mass index was the only maternal or fetal variable that showed even a slight correlation with laceration length (R = 0.30, P =.04). Episiotomy is the overriding determinant of perineal laceration length and recognized anal sphincter disruption.

Exploring emergency department 4-hour target performance and cancelled elective operations: a regression analysis of routinely collected and openly reported NHS trust data.

PubMed

Keogh, Brad; Culliford, David; Guerrero-Ludueña, Richard; Monks, Thomas

2018-05-24

To quantify the effect of intrahospital patient flow on emergency department (ED) performance targets and indicate if the expectations set by the National Health Service (NHS) England 5-year forward review are realistic in returning emergency services to previous performance levels. Linear regression analysis of routinely reported trust activity and performance data using a series of cross-sectional studies. NHS trusts in England submitting routine nationally reported measures to NHS England. 142 acute non-specialist trusts operating in England between 2012 and 2016. The primary outcome measures were proportion of 4-hour waiting time breaches and cancelled elective operations. Univariate and multivariate linear regression models were used to show relationships between the outcome measures and various measures of trust activity including empty day beds, empty night beds, day bed to night bed ratio, ED conversion ratio and delayed transfers of care. Univariate regression results using the outcome of 4-hour breaches showed clear relationships with empty night beds and ED conversion ratio between 2012 and 2016. The day bed to night bed ratio showed an increasing ability to explain variation in performance between 2015 and 2016. Delayed transfers of care showed little evidence of an association. Multivariate model results indicated that the ability of patient flow variables to explain 4-hour target performance had reduced between 2012 and 2016 (19% to 12%), and had increased in explaining cancelled elective operations (7% to 17%). The flow of patients through trusts is shown to influence ED performance; however, performance has become less explainable by intratrust patient flow between 2012 and 2016. Some commonly stated explanatory factors such as delayed transfers of care showed limited evidence of being related. The results indicate some of the measures proposed by NHS England to reduce pressure on EDs may not have the desired impact on returning services to previous performance levels. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Risk Factors Associated with Preterm Delivery after Fetoscopic Laser Surgery for Twin Twin Transfusion Syndrome

PubMed Central

PAPANNA, Ramesha; BLOCK-ABRAHAM, Dana; Mann, Lovepreet K; BUHIMSCHI, Irina A.; BEBBINGTON, Michael; GARCIA, Elisa; KAHLEK, Nahla; HARMAN, Christopher; JOHNSON, Anthony; BASCHAT, Ahmet; MOISE, Kenneth J.

2014-01-01

OBJECTIVE Despite improved perinatal survival following fetoscopic laser surgery (FLS) for twin twin transfusion syndrome (TTTS), prematurity remains an important contributor to perinatal mortality and morbidity. The objective of the study was to identify risk factors for complicated preterm delivery after FLS. STUDY DESIGN Retrospective cohort study of prospectively collected data on maternal/fetal demographics and pre-operative, operative and post-operative variables of 459 patients treated in 3 U.S. fetal centers. Multivariate linear regression was performed to identify significant risk factors associated with preterm delivery, which was cross-validated using K-fold method. Multivariate logistic regression was performed to identify risk factors for early vs. late preterm delivery based on median gestational age at delivery of 32 weeks. RESULTS There were significant differences in case selection and outcomes between the centers. After controlling for the center of surgery, a multivariate analysis indicated a lower maternal age at procedure, history of previous prematurity, shortened cervical length, use of amnioinfusion, 12 Fr cannula diameter, lack of a collagen plug placement and iatrogenic preterm premature rupture of membranes (iPPROM) were significantly associated with a lower gestational age at delivery. CONCLUSION Specific fetal/maternal and operative variables are associated with preterm delivery after FLS for the treatment of TTTS. Further studies to modify some of these variables may decrease the perinatal morbidity after laser therapy. PMID:24013922

Risk prediction for myocardial infarction via generalized functional regression models.

PubMed

Ieva, Francesca; Paganoni, Anna M

2016-08-01

In this paper, we propose a generalized functional linear regression model for a binary outcome indicating the presence/absence of a cardiac disease with multivariate functional data among the relevant predictors. In particular, the motivating aim is the analysis of electrocardiographic traces of patients whose pre-hospital electrocardiogram (ECG) has been sent to 118 Dispatch Center of Milan (the Italian free-toll number for emergencies) by life support personnel of the basic rescue units. The statistical analysis starts with a preprocessing of ECGs treated as multivariate functional data. The signals are reconstructed from noisy observations. The biological variability is then removed by a nonlinear registration procedure based on landmarks. Thus, in order to perform a data-driven dimensional reduction, a multivariate functional principal component analysis is carried out on the variance-covariance matrix of the reconstructed and registered ECGs and their first derivatives. We use the scores of the Principal Components decomposition as covariates in a generalized linear model to predict the presence of the disease in a new patient. Hence, a new semi-automatic diagnostic procedure is proposed to estimate the risk of infarction (in the case of interest, the probability of being affected by Left Bundle Brunch Block). The performance of this classification method is evaluated and compared with other methods proposed in literature. Finally, the robustness of the procedure is checked via leave-j-out techniques. © The Author(s) 2013.

Multivariate Bias Correction Procedures for Improving Water Quality Predictions from the SWAT Model

NASA Astrophysics Data System (ADS)

Arumugam, S.; Libera, D.

2017-12-01

Water quality observations are usually not available on a continuous basis for longer than 1-2 years at a time over a decadal period given the labor requirements making calibrating and validating mechanistic models difficult. Further, any physical model predictions inherently have bias (i.e., under/over estimation) and require post-simulation techniques to preserve the long-term mean monthly attributes. This study suggests a multivariate bias-correction technique and compares to a common technique in improving the performance of the SWAT model in predicting daily streamflow and TN loads across the southeast based on split-sample validation. The approach is a dimension reduction technique, canonical correlation analysis (CCA) that regresses the observed multivariate attributes with the SWAT model simulated values. The common approach is a regression based technique that uses an ordinary least squares regression to adjust model values. The observed cross-correlation between loadings and streamflow is better preserved when using canonical correlation while simultaneously reducing individual biases. Additionally, canonical correlation analysis does a better job in preserving the observed joint likelihood of observed streamflow and loadings. These procedures were applied to 3 watersheds chosen from the Water Quality Network in the Southeast Region; specifically, watersheds with sufficiently large drainage areas and number of observed data points. The performance of these two approaches are compared for the observed period and over a multi-decadal period using loading estimates from the USGS LOADEST model. Lastly, the CCA technique is applied in a forecasting sense by using 1-month ahead forecasts of P & T from ECHAM4.5 as forcings in the SWAT model. Skill in using the SWAT model for forecasting loadings and streamflow at the monthly and seasonal timescale is also discussed.

Cement Leakage in Percutaneous Vertebral Augmentation for Osteoporotic Vertebral Compression Fractures: Analysis of Risk Factors.

PubMed

Xie, Weixing; Jin, Daxiang; Ma, Hui; Ding, Jinyong; Xu, Jixi; Zhang, Shuncong; Liang, De

2016-05-01

The risk factors for cement leakage were retrospectively reviewed in 192 patients who underwent percutaneous vertebral augmentation (PVA). To discuss the factors related to the cement leakage in PVA procedure for the treatment of osteoporotic vertebral compression fractures. PVA is widely applied for the treatment of osteoporotic vertebral fractures. Cement leakage is a major complication of this procedure. The risk factors for cement leakage were controversial. A retrospective review of 192 patients who underwent PVA was conducted. The following data were recorded: age, sex, bone density, number of fractured vertebrae before surgery, number of treated vertebrae, severity of the treated vertebrae, operative approach, volume of injected bone cement, preoperative vertebral compression ratio, preoperative local kyphosis angle, intraosseous clefts, preoperative vertebral cortical bone defect, and ratio and type of cement leakage. To study the correlation between each factor and cement leakage ratio, bivariate regression analysis was employed to perform univariate analysis, whereas multivariate linear regression analysis was employed to perform multivariate analysis. The study included 192 patients (282 treated vertebrae), and cement leakage occurred in 100 vertebrae (35.46%). The vertebrae with preoperative cortical bone defects generally exhibited higher cement leakage ratio, and the leakage is typically type C. Vertebrae with intact cortical bones before the procedure tend to experience type S leakage. Univariate analysis showed that patient age, bone density, number of fractured vertebrae before surgery, and vertebral cortical bone were associated with cement leakage ratio (P<0.05). Multivariate analysis showed that the main factors influencing bone cement leakage are bone density and vertebral cortical bone defect, with standardized partial regression coefficients of -0.085 and 0.144, respectively. High bone density and vertebral cortical bone defect are independent risk factors associated with bone cement leakage.

Factors affecting the outcome of excimer laser photorefractive keratectomy: a preliminary multivariable regression analysis

NASA Astrophysics Data System (ADS)

Maguen, Ezra I.; Papaioannou, Thanassis; Nesburn, Anthony B.; Salz, James J.; Warren, Cathy; Grundfest, Warren S.

1996-05-01

Multivariable regression analysis was used to evaluate the combined effects of some preoperative and operative variables on the change of refraction following excimer laser photorefractive keratectomy for myopia (PRK). This analysis was performed on 152 eyes (at 6 months postoperatively) and 156 eyes (at 12 months postoperatively). The following variables were considered: intended refractive correction, patient age, treatment zone, central corneal thickness, average corneal curvature, and intraocular pressure. At 6 months after surgery, the cumulative R2 was 0.43 with 0.38 attributed to the intended correction and 0.06 attributed to the preoperative corneal curvature. At 12 months, the cumulative R2 was 0.37 where 0.33 was attributed to the intended correction, 0.02 to the preoperative corneal curvature, and 0.01 to both preoperative corneal thickness and to the patient age. Further model augmentation is necessary to account for the remaining variability and the behavior of the residuals.

Specific prognostic factors for secondary pancreatic infection in severe acute pancreatitis.

PubMed

Armengol-Carrasco, M; Oller, B; Escudero, L E; Roca, J; Gener, J; Rodríguez, N; del Moral, P; Moreno, P

1999-01-01

The aim of the present study was to investigate whether there are specific prognostic factors to predict the development of secondary pancreatic infection (SPI) in severe acute pancreatitis in order to perform a computed tomography-fine needle aspiration with bacteriological sampling at the right moment and confirm the diagnosis. Twenty-five clinical and laboratory parameters were determined sequentially in 150 patients with severe acute pancreatitis (SAP) and univariate, and multivariate regression analyses were done looking for correlation with the development of SPI. Only APACHE II score and C-reactive protein levels were related to the development of SPI in the multivariate analysis. A regression equation was designed using these two parameters, and empiric cut-off points defined the subgroup of patients at high risk of developing secondary pancreatic infection. The results showed that it is possible to predict SPI during SAP allowing bacteriological confirmation and early treatment of this severe condition.

Method for enhanced accuracy in predicting peptides using liquid separations or chromatography

DOEpatents

Kangas, Lars J.; Auberry, Kenneth J.; Anderson, Gordon A.; Smith, Richard D.

2006-11-14

A method for predicting the elution time of a peptide in chromatographic and electrophoretic separations by first providing a data set of known elution times of known peptides, then creating a plurality of vectors, each vector having a plurality of dimensions, and each dimension representing the elution time of amino acids present in each of these known peptides from the data set. The elution time of any protein is then be predicted by first creating a vector by assigning dimensional values for the elution time of amino acids of at least one hypothetical peptide and then calculating a predicted elution time for the vector by performing a multivariate regression of the dimensional values of the hypothetical peptide using the dimensional values of the known peptides. Preferably, the multivariate regression is accomplished by the use of an artificial neural network and the elution times are first normalized using a transfer function.

Endpoint in plasma etch process using new modified w-multivariate charts and windowed regression

NASA Astrophysics Data System (ADS)

Zakour, Sihem Ben; Taleb, Hassen

2017-09-01

Endpoint detection is very important undertaking on the side of getting a good understanding and figuring out if a plasma etching process is done in the right way, especially if the etched area is very small (0.1%). It truly is a crucial part of supplying repeatable effects in every single wafer. When the film being etched has been completely cleared, the endpoint is reached. To ensure the desired device performance on the produced integrated circuit, the high optical emission spectroscopy (OES) sensor is employed. The huge number of gathered wavelengths (profiles) is then analyzed and pre-processed using a new proposed simple algorithm named Spectra peak selection (SPS) to select the important wavelengths, then we employ wavelet analysis (WA) to enhance the performance of detection by suppressing noise and redundant information. The selected and treated OES wavelengths are then used in modified multivariate control charts (MEWMA and Hotelling) for three statistics (mean, SD and CV) and windowed polynomial regression for mean. The employ of three aforementioned statistics is motivated by controlling mean shift, variance shift and their ratio (CV) if both mean and SD are not stable. The control charts show their performance in detecting endpoint especially W-mean Hotelling chart and the worst result is given by CV statistic. As the best detection of endpoint is given by the W-Hotelling mean statistic, this statistic will be used to construct a windowed wavelet Hotelling polynomial regression. This latter can only identify the window containing endpoint phenomenon.

Applied Statistics: From Bivariate through Multivariate Techniques [with CD-ROM

ERIC Educational Resources Information Center

Warner, Rebecca M.

2007-01-01

This book provides a clear introduction to widely used topics in bivariate and multivariate statistics, including multiple regression, discriminant analysis, MANOVA, factor analysis, and binary logistic regression. The approach is applied and does not require formal mathematics; equations are accompanied by verbal explanations. Students are asked…

PHI and PCA3 improve the prognostic performance of PRIAS and Epstein criteria in predicting insignificant prostate cancer in men eligible for active surveillance.

PubMed

Cantiello, Francesco; Russo, Giorgio Ivan; Cicione, Antonio; Ferro, Matteo; Cimino, Sebastiano; Favilla, Vincenzo; Perdonà, Sisto; De Cobelli, Ottavio; Magno, Carlo; Morgia, Giuseppe; Damiano, Rocco

2016-04-01

To assess the performance of prostate health index (PHI) and prostate cancer antigen 3 (PCA3) when added to the PRIAS or Epstein criteria in predicting the presence of pathologically insignificant prostate cancer (IPCa) in patients who underwent radical prostatectomy (RP) but eligible for active surveillance (AS). An observational retrospective study was performed in 188 PCa patients treated with laparoscopic or robot-assisted RP but eligible for AS according to Epstein or PRIAS criteria. Blood and urinary specimens were collected before initial prostate biopsy for PHI and PCA3 measurements. Multivariate logistic regression analyses and decision curve analysis were carried out to identify predictors of IPCa using the updated ERSPC definition. At the multivariate analyses, the inclusion of both PCA3 and PHI significantly increased the accuracy of the Epstein multivariate model in predicting IPCa with an increase of 17 % (AUC = 0.77) and of 32 % (AUC = 0.92), respectively. The inclusion of both PCA3 and PHI also increased the predictive accuracy of the PRIAS multivariate model with an increase of 29 % (AUC = 0.87) and of 39 % (AUC = 0.97), respectively. DCA revealed that the multivariable models with the addition of PHI or PCA3 showed a greater net benefit and performed better than the reference models. In a direct comparison, PHI outperformed PCA3 performance resulting in higher net benefit. In a same cohort of patients eligible for AS, the addition of PHI and PCA3 to Epstein or PRIAS models improved their prognostic performance. PHI resulted in greater net benefit in predicting IPCa compared to PCA3.

Effect of Contact Damage on the Strength of Ceramic Materials.

DTIC Science & Technology

1982-10-01

variables that are important to erosion, and a multivariate , linear regression analysis is used to fit the data to the dimensional analysis. The...of Equations 7 and 8 by a multivariable regression analysis (room tem- perature data) Exponent Regression Standard error Computed coefficient of...1980) 593. WEAVER, Proc. Brit. Ceram. Soc. 22 (1973) 125. 39. P. W. BRIDGMAN, "Dimensional Analaysis ", (Yale 18. R. W. RICE, S. W. FREIMAN and P. F

Physical function in older men with hyperkyphosis.

PubMed

Katzman, Wendy B; Harrison, Stephanie L; Fink, Howard A; Marshall, Lynn M; Orwoll, Eric; Barrett-Connor, Elizabeth; Cawthon, Peggy M; Kado, Deborah M

2015-05-01

Age-related hyperkyphosis has been associated with poor physical function and is a well-established predictor of adverse health outcomes in older women, but its impact on health in older men is less well understood. We conducted a cross-sectional study to evaluate the association of hyperkyphosis and physical function in 2,363 men, aged 71-98 (M = 79) from the Osteoporotic Fractures in Men Study. Kyphosis was measured using the Rancho Bernardo Study block method. Measurements of grip strength and lower extremity function, including gait speed over 6 m, narrow walk (measure of dynamic balance), repeated chair stands ability and time, and lower extremity power (Nottingham Power Rig) were included separately as primary outcomes. We investigated associations of kyphosis and each outcome in age-adjusted and multivariable linear or logistic regression models, controlling for age, clinic, education, race, bone mineral density, height, weight, diabetes, and physical activity. In multivariate linear regression, we observed a dose-related response of worse scores on each lower extremity physical function test as number of blocks increased, p for trend ≤.001. Using a cutoff of ≥4 blocks, 20% (N = 469) of men were characterized with hyperkyphosis. In multivariate logistic regression, men with hyperkyphosis had increased odds (range 1.5-1.8) of being in the worst quartile of performing lower extremity physical function tasks (p < .001 for each outcome). Kyphosis was not associated with grip strength in any multivariate analysis. Hyperkyphosis is associated with impaired lower extremity physical function in older men. Further studies are needed to determine the direction of causality. © The Author 2014. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Multiple imputation for handling missing outcome data when estimating the relative risk.

PubMed

Sullivan, Thomas R; Lee, Katherine J; Ryan, Philip; Salter, Amy B

2017-09-06

Multiple imputation is a popular approach to handling missing data in medical research, yet little is known about its applicability for estimating the relative risk. Standard methods for imputing incomplete binary outcomes involve logistic regression or an assumption of multivariate normality, whereas relative risks are typically estimated using log binomial models. It is unclear whether misspecification of the imputation model in this setting could lead to biased parameter estimates. Using simulated data, we evaluated the performance of multiple imputation for handling missing data prior to estimating adjusted relative risks from a correctly specified multivariable log binomial model. We considered an arbitrary pattern of missing data in both outcome and exposure variables, with missing data induced under missing at random mechanisms. Focusing on standard model-based methods of multiple imputation, missing data were imputed using multivariate normal imputation or fully conditional specification with a logistic imputation model for the outcome. Multivariate normal imputation performed poorly in the simulation study, consistently producing estimates of the relative risk that were biased towards the null. Despite outperforming multivariate normal imputation, fully conditional specification also produced somewhat biased estimates, with greater bias observed for higher outcome prevalences and larger relative risks. Deleting imputed outcomes from analysis datasets did not improve the performance of fully conditional specification. Both multivariate normal imputation and fully conditional specification produced biased estimates of the relative risk, presumably since both use a misspecified imputation model. Based on simulation results, we recommend researchers use fully conditional specification rather than multivariate normal imputation and retain imputed outcomes in the analysis when estimating relative risks. However fully conditional specification is not without its shortcomings, and so further research is needed to identify optimal approaches for relative risk estimation within the multiple imputation framework.

Local polynomial estimation of heteroscedasticity in a multivariate linear regression model and its applications in economics.

PubMed

Su, Liyun; Zhao, Yanyong; Yan, Tianshun; Li, Fenglan

2012-01-01

Multivariate local polynomial fitting is applied to the multivariate linear heteroscedastic regression model. Firstly, the local polynomial fitting is applied to estimate heteroscedastic function, then the coefficients of regression model are obtained by using generalized least squares method. One noteworthy feature of our approach is that we avoid the testing for heteroscedasticity by improving the traditional two-stage method. Due to non-parametric technique of local polynomial estimation, it is unnecessary to know the form of heteroscedastic function. Therefore, we can improve the estimation precision, when the heteroscedastic function is unknown. Furthermore, we verify that the regression coefficients is asymptotic normal based on numerical simulations and normal Q-Q plots of residuals. Finally, the simulation results and the local polynomial estimation of real data indicate that our approach is surely effective in finite-sample situations.

Voxelwise multivariate analysis of multimodality magnetic resonance imaging.

PubMed

Naylor, Melissa G; Cardenas, Valerie A; Tosun, Duygu; Schuff, Norbert; Weiner, Michael; Schwartzman, Armin

2014-03-01

Most brain magnetic resonance imaging (MRI) studies concentrate on a single MRI contrast or modality, frequently structural MRI. By performing an integrated analysis of several modalities, such as structural, perfusion-weighted, and diffusion-weighted MRI, new insights may be attained to better understand the underlying processes of brain diseases. We compare two voxelwise approaches: (1) fitting multiple univariate models, one for each outcome and then adjusting for multiple comparisons among the outcomes and (2) fitting a multivariate model. In both cases, adjustment for multiple comparisons is performed over all voxels jointly to account for the search over the brain. The multivariate model is able to account for the multiple comparisons over outcomes without assuming independence because the covariance structure between modalities is estimated. Simulations show that the multivariate approach is more powerful when the outcomes are correlated and, even when the outcomes are independent, the multivariate approach is just as powerful or more powerful when at least two outcomes are dependent on predictors in the model. However, multiple univariate regressions with Bonferroni correction remain a desirable alternative in some circumstances. To illustrate the power of each approach, we analyze a case control study of Alzheimer's disease, in which data from three MRI modalities are available. Copyright © 2013 Wiley Periodicals, Inc.

Postsecondary Student Mobility from College to University: Academic Performance of Students

ERIC Educational Resources Information Center

Gerhardt, Kris; Masakure, Oliver

2016-01-01

This paper considers the impact of transfer credits on the GPA of college-university transfer students. The data come from the academic records of students enrolled at 2 different campuses at an undergraduate university in Ontario across a 4-year period. The results from multivariate regression analyses show that the number of transfer credits is…

A multivariate analysis of clinical and morphological prognostic factors in squamous cell carcinoma of the vulva.

PubMed

Smyczek-Gargya, B; Volz, B; Geppert, M; Dietl, J

1997-01-01

Clinical and histological data of 168 patients with squamous cell carcinoma of the vulva were analyzed with respect to survival. 151 patients underwent surgery, 12 patients were treated with primary radiation and in 5 patients no treatment was performed. Follow-up lasted from at least 2 up to 22 years' posttreatment. In univariate analysis, the following factors were highly significant: presurgery lymph node status, tumor infiltration beyond the vulva, tumor grading, histological inguinal lymph node status, pre- and postsurgery tumor stage, depth of invasion and tumor diameter. In the multivariate analysis (Cox regression), the most powerful factors were shown to be histological inguinal lymph node status, tumor diameter and tumor grading. The multivariate logistic regression analysis worked out as main prognostic factors for metastases of inguinal lymph nodes: presurgery inguinal lymph node status, tumor size, depth of invasion and tumor grading. Based on these results, tumor biology seems to be the decisive factor concerning recurrence and survival. Therefore, we suggest a more conservative treatment of vulvar carcinoma. Patients with confined carcinoma to the vulva, with a tumor diameter up to 3 cm and without clinical suspected lymph nodes, should be treated by wide excision/partial vulvectomy with ipsilateral lymphadenectomy.

Transition from a multiport technique to a single-port technique for lung cancer surgery: is lymph node dissection inferior using the single-port technique?†.

PubMed

Liu, Chia-Chuan; Shih, Chih-Shiun; Pennarun, Nicolas; Cheng, Chih-Tao

2016-01-01

The feasibility and radicalism of lymph node dissection for lung cancer surgery by a single-port technique has frequently been challenged. We performed a retrospective cohort study to investigate this issue. Two chest surgeons initiated multiple-port thoracoscopic surgery in a 180-bed cancer centre in 2005 and shifted to a single-port technique gradually after 2010. Data, including demographic and clinical information, from 389 patients receiving multiport thoracoscopic lobectomy or segmentectomy and 149 consecutive patients undergoing either single-port lobectomy or segmentectomy for primary non-small-cell lung cancer were retrieved and entered for statistical analysis by multivariable linear regression models and Box-Cox transformed multivariable analysis. The mean number of total dissected lymph nodes in the lobectomy group was 28.5 ± 11.7 for the single-port group versus 25.2 ± 11.3 for the multiport group; the mean number of total dissected lymph nodes in the segmentectomy group was 19.5 ± 10.8 for the single-port group versus 17.9 ± 10.3 for the multiport group. In linear multivariable and after Box-Cox transformed multivariable analyses, the single-port approach was still associated with a higher total number of dissected lymph nodes. The total number of dissected lymph nodes for primary lung cancer surgery by single-port video-assisted thoracoscopic surgery (VATS) was higher than by multiport VATS in univariable, multivariable linear regression and Box-Cox transformed multivariable analyses. This study confirmed that highly effective lymph node dissection could be achieved through single-port VATS in our setting. © The Author 2015. Published by Oxford University Press on behalf of the European Association for Cardio-Thoracic Surgery. All rights reserved.

Modelling nitrate pollution pressure using a multivariate statistical approach: the case of Kinshasa groundwater body, Democratic Republic of Congo

NASA Astrophysics Data System (ADS)

Mfumu Kihumba, Antoine; Ndembo Longo, Jean; Vanclooster, Marnik

2016-03-01

A multivariate statistical modelling approach was applied to explain the anthropogenic pressure of nitrate pollution on the Kinshasa groundwater body (Democratic Republic of Congo). Multiple regression and regression tree models were compared and used to identify major environmental factors that control the groundwater nitrate concentration in this region. The analyses were made in terms of physical attributes related to the topography, land use, geology and hydrogeology in the capture zone of different groundwater sampling stations. For the nitrate data, groundwater datasets from two different surveys were used. The statistical models identified the topography, the residential area, the service land (cemetery), and the surface-water land-use classes as major factors explaining nitrate occurrence in the groundwater. Also, groundwater nitrate pollution depends not on one single factor but on the combined influence of factors representing nitrogen loading sources and aquifer susceptibility characteristics. The groundwater nitrate pressure was better predicted with the regression tree model than with the multiple regression model. Furthermore, the results elucidated the sensitivity of the model performance towards the method of delineation of the capture zones. For pollution modelling at the monitoring points, therefore, it is better to identify capture-zone shapes based on a conceptual hydrogeological model rather than to adopt arbitrary circular capture zones.

Successive Projections Algorithm-Multivariable Linear Regression Classifier for the Detection of Contaminants on Chicken Carcasses in Hyperspectral Images

NASA Astrophysics Data System (ADS)

Wu, W.; Chen, G. Y.; Kang, R.; Xia, J. C.; Huang, Y. P.; Chen, K. J.

2017-07-01

During slaughtering and further processing, chicken carcasses are inevitably contaminated by microbial pathogen contaminants. Due to food safety concerns, many countries implement a zero-tolerance policy that forbids the placement of visibly contaminated carcasses in ice-water chiller tanks during processing. Manual detection of contaminants is labor consuming and imprecise. Here, a successive projections algorithm (SPA)-multivariable linear regression (MLR) classifier based on an optimal performance threshold was developed for automatic detection of contaminants on chicken carcasses. Hyperspectral images were obtained using a hyperspectral imaging system. A regression model of the classifier was established by MLR based on twelve characteristic wavelengths (505, 537, 561, 562, 564, 575, 604, 627, 656, 665, 670, and 689 nm) selected by SPA , and the optimal threshold T = 1 was obtained from the receiver operating characteristic (ROC) analysis. The SPA-MLR classifier provided the best detection results when compared with the SPA-partial least squares (PLS) regression classifier and the SPA-least squares supported vector machine (LS-SVM) classifier. The true positive rate (TPR) of 100% and the false positive rate (FPR) of 0.392% indicate that the SPA-MLR classifier can utilize spatial and spectral information to effectively detect contaminants on chicken carcasses.

Evaluation of multivariate linear regression and artificial neural networks in prediction of water quality parameters

PubMed Central

2014-01-01

This paper examined the efficiency of multivariate linear regression (MLR) and artificial neural network (ANN) models in prediction of two major water quality parameters in a wastewater treatment plant. Biochemical oxygen demand (BOD) and chemical oxygen demand (COD) as well as indirect indicators of organic matters are representative parameters for sewer water quality. Performance of the ANN models was evaluated using coefficient of correlation (r), root mean square error (RMSE) and bias values. The computed values of BOD and COD by model, ANN method and regression analysis were in close agreement with their respective measured values. Results showed that the ANN performance model was better than the MLR model. Comparative indices of the optimized ANN with input values of temperature (T), pH, total suspended solid (TSS) and total suspended (TS) for prediction of BOD was RMSE = 25.1 mg/L, r = 0.83 and for prediction of COD was RMSE = 49.4 mg/L, r = 0.81. It was found that the ANN model could be employed successfully in estimating the BOD and COD in the inlet of wastewater biochemical treatment plants. Moreover, sensitive examination results showed that pH parameter have more effect on BOD and COD predicting to another parameters. Also, both implemented models have predicted BOD better than COD. PMID:24456676

Epidemiologic programs for computers and calculators. A microcomputer program for multiple logistic regression by unconditional and conditional maximum likelihood methods.

PubMed

Campos-Filho, N; Franco, E L

1989-02-01

A frequent procedure in matched case-control studies is to report results from the multivariate unmatched analyses if they do not differ substantially from the ones obtained after conditioning on the matching variables. Although conceptually simple, this rule requires that an extensive series of logistic regression models be evaluated by both the conditional and unconditional maximum likelihood methods. Most computer programs for logistic regression employ only one maximum likelihood method, which requires that the analyses be performed in separate steps. This paper describes a Pascal microcomputer (IBM PC) program that performs multiple logistic regression by both maximum likelihood estimation methods, which obviates the need for switching between programs to obtain relative risk estimates from both matched and unmatched analyses. The program calculates most standard statistics and allows factoring of categorical or continuous variables by two distinct methods of contrast. A built-in, descriptive statistics option allows the user to inspect the distribution of cases and controls across categories of any given variable.

Comparing lagged linear correlation, lagged regression, Granger causality, and vector autoregression for uncovering associations in EHR data.

PubMed

Levine, Matthew E; Albers, David J; Hripcsak, George

2016-01-01

Time series analysis methods have been shown to reveal clinical and biological associations in data collected in the electronic health record. We wish to develop reliable high-throughput methods for identifying adverse drug effects that are easy to implement and produce readily interpretable results. To move toward this goal, we used univariate and multivariate lagged regression models to investigate associations between twenty pairs of drug orders and laboratory measurements. Multivariate lagged regression models exhibited higher sensitivity and specificity than univariate lagged regression in the 20 examples, and incorporating autoregressive terms for labs and drugs produced more robust signals in cases of known associations among the 20 example pairings. Moreover, including inpatient admission terms in the model attenuated the signals for some cases of unlikely associations, demonstrating how multivariate lagged regression models' explicit handling of context-based variables can provide a simple way to probe for health-care processes that confound analyses of EHR data.

Television Viewing and Its Association with Sedentary Behaviors, Self-Rated Heath and Academic Performance among Secondary School Students in Peru.

PubMed

Sharma, Bimala; Cosme Chavez, Rosemary; Jeong, Ae Suk; Nam, Eun Woo

2017-04-05

The study assessed television viewing >2 h a day and its association with sedentary behaviors, self-rated health, and academic performance among secondary school adolescents. A cross-sectional survey was conducted among randomly selected students in Lima in 2015. We measured self-reported responses of students using a standard questionnaire, and conducted in-depth interviews with 10 parents and 10 teachers. Chi-square test, correlation and multivariate logistic regression analysis were performed among 1234 students, and thematic analysis technique was used for qualitative information. A total of 23.1% adolescents reported watching television >2 h a day. Qualitative findings also show that adolescents spend most of their leisure time watching television, playing video games or using the Internet. Television viewing had a significant positive correlation with video game use in males and older adolescents, with Internet use in both sexes, and a negative correlation with self-rated health and academic performance in females. Multivariate logistic regression analysis shows that television viewing >2 h a day, independent of physical activity was associated with video games use >2 h a day, Internet use >2 h a day, poor/fair self-rated health and poor self-reported academic performance. Television viewing time and sex had a significant interaction effect on both video game use >2 h a day and Internet use >2 h a day. Reducing television viewing time may be an effective strategy for improving health and academic performance in adolescents.

Television Viewing and Its Association with Sedentary Behaviors, Self-Rated Health and Academic Performance among Secondary School Students in Peru

PubMed Central

Sharma, Bimala; Cosme Chavez, Rosemary; Jeong, Ae Suk; Nam, Eun Woo

2017-01-01

The study assessed television viewing >2 h a day and its association with sedentary behaviors, self-rated health, and academic performance among secondary school adolescents. A cross-sectional survey was conducted among randomly selected students in Lima in 2015. We measured self-reported responses of students using a standard questionnaire, and conducted in-depth interviews with 10 parents and 10 teachers. Chi-square test, correlation and multivariate logistic regression analysis were performed among 1234 students, and thematic analysis technique was used for qualitative information. A total of 23.1% adolescents reported watching television >2 h a day. Qualitative findings also show that adolescents spend most of their leisure time watching television, playing video games or using the Internet. Television viewing had a significant positive correlation with video game use in males and older adolescents, with Internet use in both sexes, and a negative correlation with self-rated health and academic performance in females. Multivariate logistic regression analysis shows that television viewing >2 h a day, independent of physical activity was associated with video games use >2 h a day, Internet use >2 h a day, poor/fair self-rated health and poor self-reported academic performance. Television viewing time and sex had a significant interaction effect on both video game use >2 h a day and Internet use >2 h a day. Reducing television viewing time may be an effective strategy for improving health and academic performance in adolescents. PMID:28379202

Genetic parameters for growth characteristics of free-range chickens under univariate random regression models.

PubMed

Rovadoscki, Gregori A; Petrini, Juliana; Ramirez-Diaz, Johanna; Pertile, Simone F N; Pertille, Fábio; Salvian, Mayara; Iung, Laiza H S; Rodriguez, Mary Ana P; Zampar, Aline; Gaya, Leila G; Carvalho, Rachel S B; Coelho, Antonio A D; Savino, Vicente J M; Coutinho, Luiz L; Mourão, Gerson B

2016-09-01

Repeated measures from the same individual have been analyzed by using repeatability and finite dimension models under univariate or multivariate analyses. However, in the last decade, the use of random regression models for genetic studies with longitudinal data have become more common. Thus, the aim of this research was to estimate genetic parameters for body weight of four experimental chicken lines by using univariate random regression models. Body weight data from hatching to 84 days of age (n = 34,730) from four experimental free-range chicken lines (7P, Caipirão da ESALQ, Caipirinha da ESALQ and Carijó Barbado) were used. The analysis model included the fixed effects of contemporary group (gender and rearing system), fixed regression coefficients for age at measurement, and random regression coefficients for permanent environmental effects and additive genetic effects. Heterogeneous variances for residual effects were considered, and one residual variance was assigned for each of six subclasses of age at measurement. Random regression curves were modeled by using Legendre polynomials of the second and third orders, with the best model chosen based on the Akaike Information Criterion, Bayesian Information Criterion, and restricted maximum likelihood. Multivariate analyses under the same animal mixed model were also performed for the validation of the random regression models. The Legendre polynomials of second order were better for describing the growth curves of the lines studied. Moderate to high heritabilities (h(2) = 0.15 to 0.98) were estimated for body weight between one and 84 days of age, suggesting that selection for body weight at all ages can be used as a selection criteria. Genetic correlations among body weight records obtained through multivariate analyses ranged from 0.18 to 0.96, 0.12 to 0.89, 0.06 to 0.96, and 0.28 to 0.96 in 7P, Caipirão da ESALQ, Caipirinha da ESALQ, and Carijó Barbado chicken lines, respectively. Results indicate that genetic gain for body weight can be achieved by selection. Also, selection for body weight at 42 days of age can be maintained as a selection criterion. © 2016 Poultry Science Association Inc.

Using Multivariate Regression Model with Least Absolute Shrinkage and Selection Operator (LASSO) to Predict the Incidence of Xerostomia after Intensity-Modulated Radiotherapy for Head and Neck Cancer

PubMed Central

Ting, Hui-Min; Chang, Liyun; Huang, Yu-Jie; Wu, Jia-Ming; Wang, Hung-Yu; Horng, Mong-Fong; Chang, Chun-Ming; Lan, Jen-Hong; Huang, Ya-Yu; Fang, Fu-Min; Leung, Stephen Wan

2014-01-01

Purpose The aim of this study was to develop a multivariate logistic regression model with least absolute shrinkage and selection operator (LASSO) to make valid predictions about the incidence of moderate-to-severe patient-rated xerostomia among head and neck cancer (HNC) patients treated with IMRT. Methods and Materials Quality of life questionnaire datasets from 206 patients with HNC were analyzed. The European Organization for Research and Treatment of Cancer QLQ-H&N35 and QLQ-C30 questionnaires were used as the endpoint evaluation. The primary endpoint (grade 3+ xerostomia) was defined as moderate-to-severe xerostomia at 3 (XER3m) and 12 months (XER12m) after the completion of IMRT. Normal tissue complication probability (NTCP) models were developed. The optimal and suboptimal numbers of prognostic factors for a multivariate logistic regression model were determined using the LASSO with bootstrapping technique. Statistical analysis was performed using the scaled Brier score, Nagelkerke R2, chi-squared test, Omnibus, Hosmer-Lemeshow test, and the AUC. Results Eight prognostic factors were selected by LASSO for the 3-month time point: Dmean-c, Dmean-i, age, financial status, T stage, AJCC stage, smoking, and education. Nine prognostic factors were selected for the 12-month time point: Dmean-i, education, Dmean-c, smoking, T stage, baseline xerostomia, alcohol abuse, family history, and node classification. In the selection of the suboptimal number of prognostic factors by LASSO, three suboptimal prognostic factors were fine-tuned by Hosmer-Lemeshow test and AUC, i.e., Dmean-c, Dmean-i, and age for the 3-month time point. Five suboptimal prognostic factors were also selected for the 12-month time point, i.e., Dmean-i, education, Dmean-c, smoking, and T stage. The overall performance for both time points of the NTCP model in terms of scaled Brier score, Omnibus, and Nagelkerke R2 was satisfactory and corresponded well with the expected values. Conclusions Multivariate NTCP models with LASSO can be used to predict patient-rated xerostomia after IMRT. PMID:24586971

Using multivariate regression model with least absolute shrinkage and selection operator (LASSO) to predict the incidence of Xerostomia after intensity-modulated radiotherapy for head and neck cancer.

PubMed

Lee, Tsair-Fwu; Chao, Pei-Ju; Ting, Hui-Min; Chang, Liyun; Huang, Yu-Jie; Wu, Jia-Ming; Wang, Hung-Yu; Horng, Mong-Fong; Chang, Chun-Ming; Lan, Jen-Hong; Huang, Ya-Yu; Fang, Fu-Min; Leung, Stephen Wan

2014-01-01

The aim of this study was to develop a multivariate logistic regression model with least absolute shrinkage and selection operator (LASSO) to make valid predictions about the incidence of moderate-to-severe patient-rated xerostomia among head and neck cancer (HNC) patients treated with IMRT. Quality of life questionnaire datasets from 206 patients with HNC were analyzed. The European Organization for Research and Treatment of Cancer QLQ-H&N35 and QLQ-C30 questionnaires were used as the endpoint evaluation. The primary endpoint (grade 3(+) xerostomia) was defined as moderate-to-severe xerostomia at 3 (XER3m) and 12 months (XER12m) after the completion of IMRT. Normal tissue complication probability (NTCP) models were developed. The optimal and suboptimal numbers of prognostic factors for a multivariate logistic regression model were determined using the LASSO with bootstrapping technique. Statistical analysis was performed using the scaled Brier score, Nagelkerke R(2), chi-squared test, Omnibus, Hosmer-Lemeshow test, and the AUC. Eight prognostic factors were selected by LASSO for the 3-month time point: Dmean-c, Dmean-i, age, financial status, T stage, AJCC stage, smoking, and education. Nine prognostic factors were selected for the 12-month time point: Dmean-i, education, Dmean-c, smoking, T stage, baseline xerostomia, alcohol abuse, family history, and node classification. In the selection of the suboptimal number of prognostic factors by LASSO, three suboptimal prognostic factors were fine-tuned by Hosmer-Lemeshow test and AUC, i.e., Dmean-c, Dmean-i, and age for the 3-month time point. Five suboptimal prognostic factors were also selected for the 12-month time point, i.e., Dmean-i, education, Dmean-c, smoking, and T stage. The overall performance for both time points of the NTCP model in terms of scaled Brier score, Omnibus, and Nagelkerke R(2) was satisfactory and corresponded well with the expected values. Multivariate NTCP models with LASSO can be used to predict patient-rated xerostomia after IMRT.

The association between a body shape index and cardiovascular risk in overweight and obese children and adolescents.

PubMed

Mameli, Chiara; Krakauer, Nir Y; Krakauer, Jesse C; Bosetti, Alessandra; Ferrari, Chiara Matilde; Moiana, Norma; Schneider, Laura; Borsani, Barbara; Genoni, Teresa; Zuccotti, Gianvincenzo

2018-01-01

A Body Shape Index (ABSI) and normalized hip circumference (Hip Index, HI) have been recently shown to be strong risk factors for mortality and for cardiovascular disease in adults. We conducted an observational cross-sectional study to evaluate the relationship between ABSI, HI and cardiometabolic risk factors and obesity-related comorbidities in overweight and obese children and adolescents aged 2-18 years. We performed multivariate linear and logistic regression analyses with BMI, ABSI, and HI age and sex normalized z scores as predictors to examine the association with cardiometabolic risk markers (systolic and diastolic blood pressure, fasting glucose and insulin, total cholesterol and its components, transaminases, fat mass % detected by bioelectrical impedance analysis) and obesity-related conditions (including hepatic steatosis and metabolic syndrome). We recruited 217 patients (114 males), mean age 11.3 years. Multivariate linear regression showed a significant association of ABSI z score with 10 out of 15 risk markers expressed as continuous variables, while BMI z score showed a significant correlation with 9 and HI only with 1. In multivariate logistic regression to predict occurrence of obesity-related conditions and above-threshold values of risk factors, BMI z score was significantly correlated to 7 out of 12, ABSI to 5, and HI to 1. Overall, ABSI is an independent anthropometric index that was significantly associated with cardiometabolic risk markers in a pediatric population affected by overweight and obesity.

Visual Analysis of North Atlantic Hurricane Trends Using Parallel Coordinates and Statistical Techniques

DTIC Science & Technology

2008-07-07

analyzing multivariate data sets. The system was developed using the Java Development Kit (JDK) version 1.5; and it yields interactive performance on a... script and captures output from the MATLAB’s “regress” and “stepwisefit” utilities that perform simple and stepwise regression, respectively. The MATLAB...Statistical Association, vol. 85, no. 411, pp. 664–675, 1990. [9] H. Hauser, F. Ledermann, and H. Doleisch, “ Angular brushing of extended parallel coordinates

Simple agrometeorological models for estimating Guineagrass yield in Southeast Brazil.

PubMed

Pezzopane, José Ricardo Macedo; da Cruz, Pedro Gomes; Santos, Patricia Menezes; Bosi, Cristiam; de Araujo, Leandro Coelho

2014-09-01

The objective of this work was to develop and evaluate agrometeorological models to simulate the production of Guineagrass. For this purpose, we used forage yield from 54 growing periods between December 2004-January 2007 and April 2010-March 2012 in irrigated and non-irrigated pastures in São Carlos, São Paulo state, Brazil (latitude 21°57'42″ S, longitude 47°50'28″ W and altitude 860 m). Initially we performed linear regressions between the agrometeorological variables and the average dry matter accumulation rate for irrigated conditions. Then we determined the effect of soil water availability on the relative forage yield considering irrigated and non-irrigated pastures, by means of segmented linear regression among water balance and relative production variables (dry matter accumulation rates with and without irrigation). The models generated were evaluated with independent data related to 21 growing periods without irrigation in the same location, from eight growing periods in 2000 and 13 growing periods between December 2004-January 2007 and April 2010-March 2012. The results obtained show the satisfactory predictive capacity of the agrometeorological models under irrigated conditions based on univariate regression (mean temperature, minimum temperature and potential evapotranspiration or degreedays) or multivariate regression. The response of irrigation on production was well correlated with the climatological water balance variables (ratio between actual and potential evapotranspiration or between actual and maximum soil water storage). The models that performed best for estimating Guineagrass yield without irrigation were based on minimum temperature corrected by relative soil water storage, determined by the ratio between the actual soil water storage and the soil water holding capacity.irrigation in the same location, in 2000, 2010 and 2011. The results obtained show the satisfactory predictive capacity of the agrometeorological models under irrigated conditions based on univariate regression (mean temperature, potential evapotranspiration or degree-days) or multivariate regression. The response of irrigation on production was well correlated with the climatological water balance variables (ratio between actual and potential evapotranspiration or between actual and maximum soil water storage). The models that performed best for estimating Guineagrass yield without irrigation were based on degree-days corrected by the water deficit factor.

A matrix-based method of moments for fitting the multivariate random effects model for meta-analysis and meta-regression

PubMed Central

Jackson, Dan; White, Ian R; Riley, Richard D

2013-01-01

Multivariate meta-analysis is becoming more commonly used. Methods for fitting the multivariate random effects model include maximum likelihood, restricted maximum likelihood, Bayesian estimation and multivariate generalisations of the standard univariate method of moments. Here, we provide a new multivariate method of moments for estimating the between-study covariance matrix with the properties that (1) it allows for either complete or incomplete outcomes and (2) it allows for covariates through meta-regression. Further, for complete data, it is invariant to linear transformations. Our method reduces to the usual univariate method of moments, proposed by DerSimonian and Laird, in a single dimension. We illustrate our method and compare it with some of the alternatives using a simulation study and a real example. PMID:23401213

Transport modeling and multivariate adaptive regression splines for evaluating performance of ASR systems in freshwater aquifers

NASA Astrophysics Data System (ADS)

Forghani, Ali; Peralta, Richard C.

2017-10-01

The study presents a procedure using solute transport and statistical models to evaluate the performance of aquifer storage and recovery (ASR) systems designed to earn additional water rights in freshwater aquifers. The recovery effectiveness (REN) index quantifies the performance of these ASR systems. REN is the proportion of the injected water that the same ASR well can recapture during subsequent extraction periods. To estimate REN for individual ASR wells, the presented procedure uses finely discretized groundwater flow and contaminant transport modeling. Then, the procedure uses multivariate adaptive regression splines (MARS) analysis to identify the significant variables affecting REN, and to identify the most recovery-effective wells. Achieving REN values close to 100% is the desire of the studied 14-well ASR system operator. This recovery is feasible for most of the ASR wells by extracting three times the injectate volume during the same year as injection. Most of the wells would achieve RENs below 75% if extracting merely the same volume as they injected. In other words, recovering almost all the same water molecules that are injected requires having a pre-existing water right to extract groundwater annually. MARS shows that REN most significantly correlates with groundwater flow velocity, or hydraulic conductivity and hydraulic gradient. MARS results also demonstrate that maximizing REN requires utilizing the wells located in areas with background Darcian groundwater velocities less than 0.03 m/d. The study also highlights the superiority of MARS over regular multiple linear regressions to identify the wells that can provide the maximum REN. This is the first reported application of MARS for evaluating performance of an ASR system in fresh water aquifers.

SMURC: High-Dimension Small-Sample Multivariate Regression With Covariance Estimation.

PubMed

Bayar, Belhassen; Bouaynaya, Nidhal; Shterenberg, Roman

2017-03-01

We consider a high-dimension low sample-size multivariate regression problem that accounts for correlation of the response variables. The system is underdetermined as there are more parameters than samples. We show that the maximum likelihood approach with covariance estimation is senseless because the likelihood diverges. We subsequently propose a normalization of the likelihood function that guarantees convergence. We call this method small-sample multivariate regression with covariance (SMURC) estimation. We derive an optimization problem and its convex approximation to compute SMURC. Simulation results show that the proposed algorithm outperforms the regularized likelihood estimator with known covariance matrix and the sparse conditional Gaussian graphical model. We also apply SMURC to the inference of the wing-muscle gene network of the Drosophila melanogaster (fruit fly).

A graphical method to evaluate spectral preprocessing in multivariate regression calibrations: example with Savitzky-Golay filters and partial least squares regression.

PubMed

Delwiche, Stephen R; Reeves, James B

2010-01-01

In multivariate regression analysis of spectroscopy data, spectral preprocessing is often performed to reduce unwanted background information (offsets, sloped baselines) or accentuate absorption features in intrinsically overlapping bands. These procedures, also known as pretreatments, are commonly smoothing operations or derivatives. While such operations are often useful in reducing the number of latent variables of the actual decomposition and lowering residual error, they also run the risk of misleading the practitioner into accepting calibration equations that are poorly adapted to samples outside of the calibration. The current study developed a graphical method to examine this effect on partial least squares (PLS) regression calibrations of near-infrared (NIR) reflection spectra of ground wheat meal with two analytes, protein content and sodium dodecyl sulfate sedimentation (SDS) volume (an indicator of the quantity of the gluten proteins that contribute to strong doughs). These two properties were chosen because of their differing abilities to be modeled by NIR spectroscopy: excellent for protein content, fair for SDS sedimentation volume. To further demonstrate the potential pitfalls of preprocessing, an artificial component, a randomly generated value, was included in PLS regression trials. Savitzky-Golay (digital filter) smoothing, first-derivative, and second-derivative preprocess functions (5 to 25 centrally symmetric convolution points, derived from quadratic polynomials) were applied to PLS calibrations of 1 to 15 factors. The results demonstrated the danger of an over reliance on preprocessing when (1) the number of samples used in a multivariate calibration is low (<50), (2) the spectral response of the analyte is weak, and (3) the goodness of the calibration is based on the coefficient of determination (R(2)) rather than a term based on residual error. The graphical method has application to the evaluation of other preprocess functions and various types of spectroscopy data.

Prognostic Value of Pretherapeutic Tumor-to-Blood Standardized Uptake Ratio in Patients with Esophageal Carcinoma.

PubMed

Bütof, Rebecca; Hofheinz, Frank; Zöphel, Klaus; Stadelmann, Tobias; Schmollack, Julia; Jentsch, Christina; Löck, Steffen; Kotzerke, Jörg; Baumann, Michael; van den Hoff, Jörg

2015-08-01

Despite ongoing efforts to develop new treatment options, the prognosis for patients with inoperable esophageal carcinoma is still poor and the reliability of individual therapy outcome prediction based on clinical parameters is not convincing. The aim of this work was to investigate whether PET can provide independent prognostic information in such a patient group and whether the tumor-to-blood standardized uptake ratio (SUR) can improve the prognostic value of tracer uptake values. (18)F-FDG PET/CT was performed in 130 consecutive patients (mean age ± SD, 63 ± 11 y; 113 men, 17 women) with newly diagnosed esophageal cancer before definitive radiochemotherapy. In the PET images, the metabolically active tumor volume (MTV) of the primary tumor was delineated with an adaptive threshold method. The blood standardized uptake value (SUV) was determined by manually delineating the aorta in the low-dose CT. SUR values were computed as the ratio of tumor SUV and blood SUV. Uptake values were scan-time-corrected to 60 min after injection. Univariate Cox regression and Kaplan-Meier analysis with respect to overall survival (OS), distant metastases-free survival (DM), and locoregional tumor control (LRC) was performed. Additionally, a multivariate Cox regression including clinically relevant parameters was performed. In multivariate Cox regression with respect to OS, including T stage, N stage, and smoking state, MTV- and SUR-based parameters were significant prognostic factors for OS with similar effect size. Multivariate analysis with respect to DM revealed smoking state, MTV, and all SUR-based parameters as significant prognostic factors. The highest hazard ratios (HRs) were found for scan-time-corrected maximum SUR (HR = 3.9) and mean SUR (HR = 4.4). None of the PET parameters was associated with LRC. Univariate Cox regression with respect to LRC revealed a significant effect only for N stage greater than 0 (P = 0.048). PET provides independent prognostic information for OS and DM but not for LRC in patients with locally advanced esophageal carcinoma treated with definitive radiochemotherapy in addition to clinical parameters. Among the investigated uptake-based parameters, only SUR was an independent prognostic factor for OS and DM. These results suggest that the prognostic value of tracer uptake can be improved when characterized by SUR instead of SUV. Further investigations are required to confirm these preliminary results. © 2015 by the Society of Nuclear Medicine and Molecular Imaging, Inc.

Multivariate regression model for predicting lumber grade volumes of northern red oak sawlogs

Treesearch

Daniel A. Yaussy; Robert L. Brisbin

1983-01-01

A multivariate regression model was developed to predict green board-foot yields for the seven common factory lumber grades processed from northern red oak (Quercus rubra L.) factory grade logs. The model uses the standard log measurements of grade, scaling diameter, length, and percent defect. It was validated with an independent data set. The model...

Predictive and mechanistic multivariate linear regression models for reaction development

PubMed Central

Santiago, Celine B.; Guo, Jing-Yao

2018-01-01

Multivariate Linear Regression (MLR) models utilizing computationally-derived and empirically-derived physical organic molecular descriptors are described in this review. Several reports demonstrating the effectiveness of this methodological approach towards reaction optimization and mechanistic interrogation are discussed. A detailed protocol to access quantitative and predictive MLR models is provided as a guide for model development and parameter analysis. PMID:29719711

Multivariate regression model for predicting yields of grade lumber from yellow birch sawlogs

Treesearch

Andrew F. Howard; Daniel A. Yaussy

1986-01-01

A multivariate regression model was developed to predict green board-foot yields for the common grades of factory lumber processed from yellow birch factory-grade logs. The model incorporates the standard log measurements of scaling diameter, length, proportion of scalable defects, and the assigned USDA Forest Service log grade. Differences in yields between band and...

Validation of cross-sectional time series and multivariate adaptive regression splines models for the prediction of energy expenditure in children and adolescents using doubly labeled water

USDA-ARS?s Scientific Manuscript database

Accurate, nonintrusive, and inexpensive techniques are needed to measure energy expenditure (EE) in free-living populations. Our primary aim in this study was to validate cross-sectional time series (CSTS) and multivariate adaptive regression splines (MARS) models based on observable participant cha...

Higher-order Multivariable Polynomial Regression to Estimate Human Affective States

NASA Astrophysics Data System (ADS)

Wei, Jie; Chen, Tong; Liu, Guangyuan; Yang, Jiemin

2016-03-01

From direct observations, facial, vocal, gestural, physiological, and central nervous signals, estimating human affective states through computational models such as multivariate linear-regression analysis, support vector regression, and artificial neural network, have been proposed in the past decade. In these models, linear models are generally lack of precision because of ignoring intrinsic nonlinearities of complex psychophysiological processes; and nonlinear models commonly adopt complicated algorithms. To improve accuracy and simplify model, we introduce a new computational modeling method named as higher-order multivariable polynomial regression to estimate human affective states. The study employs standardized pictures in the International Affective Picture System to induce thirty subjects’ affective states, and obtains pure affective patterns of skin conductance as input variables to the higher-order multivariable polynomial model for predicting affective valence and arousal. Experimental results show that our method is able to obtain efficient correlation coefficients of 0.98 and 0.96 for estimation of affective valence and arousal, respectively. Moreover, the method may provide certain indirect evidences that valence and arousal have their brain’s motivational circuit origins. Thus, the proposed method can serve as a novel one for efficiently estimating human affective states.

Higher-order Multivariable Polynomial Regression to Estimate Human Affective States

PubMed Central

Wei, Jie; Chen, Tong; Liu, Guangyuan; Yang, Jiemin

2016-01-01

From direct observations, facial, vocal, gestural, physiological, and central nervous signals, estimating human affective states through computational models such as multivariate linear-regression analysis, support vector regression, and artificial neural network, have been proposed in the past decade. In these models, linear models are generally lack of precision because of ignoring intrinsic nonlinearities of complex psychophysiological processes; and nonlinear models commonly adopt complicated algorithms. To improve accuracy and simplify model, we introduce a new computational modeling method named as higher-order multivariable polynomial regression to estimate human affective states. The study employs standardized pictures in the International Affective Picture System to induce thirty subjects’ affective states, and obtains pure affective patterns of skin conductance as input variables to the higher-order multivariable polynomial model for predicting affective valence and arousal. Experimental results show that our method is able to obtain efficient correlation coefficients of 0.98 and 0.96 for estimation of affective valence and arousal, respectively. Moreover, the method may provide certain indirect evidences that valence and arousal have their brain’s motivational circuit origins. Thus, the proposed method can serve as a novel one for efficiently estimating human affective states. PMID:26996254

KMgene: a unified R package for gene-based association analysis for complex traits.

PubMed

Yan, Qi; Fang, Zhou; Chen, Wei; Stegle, Oliver

2018-02-09

In this report, we introduce an R package KMgene for performing gene-based association tests for familial, multivariate or longitudinal traits using kernel machine (KM) regression under a generalized linear mixed model (GLMM) framework. Extensive simulations were performed to evaluate the validity of the approaches implemented in KMgene. http://cran.r-project.org/web/packages/KMgene. qi.yan@chp.edu or wei.chen@chp.edu. Supplementary data are available at Bioinformatics online. © The Author(s) 2018. Published by Oxford University Press.

Regression Models For Multivariate Count Data

PubMed Central

Zhang, Yiwen; Zhou, Hua; Zhou, Jin; Sun, Wei

2016-01-01

Data with multivariate count responses frequently occur in modern applications. The commonly used multinomial-logit model is limiting due to its restrictive mean-variance structure. For instance, analyzing count data from the recent RNA-seq technology by the multinomial-logit model leads to serious errors in hypothesis testing. The ubiquity of over-dispersion and complicated correlation structures among multivariate counts calls for more flexible regression models. In this article, we study some generalized linear models that incorporate various correlation structures among the counts. Current literature lacks a treatment of these models, partly due to the fact that they do not belong to the natural exponential family. We study the estimation, testing, and variable selection for these models in a unifying framework. The regression models are compared on both synthetic and real RNA-seq data. PMID:28348500

Regression Models For Multivariate Count Data.

PubMed

Zhang, Yiwen; Zhou, Hua; Zhou, Jin; Sun, Wei

2017-01-01

Data with multivariate count responses frequently occur in modern applications. The commonly used multinomial-logit model is limiting due to its restrictive mean-variance structure. For instance, analyzing count data from the recent RNA-seq technology by the multinomial-logit model leads to serious errors in hypothesis testing. The ubiquity of over-dispersion and complicated correlation structures among multivariate counts calls for more flexible regression models. In this article, we study some generalized linear models that incorporate various correlation structures among the counts. Current literature lacks a treatment of these models, partly due to the fact that they do not belong to the natural exponential family. We study the estimation, testing, and variable selection for these models in a unifying framework. The regression models are compared on both synthetic and real RNA-seq data.

Multi-analyte quantification in bioprocesses by Fourier-transform-infrared spectroscopy by partial least squares regression and multivariate curve resolution.

PubMed

Koch, Cosima; Posch, Andreas E; Goicoechea, Héctor C; Herwig, Christoph; Lendl, Bernhard

2014-01-07

This paper presents the quantification of Penicillin V and phenoxyacetic acid, a precursor, inline during Pencillium chrysogenum fermentations by FTIR spectroscopy and partial least squares (PLS) regression and multivariate curve resolution - alternating least squares (MCR-ALS). First, the applicability of an attenuated total reflection FTIR fiber optic probe was assessed offline by measuring standards of the analytes of interest and investigating matrix effects of the fermentation broth. Then measurements were performed inline during four fed-batch fermentations with online HPLC for the determination of Penicillin V and phenoxyacetic acid as reference analysis. PLS and MCR-ALS models were built using these data and validated by comparison of single analyte spectra with the selectivity ratio of the PLS models and the extracted spectral traces of the MCR-ALS models, respectively. The achieved root mean square errors of cross-validation for the PLS regressions were 0.22 g L(-1) for Penicillin V and 0.32 g L(-1) for phenoxyacetic acid and the root mean square errors of prediction for MCR-ALS were 0.23 g L(-1) for Penicillin V and 0.15 g L(-1) for phenoxyacetic acid. A general work-flow for building and assessing chemometric regression models for the quantification of multiple analytes in bioprocesses by FTIR spectroscopy is given. Copyright © 2013 The Authors. Published by Elsevier B.V. All rights reserved.

Controlled pattern imputation for sensitivity analysis of longitudinal binary and ordinal outcomes with nonignorable dropout.

PubMed

Tang, Yongqiang

2018-04-30

The controlled imputation method refers to a class of pattern mixture models that have been commonly used as sensitivity analyses of longitudinal clinical trials with nonignorable dropout in recent years. These pattern mixture models assume that participants in the experimental arm after dropout have similar response profiles to the control participants or have worse outcomes than otherwise similar participants who remain on the experimental treatment. In spite of its popularity, the controlled imputation has not been formally developed for longitudinal binary and ordinal outcomes partially due to the lack of a natural multivariate distribution for such endpoints. In this paper, we propose 2 approaches for implementing the controlled imputation for binary and ordinal data based respectively on the sequential logistic regression and the multivariate probit model. Efficient Markov chain Monte Carlo algorithms are developed for missing data imputation by using the monotone data augmentation technique for the sequential logistic regression and a parameter-expanded monotone data augmentation scheme for the multivariate probit model. We assess the performance of the proposed procedures by simulation and the analysis of a schizophrenia clinical trial and compare them with the fully conditional specification, last observation carried forward, and baseline observation carried forward imputation methods. Copyright © 2018 John Wiley & Sons, Ltd.

LASSO NTCP predictors for the incidence of xerostomia in patients with head and neck squamous cell carcinoma and nasopharyngeal carcinoma

PubMed Central

Lee, Tsair-Fwu; Liou, Ming-Hsiang; Huang, Yu-Jie; Chao, Pei-Ju; Ting, Hui-Min; Lee, Hsiao-Yi

2014-01-01

To predict the incidence of moderate-to-severe patient-reported xerostomia among head and neck squamous cell carcinoma (HNSCC) and nasopharyngeal carcinoma (NPC) patients treated with intensity-modulated radiotherapy (IMRT). Multivariable normal tissue complication probability (NTCP) models were developed by using quality of life questionnaire datasets from 152 patients with HNSCC and 84 patients with NPC. The primary endpoint was defined as moderate-to-severe xerostomia after IMRT. The numbers of predictive factors for a multivariable logistic regression model were determined using the least absolute shrinkage and selection operator (LASSO) with bootstrapping technique. Four predictive models were achieved by LASSO with the smallest number of factors while preserving predictive value with higher AUC performance. For all models, the dosimetric factors for the mean dose given to the contralateral and ipsilateral parotid gland were selected as the most significant predictors. Followed by the different clinical and socio-economic factors being selected, namely age, financial status, T stage, and education for different models were chosen. The predicted incidence of xerostomia for HNSCC and NPC patients can be improved by using multivariable logistic regression models with LASSO technique. The predictive model developed in HNSCC cannot be generalized to NPC cohort treated with IMRT without validation and vice versa. PMID:25163814

Estimation of soil cation exchange capacity using Genetic Expression Programming (GEP) and Multivariate Adaptive Regression Splines (MARS)

NASA Astrophysics Data System (ADS)

Emamgolizadeh, S.; Bateni, S. M.; Shahsavani, D.; Ashrafi, T.; Ghorbani, H.

2015-10-01

The soil cation exchange capacity (CEC) is one of the main soil chemical properties, which is required in various fields such as environmental and agricultural engineering as well as soil science. In situ measurement of CEC is time consuming and costly. Hence, numerous studies have used traditional regression-based techniques to estimate CEC from more easily measurable soil parameters (e.g., soil texture, organic matter (OM), and pH). However, these models may not be able to adequately capture the complex and highly nonlinear relationship between CEC and its influential soil variables. In this study, Genetic Expression Programming (GEP) and Multivariate Adaptive Regression Splines (MARS) were employed to estimate CEC from more readily measurable soil physical and chemical variables (e.g., OM, clay, and pH) by developing functional relations. The GEP- and MARS-based functional relations were tested at two field sites in Iran. Results showed that GEP and MARS can provide reliable estimates of CEC. Also, it was found that the MARS model (with root-mean-square-error (RMSE) of 0.318 Cmol+ kg-1 and correlation coefficient (R2) of 0.864) generated slightly better results than the GEP model (with RMSE of 0.270 Cmol+ kg-1 and R2 of 0.807). The performance of GEP and MARS models was compared with two existing approaches, namely artificial neural network (ANN) and multiple linear regression (MLR). The comparison indicated that MARS and GEP outperformed the MLP model, but they did not perform as good as ANN. Finally, a sensitivity analysis was conducted to determine the most and the least influential variables affecting CEC. It was found that OM and pH have the most and least significant effect on CEC, respectively.

Gas-induced susceptibility artefacts on diffusion-weighted MRI of the rectum at 1.5 T - Effect of applying a micro-enema to improve image quality.

PubMed

van Griethuysen, Joost J M; Bus, Elyse M; Hauptmann, Michael; Lahaye, Max J; Maas, Monique; Ter Beek, Leon C; Beets, Geerard L; Bakers, Frans C H; Beets-Tan, Regina G H; Lambregts, Doenja M J

2018-02-01

Assess whether application of a micro-enema can reduce gas-induced susceptibility artefacts in Single-shot Echo Planar Imaging (EPI) Diffusion-weighted imaging of the rectum at 1.5 T. Retrospective analysis of n = 50 rectal cancer patients who each underwent multiple DWI-MRIs (1.5 T) from 2012 to 2016 as part of routine follow-up during a watch-and-wait approach after chemoradiotherapy. From March 2014 DWI-MRIs were routinely acquired after application of a preparatory micro-enema (Microlax ® ; 5 ml; self-administered shortly before acquisition); before March 2014 no bowel preparation was given. In total, 335 scans were scored by an experienced reader for the presence/severity of gas-artefacts (on b1000 DWI), ranging from 0 (no artefact) to 5 (severe artefact). A score ≥3 (moderate-severe) was considered a clinically relevant artefact. A random sample of 100 scans was re-assessed by a second independent reader to study inter-observer effects. Scores were compared between the scans performed without and with a preparatory micro-enema using univariable and multivariable logistic regression taking into account potential confounding factors (age/gender, acquisition parameters, MRI-hardware, rectoscopy prior to MRI). Clinically relevant gas-artefacts were seen in 24.3% (no micro-enema) vs. 3.7% (micro-enema), odds ratios were 0.118 in univariable and 0.230 in multivariable regression (P = 0.0005 and 0.0291). Mean severity score (±SD) was 1.19 ± 1.71 (no-enema) vs 0.32 ± 0.77 (micro-enema), odds ratios were 0.321 (P < 0.0001) and 0.489 (P = 0.0461) in uni- and multivariable regression, respectively. Inter-observer agreement was excellent (κ0.85). Use of a preparatory micro-enema shortly before rectal EPI-DWI examinations performed at 1.5 T MRI significantly reduces both the incidence and severity of gas-induced artefacts, compared to examinations performed without bowel preparation. Copyright © 2017 Elsevier B.V. All rights reserved.

Assessing the response of area burned to changing climate in western boreal North America using a Multivariate Adaptive Regression Splines (MARS) approach

Treesearch

Michael S. Balshi; A. David McGuire; Paul Duffy; Mike Flannigan; John Walsh; Jerry Melillo

2009-01-01

We developed temporally and spatially explicit relationships between air temperature and fuel moisture codes derived from the Canadian Fire Weather Index System to estimate annual area burned at 2.5o (latitude x longitude) resolution using a Multivariate Adaptive Regression Spline (MARS) approach across Alaska and Canada. Burned area was...

Characterizing multivariate decoding models based on correlated EEG spectral features

PubMed Central

McFarland, Dennis J.

2013-01-01

Objective Multivariate decoding methods are popular techniques for analysis of neurophysiological data. The present study explored potential interpretative problems with these techniques when predictors are correlated. Methods Data from sensorimotor rhythm-based cursor control experiments was analyzed offline with linear univariate and multivariate models. Features were derived from autoregressive (AR) spectral analysis of varying model order which produced predictors that varied in their degree of correlation (i.e., multicollinearity). Results The use of multivariate regression models resulted in much better prediction of target position as compared to univariate regression models. However, with lower order AR features interpretation of the spectral patterns of the weights was difficult. This is likely to be due to the high degree of multicollinearity present with lower order AR features. Conclusions Care should be exercised when interpreting the pattern of weights of multivariate models with correlated predictors. Comparison with univariate statistics is advisable. Significance While multivariate decoding algorithms are very useful for prediction their utility for interpretation may be limited when predictors are correlated. PMID:23466267

The PIT-trap-A "model-free" bootstrap procedure for inference about regression models with discrete, multivariate responses.

PubMed

Warton, David I; Thibaut, Loïc; Wang, Yi Alice

2017-01-01

Bootstrap methods are widely used in statistics, and bootstrapping of residuals can be especially useful in the regression context. However, difficulties are encountered extending residual resampling to regression settings where residuals are not identically distributed (thus not amenable to bootstrapping)-common examples including logistic or Poisson regression and generalizations to handle clustered or multivariate data, such as generalised estimating equations. We propose a bootstrap method based on probability integral transform (PIT-) residuals, which we call the PIT-trap, which assumes data come from some marginal distribution F of known parametric form. This method can be understood as a type of "model-free bootstrap", adapted to the problem of discrete and highly multivariate data. PIT-residuals have the key property that they are (asymptotically) pivotal. The PIT-trap thus inherits the key property, not afforded by any other residual resampling approach, that the marginal distribution of data can be preserved under PIT-trapping. This in turn enables the derivation of some standard bootstrap properties, including second-order correctness of pivotal PIT-trap test statistics. In multivariate data, bootstrapping rows of PIT-residuals affords the property that it preserves correlation in data without the need for it to be modelled, a key point of difference as compared to a parametric bootstrap. The proposed method is illustrated on an example involving multivariate abundance data in ecology, and demonstrated via simulation to have improved properties as compared to competing resampling methods.

The PIT-trap—A “model-free” bootstrap procedure for inference about regression models with discrete, multivariate responses

PubMed Central

Thibaut, Loïc; Wang, Yi Alice

2017-01-01

Bootstrap methods are widely used in statistics, and bootstrapping of residuals can be especially useful in the regression context. However, difficulties are encountered extending residual resampling to regression settings where residuals are not identically distributed (thus not amenable to bootstrapping)—common examples including logistic or Poisson regression and generalizations to handle clustered or multivariate data, such as generalised estimating equations. We propose a bootstrap method based on probability integral transform (PIT-) residuals, which we call the PIT-trap, which assumes data come from some marginal distribution F of known parametric form. This method can be understood as a type of “model-free bootstrap”, adapted to the problem of discrete and highly multivariate data. PIT-residuals have the key property that they are (asymptotically) pivotal. The PIT-trap thus inherits the key property, not afforded by any other residual resampling approach, that the marginal distribution of data can be preserved under PIT-trapping. This in turn enables the derivation of some standard bootstrap properties, including second-order correctness of pivotal PIT-trap test statistics. In multivariate data, bootstrapping rows of PIT-residuals affords the property that it preserves correlation in data without the need for it to be modelled, a key point of difference as compared to a parametric bootstrap. The proposed method is illustrated on an example involving multivariate abundance data in ecology, and demonstrated via simulation to have improved properties as compared to competing resampling methods. PMID:28738071

Models of Marine Fish Biodiversity: Assessing Predictors from Three Habitat Classification Schemes.

PubMed

Yates, Katherine L; Mellin, Camille; Caley, M Julian; Radford, Ben T; Meeuwig, Jessica J

2016-01-01

Prioritising biodiversity conservation requires knowledge of where biodiversity occurs. Such knowledge, however, is often lacking. New technologies for collecting biological and physical data coupled with advances in modelling techniques could help address these gaps and facilitate improved management outcomes. Here we examined the utility of environmental data, obtained using different methods, for developing models of both uni- and multivariate biodiversity metrics. We tested which biodiversity metrics could be predicted best and evaluated the performance of predictor variables generated from three types of habitat data: acoustic multibeam sonar imagery, predicted habitat classification, and direct observer habitat classification. We used boosted regression trees (BRT) to model metrics of fish species richness, abundance and biomass, and multivariate regression trees (MRT) to model biomass and abundance of fish functional groups. We compared model performance using different sets of predictors and estimated the relative influence of individual predictors. Models of total species richness and total abundance performed best; those developed for endemic species performed worst. Abundance models performed substantially better than corresponding biomass models. In general, BRT and MRTs developed using predicted habitat classifications performed less well than those using multibeam data. The most influential individual predictor was the abiotic categorical variable from direct observer habitat classification and models that incorporated predictors from direct observer habitat classification consistently outperformed those that did not. Our results show that while remotely sensed data can offer considerable utility for predictive modelling, the addition of direct observer habitat classification data can substantially improve model performance. Thus it appears that there are aspects of marine habitats that are important for modelling metrics of fish biodiversity that are not fully captured by remotely sensed data. As such, the use of remotely sensed data to model biodiversity represents a compromise between model performance and data availability.

Models of Marine Fish Biodiversity: Assessing Predictors from Three Habitat Classification Schemes

PubMed Central

Yates, Katherine L.; Mellin, Camille; Caley, M. Julian; Radford, Ben T.; Meeuwig, Jessica J.

2016-01-01

Prioritising biodiversity conservation requires knowledge of where biodiversity occurs. Such knowledge, however, is often lacking. New technologies for collecting biological and physical data coupled with advances in modelling techniques could help address these gaps and facilitate improved management outcomes. Here we examined the utility of environmental data, obtained using different methods, for developing models of both uni- and multivariate biodiversity metrics. We tested which biodiversity metrics could be predicted best and evaluated the performance of predictor variables generated from three types of habitat data: acoustic multibeam sonar imagery, predicted habitat classification, and direct observer habitat classification. We used boosted regression trees (BRT) to model metrics of fish species richness, abundance and biomass, and multivariate regression trees (MRT) to model biomass and abundance of fish functional groups. We compared model performance using different sets of predictors and estimated the relative influence of individual predictors. Models of total species richness and total abundance performed best; those developed for endemic species performed worst. Abundance models performed substantially better than corresponding biomass models. In general, BRT and MRTs developed using predicted habitat classifications performed less well than those using multibeam data. The most influential individual predictor was the abiotic categorical variable from direct observer habitat classification and models that incorporated predictors from direct observer habitat classification consistently outperformed those that did not. Our results show that while remotely sensed data can offer considerable utility for predictive modelling, the addition of direct observer habitat classification data can substantially improve model performance. Thus it appears that there are aspects of marine habitats that are important for modelling metrics of fish biodiversity that are not fully captured by remotely sensed data. As such, the use of remotely sensed data to model biodiversity represents a compromise between model performance and data availability. PMID:27333202

Lameness detection in dairy cattle: single predictor v. multivariate analysis of image-based posture processing and behaviour and performance sensing.

PubMed

Van Hertem, T; Bahr, C; Schlageter Tello, A; Viazzi, S; Steensels, M; Romanini, C E B; Lokhorst, C; Maltz, E; Halachmi, I; Berckmans, D

2016-09-01

The objective of this study was to evaluate if a multi-sensor system (milk, activity, body posture) was a better classifier for lameness than the single-sensor-based detection models. Between September 2013 and August 2014, 3629 cow observations were collected on a commercial dairy farm in Belgium. Human locomotion scoring was used as reference for the model development and evaluation. Cow behaviour and performance was measured with existing sensors that were already present at the farm. A prototype of three-dimensional-based video recording system was used to quantify automatically the back posture of a cow. For the single predictor comparisons, a receiver operating characteristics curve was made. For the multivariate detection models, logistic regression and generalized linear mixed models (GLMM) were developed. The best lameness classification model was obtained by the multi-sensor analysis (area under the receiver operating characteristics curve (AUC)=0.757±0.029), containing a combination of milk and milking variables, activity and gait and posture variables from videos. Second, the multivariate video-based system (AUC=0.732±0.011) performed better than the multivariate milk sensors (AUC=0.604±0.026) and the multivariate behaviour sensors (AUC=0.633±0.018). The video-based system performed better than the combined behaviour and performance-based detection model (AUC=0.669±0.028), indicating that it is worthwhile to consider a video-based lameness detection system, regardless the presence of other existing sensors in the farm. The results suggest that Θ2, the feature variable for the back curvature around the hip joints, with an AUC of 0.719 is the best single predictor variable for lameness detection based on locomotion scoring. In general, this study showed that the video-based back posture monitoring system is outperforming the behaviour and performance sensing techniques for locomotion scoring-based lameness detection. A GLMM with seven specific variables (walking speed, back posture measurement, daytime activity, milk yield, lactation stage, milk peak flow rate and milk peak conductivity) is the best combination of variables for lameness classification. The accuracy on four-level lameness classification was 60.3%. The accuracy improved to 79.8% for binary lameness classification. The binary GLMM obtained a sensitivity of 68.5% and a specificity of 87.6%, which both exceed the sensitivity (52.1%±4.7%) and specificity (83.2%±2.3%) of the multi-sensor logistic regression model. This shows that the repeated measures analysis in the GLMM, taking into account the individual history of the animal, outperforms the classification when thresholds based on herd level (a statistical population) are used.

Wavelet analysis techniques applied to removing varying spectroscopic background in calibration model for pear sugar content

NASA Astrophysics Data System (ADS)

Liu, Yande; Ying, Yibin; Lu, Huishan; Fu, Xiaping

2005-11-01

A new method is proposed to eliminate the varying background and noise simultaneously for multivariate calibration of Fourier transform near infrared (FT-NIR) spectral signals. An ideal spectrum signal prototype was constructed based on the FT-NIR spectrum of fruit sugar content measurement. The performances of wavelet based threshold de-noising approaches via different combinations of wavelet base functions were compared. Three families of wavelet base function (Daubechies, Symlets and Coiflets) were applied to estimate the performance of those wavelet bases and threshold selection rules by a series of experiments. The experimental results show that the best de-noising performance is reached via the combinations of Daubechies 4 or Symlet 4 wavelet base function. Based on the optimization parameter, wavelet regression models for sugar content of pear were also developed and result in a smaller prediction error than a traditional Partial Least Squares Regression (PLSR) mode.

A climate-based multivariate extreme emulator of met-ocean-hydrological events for coastal flooding

NASA Astrophysics Data System (ADS)

Camus, Paula; Rueda, Ana; Mendez, Fernando J.; Tomas, Antonio; Del Jesus, Manuel; Losada, Iñigo J.

2015-04-01

Atmosphere-ocean general circulation models (AOGCMs) are useful to analyze large-scale climate variability (long-term historical periods, future climate projections). However, applications such as coastal flood modeling require climate information at finer scale. Besides, flooding events depend on multiple climate conditions: waves, surge levels from the open-ocean and river discharge caused by precipitation. Therefore, a multivariate statistical downscaling approach is adopted to reproduce relationships between variables and due to its low computational cost. The proposed method can be considered as a hybrid approach which combines a probabilistic weather type downscaling model with a stochastic weather generator component. Predictand distributions are reproduced modeling the relationship with AOGCM predictors based on a physical division in weather types (Camus et al., 2012). The multivariate dependence structure of the predictand (extreme events) is introduced linking the independent marginal distributions of the variables by a probabilistic copula regression (Ben Ayala et al., 2014). This hybrid approach is applied for the downscaling of AOGCM data to daily precipitation and maximum significant wave height and storm-surge in different locations along the Spanish coast. Reanalysis data is used to assess the proposed method. A commonly predictor for the three variables involved is classified using a regression-guided clustering algorithm. The most appropriate statistical model (general extreme value distribution, pareto distribution) for daily conditions is fitted. Stochastic simulation of the present climate is performed obtaining the set of hydraulic boundary conditions needed for high resolution coastal flood modeling. References: Camus, P., Menéndez, M., Méndez, F.J., Izaguirre, C., Espejo, A., Cánovas, V., Pérez, J., Rueda, A., Losada, I.J., Medina, R. (2014b). A weather-type statistical downscaling framework for ocean wave climate. Journal of Geophysical Research, doi: 10.1002/2014JC010141. Ben Ayala, M.A., Chebana, F., Ouarda, T.B.M.J. (2014). Probabilistic Gaussian Copula Regression Model for Multisite and Multivariable Downscaling, Journal of Climate, 27, 3331-3347.

Optoelectronic instrumentation enhancement using data mining feedback for a 3D measurement system

NASA Astrophysics Data System (ADS)

Flores-Fuentes, Wendy; Sergiyenko, Oleg; Gonzalez-Navarro, Félix F.; Rivas-López, Moisés; Hernandez-Balbuena, Daniel; Rodríguez-Quiñonez, Julio C.; Tyrsa, Vera; Lindner, Lars

2016-12-01

3D measurement by a cyber-physical system based on optoelectronic scanning instrumentation has been enhanced by outliers and regression data mining feedback. The prototype has applications in (1) industrial manufacturing systems that include: robotic machinery, embedded vision, and motion control, (2) health care systems for measurement scanning, and (3) infrastructure by providing structural health monitoring. This paper presents new research performed in data processing of a 3D measurement vision sensing database. Outliers from multivariate data have been detected and removal to improve artificial intelligence regression algorithm results. Physical measurement error regression data has been used for 3D measurements error correction. Concluding, that the joint of physical phenomena, measurement and computation is an effectiveness action for feedback loops in the control of industrial, medical and civil tasks.

Reduced rank regression via adaptive nuclear norm penalization

PubMed Central

Chen, Kun; Dong, Hongbo; Chan, Kung-Sik

2014-01-01

Summary We propose an adaptive nuclear norm penalization approach for low-rank matrix approximation, and use it to develop a new reduced rank estimation method for high-dimensional multivariate regression. The adaptive nuclear norm is defined as the weighted sum of the singular values of the matrix, and it is generally non-convex under the natural restriction that the weight decreases with the singular value. However, we show that the proposed non-convex penalized regression method has a global optimal solution obtained from an adaptively soft-thresholded singular value decomposition. The method is computationally efficient, and the resulting solution path is continuous. The rank consistency of and prediction/estimation performance bounds for the estimator are established for a high-dimensional asymptotic regime. Simulation studies and an application in genetics demonstrate its efficacy. PMID:25045172

Evaluation of a Pharmacokinetic-Pharmacodynamic Model for Hypouricemic Effects of Febuxostat Using Datasets Obtained from Real-world Patients.

PubMed

Hirai, Toshinori; Itoh, Toshimasa; Kimura, Toshimi; Echizen, Hirotoshi

2018-06-06

Febuxostat is an active xanthine oxidase (XO) inhibitor that is widely used in the hyperuricemia treatment. We aimed to evaluate the predictive performance of a pharmacokinetic-pharmacodynamic (PK-PD) model for hypouricemic effects of febuxostat. Previously, we have formulated a PK--PD model for predicting hypouricemic effects of febuxostat as a function of baseline serum urate levels, body weight, renal function, and drug dose using datasets reported in preapproval studies (Hirai T et al., Biol Pharm Bull 2016; 39: 1013-21). Using an updated model with sensitivity analysis, we examined the predictive performance of the PK-PD model using datasets obtained from the medical records of patients who received febuxostat from March 2011 to December 2015 at Tokyo Women's Medical University Hospital. Multivariate regression analysis was performed to explore clinical variables to improve the predictive performance of the model. A total of 1,199 serum urate data were retrieved from 168 patients (age: 60.5 ±17.7 years, 71.4% males) who received febuxostat as hyperuricemia treatment. There was a significant correlation (r=0.68, p<0.01) between serum urate levels observed and those predicted by the modified PK-PD model. A multivariate regression analysis revealed that the predictive performance of the model may be improved further by considering comorbidities, such as diabetes mellitus, estimated glomerular filtration rate (eGFR), and co-administration of loop diuretics (r = 0.77, p<0.01). The PK-PD model may be useful for predicting individualized maintenance doses of febuxostat in real-world patients. This article is protected by copyright. All rights reserved.

Cognitive Complaints After Breast Cancer Treatments: Examining the Relationship With Neuropsychological Test Performance

PubMed Central

2013-01-01

Background Cognitive complaints are reported frequently after breast cancer treatments. Their association with neuropsychological (NP) test performance is not well-established. Methods Early-stage, posttreatment breast cancer patients were enrolled in a prospective, longitudinal, cohort study prior to starting endocrine therapy. Evaluation included an NP test battery and self-report questionnaires assessing symptoms, including cognitive complaints. Multivariable regression models assessed associations among cognitive complaints, mood, treatment exposures, and NP test performance. Results One hundred eighty-nine breast cancer patients, aged 21–65 years, completed the evaluation; 23.3% endorsed higher memory complaints and 19.0% reported higher executive function complaints (>1 SD above the mean for healthy control sample). Regression modeling demonstrated a statistically significant association of higher memory complaints with combined chemotherapy and radiation treatments (P = .01), poorer NP verbal memory performance (P = .02), and higher depressive symptoms (P < .001), controlling for age and IQ. For executive functioning complaints, multivariable modeling controlling for age, IQ, and other confounds demonstrated statistically significant associations with better NP visual memory performance (P = .03) and higher depressive symptoms (P < .001), whereas combined chemotherapy and radiation treatment (P = .05) approached statistical significance. Conclusions About one in five post–adjuvant treatment breast cancer patients had elevated memory and/or executive function complaints that were statistically significantly associated with domain-specific NP test performances and depressive symptoms; combined chemotherapy and radiation treatment was also statistically significantly associated with memory complaints. These results and other emerging studies suggest that subjective cognitive complaints in part reflect objective NP performance, although their etiology and biology appear to be multifactorial, motivating further transdisciplinary research. PMID:23606729

Multivariate modelling of density, strength, and stiffness from near infared for mature, juvenile, and pith wood of longleaf pine (Pinus Palustris)

Treesearch

Brian K. Via; Todd F. Shupe; Leslie H. Groom; Michael Stine; Chi-Leung So

2003-01-01

In manufacturing, monitoring the mechanical properties of wood with near infrared spectroscopy (NIR) is an attractive alternative to more conventional methods. However, no attention has been given to see if models differ between juvenile and mature wood. Additionally, it would be convenient if multiple linear regression (MLR) could perform well in the place of more...

Factors Controlling Sediment Load in The Central Anatolia Region of Turkey: Ankara River Basin.

PubMed

Duru, Umit; Wohl, Ellen; Ahmadi, Mehdi

2017-05-01

Better understanding of the factors controlling sediment load at a catchment scale can facilitate estimation of soil erosion and sediment transport rates. The research summarized here enhances understanding of correlations between potential control variables on suspended sediment loads. The Soil and Water Assessment Tool was used to simulate flow and sediment at the Ankara River basin. Multivariable regression analysis and principal component analysis were then performed between sediment load and controlling variables. The physical variables were either directly derived from a Digital Elevation Model or from field maps or computed using established equations. Mean observed sediment rate is 6697 ton/year and mean sediment yield is 21 ton/y/km² from the gage. Soil and Water Assessment Tool satisfactorily simulated observed sediment load with Nash-Sutcliffe efficiency, relative error, and coefficient of determination (R²) values of 0.81, -1.55, and 0.93, respectively in the catchment. Therefore, parameter values from the physically based model were applied to the multivariable regression analysis as well as principal component analysis. The results indicate that stream flow, drainage area, and channel width explain most of the variability in sediment load among the catchments. The implications of the results, efficient siltation management practices in the catchment should be performed to stream flow, drainage area, and channel width.

Factors Controlling Sediment Load in The Central Anatolia Region of Turkey: Ankara River Basin

NASA Astrophysics Data System (ADS)

Duru, Umit; Wohl, Ellen; Ahmadi, Mehdi

2017-05-01

Better understanding of the factors controlling sediment load at a catchment scale can facilitate estimation of soil erosion and sediment transport rates. The research summarized here enhances understanding of correlations between potential control variables on suspended sediment loads. The Soil and Water Assessment Tool was used to simulate flow and sediment at the Ankara River basin. Multivariable regression analysis and principal component analysis were then performed between sediment load and controlling variables. The physical variables were either directly derived from a Digital Elevation Model or from field maps or computed using established equations. Mean observed sediment rate is 6697 ton/year and mean sediment yield is 21 ton/y/km² from the gage. Soil and Water Assessment Tool satisfactorily simulated observed sediment load with Nash-Sutcliffe efficiency, relative error, and coefficient of determination ( R²) values of 0.81, -1.55, and 0.93, respectively in the catchment. Therefore, parameter values from the physically based model were applied to the multivariable regression analysis as well as principal component analysis. The results indicate that stream flow, drainage area, and channel width explain most of the variability in sediment load among the catchments. The implications of the results, efficient siltation management practices in the catchment should be performed to stream flow, drainage area, and channel width.

The impact of moderate wine consumption on the risk of developing prostate cancer.

PubMed

Vartolomei, Mihai Dorin; Kimura, Shoji; Ferro, Matteo; Foerster, Beat; Abufaraj, Mohammad; Briganti, Alberto; Karakiewicz, Pierre I; Shariat, Shahrokh F

2018-01-01

To investigate the impact of moderate wine consumption on the risk of prostate cancer (PCa). We focused on the differential effect of moderate consumption of red versus white wine. This study was a meta-analysis that includes data from case-control and cohort studies. A systematic search of Web of Science, Medline/PubMed, and Cochrane library was performed on December 1, 2017. Studies were deemed eligible if they assessed the risk of PCa due to red, white, or any wine using multivariable logistic regression analysis. We performed a formal meta-analysis for the risk of PCa according to moderate wine and wine type consumption (white or red). Heterogeneity between studies was assessed using Cochrane's Q test and I 2 statistics. Publication bias was assessed using Egger's regression test. A total of 930 abstracts and titles were initially identified. After removal of duplicates, reviews, and conference abstracts, 83 full-text original articles were screened. Seventeen studies (611,169 subjects) were included for final evaluation and fulfilled the inclusion criteria. In the case of moderate wine consumption: the pooled risk ratio (RR) for the risk of PCa was 0.98 (95% CI 0.92-1.05, p =0.57) in the multivariable analysis. Moderate white wine consumption increased the risk of PCa with a pooled RR of 1.26 (95% CI 1.10-1.43, p =0.001) in the multi-variable analysis. Meanwhile, moderate red wine consumption had a protective role reducing the risk by 12% (RR 0.88, 95% CI 0.78-0.999, p =0.047) in the multivariable analysis that comprised 222,447 subjects. In this meta-analysis, moderate wine consumption did not impact the risk of PCa. Interestingly, regarding the type of wine, moderate consumption of white wine increased the risk of PCa, whereas moderate consumption of red wine had a protective effect. Further analyses are needed to assess the differential molecular effect of white and red wine conferring their impact on PCa risk.

Multicollinearity in spatial genetics: separating the wheat from the chaff using commonality analyses.

PubMed

Prunier, J G; Colyn, M; Legendre, X; Nimon, K F; Flamand, M C

2015-01-01

Direct gradient analyses in spatial genetics provide unique opportunities to describe the inherent complexity of genetic variation in wildlife species and are the object of many methodological developments. However, multicollinearity among explanatory variables is a systemic issue in multivariate regression analyses and is likely to cause serious difficulties in properly interpreting results of direct gradient analyses, with the risk of erroneous conclusions, misdirected research and inefficient or counterproductive conservation measures. Using simulated data sets along with linear and logistic regressions on distance matrices, we illustrate how commonality analysis (CA), a detailed variance-partitioning procedure that was recently introduced in the field of ecology, can be used to deal with nonindependence among spatial predictors. By decomposing model fit indices into unique and common (or shared) variance components, CA allows identifying the location and magnitude of multicollinearity, revealing spurious correlations and thus thoroughly improving the interpretation of multivariate regressions. Despite a few inherent limitations, especially in the case of resistance model optimization, this review highlights the great potential of CA to account for complex multicollinearity patterns in spatial genetics and identifies future applications and lines of research. We strongly urge spatial geneticists to systematically investigate commonalities when performing direct gradient analyses. © 2014 John Wiley & Sons Ltd.

Lumbar subcutaneous edema and degenerative spinal disease in patients with low back pain: a retrospective MRI study.

PubMed

Quattrocchi, C C; Giona, A; Di Martino, A; Gaudino, F; Mallio, C A; Errante, Y; Occhicone, F; Vitali, M A; Zobel, B B; Denaro, V

2015-08-01

This study was designed to determine the association between LSE, spondylolisthesis, facet arthropathy, lumbar canal stenosis, BMI, radiculopathy and bone marrow edema at conventional lumbar spine MR imaging. This is a retrospective radiological study; 441 consecutive patients with low back pain (224 men and 217 women; mean age 57.3 years; mean BMI 26) underwent conventional lumbar MRI using a 1.5-T magnet (Avanto, Siemens). Lumbar MR images were reviewed by consensus for the presence of LSE, spondylolisthesis, facet arthropathy, lumbar canal stenosis, radiculopathy and bone marrow edema. Descriptive statistics and association studies were conducted using STATA software 11.0. Association studies have been performed using linear univariate regression analysis and multivariate regression analysis, considering LSE as response variable. The overall prevalence of LSE was 40%; spondylolisthesis (p = 0.01), facet arthropathy (p < 0.001), BMI (p = 0.008) and lumbar canal stenosis (p < 0.001) were included in the multivariate regression model, whereas bone marrow edema, radiculopathy and age were not. LSE is highly associated with spondylolisthesis, facet arthropathy and BMI, suggesting underestimation of its clinical impact as an integral component in chronic lumbar back pain. Longitudinal simultaneous X-ray/MRI studies should be conducted to test the relationship of LSE with lumbar spinal instability and low back pain.

Sparse partial least squares regression for simultaneous dimension reduction and variable selection

PubMed Central

Chun, Hyonho; Keleş, Sündüz

2010-01-01

Partial least squares regression has been an alternative to ordinary least squares for handling multicollinearity in several areas of scientific research since the 1960s. It has recently gained much attention in the analysis of high dimensional genomic data. We show that known asymptotic consistency of the partial least squares estimator for a univariate response does not hold with the very large p and small n paradigm. We derive a similar result for a multivariate response regression with partial least squares. We then propose a sparse partial least squares formulation which aims simultaneously to achieve good predictive performance and variable selection by producing sparse linear combinations of the original predictors. We provide an efficient implementation of sparse partial least squares regression and compare it with well-known variable selection and dimension reduction approaches via simulation experiments. We illustrate the practical utility of sparse partial least squares regression in a joint analysis of gene expression and genomewide binding data. PMID:20107611

Predictors of Performance on the MMSE and the DRS-2 Among American Indian Elders

PubMed Central

Jervis, Lori L.; Fickenscher, Alexandra; Beals, Janette; Cullum, C. Munro; Novins, Douglas K.; Manson, Spero M.; Arciniegas, David B.

2015-01-01

Little is known about factors that predict older American Indians’ performance on cognitive tests. This study examined 137 American Indian elders’ performance on the MMSE and the Dementia Rating Scale—Second Edition (DRS-2). Multivariate regression identified younger age, more education, not receiving Supplemental Security Income, and frequent receipt of needed health care as predictors of better performance on the MMSE. Better performance on the DRS-2 was predicted by more education, boarding school attendance, not receiving Supplemental Security Income, and frequent receipt of needed health care. This study points to the importance of economic and educational factors on cognitive test performance among American Indian elders. PMID:21037127

Performance characteristics of LOX-H2, tangential-entry, swirl-coaxial, rocket injectors

NASA Technical Reports Server (NTRS)

Howell, Doug; Petersen, Eric; Clark, Jim

1993-01-01

Development of a high performing swirl-coaxial injector requires an understanding of fundamental performance characteristics. This paper addresses the findings of studies on cold flow atomic characterizations which provided information on the influence of fluid properties and element operating conditions on the produced droplet sprays. These findings are applied to actual rocket conditions. The performance characteristics of swirl-coaxial injection elements under multi-element hot-fire conditions were obtained by analysis of combustion performance data from three separate test series. The injection elements are described and test results are analyzed using multi-variable linear regression. A direct comparison of test results indicated that reduced fuel injection velocity improved injection element performance through improved propellant mixing.

Predictors of performance on the MMSE and the DRS-2 among American Indian elders.

PubMed

Jervis, Lori L; Fickenscher, Alexandra; Beals, Janette; Cullum, C Munro; Novins, Douglas K; Manson, Spero M; Arciniegas, David B

2010-01-01

Little is known about factors that predict older American Indians' performance on cognitive tests. This study examined 137 American Indian elders' performance on the MMSE and the Dementia Rating Scale-Second Edition (DRS-2). Multivariate regression identified younger age, more education, not receiving Supplemental Security Income, and frequent receipt of needed health care as predictors of better performance on the MMSE. Better performance on the DRS-2 was predicted by more education, boarding school attendance, not receiving Supplemental Security Income, and frequent receipt of needed health care. This study points to the importance of economic and educational factors on cognitive test performance among American Indian elders.

Changes of visual-field global indices after cataract surgery in primary open-angle glaucoma patients.

PubMed

Seol, Bo Ram; Jeoung, Jin Wook; Park, Ki Ho

2016-11-01

To determine changes of visual-field (VF) global indices after cataract surgery and the factors associated with the effect of cataracts on those indices in primary open-angle glaucoma (POAG) patients. A retrospective chart review of 60 POAG patients who had undergone phacoemulsification and intraocular lens insertion was conducted. All of the patients were evaluated with standard automated perimetry (SAP; 30-2 Swedish interactive threshold algorithm; Carl Zeiss Meditec Inc.) before and after surgery. VF global indices before surgery were compared with those after surgery. The best-corrected visual acuity, intraocular pressure (IOP), number of glaucoma medications before surgery, mean total deviation (TD) values, mean pattern deviation (PD) value, and mean TD-PD value were also compared with the corresponding postoperative values. Additionally, postoperative peak IOP and mean IOP were evaluated. Univariate and multivariate logistic regression analyses were performed to identify the factors associated with the effect of cataract on global indices. Mean deviation (MD) after cataract surgery was significantly improved compared with the preoperative MD. Pattern standard deviation (PSD) and visual-field index (VFI) after surgery were similar to those before surgery. Also, mean TD and mean TD-PD were significantly improved after surgery. The posterior subcapsular cataract (PSC) type showed greater MD changes than did the non-PSC type in both the univariate and multivariate logistic regression analyses. In the univariate logistic regression analysis, the preoperative TD-PD value and type of cataract were associated with MD change. However, in the multivariate logistic regression analysis, type of cataract was the only associated factor. None of the other factors was associated with MD change. MD was significantly affected by cataracts, whereas PSD and VFI were not. Most notably, the PSC type showed better MD improvement compared with the non-PSC type after cataract surgery. Clinicians therefore should carefully analyze VF examination results for POAG patients with the PSC type.

Comparison of Nine Statistical Model Based Warfarin Pharmacogenetic Dosing Algorithms Using the Racially Diverse International Warfarin Pharmacogenetic Consortium Cohort Database

PubMed Central

Liu, Rong; Li, Xi; Zhang, Wei; Zhou, Hong-Hao

2015-01-01

Objective Multiple linear regression (MLR) and machine learning techniques in pharmacogenetic algorithm-based warfarin dosing have been reported. However, performances of these algorithms in racially diverse group have never been objectively evaluated and compared. In this literature-based study, we compared the performances of eight machine learning techniques with those of MLR in a large, racially-diverse cohort. Methods MLR, artificial neural network (ANN), regression tree (RT), multivariate adaptive regression splines (MARS), boosted regression tree (BRT), support vector regression (SVR), random forest regression (RFR), lasso regression (LAR) and Bayesian additive regression trees (BART) were applied in warfarin dose algorithms in a cohort from the International Warfarin Pharmacogenetics Consortium database. Covariates obtained by stepwise regression from 80% of randomly selected patients were used to develop algorithms. To compare the performances of these algorithms, the mean percentage of patients whose predicted dose fell within 20% of the actual dose (mean percentage within 20%) and the mean absolute error (MAE) were calculated in the remaining 20% of patients. The performances of these techniques in different races, as well as the dose ranges of therapeutic warfarin were compared. Robust results were obtained after 100 rounds of resampling. Results BART, MARS and SVR were statistically indistinguishable and significantly out performed all the other approaches in the whole cohort (MAE: 8.84–8.96 mg/week, mean percentage within 20%: 45.88%–46.35%). In the White population, MARS and BART showed higher mean percentage within 20% and lower mean MAE than those of MLR (all p values < 0.05). In the Asian population, SVR, BART, MARS and LAR performed the same as MLR. MLR and LAR optimally performed among the Black population. When patients were grouped in terms of warfarin dose range, all machine learning techniques except ANN and LAR showed significantly higher mean percentage within 20%, and lower MAE (all p values < 0.05) than MLR in the low- and high- dose ranges. Conclusion Overall, machine learning-based techniques, BART, MARS and SVR performed superior than MLR in warfarin pharmacogenetic dosing. Differences of algorithms’ performances exist among the races. Moreover, machine learning-based algorithms tended to perform better in the low- and high- dose ranges than MLR. PMID:26305568

Characterizing multivariate decoding models based on correlated EEG spectral features.

PubMed

McFarland, Dennis J

2013-07-01

Multivariate decoding methods are popular techniques for analysis of neurophysiological data. The present study explored potential interpretative problems with these techniques when predictors are correlated. Data from sensorimotor rhythm-based cursor control experiments was analyzed offline with linear univariate and multivariate models. Features were derived from autoregressive (AR) spectral analysis of varying model order which produced predictors that varied in their degree of correlation (i.e., multicollinearity). The use of multivariate regression models resulted in much better prediction of target position as compared to univariate regression models. However, with lower order AR features interpretation of the spectral patterns of the weights was difficult. This is likely to be due to the high degree of multicollinearity present with lower order AR features. Care should be exercised when interpreting the pattern of weights of multivariate models with correlated predictors. Comparison with univariate statistics is advisable. While multivariate decoding algorithms are very useful for prediction their utility for interpretation may be limited when predictors are correlated. Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

[Travel time and participation in breast cancer screening in a region with high population dispersion].

PubMed

Borda, Alfredo; Sanz, Belén; Otero, Laura; Blasco, Teresa; García-Gómez, Francisco J; de Andrés, Fuencisla

2011-01-01

To analyze the association between travel time and participation in a breast cancer screening program adjusted for contextual variables in the province of Segovia (Spain). We performed an ecological study using the following data sources: the Breast Cancer Early Detection Program of the Primary Care Management of Segovia, the Population and Housing Census for 2001 and the municipal register for 2006-2007. The study period comprised January 2006 to December 2007. Dependent variables consisted of the municipal participation rate and the desired level of municipal participation (greater than or equal to 70%). The key independent variable was travel time from the municipality to the mammography unit. Covariables consisted of the municipalities' demographic and socioeconomic factors. We performed univariate and multivariate Poisson regression analyses of the participation rate, and logistic regression of the desired participation level. The sample was composed of 178 municipalities. The mean participation rate was 75.2%. The desired level of participation (≥ 70%) was achieved in 119 municipalities (67%). In the multivariate Poisson and logistic regression analyses, longer travel time was associated with a lower participation rate and with lower participation after adjustment was made for geographic density, age, socioeconomic status and dependency ratio, with a relative risk index of 0.88 (95% CI: 0.81-0.96) and an odds ratio of 0.22 (95% CI: 0.1-0.47), respectively. Travel time to the mammography unit may help to explain participation in breast cancer screening programs. Copyright © 2010 SESPAS. Published by Elsevier Espana. All rights reserved.

NOCTURNAL ENURESIS AS A RISK FACTOR FOR FALLS IN OLDER COMMUNITY-DWELLING WOMEN WITH URINARY INCONTINENCE

PubMed Central

Pahwa, Avita K.; Andy, Uduak U.; Newman, Diane K.; Stambakio, Hanna; Schmitz, Kathryn H.; Arya, Lily A.

2016-01-01

Purpose To determine the association between urinary symptoms, fall risk and physical limitations in older community-dwelling women with urinary incontinence (UI). Materials and Methods In-depth assessment of day and nighttime urinary symptoms, fall risk, physical function, physical performance tests and mental function in older community-dwelling women with UI and who had not sought care for their urinary symptoms. All assessments were performed in the participants’ homes. We used univariable and multivariable linear regression to examine the relationship of urinary symptoms with fall risk, physical function, and physical performance. Results In 37 women with UI (mean age 74 ± 8.4 years), 48% were at high risk for falls. Nocturnal enuresis was reported by 50%. Increased fall risk was associated with increasing frequency of nocturnal enuresis (p=0.04), worse lower limb (p<0.001) and worse upper limb (p<0.0001) function and worse performance on a composite physical performance test of strength, gait and balance (p=0.02). Women with nocturnal enuresis had significantly lower median physical performance test scores (7, range 0, 11) than women without nocturnal enuresis (median 9, range 1, 12, p=0.04). In a multivariable regression model that included age, nocturnal enuresis episodes and physical function, only physical function was associated with increased fall risk (p<0.0001). Conclusion Nocturnal enuresis is common in older community-dwelling women with UI and may serve as a marker for fall risk even in women not seeking care for their urinary symptoms. Interventions targeting upper and lower body physical function could potentially reduce risk of falls in older women with UI. PMID:26626218

A comparison between univariate probabilistic and multivariate (logistic regression) methods for landslide susceptibility analysis: the example of the Febbraro valley (Northern Alps, Italy)

NASA Astrophysics Data System (ADS)

Rossi, M.; Apuani, T.; Felletti, F.

2009-04-01

The aim of this paper is to compare the results of two statistical methods for landslide susceptibility analysis: 1) univariate probabilistic method based on landslide susceptibility index, 2) multivariate method (logistic regression). The study area is the Febbraro valley, located in the central Italian Alps, where different types of metamorphic rocks croup out. On the eastern part of the studied basin a quaternary cover represented by colluvial and secondarily, by glacial deposits, is dominant. In this study 110 earth flows, mainly located toward NE portion of the catchment, were analyzed. They involve only the colluvial deposits and their extension mainly ranges from 36 to 3173 m2. Both statistical methods require to establish a spatial database, in which each landslide is described by several parameters that can be assigned using a main scarp central point of landslide. The spatial database is constructed using a Geographical Information System (GIS). Each landslide is described by several parameters corresponding to the value of main scarp central point of the landslide. Based on bibliographic review a total of 15 predisposing factors were utilized. The width of the intervals, in which the maps of the predisposing factors have to be reclassified, has been defined assuming constant intervals to: elevation (100 m), slope (5 °), solar radiation (0.1 MJ/cm2/year), profile curvature (1.2 1/m), tangential curvature (2.2 1/m), drainage density (0.5), lineament density (0.00126). For the other parameters have been used the results of the probability-probability plots analysis and the statistical indexes of landslides site. In particular slope length (0 ÷ 2, 2 ÷ 5, 5 ÷ 10, 10 ÷ 20, 20 ÷ 35, 35 ÷ 260), accumulation flow (0 ÷ 1, 1 ÷ 2, 2 ÷ 5, 5 ÷ 12, 12 ÷ 60, 60 ÷27265), Topographic Wetness Index 0 ÷ 0.74, 0.74 ÷ 1.94, 1.94 ÷ 2.62, 2.62 ÷ 3.48, 3.48 ÷ 6,00, 6.00 ÷ 9.44), Stream Power Index (0 ÷ 0.64, 0.64 ÷ 1.28, 1.28 ÷ 1.81, 1.81 ÷ 4.20, 4.20 ÷ 9.40). Geological map and land use map were also used, considering geological and land use properties as categorical variables. Appling the univariate probabilistic method the Landslide Susceptibility Index (LSI) is defined as the sum of the ratio Ra/Rb calculated for each predisposing factor, where Ra is the ratio between number of pixel of class and the total number of pixel of the study area, and Rb is the ratio between number of landslides respect to the pixel number of the interval area. From the analysis of the Ra/Rb ratio the relationship between landslide occurrence and predisposing factors were defined. Then the equation of LSI was used in GIS to trace the landslide susceptibility maps. The multivariate method for landslide susceptibility analysis, based on logistic regression, was performed starting from the density maps of the predisposing factors, calculated with the intervals defined above using the equation Rb/Rbtot, where Rbtot is a sum of all Rb values. Using stepwise forward algorithms the logistic regression was performed in two successive steps: first a univariate logistic regression is used to choose the most significant predisposing factors, then the multivariate logistic regression can be performed. The univariate regression highlighted the importance of the following factors: elevation, accumulation flow, drainage density, lineament density, geology and land use. When the multivariate regression was applied the number of controlling factors was reduced neglecting the geological properties. The resulting final susceptibility equation is: P = 1 / (1 + exp-(6.46-22.34*elevation-5.33*accumulation flow-7.99* drainage density-4.47*lineament density-17.31*land use)) and using this equation the susceptibility maps were obtained. To easy compare the results of the two methodologies, the susceptibility maps were reclassified in five susceptibility intervals (very high, high, moderate, low and very low) using natural breaks. Then the maps were validated using two cumulative distribution curves, one related to the landslides (number of landslides in each susceptibility class) and one to the basin (number of pixel covering each class). Comparing the curves for each method, it results that the two approaches (univariate and multivariate) are appropriate, providing acceptable results. In both maps the distribution of high susceptibility condition is mainly localized on the left slope of the catchment in agreement with the field evidences. The comparison between the methods was obtained by subtraction of the two maps. This operation shows that about 40% of the basin is classified by the same class of susceptibility. In general the univariate probabilistic method tends to overestimate the areal extension of the high susceptibility class with respect to the maps obtained by the logistic regression method.

Firefly algorithm versus genetic algorithm as powerful variable selection tools and their effect on different multivariate calibration models in spectroscopy: A comparative study

NASA Astrophysics Data System (ADS)

Attia, Khalid A. M.; Nassar, Mohammed W. I.; El-Zeiny, Mohamed B.; Serag, Ahmed

2017-01-01

For the first time, a new variable selection method based on swarm intelligence namely firefly algorithm is coupled with three different multivariate calibration models namely, concentration residual augmented classical least squares, artificial neural network and support vector regression in UV spectral data. A comparative study between the firefly algorithm and the well-known genetic algorithm was developed. The discussion revealed the superiority of using this new powerful algorithm over the well-known genetic algorithm. Moreover, different statistical tests were performed and no significant differences were found between all the models regarding their predictabilities. This ensures that simpler and faster models were obtained without any deterioration of the quality of the calibration.

Longitudinal assessment of treatment effects on pulmonary ventilation using 1H/3He MRI multivariate templates

NASA Astrophysics Data System (ADS)

Tustison, Nicholas J.; Contrella, Benjamin; Altes, Talissa A.; Avants, Brian B.; de Lange, Eduard E.; Mugler, John P.

2013-03-01

The utitlity of pulmonary functional imaging techniques, such as hyperpolarized 3He MRI, has encouraged their inclusion in research studies for longitudinal assessment of disease progression and the study of treatment effects. We present methodology for performing voxelwise statistical analysis of ventilation maps derived from hyper­ polarized 3He MRI which incorporates multivariate template construction using simultaneous acquisition of IH and 3He images. Additional processing steps include intensity normalization, bias correction, 4-D longitudinal segmentation, and generation of expected ventilation maps prior to voxelwise regression analysis. Analysis is demonstrated on a cohort of eight individuals with diagnosed cystic fibrosis (CF) undergoing treatment imaged five times every two weeks with a prescribed treatment schedule.

Differences in passenger car and large truck involved crash frequencies at urban signalized intersections: an exploratory analysis.

PubMed

Dong, Chunjiao; Clarke, David B; Richards, Stephen H; Huang, Baoshan

2014-01-01

The influence of intersection features on safety has been examined extensively because intersections experience a relatively large proportion of motor vehicle conflicts and crashes. Although there are distinct differences between passenger cars and large trucks-size, operating characteristics, dimensions, and weight-modeling crash counts across vehicle types is rarely addressed. This paper develops and presents a multivariate regression model of crash frequencies by collision vehicle type using crash data for urban signalized intersections in Tennessee. In addition, the performance of univariate Poisson-lognormal (UVPLN), multivariate Poisson (MVP), and multivariate Poisson-lognormal (MVPLN) regression models in establishing the relationship between crashes, traffic factors, and geometric design of roadway intersections is investigated. Bayesian methods are used to estimate the unknown parameters of these models. The evaluation results suggest that the MVPLN model possesses most of the desirable statistical properties in developing the relationships. Compared to the UVPLN and MVP models, the MVPLN model better identifies significant factors and predicts crash frequencies. The findings suggest that traffic volume, truck percentage, lighting condition, and intersection angle significantly affect intersection safety. Important differences in car, car-truck, and truck crash frequencies with respect to various risk factors were found to exist between models. The paper provides some new or more comprehensive observations that have not been covered in previous studies. Copyright © 2013 Elsevier Ltd. All rights reserved.

Arteriopathy after transarterial chemo-lipiodolization for hepatocellular carcinoma.

PubMed

Matsui, Y; Figi, A; Horikawa, M; Jahangiri Noudeh, Y; Tomozawa, Y; Hashimoto, K; Kaufman, J A; Farsad, K

2017-12-01

The purpose of this study was to investigate the incidence of and the risk factors for arteriopathy in hepatic arteries after transarterial chemo-lipiodolization in patients with hepatocellular carcinoma and the subsequent treatment strategy changes due to arteriopathy. A total of 365 arteries in 167 patients (126 men and 41 women; mean age, 60.4±15.0 [SD] years [range: 18-87 years]) were evaluated for the development of arteriopathy after chemo-lipiodolization with epirubicin- or doxorubicin-Lipiodol ® emulsion. The development of arteriopathy after chemo-lipiodolization was assessed on arteriograms performed during subsequent transarterial treatments. The treatment strategy changes due to arteriopathy, including change in the chemo-lipiodolization method and the application of alternative therapies was also investigated. Univariate and multivariate binary logistic regression models were used to identify risk factors for arteriopathy and subsequent treatment strategy change. One hundred two (27.9%) arteriopathies were detected in 62/167 (37.1%) patients (45 men, 17 women) with a mean age of 63.3±7.1 [SD] years (age range, 50-86 years). The incidence of arteriopathy was highly patient dependent, demonstrating significant correlation in a fully-adjusted multivariate regression model (P<0.0001). Multivariate-adjusted regression analysis with adjustment for the patient effect showed a statistically significant association of super-selective chemo-lipiodolization (P=0.003) with the incidence of arteriopathy. Thirty of the 102 arteriopathies (29.4%) caused a change in treatment strategy. No factors were found to be significantly associated with the treatment strategy change. The incidence of arteriopathy after chemo-lipiodolization is 27.9%. Among them, 29.4% result in a change in treatment strategy. Copyright © 2017 Editions françaises de radiologie. Published by Elsevier Masson SAS. All rights reserved.

Predictors of unsuccessful outcome in cemented femoral revisions using bone impaction grafting; Cox regression analysis of 208 cases.

PubMed

Te Stroet, Martijn A J; Rijnen, Wim H C; Gardeniers, Jean W M; Schreurs, B Willem; Hannink, Gerjon

2016-09-29

Despite improvements in the technique of femoral impaction bone grafting, reconstruction failures still can occur. Therefore, the aim of our study was to determine risk factors for the endpoint re-revision for any reason. We used prospectively collected demographic, clinical and surgical data of all 202 patients who underwent 208 femoral revisions using the X-change Femoral Revision System (Stryker-Howmedica), fresh-frozen morcellised allograft and a cemented polished Exeter stem in our department from 1991 to 2007. Univariable and multivariable Cox regression analyses were performed to identify potential factors associated with re-revision. The mean follow-up was 10.6 (5-21) years. The cumulative re-revision rate was 6.3% (13/208). After univariable selection, sex, age, body mass index (BMI), American Association of Anesthesiologists (ASA) classification, type of removed femoral component, and mesh used for reconstruction were included in multivariable regression analysis.In the multivariable analysis, BMI was the only factor that was significantly associated with the risk of re-revision after bone impaction grafting (BMI ≥30 vs. BMI <30, HR = 6.54 [95% CI 1.89-22.65]; p = 0.003). BMI was the only factor associated with the risk of re-revision for any reason. Besides BMI also other factors, such as Endoklinik score and the type of removed femoral component, can provide guidance in the process of preclinical decision making. With the knowledge obtained from this study, preoperative patient selection, informed consent, and treatment protocols can be better adjusted to the individual patient who needs to undergo a femoral revision with impaction bone grafting.

EXTENDING MULTIVARIATE DISTANCE MATRIX REGRESSION WITH AN EFFECT SIZE MEASURE AND THE ASYMPTOTIC NULL DISTRIBUTION OF THE TEST STATISTIC

PubMed Central

McArtor, Daniel B.; Lubke, Gitta H.; Bergeman, C. S.

2017-01-01

Person-centered methods are useful for studying individual differences in terms of (dis)similarities between response profiles on multivariate outcomes. Multivariate distance matrix regression (MDMR) tests the significance of associations of response profile (dis)similarities and a set of predictors using permutation tests. This paper extends MDMR by deriving and empirically validating the asymptotic null distribution of its test statistic, and by proposing an effect size for individual outcome variables, which is shown to recover true associations. These extensions alleviate the computational burden of permutation tests currently used in MDMR and render more informative results, thus making MDMR accessible to new research domains. PMID:27738957

Extending multivariate distance matrix regression with an effect size measure and the asymptotic null distribution of the test statistic.

PubMed

McArtor, Daniel B; Lubke, Gitta H; Bergeman, C S

2017-12-01

Person-centered methods are useful for studying individual differences in terms of (dis)similarities between response profiles on multivariate outcomes. Multivariate distance matrix regression (MDMR) tests the significance of associations of response profile (dis)similarities and a set of predictors using permutation tests. This paper extends MDMR by deriving and empirically validating the asymptotic null distribution of its test statistic, and by proposing an effect size for individual outcome variables, which is shown to recover true associations. These extensions alleviate the computational burden of permutation tests currently used in MDMR and render more informative results, thus making MDMR accessible to new research domains.

Hierarchical cluster-based partial least squares regression (HC-PLSR) is an efficient tool for metamodelling of nonlinear dynamic models.

PubMed

Tøndel, Kristin; Indahl, Ulf G; Gjuvsland, Arne B; Vik, Jon Olav; Hunter, Peter; Omholt, Stig W; Martens, Harald

2011-06-01

Deterministic dynamic models of complex biological systems contain a large number of parameters and state variables, related through nonlinear differential equations with various types of feedback. A metamodel of such a dynamic model is a statistical approximation model that maps variation in parameters and initial conditions (inputs) to variation in features of the trajectories of the state variables (outputs) throughout the entire biologically relevant input space. A sufficiently accurate mapping can be exploited both instrumentally and epistemically. Multivariate regression methodology is a commonly used approach for emulating dynamic models. However, when the input-output relations are highly nonlinear or non-monotone, a standard linear regression approach is prone to give suboptimal results. We therefore hypothesised that a more accurate mapping can be obtained by locally linear or locally polynomial regression. We present here a new method for local regression modelling, Hierarchical Cluster-based PLS regression (HC-PLSR), where fuzzy C-means clustering is used to separate the data set into parts according to the structure of the response surface. We compare the metamodelling performance of HC-PLSR with polynomial partial least squares regression (PLSR) and ordinary least squares (OLS) regression on various systems: six different gene regulatory network models with various types of feedback, a deterministic mathematical model of the mammalian circadian clock and a model of the mouse ventricular myocyte function. Our results indicate that multivariate regression is well suited for emulating dynamic models in systems biology. The hierarchical approach turned out to be superior to both polynomial PLSR and OLS regression in all three test cases. The advantage, in terms of explained variance and prediction accuracy, was largest in systems with highly nonlinear functional relationships and in systems with positive feedback loops. HC-PLSR is a promising approach for metamodelling in systems biology, especially for highly nonlinear or non-monotone parameter to phenotype maps. The algorithm can be flexibly adjusted to suit the complexity of the dynamic model behaviour, inviting automation in the metamodelling of complex systems.

Hierarchical Cluster-based Partial Least Squares Regression (HC-PLSR) is an efficient tool for metamodelling of nonlinear dynamic models

PubMed Central

2011-01-01

Background Deterministic dynamic models of complex biological systems contain a large number of parameters and state variables, related through nonlinear differential equations with various types of feedback. A metamodel of such a dynamic model is a statistical approximation model that maps variation in parameters and initial conditions (inputs) to variation in features of the trajectories of the state variables (outputs) throughout the entire biologically relevant input space. A sufficiently accurate mapping can be exploited both instrumentally and epistemically. Multivariate regression methodology is a commonly used approach for emulating dynamic models. However, when the input-output relations are highly nonlinear or non-monotone, a standard linear regression approach is prone to give suboptimal results. We therefore hypothesised that a more accurate mapping can be obtained by locally linear or locally polynomial regression. We present here a new method for local regression modelling, Hierarchical Cluster-based PLS regression (HC-PLSR), where fuzzy C-means clustering is used to separate the data set into parts according to the structure of the response surface. We compare the metamodelling performance of HC-PLSR with polynomial partial least squares regression (PLSR) and ordinary least squares (OLS) regression on various systems: six different gene regulatory network models with various types of feedback, a deterministic mathematical model of the mammalian circadian clock and a model of the mouse ventricular myocyte function. Results Our results indicate that multivariate regression is well suited for emulating dynamic models in systems biology. The hierarchical approach turned out to be superior to both polynomial PLSR and OLS regression in all three test cases. The advantage, in terms of explained variance and prediction accuracy, was largest in systems with highly nonlinear functional relationships and in systems with positive feedback loops. Conclusions HC-PLSR is a promising approach for metamodelling in systems biology, especially for highly nonlinear or non-monotone parameter to phenotype maps. The algorithm can be flexibly adjusted to suit the complexity of the dynamic model behaviour, inviting automation in the metamodelling of complex systems. PMID:21627852

Liver Rapid Reference Set Application: Kevin Qu-Quest (2011) — EDRN Public Portal

Cancer.gov

We propose to evaluate the performance of a novel serum biomarker panel for early detection of hepatocellular carcinoma (HCC). This panel is based on markers from the ubiquitin-proteasome system (UPS) in combination with the existing known HCC biomarkers, namely, alpha-fetoprotein (AFP), AFP-L3%, and des-y-carboxy prothrombin (DCP). To this end, we applied multivariate logistic regression analysis to optimize this biomarker algorithm tool.

Comparison of Platelet Transfusion as Fresh Whole Blood Versus Apheresis Platelets for Massively Transfused Combat Trauma patients

DTIC Science & Technology

2011-02-01

transfusion (10 units in 24 hr) were divided into two groups: those receiving FWB (n = 85) or aPLT (n = 284) during their resuscitation . Admission...characteristics, resuscitation , and survival were compared between groups. Multivari- ate regression analyses were performed comparing sur- vival of patients at...days. CONCLUSIONS: Survival for massively transfused trauma patients receiving FWB appears to be similar to patients resuscitated with aPLT

Comparison of univariate and multivariate calibration for the determination of micronutrients in pellets of plant materials by laser induced breakdown spectrometry

NASA Astrophysics Data System (ADS)

Braga, Jez Willian Batista; Trevizan, Lilian Cristina; Nunes, Lidiane Cristina; Rufini, Iolanda Aparecida; Santos, Dário, Jr.; Krug, Francisco José

2010-01-01

The application of laser induced breakdown spectrometry (LIBS) aiming the direct analysis of plant materials is a great challenge that still needs efforts for its development and validation. In this way, a series of experimental approaches has been carried out in order to show that LIBS can be used as an alternative method to wet acid digestions based methods for analysis of agricultural and environmental samples. The large amount of information provided by LIBS spectra for these complex samples increases the difficulties for selecting the most appropriated wavelengths for each analyte. Some applications have suggested that improvements in both accuracy and precision can be achieved by the application of multivariate calibration in LIBS data when compared to the univariate regression developed with line emission intensities. In the present work, the performance of univariate and multivariate calibration, based on partial least squares regression (PLSR), was compared for analysis of pellets of plant materials made from an appropriate mixture of cryogenically ground samples with cellulose as the binding agent. The development of a specific PLSR model for each analyte and the selection of spectral regions containing only lines of the analyte of interest were the best conditions for the analysis. In this particular application, these models showed a similar performance, but PLSR seemed to be more robust due to a lower occurrence of outliers in comparison to the univariate method. Data suggests that efforts dealing with sample presentation and fitness of standards for LIBS analysis must be done in order to fulfill the boundary conditions for matrix independent development and validation.

Remote Zone Extracellular Volume and Left Ventricular Remodeling in Survivors of ST-Elevation Myocardial Infarction.

PubMed

Carberry, Jaclyn; Carrick, David; Haig, Caroline; Rauhalammi, Samuli M; Ahmed, Nadeem; Mordi, Ify; McEntegart, Margaret; Petrie, Mark C; Eteiba, Hany; Hood, Stuart; Watkins, Stuart; Lindsay, Mitchell; Davie, Andrew; Mahrous, Ahmed; Ford, Ian; Sattar, Naveed; Welsh, Paul; Radjenovic, Aleksandra; Oldroyd, Keith G; Berry, Colin

2016-08-01

The natural history and pathophysiological significance of tissue remodeling in the myocardial remote zone after acute ST-elevation myocardial infarction (STEMI) is incompletely understood. Extracellular volume (ECV) in myocardial regions of interest can now be measured with cardiac magnetic resonance imaging. Patients who sustained an acute STEMI were enrolled in a cohort study (BHF MR-MI [British Heart Foundation Magnetic Resonance Imaging in Acute ST-Segment Elevation Myocardial Infarction study]). Cardiac magnetic resonance was performed at 1.5 Tesla at 2 days and 6 months post STEMI. T1 modified Look-Locker inversion recovery mapping was performed before and 15 minutes after contrast (0.15 mmol/kg gadoterate meglumine) in 140 patients at 2 days post STEMI (mean age: 59 years, 76% male) and in 131 patients at 6 months post STEMI. Remote zone ECV was lower than infarct zone ECV (25.6±2.8% versus 51.4±8.9%; P<0.001). In multivariable regression, left ventricular ejection fraction was inversely associated with remote zone ECV (P<0.001), and diabetes mellitus was positively associated with remote zone ECV (P=0.010). No ST-segment resolution (P=0.034) and extent of ischemic area at risk (P<0.001) were multivariable associates of the change in remote zone ECV at 6 months (ΔECV). ΔECV was a multivariable associate of the change in left ventricular end-diastolic volume at 6 months (regression coefficient [95% confidence interval]: 1.43 (0.10-2.76); P=0.036). ΔECV is implicated in the pathophysiology of left ventricular remodeling post STEMI, but because the effect size is small, ΔECV has limited use as a clinical biomarker of remodeling. URL: https://www.clinicaltrials.gov. Unique identifier: NCT02072850. © 2016 The Authors.

Remote Zone Extracellular Volume and Left Ventricular Remodeling in Survivors of ST-Elevation Myocardial Infarction

PubMed Central

Carberry, Jaclyn; Carrick, David; Haig, Caroline; Rauhalammi, Samuli M.; Ahmed, Nadeem; Mordi, Ify; McEntegart, Margaret; Petrie, Mark C.; Eteiba, Hany; Hood, Stuart; Watkins, Stuart; Lindsay, Mitchell; Davie, Andrew; Mahrous, Ahmed; Ford, Ian; Sattar, Naveed; Welsh, Paul; Radjenovic, Aleksandra; Oldroyd, Keith G.

2016-01-01

The natural history and pathophysiological significance of tissue remodeling in the myocardial remote zone after acute ST-elevation myocardial infarction (STEMI) is incompletely understood. Extracellular volume (ECV) in myocardial regions of interest can now be measured with cardiac magnetic resonance imaging. Patients who sustained an acute STEMI were enrolled in a cohort study (BHF MR-MI [British Heart Foundation Magnetic Resonance Imaging in Acute ST-Segment Elevation Myocardial Infarction study]). Cardiac magnetic resonance was performed at 1.5 Tesla at 2 days and 6 months post STEMI. T1 modified Look-Locker inversion recovery mapping was performed before and 15 minutes after contrast (0.15 mmol/kg gadoterate meglumine) in 140 patients at 2 days post STEMI (mean age: 59 years, 76% male) and in 131 patients at 6 months post STEMI. Remote zone ECV was lower than infarct zone ECV (25.6±2.8% versus 51.4±8.9%; P<0.001). In multivariable regression, left ventricular ejection fraction was inversely associated with remote zone ECV (P<0.001), and diabetes mellitus was positively associated with remote zone ECV (P=0.010). No ST-segment resolution (P=0.034) and extent of ischemic area at risk (P<0.001) were multivariable associates of the change in remote zone ECV at 6 months (ΔECV). ΔECV was a multivariable associate of the change in left ventricular end-diastolic volume at 6 months (regression coefficient [95% confidence interval]: 1.43 (0.10–2.76); P=0.036). ΔECV is implicated in the pathophysiology of left ventricular remodeling post STEMI, but because the effect size is small, ΔECV has limited use as a clinical biomarker of remodeling. Clinical Trial Registration— URL: https://www.clinicaltrials.gov. Unique identifier: NCT02072850. PMID:27354423

Multivariate meta-analysis: a robust approach based on the theory of U-statistic.

PubMed

Ma, Yan; Mazumdar, Madhu

2011-10-30

Meta-analysis is the methodology for combining findings from similar research studies asking the same question. When the question of interest involves multiple outcomes, multivariate meta-analysis is used to synthesize the outcomes simultaneously taking into account the correlation between the outcomes. Likelihood-based approaches, in particular restricted maximum likelihood (REML) method, are commonly utilized in this context. REML assumes a multivariate normal distribution for the random-effects model. This assumption is difficult to verify, especially for meta-analysis with small number of component studies. The use of REML also requires iterative estimation between parameters, needing moderately high computation time, especially when the dimension of outcomes is large. A multivariate method of moments (MMM) is available and is shown to perform equally well to REML. However, there is a lack of information on the performance of these two methods when the true data distribution is far from normality. In this paper, we propose a new nonparametric and non-iterative method for multivariate meta-analysis on the basis of the theory of U-statistic and compare the properties of these three procedures under both normal and skewed data through simulation studies. It is shown that the effect on estimates from REML because of non-normal data distribution is marginal and that the estimates from MMM and U-statistic-based approaches are very similar. Therefore, we conclude that for performing multivariate meta-analysis, the U-statistic estimation procedure is a viable alternative to REML and MMM. Easy implementation of all three methods are illustrated by their application to data from two published meta-analysis from the fields of hip fracture and periodontal disease. We discuss ideas for future research based on U-statistic for testing significance of between-study heterogeneity and for extending the work to meta-regression setting. Copyright © 2011 John Wiley & Sons, Ltd.

Logistic models--an odd(s) kind of regression.

PubMed

Jupiter, Daniel C

2013-01-01

The logistic regression model bears some similarity to the multivariable linear regression with which we are familiar. However, the differences are great enough to warrant a discussion of the need for and interpretation of logistic regression. Copyright © 2013 American College of Foot and Ankle Surgeons. Published by Elsevier Inc. All rights reserved.

Solving large mixed linear models using preconditioned conjugate gradient iteration.

PubMed

Strandén, I; Lidauer, M

1999-12-01

Continuous evaluation of dairy cattle with a random regression test-day model requires a fast solving method and algorithm. A new computing technique feasible in Jacobi and conjugate gradient based iterative methods using iteration on data is presented. In the new computing technique, the calculations in multiplication of a vector by a matrix were recorded to three steps instead of the commonly used two steps. The three-step method was implemented in a general mixed linear model program that used preconditioned conjugate gradient iteration. Performance of this program in comparison to other general solving programs was assessed via estimation of breeding values using univariate, multivariate, and random regression test-day models. Central processing unit time per iteration with the new three-step technique was, at best, one-third that needed with the old technique. Performance was best with the test-day model, which was the largest and most complex model used. The new program did well in comparison to other general software. Programs keeping the mixed model equations in random access memory required at least 20 and 435% more time to solve the univariate and multivariate animal models, respectively. Computations of the second best iteration on data took approximately three and five times longer for the animal and test-day models, respectively, than did the new program. Good performance was due to fast computing time per iteration and quick convergence to the final solutions. Use of preconditioned conjugate gradient based methods in solving large breeding value problems is supported by our findings.

Using multiobjective tradeoff sets and Multivariate Regression Trees to identify critical and robust decisions for long term water utility planning

NASA Astrophysics Data System (ADS)

Smith, R.; Kasprzyk, J. R.; Balaji, R.

2017-12-01

In light of deeply uncertain factors like future climate change and population shifts, responsible resource management will require new types of information and strategies. For water utilities, this entails potential expansion and efficient management of water supply infrastructure systems for changes in overall supply; changes in frequency and severity of climate extremes such as droughts and floods; and variable demands, all while accounting for conflicting long and short term performance objectives. Multiobjective Evolutionary Algorithms (MOEAs) are emerging decision support tools that have been used by researchers and, more recently, water utilities to efficiently generate and evaluate thousands of planning portfolios. The tradeoffs between conflicting objectives are explored in an automated way to produce (often large) suites of portfolios that strike different balances of performance. Once generated, the sets of optimized portfolios are used to support relatively subjective assertions of priorities and human reasoning, leading to adoption of a plan. These large tradeoff sets contain information about complex relationships between decisions and between groups of decisions and performance that, until now, has not been quantitatively described. We present a novel use of Multivariate Regression Trees (MRTs) to analyze tradeoff sets to reveal these relationships and critical decisions. Additionally, when MRTs are applied to tradeoff sets developed for different realizations of an uncertain future, they can identify decisions that are robust across a wide range of conditions and produce fundamental insights about the system being optimized.

Learning curve for intracranial angioplasty and stenting in single center.

PubMed

Cai, Qiankun; Li, Yongkun; Xu, Gelin; Sun, Wen; Xiong, Yunyun; Sun, Wenshan; Bao, Yuanfei; Huang, Xianjun; Zhang, Yao; Zhou, Lulu; Zhu, Wusheng; Liu, Xinfeng

2014-01-01

To identify the specific caseload to overcome learning curve effect based on data from consecutive patients treated with Intracranial Angioplasty and Stenting (IAS) in our center. The Stenting and Aggressive Medical Management for Preventing Recurrent Stroke and Intracranial Stenosis trial was prematurely terminated owing to the high rate of periprocedural complications in the endovascular arm. To date, there are no data available for determining the essential caseload sufficient to overcome the learning effect and perform IAS with an acceptable level of complications. Between March 2004 and May 2012, 188 consecutive patients with 194 lesions who underwent IAS were analyzed retrospectively. The outcome variables used to assess the learning curve were periprocedural complications (included transient ischemic attack, ischemic stroke, vessel rupture, cerebral hyperperfusion syndrome, and vessel perforation). Multivariable logistic regression analysis was employed to illustrate the existence of learning curve effect on IAS. A risk-adjusted cumulative sum chart was performed to identify the specific caseload to overcome learning curve effect. The overall rate of 30-days periprocedural complications was 12.4% (24/194). After adjusting for case-mix, multivariate logistic regression analysis showed that operator experience was an independent predictor for periprocedural complications. The learning curve of IAS to overcome complications in a risk-adjusted manner was 21 cases. Operator's level of experience significantly affected the outcome of IAS. Moreover, we observed that the amount of experience sufficient for performing IAS in our center was 21 cases. Copyright © 2013 Wiley Periodicals, Inc.

Retro-regression--another important multivariate regression improvement.

PubMed

Randić, M

2001-01-01

We review the serious problem associated with instabilities of the coefficients of regression equations, referred to as the MRA (multivariate regression analysis) "nightmare of the first kind". This is manifested when in a stepwise regression a descriptor is included or excluded from a regression. The consequence is an unpredictable change of the coefficients of the descriptors that remain in the regression equation. We follow with consideration of an even more serious problem, referred to as the MRA "nightmare of the second kind", arising when optimal descriptors are selected from a large pool of descriptors. This process typically causes at different steps of the stepwise regression a replacement of several previously used descriptors by new ones. We describe a procedure that resolves these difficulties. The approach is illustrated on boiling points of nonanes which are considered (1) by using an ordered connectivity basis; (2) by using an ordering resulting from application of greedy algorithm; and (3) by using an ordering derived from an exhaustive search for optimal descriptors. A novel variant of multiple regression analysis, called retro-regression (RR), is outlined showing how it resolves the ambiguities associated with both "nightmares" of the first and the second kind of MRA.

Factors related to clinical pregnancy after vitrified-warmed embryo transfer: a retrospective and multivariate logistic regression analysis of 2313 transfer cycles.

PubMed

Shi, Wenhao; Zhang, Silin; Zhao, Wanqiu; Xia, Xue; Wang, Min; Wang, Hui; Bai, Haiyan; Shi, Juanzi

2013-07-01

What factors does multivariate logistic regression show to be significantly associated with the likelihood of clinical pregnancy in vitrified-warmed embryo transfer (VET) cycles? Assisted hatching (AH) and if the reason to freeze embryos was to avoid the risk of ovarian hyperstimulation syndrome (OHSS) were significantly positively associated with a greater likelihood of clinical pregnancy. Single factor analysis has shown AH, number of embryos transferred and the reason of freezing for OHSS to be positively and damaged blastomere to be negatively significantly associated with the chance of clinical pregnancy after VET. It remains unclear what factors would be significant after multivariate analysis. The study was a retrospective analysis of 2313 VET cycles from 1481 patients performed between January 2008 and April 2012. A multivariate logistic regression analysis was performed to identify the factors to affect clinical pregnancy outcome of VET. There were 22 candidate variables selected based on clinical experiences and the literature. With the thresholds of α entry = α removal= 0.05 for both variable entry and variable removal, eight variables were chosen to contribute the multivariable model by the bootstrap stepwise variable selection algorithm (n = 1000). Eight variables were age at controlled ovarian hyperstimulation (COH), reason for freezing, AH, endometrial thickness, damaged blastomere, number of embryos transferred, number of good-quality embryos, and blood presence on transfer catheter. A descriptive comparison of the relative importance was accomplished by the proportion of explained variation (PEV). Among the reasons for freezing, the OHSS group showed a higher OR than the surplus embryo group when compared with other reasons for VET groups (OHSS versus Other, OR: 2.145; CI: 1.4-3.286; Surplus embryos versus Other, OR: 1.152; CI: 0.761-1.743) and high PEV (marginal 2.77%, P = 0.2911; partial 1.68%; CI of area under receptor operator characteristic curve (ROC): 0.5576-0.6000). AH also showed a high OR (OR: 2.105, CI: 1.554-2.85) and high PEV (marginal 1.97%; partial 1.02%; CI of area under ROC: 0.5344-0.5647). The number of good-quality embryos showed the highest marginal PEV and partial PEV (marginal 3.91%, partial 2.28%; CI of area under ROC: 0.5886-0.6343). This was a retrospective multivariate analysis of the data obtained in 5 years from a single IVF center. Repeated cycles in the same woman were treated as independent observations, which could introduce bias. Results are based on clinical pregnancy and not live births. Prospective analysis of a larger data set from a multicenter study based on live births is necessary to confirm the findings. Paying attention to the quality of embryos, the number of good embryos, AH and the reasons for freezing that are associated with clinical pregnancy after VET will assist the improvement of success rates.

Patient characteristics of smokers undergoing lumbar spine surgery: an analysis from the Quality Outcomes Database.

PubMed

Asher, Anthony L; Devin, Clinton J; McCutcheon, Brandon; Chotai, Silky; Archer, Kristin R; Nian, Hui; Harrell, Frank E; McGirt, Matthew; Mummaneni, Praveen V; Shaffrey, Christopher I; Foley, Kevin; Glassman, Steven D; Bydon, Mohamad

2017-12-01

OBJECTIVE In this analysis the authors compare the characteristics of smokers to nonsmokers using demographic, socioeconomic, and comorbidity variables. They also investigate which of these characteristics are most strongly associated with smoking status. Finally, the authors investigate whether the association between known patient risk factors and disability outcome is differentially modified by patient smoking status for those who have undergone surgery for lumbar degeneration. METHODS A total of 7547 patients undergoing degenerative lumbar surgery were entered into a prospective multicenter registry (Quality Outcomes Database [QOD]). A retrospective analysis of the prospectively collected data was conducted. Patients were dichotomized as smokers (current smokers) and nonsmokers. Multivariable logistic regression analysis fitted for patient smoking status and subsequent measurement of variable importance was performed to identify the strongest patient characteristics associated with smoking status. Multivariable linear regression models fitted for 12-month Oswestry Disability Index (ODI) scores in subsets of smokers and nonsmokers was performed to investigate whether differential effects of risk factors by smoking status might be present. RESULTS In total, 18% (n = 1365) of patients were smokers and 82% (n = 6182) were nonsmokers. In a multivariable logistic regression analysis, the factors significantly associated with patients' smoking status were sex (p < 0.0001), age (p < 0.0001), body mass index (p < 0.0001), educational status (p < 0.0001), insurance status (p < 0.001), and employment/occupation (p = 0.0024). Patients with diabetes had lowers odds of being a smoker (p = 0.0008), while patients with coronary artery disease had greater odds of being a smoker (p = 0.044). Patients' propensity for smoking was also significantly associated with higher American Society of Anesthesiologists (ASA) class (p < 0.0001), anterior-alone surgical approach (p = 0.018), greater number of levels (p = 0.0246), decompression only (p = 0.0001), and higher baseline ODI score (p < 0.0001). In a multivariable proportional odds logistic regression model, the adjusted odds ratio of risk factors and direction of improvement in 12-month ODI scores remained similar between the subsets of smokers and nonsmokers. CONCLUSIONS Using a large, national, multiinstitutional registry, the authors described the profile of patients who undergo lumbar spine surgery and its association with their smoking status. Compared with nonsmokers, smokers were younger, male, nondiabetic, nonobese patients presenting with leg pain more so than back pain, with higher ASA classes, higher disability, less education, more likely to be unemployed, and with Medicaid/uninsured insurance status. Smoking status did not affect the association between these risk factors and 12-month ODI outcome, suggesting that interventions for modifiable risk factors are equally efficacious between smokers and nonsmokers.

Utilization of apical vaginal support procedures at time of inpatient hysterectomy performed for benign conditions: a national estimate.

PubMed

Ross, Whitney Trotter; Meister, Melanie R; Shepherd, Jonathan P; Olsen, Margaret A; Lowder, Jerry L

2017-10-01

Apical vaginal support is considered the keystone of pelvic organ support. Level I evidence supports reestablishment of apical support at time of hysterectomy, regardless of whether the hysterectomy is performed for prolapse. National rates of apical support procedure performance at time of inpatient hysterectomy have not been well described. We sought to estimate trends and factors associated with use of apical support procedures at time of inpatient hysterectomy for benign indications in a large national database. The National (Nationwide) Inpatient Sample was used to identify hysterectomies performed from 2004 through 2013 for benign indications. International Classification of Diseases, Ninth Revision, Clinical Modification codes were used to select both procedures and diagnoses. The primary outcome was performance of an apical support procedure at time of hysterectomy. Descriptive and multivariable analyses were performed. There were 3,509,230 inpatient hysterectomies performed for benign disease from 2004 through 2013. In both nonprolapse and prolapse groups, there was a significant decrease in total number of annual hysterectomies performed over the study period (P < .0001). There were 2,790,652 (79.5%) hysterectomies performed without a diagnosis of prolapse, and an apical support procedure was performed in only 85,879 (3.1%). There was a significant decrease in the proportion of hysterectomies with concurrent apical support procedure (high of 4.0% in 2004 to 2.5% in 2013, P < .0001). In the multivariable logistic regression model, increasing age, hospital type (urban teaching), hospital bed size (large and medium), and hysterectomy type (vaginal and laparoscopically assisted vaginal) were associated with performance of an apical support procedure. During the study period, 718,578 (20.5%) inpatient hysterectomies were performed for prolapse diagnoses and 266,743 (37.1%) included an apical support procedure. There was a significant increase in the proportion of hysterectomies with concurrent apical support procedure (low of 31.3% in 2005 to 49.3% in 2013, P < .0001). In the multivariable logistic regression model, increasing age, hospital type (urban teaching), hospital bed size (medium and large), and hysterectomy type (total laparoscopic and laparoscopic supracervical) were associated with performance of an apical support procedure. This national database study demonstrates that apical support procedures are not routinely performed at time of inpatient hysterectomy regardless of presence of prolapse diagnosis. Educational efforts are needed to increase awareness of the importance of reestablishing apical vaginal support at time of hysterectomy regardless of indication. Copyright © 2017 Elsevier Inc. All rights reserved.

Assessing Principal Component Regression Prediction of Neurochemicals Detected with Fast-Scan Cyclic Voltammetry

PubMed Central

2011-01-01

Principal component regression is a multivariate data analysis approach routinely used to predict neurochemical concentrations from in vivo fast-scan cyclic voltammetry measurements. This mathematical procedure can rapidly be employed with present day computer programming languages. Here, we evaluate several methods that can be used to evaluate and improve multivariate concentration determination. The cyclic voltammetric representation of the calculated regression vector is shown to be a valuable tool in determining whether the calculated multivariate model is chemically appropriate. The use of Cook’s distance successfully identified outliers contained within in vivo fast-scan cyclic voltammetry training sets. This work also presents the first direct interpretation of a residual color plot and demonstrated the effect of peak shifts on predicted dopamine concentrations. Finally, separate analyses of smaller increments of a single continuous measurement could not be concatenated without substantial error in the predicted neurochemical concentrations due to electrode drift. Taken together, these tools allow for the construction of more robust multivariate calibration models and provide the first approach to assess the predictive ability of a procedure that is inherently impossible to validate because of the lack of in vivo standards. PMID:21966586

Parameter estimation of multivariate multiple regression model using bayesian with non-informative Jeffreys’ prior distribution

NASA Astrophysics Data System (ADS)

Saputro, D. R. S.; Amalia, F.; Widyaningsih, P.; Affan, R. C.

2018-05-01

Bayesian method is a method that can be used to estimate the parameters of multivariate multiple regression model. Bayesian method has two distributions, there are prior and posterior distributions. Posterior distribution is influenced by the selection of prior distribution. Jeffreys’ prior distribution is a kind of Non-informative prior distribution. This prior is used when the information about parameter not available. Non-informative Jeffreys’ prior distribution is combined with the sample information resulting the posterior distribution. Posterior distribution is used to estimate the parameter. The purposes of this research is to estimate the parameters of multivariate regression model using Bayesian method with Non-informative Jeffreys’ prior distribution. Based on the results and discussion, parameter estimation of β and Σ which were obtained from expected value of random variable of marginal posterior distribution function. The marginal posterior distributions for β and Σ are multivariate normal and inverse Wishart. However, in calculation of the expected value involving integral of a function which difficult to determine the value. Therefore, approach is needed by generating of random samples according to the posterior distribution characteristics of each parameter using Markov chain Monte Carlo (MCMC) Gibbs sampling algorithm.

Field applications of stand-off sensing using visible/NIR multivariate optical computing

NASA Astrophysics Data System (ADS)

Eastwood, DeLyle; Soyemi, Olusola O.; Karunamuni, Jeevanandra; Zhang, Lixia; Li, Hongli; Myrick, Michael L.

2001-02-01

12 A novel multivariate visible/NIR optical computing approach applicable to standoff sensing will be demonstrated with porphyrin mixtures as examples. The ultimate goal is to develop environmental or counter-terrorism sensors for chemicals such as organophosphorus (OP) pesticides or chemical warfare simulants in the near infrared spectral region. The mathematical operation that characterizes prediction of properties via regression from optical spectra is a calculation of inner products between the spectrum and the pre-determined regression vector. The result is scaled appropriately and offset to correspond to the basis from which the regression vector is derived. The process involves collecting spectroscopic data and synthesizing a multivariate vector using a pattern recognition method. Then, an interference coating is designed that reproduces the pattern of the multivariate vector in its transmission or reflection spectrum, and appropriate interference filters are fabricated. High and low refractive index materials such as Nb2O5 and SiO2 are excellent choices for the visible and near infrared regions. The proof of concept has now been established for this system in the visible and will later be extended to chemicals such as OP compounds in the near and mid-infrared.

Assessing principal component regression prediction of neurochemicals detected with fast-scan cyclic voltammetry.

PubMed

Keithley, Richard B; Wightman, R Mark

2011-06-07

Principal component regression is a multivariate data analysis approach routinely used to predict neurochemical concentrations from in vivo fast-scan cyclic voltammetry measurements. This mathematical procedure can rapidly be employed with present day computer programming languages. Here, we evaluate several methods that can be used to evaluate and improve multivariate concentration determination. The cyclic voltammetric representation of the calculated regression vector is shown to be a valuable tool in determining whether the calculated multivariate model is chemically appropriate. The use of Cook's distance successfully identified outliers contained within in vivo fast-scan cyclic voltammetry training sets. This work also presents the first direct interpretation of a residual color plot and demonstrated the effect of peak shifts on predicted dopamine concentrations. Finally, separate analyses of smaller increments of a single continuous measurement could not be concatenated without substantial error in the predicted neurochemical concentrations due to electrode drift. Taken together, these tools allow for the construction of more robust multivariate calibration models and provide the first approach to assess the predictive ability of a procedure that is inherently impossible to validate because of the lack of in vivo standards.

A refined method for multivariate meta-analysis and meta-regression.

PubMed

Jackson, Daniel; Riley, Richard D

2014-02-20

Making inferences about the average treatment effect using the random effects model for meta-analysis is problematic in the common situation where there is a small number of studies. This is because estimates of the between-study variance are not precise enough to accurately apply the conventional methods for testing and deriving a confidence interval for the average effect. We have found that a refined method for univariate meta-analysis, which applies a scaling factor to the estimated effects' standard error, provides more accurate inference. We explain how to extend this method to the multivariate scenario and show that our proposal for refined multivariate meta-analysis and meta-regression can provide more accurate inferences than the more conventional approach. We explain how our proposed approach can be implemented using standard output from multivariate meta-analysis software packages and apply our methodology to two real examples. Copyright © 2013 John Wiley & Sons, Ltd.

Newer classification and regression tree techniques: Bagging and Random Forests for ecological prediction

Treesearch

Anantha M. Prasad; Louis R. Iverson; Andy Liaw; Andy Liaw

2006-01-01

We evaluated four statistical models - Regression Tree Analysis (RTA), Bagging Trees (BT), Random Forests (RF), and Multivariate Adaptive Regression Splines (MARS) - for predictive vegetation mapping under current and future climate scenarios according to the Canadian Climate Centre global circulation model.

Artificial neural network, genetic algorithm, and logistic regression applications for predicting renal colic in emergency settings.

PubMed

Eken, Cenker; Bilge, Ugur; Kartal, Mutlu; Eray, Oktay

2009-06-03

Logistic regression is the most common statistical model for processing multivariate data in the medical literature. Artificial intelligence models like an artificial neural network (ANN) and genetic algorithm (GA) may also be useful to interpret medical data. The purpose of this study was to perform artificial intelligence models on a medical data sheet and compare to logistic regression. ANN, GA, and logistic regression analysis were carried out on a data sheet of a previously published article regarding patients presenting to an emergency department with flank pain suspicious for renal colic. The study population was composed of 227 patients: 176 patients had a diagnosis of urinary stone, while 51 ultimately had no calculus. The GA found two decision rules in predicting urinary stones. Rule 1 consisted of being male, pain not spreading to back, and no fever. In rule 2, pelvicaliceal dilatation on bedside ultrasonography replaced no fever. ANN, GA rule 1, GA rule 2, and logistic regression had a sensitivity of 94.9, 67.6, 56.8, and 95.5%, a specificity of 78.4, 76.47, 86.3, and 47.1%, a positive likelihood ratio of 4.4, 2.9, 4.1, and 1.8, and a negative likelihood ratio of 0.06, 0.42, 0.5, and 0.09, respectively. The area under the curve was found to be 0.867, 0.720, 0.715, and 0.713 for all applications, respectively. Data mining techniques such as ANN and GA can be used for predicting renal colic in emergency settings and to constitute clinical decision rules. They may be an alternative to conventional multivariate analysis applications used in biostatistics.

Structural Analysis of Women’s Heptathlon

PubMed Central

Gassmann, Freya; Fröhlich, Michael; Emrich, Eike

2016-01-01

The heptathlon comprises the results of seven single disciplines, assuming an equal influence from each discipline, depending on the measured performance. Data analysis was based on the data recorded for the individual performances of the 10 winning heptathletes in the World Athletics Championships from 1987 to 2013 and the Olympic Games from 1988 to 2012. In addition to descriptive analysis methods, correlations, bivariate and multivariate linear regressions, and panel data regressions were used. The transformation of the performances from seconds, centimeters, and meters into points showed that the individual disciplines do not equally affect the overall competition result. The currently valid conversion formula for the run, jump, and throw disciplines prefers the sprint and jump disciplines but penalizes the athletes performing in the 800 m run, javelin throw, and shotput disciplines. Furthermore, 21% to 48% of the variance of the sum of points can be attributed to the performances in the disciplines of long jump, 200 m sprint, 100 m hurdles, and high jump. To balance the effects of the single disciplines in the heptathlon, the formula to calculate points should be reevaluated. PMID:29910260

Functional Relationships and Regression Analysis.

ERIC Educational Resources Information Center

Preece, Peter F. W.

1978-01-01

Using a degenerate multivariate normal model for the distribution of organismic variables, the form of least-squares regression analysis required to estimate a linear functional relationship between variables is derived. It is suggested that the two conventional regression lines may be considered to describe functional, not merely statistical,…

Racial Differences in Circulating Natriuretic Peptide Levels: The Atherosclerosis Risk in Communities Study

PubMed Central

Gupta, Deepak K; Claggett, Brian; Wells, Quinn; Cheng, Susan; Li, Man; Maruthur, Nisa; Selvin, Elizabeth; Coresh, Josef; Konety, Suma; Butler, Kenneth R; Mosley, Thomas; Boerwinkle, Eric; Hoogeveen, Ron; Ballantyne, Christie M; Solomon, Scott D

2015-01-01

Background Natriuretic peptides promote natriuresis, diuresis, and vasodilation. Experimental deficiency of natriuretic peptides leads to hypertension (HTN) and cardiac hypertrophy, conditions more common among African Americans. Hospital-based studies suggest that African Americans may have reduced circulating natriuretic peptides, as compared to Caucasians, but definitive data from community-based cohorts are lacking. Methods and Results We examined plasma N-terminal pro B-type natriuretic peptide (NTproBNP) levels according to race in 9137 Atherosclerosis Risk in Communities (ARIC) Study participants (22% African American) without prevalent cardiovascular disease at visit 4 (1996–1998). Multivariable linear and logistic regression analyses were performed adjusting for clinical covariates. Among African Americans, percent European ancestry was determined from genetic ancestry informative markers and then examined in relation to NTproBNP levels in multivariable linear regression analysis. NTproBNP levels were significantly lower in African Americans (median, 43 pg/mL; interquartile range [IQR], 18, 88) than Caucasians (median, 68 pg/mL; IQR, 36, 124; P<0.0001). In multivariable models, adjusted log NTproBNP levels were 40% lower (95% confidence interval [CI], −43, −36) in African Americans, compared to Caucasians, which was consistent across subgroups of age, gender, HTN, diabetes, insulin resistance, and obesity. African-American race was also significantly associated with having nondetectable NTproBNP (adjusted OR, 5.74; 95% CI, 4.22, 7.80). In multivariable analyses in African Americans, a 10% increase in genetic European ancestry was associated with a 7% (95% CI, 1, 13) increase in adjusted log NTproBNP. Conclusions African Americans have lower levels of plasma NTproBNP than Caucasians, which may be partially owing to genetic variation. Low natriuretic peptide levels in African Americans may contribute to the greater risk for HTN and its sequalae in this population. PMID:25999400

Overexpression of epithelial cell adhesion molecule protein is associated with favorable prognosis in an unselected cohort of ovarian cancer patients.

PubMed

Battista, Marco Johannes; Cotarelo, Cristina; Jakobi, Sina; Steetskamp, Joscha; Makris, Georgios; Sicking, Isabel; Weyer, Veronika; Schmidt, Marcus

2014-07-01

The aim of this study was to evaluate the prognostic influence of epithelial cell adhesion molecule (EpCAM) in an unselected cohort of ovarian cancer (OC) patients. Expression of EpCAM was determined by immunohistochemistry in an unselected cohort of 117 patients with OC. Univariable and multivariable Cox regression analyses adjusted for age, tumor stage, histological grading, histological subtype, postoperative tumor burden and completeness of chemotherapy were performed in order to determine the prognostic influence of EpCAM. The Kaplan-Meier method is used to estimate survival rates. Univariable Cox regression analysis showed that overexpression of EpCAM is associated with favorable prognosis in terms of progression-free survival (PFS) (p = 0.011) and disease-specific survival (DSS) (p = 0.003). In multivariable Cox regression analysis, overexpression of EpCAM retains its significance independent of established prognostic factors for longer PFS [hazard ratios (HR) 0.408, 95 % confidence interval (CI) 0.197-0.846, p = 0.003] but not for PFS (HR 0.666, 95 % CI 0.366-1.212, p = 0.183). Kaplan-Meier plots demonstrate an influence on 5-year PFS rates (0 vs. 27.6 %, p = 0.048) and DSS rates (11.8 vs. 54.0 %, p = 0.018). These findings support the hypothesis that the expression of EpCAM is associated with favorable prognosis in OC.

Load cell having strain gauges of arbitrary location

DOEpatents

Spletzer, Barry [Albuquerque, NM

2007-03-13

A load cell utilizes a plurality of strain gauges mounted upon the load cell body such that there are six independent load-strain relations. Load is determined by applying the inverse of a load-strain sensitivity matrix to a measured strain vector. The sensitivity matrix is determined by performing a multivariate regression technique on a set of known loads correlated to the resulting strains. Temperature compensation is achieved by configuring the strain gauges as co-located orthogonal pairs.

Gender differences in health-related quality of life of adolescents with cystic fibrosis

PubMed Central

Arrington-Sanders, Renata; Yi, Michael S; Tsevat, Joel; Wilmott, Robert W; Mrus, Joseph M; Britto, Maria T

2006-01-01

Background Female patients with cystic fibrosis (CF) have consistently poorer survival rates than males across all ages. To determine if gender differences exist in health-related quality of life (HRQOL) of adolescent patients with CF, we performed a cross-section analysis of CF patients recruited from 2 medical centers in 2 cities during 1997–2001. Methods We used the 87-item child self-report form of the Child Health Questionnaire to measure 12 health domains. Data was also collected on age and forced expiratory volume in 1 second (FEV1). We analyzed data from 98 subjects and performed univariate analyses and linear regression or ordinal logistic regression for multivariable analyses. Results The mean (SD) age was 14.6 (2.5) years; 50 (51.0%) were female; and mean FEV1 was 71.6% (25.6%) of predicted. There were no statistically significant gender differences in age or FEV1. In univariate analyses, females reported significantly poorer HRQOL in 5 of the 12 domains. In multivariable analyses controlling for FEV1 and age, we found that female gender was associated with significantly lower global health (p < 0.05), mental health (p < 0.01), and general health perceptions (p < 0.05) scores. Conclusion Further research will need to focus on the causes of these differences in HRQOL and on potential interventions to improve HRQOL of adolescent patients with CF. PMID:16433917

High-throughput quantitative biochemical characterization of algal biomass by NIR spectroscopy; multiple linear regression and multivariate linear regression analysis.

PubMed

Laurens, L M L; Wolfrum, E J

2013-12-18

One of the challenges associated with microalgal biomass characterization and the comparison of microalgal strains and conversion processes is the rapid determination of the composition of algae. We have developed and applied a high-throughput screening technology based on near-infrared (NIR) spectroscopy for the rapid and accurate determination of algal biomass composition. We show that NIR spectroscopy can accurately predict the full composition using multivariate linear regression analysis of varying lipid, protein, and carbohydrate content of algal biomass samples from three strains. We also demonstrate a high quality of predictions of an independent validation set. A high-throughput 96-well configuration for spectroscopy gives equally good prediction relative to a ring-cup configuration, and thus, spectra can be obtained from as little as 10-20 mg of material. We found that lipids exhibit a dominant, distinct, and unique fingerprint in the NIR spectrum that allows for the use of single and multiple linear regression of respective wavelengths for the prediction of the biomass lipid content. This is not the case for carbohydrate and protein content, and thus, the use of multivariate statistical modeling approaches remains necessary.

Symptoms of musculoskeletal disorders among ammunition factory workers in Turkey.

PubMed

Pinar, Tevfik; Cakmak, Z Aytul; Saygun, Meral; Akdur, Recep; Ulu, Nuriye; Keles, Isik; Saylam, Hamdi Saim

2013-01-01

The aim of this study was to assess the prevalence of symptoms of work-related musculoskeletal disorders (MSDs) and to determine the risk factors among ammunition factory workers in Turkey. This cross-sectional study was performed on 955 ammunition factory workers. Potential risk factors were investigated with a questionnaire and multivariate logistic regression analysis was performed. During the previous year, 39.3% of ammunition workers experienced symptoms of work-related MSDs. Logistic regression analysis showed smoking (odds ratio [OR] = 1.372), chronic diseases (OR = 1.795), body mass index (BMI; overweight) (OR = 1.631), working year (OR = 1.509), cold temperature (OR = 1.838), and work load (OR = 2.210) were significant independent risk factors for the development of symptoms of MSDs. It was found that both work-related conditions and personal and environmental factors are important for the development of occupational MSDs.

Firefly algorithm versus genetic algorithm as powerful variable selection tools and their effect on different multivariate calibration models in spectroscopy: A comparative study.

PubMed

Attia, Khalid A M; Nassar, Mohammed W I; El-Zeiny, Mohamed B; Serag, Ahmed

2017-01-05

For the first time, a new variable selection method based on swarm intelligence namely firefly algorithm is coupled with three different multivariate calibration models namely, concentration residual augmented classical least squares, artificial neural network and support vector regression in UV spectral data. A comparative study between the firefly algorithm and the well-known genetic algorithm was developed. The discussion revealed the superiority of using this new powerful algorithm over the well-known genetic algorithm. Moreover, different statistical tests were performed and no significant differences were found between all the models regarding their predictabilities. This ensures that simpler and faster models were obtained without any deterioration of the quality of the calibration. Copyright © 2016 Elsevier B.V. All rights reserved.

Correlates of consistent condom use among men who have sex with men recruited through the Internet in Huzhou city: a cross-sectional survey.

PubMed

Jin, Meihua; Yang, Zhongrong; Dong, Zhengquan; Han, Jiankang

2013-12-01

There is growing evidence that men who have sex with men (MSM) are currently a group at high risk of HIV infection in China. Our study aims to know the factors affecting consistent condom use among MSM recruited through the internet in Huzhou city. An anonymous cross-sectional study was conducted by recruiting 410 MSM living in Huzhou city via the Internet. The socio-demographic profiles (age, education level, employment status, etc.) and sexual risk behaviors of the respondents were investigated. Bivariate logistic regression analyses were performed to compare the differences between consistent condom users and inconsistent condom users. Variables with significant bivariate between groups' differences were used as candidate variables in a stepwise multivariate logistic regression model. All statistical analyses were performed using SPSS for Windows 17.0, and a p value < 0.05 was considered to be statistically significant. According to their condom use, sixty-eight respondents were classified into two groups. One is consistent condom users, and the other is inconsistent condom users. Multivariate logistic regression showed that respondents who had a comprehensive knowledge of HIV (OR = 4.08, 95% CI: 1.85-8.99), who had sex with male sex workers (OR = 15.30, 95% CI: 5.89-39.75) and who had not drunk alcohol before sex (OR = 3.10, 95% CI: 1.38-6.95) were more likely to be consistent condom users. Consistent condom use among MSM was associated with comprehensive knowledge of HIV and a lack of alcohol use before sexual contact. As a result, reducing alcohol consumption and enhancing education regarding the risks of HIV among sexually active MSM would be effective in preventing of HIV transmission.

Trainee-Associated Factors and Proficiency at Percutaneous Nephrolithotomy.

PubMed

Aghamir, Seyed Mohammad Kazem; Behtash, Negar; Hamidi, Morteza; Farahmand, Hasan; Salavati, Alborz; Mortaz Hejri, Sara

2017-07-01

Percutaneous nephrolithotomy (PNL) is a complicated procedure for urology trainees. This study was designed to investigate the effect of trainees' ages and previous experience, as well as the number of operated cases, on proficiency at PNL by using patient outcomes. A cross sectional observational study was designed during a five-year period. Trainees in PNL fellowship programs were included. At the end of the program, the trainees' performance in PNL was assessed regarding five competencies and scored 1-5. If the overall score was 4 or above, the trainee was considered as proficient. The trainees' age at the beginning of the program and the years passed from their residency graduation were asked and recorded. Also, the number of PNL cases operated by each trainee was obtained via their logbooks. The age, years passed from graduation, and number of operated cases were compared between two groups of proficient and non-proficient trainees. Univariate and multivariate binary logistic regression analysis was applied to estimate the effect of aforementioned variables on the occurrence of the proficiency. Forty-two trainees were included in the study. The mean and standard deviation for the overall score were 3.40 (out of 5) and 0.67, respectively. Eleven trainees (26.2%) recognized as proficient in performing PNL. Univariate regression analysis indicated that each of three variables (age, years passed from graduation and number of operated cases) had statistically significant effect on proficiency. However, the multivariate regression analysis revealed that just the number of cases had significant effect on achieving proficiency. Although it might be assumed that trainees' age negatively correlates with their scores, in fact, it is their amount of practice that makes a difference. A certain number of cases is required to be operated by a trainee in order to reach the desired competency in PNL.

Impact of Delayed Time to Advanced Imaging on Missed Appointments Across Different Demographic and Socioeconomic Factors.

PubMed

Daye, Dania; Carrodeguas, Emmanuel; Glover, McKinley; Guerrier, Claude Emmanuel; Harvey, H Benjamin; Flores, Efrén J

2018-05-01

The aim of this study was to investigate the impact of wait days (WDs) on missed outpatient MRI appointments across different demographic and socioeconomic factors. An institutional review board-approved retrospective study was conducted among adult patients scheduled for outpatient MRI during a 12-month period. Scheduling data and demographic information were obtained. Imaging missed appointments were defined as missed scheduled imaging encounters. WDs were defined as the number of days from study order to appointment. Multivariate logistic regression was applied to assess the contribution of race and socioeconomic factors to missed appointments. Linear regression was performed to assess the relationship between missed appointment rates and WDs stratified by race, income, and patient insurance groups with analysis of covariance statistics. A total of 42,727 patients met the inclusion criteria. Mean WDs were 7.95 days. Multivariate regression showed increased odds ratio for missed appointments for patients with increased WDs (7-21 days: odds ratio [OR], 1.39; >21 days: OR, 1.77), African American patients (OR, 1.71), Hispanic patients (OR, 1.30), patients with noncommercial insurance (OR, 2.00-2.55), and those with imaging performed at the main hospital campus (OR, 1.51). Missed appointment rate linearly increased with WDs, with analysis of covariance revealing underrepresented minorities and Medicaid insurance as significant effect modifiers. Increased WDs for advanced imaging significantly increases the likelihood of missed appointments. This effect is most pronounced among underrepresented minorities and patients with lower socioeconomic status. Efforts to reduce WDs may improve equity in access to and utilization of advanced diagnostic imaging for all patients. Copyright © 2018. Published by Elsevier Inc.

Sex is not everything: the role of gender in early performance of a fundamental laparoscopic skill.

PubMed

Kolozsvari, Nicoleta O; Andalib, Amin; Kaneva, Pepa; Cao, Jiguo; Vassiliou, Melina C; Fried, Gerald M; Feldman, Liane S

2011-04-01

Existing literature on the acquisition of surgical skills suggests that women generally perform worse than men. This literature is limited by looking at an arbitrary number of trials and not adjusting for potential confounders. The objective of this study was to evaluate the impact of gender on the learning curve for a fundamental laparoscopic task. Thirty-two medical students performed the FLS peg transfer task and their scores were plotted to generate a learning curve. Nonlinear regression was used to estimate learning plateau and learning rate. Variables that may affect performance were assessed using a questionnaire. Innate visual-spatial abilities were evaluated using tests for spatial orientation, spatial scanning, and perceptual abilities. Score on first peg transfer attempt, learning plateau, and learning rate were compared for men and women using Student's t test. Innate abilities were correlated to simulator performance using Pearson's coefficient. Multivariate linear regression was used to investigate the effect of gender on early laparoscopic performance after adjusting for factors found significant on univariate analysis. Statistical significance was defined as P < 0.05. Nineteen men and 13 women participated in the study; 30 were right-handed, 12 reported high interest in surgery, and 26 had video game experience. There were no differences between men and women in initial peg transfer score, learning plateau, or learning rate. Initial peg transfer score and learning rate were higher in subjects who reported having a high interest in surgery (P = 0.02, P = 0.03). Initial score also correlated with perceptual ability score (P = 0.03). In multivariate analysis, only surgical interest remained a significant predictor of score on first peg transfer (P = 0.03) and learning rate (P = 0.02), while gender had no significant relationship to early performance. Gender did not affect the learning curve for a fundamental laparoscopic task, while interest in surgery and perceptual abilities did influence early performance.

Flood-frequency prediction methods for unregulated streams of Tennessee, 2000

USGS Publications Warehouse

Law, George S.; Tasker, Gary D.

2003-01-01

Up-to-date flood-frequency prediction methods for unregulated, ungaged rivers and streams of Tennessee have been developed. Prediction methods include the regional-regression method and the newer region-of-influence method. The prediction methods were developed using stream-gage records from unregulated streams draining basins having from 1 percent to about 30 percent total impervious area. These methods, however, should not be used in heavily developed or storm-sewered basins with impervious areas greater than 10 percent. The methods can be used to estimate 2-, 5-, 10-, 25-, 50-, 100-, and 500-year recurrence-interval floods of most unregulated rural streams in Tennessee. A computer application was developed that automates the calculation of flood frequency for unregulated, ungaged rivers and streams of Tennessee. Regional-regression equations were derived by using both single-variable and multivariable regional-regression analysis. Contributing drainage area is the explanatory variable used in the single-variable equations. Contributing drainage area, main-channel slope, and a climate factor are the explanatory variables used in the multivariable equations. Deleted-residual standard error for the single-variable equations ranged from 32 to 65 percent. Deleted-residual standard error for the multivariable equations ranged from 31 to 63 percent. These equations are included in the computer application to allow easy comparison of results produced by the different methods. The region-of-influence method calculates multivariable regression equations for each ungaged site and recurrence interval using basin characteristics from 60 similar sites selected from the study area. Explanatory variables that may be used in regression equations computed by the region-of-influence method include contributing drainage area, main-channel slope, a climate factor, and a physiographic-region factor. Deleted-residual standard error for the region-of-influence method tended to be only slightly smaller than those for the regional-regression method and ranged from 27 to 62 percent.

Multivariate logistic regression analysis of postoperative complications and risk model establishment of gastrectomy for gastric cancer: A single-center cohort report.

PubMed

Zhou, Jinzhe; Zhou, Yanbing; Cao, Shougen; Li, Shikuan; Wang, Hao; Niu, Zhaojian; Chen, Dong; Wang, Dongsheng; Lv, Liang; Zhang, Jian; Li, Yu; Jiao, Xuelong; Tan, Xiaojie; Zhang, Jianli; Wang, Haibo; Zhang, Bingyuan; Lu, Yun; Sun, Zhenqing

2016-01-01

Reporting of surgical complications is common, but few provide information about the severity and estimate risk factors of complications. If have, but lack of specificity. We retrospectively analyzed data on 2795 gastric cancer patients underwent surgical procedure at the Affiliated Hospital of Qingdao University between June 2007 and June 2012, established multivariate logistic regression model to predictive risk factors related to the postoperative complications according to the Clavien-Dindo classification system. Twenty-four out of 86 variables were identified statistically significant in univariate logistic regression analysis, 11 significant variables entered multivariate analysis were employed to produce the risk model. Liver cirrhosis, diabetes mellitus, Child classification, invasion of neighboring organs, combined resection, introperative transfusion, Billroth II anastomosis of reconstruction, malnutrition, surgical volume of surgeons, operating time and age were independent risk factors for postoperative complications after gastrectomy. Based on logistic regression equation, p=Exp∑BiXi / (1+Exp∑BiXi), multivariate logistic regression predictive model that calculated the risk of postoperative morbidity was developed, p = 1/(1 + e((4.810-1.287X1-0.504X2-0.500X3-0.474X4-0.405X5-0.318X6-0.316X7-0.305X8-0.278X9-0.255X10-0.138X11))). The accuracy, sensitivity and specificity of the model to predict the postoperative complications were 86.7%, 76.2% and 88.6%, respectively. This risk model based on Clavien-Dindo grading severity of complications system and logistic regression analysis can predict severe morbidity specific to an individual patient's risk factors, estimate patients' risks and benefits of gastric surgery as an accurate decision-making tool and may serve as a template for the development of risk models for other surgical groups.

PARAMETRIC AND NON PARAMETRIC (MARS: MULTIVARIATE ADDITIVE REGRESSION SPLINES) LOGISTIC REGRESSIONS FOR PREDICTION OF A DICHOTOMOUS RESPONSE VARIABLE WITH AN EXAMPLE FOR PRESENCE/ABSENCE OF AMPHIBIANS

EPA Science Inventory

The purpose of this report is to provide a reference manual that could be used by investigators for making informed use of logistic regression using two methods (standard logistic regression and MARS). The details for analyses of relationships between a dependent binary response ...

Causal diagrams and multivariate analysis II: precision work.

PubMed

Jupiter, Daniel C

2014-01-01

In this Investigators' Corner, I continue my discussion of when and why we researchers should include variables in multivariate regression. My examination focuses on studies comparing treatment groups and situations for which we can either exclude variables from multivariate analyses or include them for reasons of precision. Copyright © 2014 American College of Foot and Ankle Surgeons. Published by Elsevier Inc. All rights reserved.

Comparative Effectiveness of Emergency Resuscitative Thoracotomy versus Closed Chest Compressions among Patients with Critical Blunt Trauma: A Nationwide Cohort Study in Japan.

PubMed

Suzuki, Kodai; Inoue, Shigeaki; Morita, Seiji; Watanabe, Nobuo; Shintani, Ayumi; Inokuchi, Sadaki; Ogura, Shinji

2016-01-01

Although emergency resuscitative thoracotomy is performed as a salvage maneuver for critical blunt trauma patients, evidence supporting superior effectiveness of emergency resuscitative thoracotomy compared to conventional closed-chest compressions remains insufficient. The objective of this study was to investigate whether emergency resuscitative thoracotomy at the emergency department or in the operating room was associated with favourable outcomes after blunt trauma and to compare its effectiveness with that of closed-chest compressions. This was a retrospective nationwide cohort study. Data were obtained from the Japan Trauma Data Bank for the period between 2004 and 2012. The primary and secondary outcomes were patient survival rates 24 h and 28 d after emergency department arrival. Statistical analyses were performed using multivariable generalized mixed-effects regression analysis. We adjusted for the effects of different hospitals by introducing random intercepts in regression analysis to account for the differential quality of emergency resuscitative thoracotomy at hospitals where patients in cardiac arrest were treated. Sensitivity analyses were performed using propensity score matching. In total, 1,377 consecutive, critical blunt trauma patients who received cardiopulmonary resuscitation in the emergency department or operating room were included in the study. Of these patients, 484 (35.1%) underwent emergency resuscitative thoracotomy and 893 (64.9%) received closed-chest compressions. Compared to closed-chest compressions, emergency resuscitative thoracotomy was associated with lower survival rate 24 h after emergency department arrival (4.5% vs. 17.5%, respectively, P < 0.001) and 28 d after arrival (1.2% vs. 6.0%, respectively, P < 0.001). Multivariable generalized mixed-effects regression analysis with and without a propensity score-matched dataset revealed that the odds ratio for an unfavorable survival rate after 24 h was lower for emergency resuscitative thoracotomy than for closed-chest compressions (P < 0.001). Emergency resuscitative thoracotomy was independently associated with decreased odds of a favorable survival rate compared to closed-chest compressions.

[Study on the correlation among adolescents' family function, negative life events stress amount and suicide ideation].

PubMed

Zhang, Dongdong; Chen, Ling; Yin, Dan; Miao, Jinping; Sun, Yehuan

2014-07-01

To explore the correlation between suicide ideation and family function & negative life events, as well as other influential factors in adolescents, thus present a theoretical base for clinicians and school staff to develop intervention for those problems. By adopting current situation random sampling method, Self-Rating Idea of Suicide Scale, Adolescent Self-Rating Life Events Check List and Family APGAR Index were used to assess adolescents at random in a hygiene vocational school in Changzhou City, Jiangsu Province and a collage in Wuhu City, Anhui Province. 3700 questionnaires were granted, 3675 questionnaires were collected, among which 3620 were valid. Chi-square test, t-test, and univariate logistic regression were employed in univariate analysis, multivariate logistic regression was used in multivariate analysis. The detection rate of suicide ideation is 7.0%, and the top five suicide ideation characteristics were: poor academic performance (33.6%), serious family functional impairment (25.8%), lower-middle academic performance (11.7%), bad economic conditions (10.8%) and study in Grade Three (9.9%). Multiple logistic regression showed that the following three high-level stress amount in negative life events are most crucial for suicide ideation. They are "relationships" (OR = 1.135, 95% CI 1.071 - 1. 202), "academic pressure" (OR = 1.169, 95% CI 1.101 - 1.241), and "external events" (OR = 1.278, 95% CI 1.187 - 1.376). What' s more, the stress of attending higher grades (OR = 1.980, 95% CI 1.302 - 3.008), poor academic performance (OR = 7.206, 95% CI 1.745 - 9.789), moderate family functional impairment (OR = 2.562, 95% CI 1.527 - 2.892) and its serious level (OR = 8.287, 95% CI 3.154 - 6.917) are also influential factors for suicide ideation. Severe family functional impairment and high-level stress amount of negative life events produced the main factors of suicide ideation. Therefore, necessary and sufficient support should be given to adolescents by families and schools.

Is there a relationship between periodontal conditions and number of medications among the elderly?

PubMed

Natto, Zuhair S; Aladmawy, Majdi; Alshaeri, Heba K; Alasqah, Mohammed; Papas, Athena

2016-03-01

To investigate possible correlations of clinical attachment level and pocket depth with number of medications in elderly individuals. Intra-oral examinations for 139 patients visiting Tufts dental clinic were done. Periodontal assessments were performed with a manual UNC-15 periodontal probe to measure probing depth (PD) and clinical attachment level (CAL) at 6 sites. Complete lists of patients' medications were obtained during the examinations. Statistical analysis involved Kruskal-Wallis, chi square and multivariate logistic regression analyses. Age and health status attained statistical significance (p< 0.05), in contingency table analysis with number of medications. Number of medications had an effect on CAL: increased attachment loss was observed when 4 or more medications were being taken by the patient. Number of medications did not have any effect on periodontal PD. In multivariate logistic regression analysis, 6 or more medications had a higher risk of attachment loss (>3mm) when compared to the no-medication group, in crude OR (1.20, 95% CI:0.22-6.64), and age adjusted (OR=1.16, 95% CI:0.21-6.45), but not with the multivariate model (OR=0.71, 95% CI:0.11-4.39). CAL seems to be more sensitive to the number of medications taken, when compared to PD. However, it is not possible to discriminate at exactly what number of drug combinations the breakdown in CAL will happen. We need to do further analysis, including more subjects, to understand the possible synergistic mechanisms for different drug and periodontal responses.

Self-declared stock ownership and association with positive trial outcome in randomized controlled trials with binary outcomes published in general medical journals: a cross-sectional study.

PubMed

Falk Delgado, Alberto; Falk Delgado, Anna

2017-07-26

Describe the prevalence and types of conflicts of interest (COI) in published randomized controlled trials (RCTs) in general medical journals with a binary primary outcome and assess the association between conflicts of interest and favorable outcome. Parallel-group RCTs with a binary primary outcome published in three general medical journals during 2013-2015 were identified. COI type, funding source, and outcome were extracted. Binomial logistic regression model was performed to assess association between COI and funding source with outcome. A total of 509 consecutive parallel-group RCTs were included in the study. COI was reported in 74% in mixed funded RCTs and in 99% in for-profit funded RCTs. Stock ownership was reported in none of the non-profit RCTs, in 7% of mixed funded RCTs, and in 50% of for-profit funded RCTs. Mixed-funded RCTs had employees from the funding company in 11% and for-profit RCTs in 76%. Multivariable logistic regression revealed that stock ownership in the funding company among any of the authors was associated with a favorable outcome (odds ratio = 3.53; 95% confidence interval = 1.59-7.86; p < 0.01). COI in for-profit funded RCTs is extensive, because the factors related to COI are not fully independent, a multivariable analysis should be cautiously interpreted. However, after multivariable adjustment only stock ownership from the funding company among authors is associated with a favorable outcome.

Association between serum CA 19-9 and metabolic syndrome: A cross-sectional study.

PubMed

Du, Rui; Cheng, Di; Lin, Lin; Sun, Jichao; Peng, Kui; Xu, Yu; Xu, Min; Chen, Yuhong; Bi, Yufang; Wang, Weiqing; Lu, Jieli; Ning, Guang

2017-11-01

Increasing evidence suggests that serum CA 19-9 is associated with abnormal glucose metabolism. However, data on the association between CA 19-9 and metabolic syndrome is limited. The aim of the present study was to investigate the association between serum CA 19-9 and metabolic syndrome. A cross-sectional study was conducted on 3641 participants aged ≥40 years from the Songnan Community, Baoshan District in Shanghai, China. Logistic regression analysis was used to evaluate the association between serum CA 19-9 and metabolic syndrome. Multivariate logistic regression analysis showed that compared with participants in the first tertile of serum CA 19-9, those in the second and third tertiles had increased odds ratios (OR) for prevalent metabolic syndrome (multivariate adjusted OR 1.46 [95% confidence interval {CI} 1.11-1.92] and 1.51 [95% CI 1.14-1.98]; P trend  = 0.005). In addition, participants with elevated serum CA 19-9 (≥37 U/mL) had an increased risk of prevalent metabolic syndrome compared with those with serum CA 19-9 < 37 U/mL (multivariate adjusted OR 2.10; 95% CI 1.21-3.65). Serum CA 19-9 is associated with an increased risk of prevalent metabolic syndrome. In order to confirm this association and identify potential mechanisms, prospective cohort and mechanic studies should be performed. © 2017 Ruijin Hospital, Shanghai Jiaotong University School of Medicine and John Wiley & Sons Australia, Ltd.

Why are we regressing?

PubMed

Jupiter, Daniel C

2012-01-01

In this first of a series of statistical methodology commentaries for the clinician, we discuss the use of multivariate linear regression. Copyright © 2012 American College of Foot and Ankle Surgeons. Published by Elsevier Inc. All rights reserved.

The impact of moderate wine consumption on the risk of developing prostate cancer

PubMed Central

Ferro, Matteo; Foerster, Beat; Abufaraj, Mohammad; Briganti, Alberto; Karakiewicz, Pierre I; Shariat, Shahrokh F

2018-01-01

Objective To investigate the impact of moderate wine consumption on the risk of prostate cancer (PCa). We focused on the differential effect of moderate consumption of red versus white wine. Design This study was a meta-analysis that includes data from case–control and cohort studies. Materials and methods A systematic search of Web of Science, Medline/PubMed, and Cochrane library was performed on December 1, 2017. Studies were deemed eligible if they assessed the risk of PCa due to red, white, or any wine using multivariable logistic regression analysis. We performed a formal meta-analysis for the risk of PCa according to moderate wine and wine type consumption (white or red). Heterogeneity between studies was assessed using Cochrane’s Q test and I2 statistics. Publication bias was assessed using Egger’s regression test. Results A total of 930 abstracts and titles were initially identified. After removal of duplicates, reviews, and conference abstracts, 83 full-text original articles were screened. Seventeen studies (611,169 subjects) were included for final evaluation and fulfilled the inclusion criteria. In the case of moderate wine consumption: the pooled risk ratio (RR) for the risk of PCa was 0.98 (95% CI 0.92–1.05, p=0.57) in the multivariable analysis. Moderate white wine consumption increased the risk of PCa with a pooled RR of 1.26 (95% CI 1.10–1.43, p=0.001) in the multi-variable analysis. Meanwhile, moderate red wine consumption had a protective role reducing the risk by 12% (RR 0.88, 95% CI 0.78–0.999, p=0.047) in the multivariable analysis that comprised 222,447 subjects. Conclusions In this meta-analysis, moderate wine consumption did not impact the risk of PCa. Interestingly, regarding the type of wine, moderate consumption of white wine increased the risk of PCa, whereas moderate consumption of red wine had a protective effect. Further analyses are needed to assess the differential molecular effect of white and red wine conferring their impact on PCa risk. PMID:29713200

Use of Diuretics is not associated with mortality in patients admitted to the emergency department: results from a cross-sectional study.

PubMed

Haider, Dominik G; Lindner, Gregor; Wolzt, Michael; Leichtle, Alexander Benedikt; Fiedler, Georg-Martin; Sauter, Thomas C; Fuhrmann, Valentin; Exadaktylos, Aristomenis K

2016-02-01

Patients with diuretic therapy are at risk for drug-induced adverse reactions. It is unknown if presence of diuretic therapy at hospital emergency room admission is associated with mortality. In this cross sectional analysis, all emergency room patients 2010 and 2011 at the Inselspital Bern, Switzerland were included. A multivariable logistic regression model was performed to assess the association between pre-existing diuretic medication and 28 day mortality. Twenty-two thousand two hundred thirty-nine subjects were included in the analysis. A total of 8.5%, 2.5%, and 0.4% of patients used one, two, or three or more diuretics. In univariate analysis spironolactone, torasemide and chlortalidone use were associated with 28 day mortality (all p < 0.05). In a multivariate cox regression model no association with mortality was detectable (p > 0.05). No difference existed between patients with or without diuretic therapy (P > 0.05). Age and creatinine were independent risk factors for mortaliy (both p < 0.05). Use of diuretics is not associated with mortality in an unselected cohort of patients presenting in an emergency room.

Independent risk factors of morbidity in penetrating colon injuries.

PubMed

Girgin, Sadullah; Gedik, Ercan; Uysal, Ersin; Taçyildiz, Ibrahim Halil

2009-05-01

The present study explored the factors effective on colon-related morbidity in patients with penetrating injury of the colon. The medical records of 196 patients were reviewed for variables including age, gender, factor of trauma, time between injury and operation, shock, duration of operation, Penetrating Abdominal Trauma Index (PATI), Injury Severity Score (ISS), site of colon injury, Colon Injury Score, fecal contamination, number of associated intra- and extraabdominal organ injuries, units of transfused blood within the first 24 hours, and type of surgery. In order to determine the independent risk factors, multivariate logistic regression analysis was performed. Gunshot wounds, interval between injury and operation > or =6 hours, shock, duration of the operation > or =6 hours, PATI > or =25, ISS > or =20, Colon Injury Score > or = grade 3, major fecal contamination, number of associated intraabdominal organ injuries >2, number of associated extraabdominal organ injuries >2, multiple blood transfusions, and diversion were significantly associated with morbidity. Multivariate logistic regression analysis showed diversion and transfusion of > or =4 units in the first 24 hours as independent risk factors affecting colon-related morbidity. Diversion and transfusion of > or =4 units in the first 24 hours were determined to be independent risk factors for colon-related morbidity.

Combining fibre optic Raman spectroscopy and tactile resonance measurement for tissue characterization

NASA Astrophysics Data System (ADS)

Candefjord, Stefan; Nyberg, Morgan; Jalkanen, Ville; Ramser, Kerstin; Lindahl, Olof A.

2010-12-01

Tissue characterization is fundamental for identification of pathological conditions. Raman spectroscopy (RS) and tactile resonance measurement (TRM) are two promising techniques that measure biochemical content and stiffness, respectively. They have potential to complement the golden standard--histological analysis. By combining RS and TRM, complementary information about tissue content can be obtained and specific drawbacks can be avoided. The aim of this study was to develop a multivariate approach to compare RS and TRM information. The approach was evaluated on measurements at the same points on porcine abdominal tissue. The measurement points were divided into five groups by multivariate analysis of the RS data. A regression analysis was performed and receiver operating characteristic (ROC) curves were used to compare the RS and TRM data. TRM identified one group efficiently (area under ROC curve 0.99). The RS data showed that the proportion of saturated fat was high in this group. The regression analysis showed that stiffness was mainly determined by the amount of fat and its composition. We concluded that RS provided additional, important information for tissue identification that was not provided by TRM alone. The results are promising for development of a method combining RS and TRM for intraoperative tissue characterization.

Social participation after successful kidney transplantation.

PubMed

van der Mei, Sijrike F; van Sonderen, Eric L P; van Son, Willem J; de Jong, Paul E; Groothoff, Johan W; van den Heuvel, Wim J A

2007-03-30

To explore and describe the degree of social participation after kidney transplantation and to examine associated factors. A cross-sectional study on 239 adult patients 1-7.3 years after kidney transplantation was performed via in-home interviews on participation in obligatory activities (i.e., employment, education, household tasks) and leisure activities (volunteer work, assisting others, recreation, sports, clubs/associations, socializing, going out). Kidney transplantation patients had a lower educational level, spent less time on obligatory activities, had part-time jobs more often, and participated less in sports compared to a control group from the general population. No difference was found in socializing, church attendance, volunteer work and going out. Multivariate regression analysis showed a negative association of age and a positive association of educational status and time since transplantation with obligatory participation. Multivariate logistic regression showed positive associations of education and time since transplantation with volunteer work; age was negatively and education positively associated with sports and going out, whereas living arrangement was also associated with going out. Although kidney transplantation patients participate less in employment and sports, they do participate in household tasks, volunteer work, going out, socializing and other leisure activities. Participation is associated with factors as age, educational status and time since transplantation.

Optimization of large animal MI models; a systematic analysis of control groups from preclinical studies.

PubMed

Zwetsloot, P P; Kouwenberg, L H J A; Sena, E S; Eding, J E; den Ruijter, H M; Sluijter, J P G; Pasterkamp, G; Doevendans, P A; Hoefer, I E; Chamuleau, S A J; van Hout, G P J; Jansen Of Lorkeers, S J

2017-10-27

Large animal models are essential for the development of novel therapeutics for myocardial infarction. To optimize translation, we need to assess the effect of experimental design on disease outcome and model experimental design to resemble the clinical course of MI. The aim of this study is therefore to systematically investigate how experimental decisions affect outcome measurements in large animal MI models. We used control animal-data from two independent meta-analyses of large animal MI models. All variables of interest were pre-defined. We performed univariable and multivariable meta-regression to analyze whether these variables influenced infarct size and ejection fraction. Our analyses incorporated 246 relevant studies. Multivariable meta-regression revealed that infarct size and cardiac function were influenced independently by choice of species, sex, co-medication, occlusion type, occluded vessel, quantification method, ischemia duration and follow-up duration. We provide strong systematic evidence that commonly used endpoints significantly depend on study design and biological variation. This makes direct comparison of different study-results difficult and calls for standardized models. Researchers should take this into account when designing large animal studies to most closely mimic the clinical course of MI and enable translational success.

Application of least square support vector machine and multivariate adaptive regression spline models in long term prediction of river water pollution

NASA Astrophysics Data System (ADS)

Kisi, Ozgur; Parmar, Kulwinder Singh

2016-03-01

This study investigates the accuracy of least square support vector machine (LSSVM), multivariate adaptive regression splines (MARS) and M5 model tree (M5Tree) in modeling river water pollution. Various combinations of water quality parameters, Free Ammonia (AMM), Total Kjeldahl Nitrogen (TKN), Water Temperature (WT), Total Coliform (TC), Fecal Coliform (FC) and Potential of Hydrogen (pH) monitored at Nizamuddin, Delhi Yamuna River in India were used as inputs to the applied models. Results indicated that the LSSVM and MARS models had almost same accuracy and they performed better than the M5Tree model in modeling monthly chemical oxygen demand (COD). The average root mean square error (RMSE) of the LSSVM and M5Tree models was decreased by 1.47% and 19.1% using MARS model, respectively. Adding TC input to the models did not increase their accuracy in modeling COD while adding FC and pH inputs to the models generally decreased the accuracy. The overall results indicated that the MARS and LSSVM models could be successfully used in estimating monthly river water pollution level by using AMM, TKN and WT parameters as inputs.

Study of cyanotoxins presence from experimental cyanobacteria concentrations using a new data mining methodology based on multivariate adaptive regression splines in Trasona reservoir (Northern Spain).

PubMed

Garcia Nieto, P J; Sánchez Lasheras, F; de Cos Juez, F J; Alonso Fernández, J R

2011-11-15

There is an increasing need to describe cyanobacteria blooms since some cyanobacteria produce toxins, termed cyanotoxins. These latter can be toxic and dangerous to humans as well as other animals and life in general. It must be remarked that the cyanobacteria are reproduced explosively under certain conditions. This results in algae blooms, which can become harmful to other species if the cyanobacteria involved produce cyanotoxins. In this research work, the evolution of cyanotoxins in Trasona reservoir (Principality of Asturias, Northern Spain) was studied with success using the data mining methodology based on multivariate adaptive regression splines (MARS) technique. The results of the present study are two-fold. On one hand, the importance of the different kind of cyanobacteria over the presence of cyanotoxins in the reservoir is presented through the MARS model and on the other hand a predictive model able to forecast the possible presence of cyanotoxins in a short term was obtained. The agreement of the MARS model with experimental data confirmed the good performance of the same one. Finally, conclusions of this innovative research are exposed. Copyright © 2011 Elsevier B.V. All rights reserved.

PM10 modeling in the Oviedo urban area (Northern Spain) by using multivariate adaptive regression splines

NASA Astrophysics Data System (ADS)

Nieto, Paulino José García; Antón, Juan Carlos Álvarez; Vilán, José Antonio Vilán; García-Gonzalo, Esperanza

2014-10-01

The aim of this research work is to build a regression model of the particulate matter up to 10 micrometers in size (PM10) by using the multivariate adaptive regression splines (MARS) technique in the Oviedo urban area (Northern Spain) at local scale. This research work explores the use of a nonparametric regression algorithm known as multivariate adaptive regression splines (MARS) which has the ability to approximate the relationship between the inputs and outputs, and express the relationship mathematically. In this sense, hazardous air pollutants or toxic air contaminants refer to any substance that may cause or contribute to an increase in mortality or serious illness, or that may pose a present or potential hazard to human health. To accomplish the objective of this study, the experimental dataset of nitrogen oxides (NOx), carbon monoxide (CO), sulfur dioxide (SO2), ozone (O3) and dust (PM10) were collected over 3 years (2006-2008) and they are used to create a highly nonlinear model of the PM10 in the Oviedo urban nucleus (Northern Spain) based on the MARS technique. One main objective of this model is to obtain a preliminary estimate of the dependence between PM10 pollutant in the Oviedo urban area at local scale. A second aim is to determine the factors with the greatest bearing on air quality with a view to proposing health and lifestyle improvements. The United States National Ambient Air Quality Standards (NAAQS) establishes the limit values of the main pollutants in the atmosphere in order to ensure the health of healthy people. Firstly, this MARS regression model captures the main perception of statistical learning theory in order to obtain a good prediction of the dependence among the main pollutants in the Oviedo urban area. Secondly, the main advantages of MARS are its capacity to produce simple, easy-to-interpret models, its ability to estimate the contributions of the input variables, and its computational efficiency. Finally, on the basis of these numerical calculations, using the multivariate adaptive regression splines (MARS) technique, conclusions of this research work are exposed.

Diagnostic performance of procalcitonin for hospitalised children with acute pyelonephritis presenting to the paediatric emergency department.

PubMed

Chen, Shan-Ming; Chang, Hung-Ming; Hung, Tung-Wei; Chao, Yu-Hua; Tsai, Jeng-Dau; Lue, Ko-Huang; Sheu, Ji-Nan

2013-05-01

Urinary tract infection (UTI) is a common bacterial infection in children that can result in permanent renal damage. This study prospectively assessed the diagnostic performance of procalcitonin (PCT) for predicting acute pyelonephritis (APN) among children with febrile UTI presenting to the paediatric emergency department (ED). Children aged ≤10 years with febrile UTI admitted to hospital from the paediatric ED were prospectively studied. Blood PCT, C reactive protein (CRP) and white blood cell (WBC) count were measured in the ED. Sensitivity, specificity, predictive values, multilevel likelihood ratios, receiver operating characteristic (ROC) curve analysis and multivariate logistic regression were used to assess quantitative variables for diagnosing APN. The 136 enrolled patients (56 boys and 80 girls; age range 1 month to 10 years) were divided into APN (n=87) and lower UTI (n=49) groups according to (99m)Tc-dimercaptosuccinic acid scan results. The cut-off value for maximum diagnostic performance of PCT was 1.3 ng/ml (sensitivity 86.2%, specificity 89.8%). By multivariate regression analysis, only PCT and CRP were retained as significant predictors of APN. Comparing ROC curves, PCT had a significantly greater area under the curve than CRP, WBC count and fever for differentiating between APN and lower UTI. PCT has better sensitivity and specificity than CRP and WBC count for distinguishing between APN and lower UTI. PCT is a valuable marker for predicting APN in children with febrile UTI. It may be considered in the initial investigation and therapeutic strategies for children presenting to the ED.

Using Multivariate Adaptive Regression Spline and Artificial Neural Network to Simulate Urbanization in Mumbai, India

NASA Astrophysics Data System (ADS)

Ahmadlou, M.; Delavar, M. R.; Tayyebi, A.; Shafizadeh-Moghadam, H.

2015-12-01

Land use change (LUC) models used for modelling urban growth are different in structure and performance. Local models divide the data into separate subsets and fit distinct models on each of the subsets. Non-parametric models are data driven and usually do not have a fixed model structure or model structure is unknown before the modelling process. On the other hand, global models perform modelling using all the available data. In addition, parametric models have a fixed structure before the modelling process and they are model driven. Since few studies have compared local non-parametric models with global parametric models, this study compares a local non-parametric model called multivariate adaptive regression spline (MARS), and a global parametric model called artificial neural network (ANN) to simulate urbanization in Mumbai, India. Both models determine the relationship between a dependent variable and multiple independent variables. We used receiver operating characteristic (ROC) to compare the power of the both models for simulating urbanization. Landsat images of 1991 (TM) and 2010 (ETM+) were used for modelling the urbanization process. The drivers considered for urbanization in this area were distance to urban areas, urban density, distance to roads, distance to water, distance to forest, distance to railway, distance to central business district, number of agricultural cells in a 7 by 7 neighbourhoods, and slope in 1991. The results showed that the area under the ROC curve for MARS and ANN was 94.77% and 95.36%, respectively. Thus, ANN performed slightly better than MARS to simulate urban areas in Mumbai, India.

Lithium might be associated with better decision-making performance in euthymic bipolar patients.

PubMed

Adida, Marc; Jollant, Fabrice; Clark, Luke; Guillaume, Sebastien; Goodwin, Guy M; Azorin, Jean-Michel; Courtet, Philippe

2015-06-01

Bipolar disorder is associated with impaired decision-making. Little is known about how treatment, especially lithium, influences decision-making abilities in bipolar patients when euthymic. We aimed at testing for an association between lithium medication and decision-making performance in remitted bipolar patients. Decision-making was measured using the Iowa Gambling Task in 3 groups of subjects: 34 and 56 euthymic outpatients with bipolar disorder, treated with lithium (monotherapy and lithium combined with anticonvulsant or antipsychotic) and without lithium (anticonvulsant, antipsychotic and combination treatment), respectively, and 152 matched healthy controls. Performance was compared between the 3 groups. In the 90 euthymic patients, the relationship between different sociodemographic and clinical variables and decision-making was assessed by stepwise multivariate regression analysis. Euthymic patients with lithium (p=0.007) and healthy controls (p=0.001) selected significantly more cards from the safe decks than euthymic patients without lithium, with no significant difference between euthymic patients with lithium and healthy controls (p=0.9). In the 90 euthymic patients, the stepwise linear multivariate regression revealed that decision-making was significantly predicted (p<0.001) by lithium dose, level of education and no family history of bipolar disorder (all p≤0.01). Because medication was not randomized, it was not possible to discriminate the effect of different medications. Lithium medication might be associated with better decision-making in remitted bipolar patients. A randomized trial is required to test for the hypothesis that lithium, but not other mood stabilizers, may specifically improve decision-making abilities in bipolar disorder. Copyright © 2015 Elsevier B.V. and ECNP. All rights reserved.

Estimating Soil Cation Exchange Capacity from Soil Physical and Chemical Properties

NASA Astrophysics Data System (ADS)

Bateni, S. M.; Emamgholizadeh, S.; Shahsavani, D.

2014-12-01

The soil Cation Exchange Capacity (CEC) is an important soil characteristic that has many applications in soil science and environmental studies. For example, CEC influences soil fertility by controlling the exchange of ions in the soil. Measurement of CEC is costly and difficult. Consequently, several studies attempted to obtain CEC from readily measurable soil physical and chemical properties such as soil pH, organic matter, soil texture, bulk density, and particle size distribution. These studies have often used multiple regression or artificial neural network models. Regression-based models cannot capture the intricate relationship between CEC and soil physical and chemical attributes and provide inaccurate CEC estimates. Although neural network models perform better than regression methods, they act like a black-box and cannot generate an explicit expression for retrieval of CEC from soil properties. In a departure with regression and neural network models, this study uses Genetic Expression Programming (GEP) and Multivariate Adaptive Regression Splines (MARS) to estimate CEC from easily measurable soil variables such as clay, pH, and OM. CEC estimates from GEP and MARS are compared with measurements at two field sites in Iran. Results show that GEP and MARS can estimate CEC accurately. Also, the MARS model performs slightly better than GEP. Finally, a sensitivity test indicates that organic matter and pH have respectively the least and the most significant impact on CEC.

A refined method for multivariate meta-analysis and meta-regression

PubMed Central

Jackson, Daniel; Riley, Richard D

2014-01-01

Making inferences about the average treatment effect using the random effects model for meta-analysis is problematic in the common situation where there is a small number of studies. This is because estimates of the between-study variance are not precise enough to accurately apply the conventional methods for testing and deriving a confidence interval for the average effect. We have found that a refined method for univariate meta-analysis, which applies a scaling factor to the estimated effects’ standard error, provides more accurate inference. We explain how to extend this method to the multivariate scenario and show that our proposal for refined multivariate meta-analysis and meta-regression can provide more accurate inferences than the more conventional approach. We explain how our proposed approach can be implemented using standard output from multivariate meta-analysis software packages and apply our methodology to two real examples. © 2013 The Authors. Statistics in Medicine published by John Wiley & Sons, Ltd. PMID:23996351

Multivariate meta-analysis for non-linear and other multi-parameter associations

PubMed Central

Gasparrini, A; Armstrong, B; Kenward, M G

2012-01-01

In this paper, we formalize the application of multivariate meta-analysis and meta-regression to synthesize estimates of multi-parameter associations obtained from different studies. This modelling approach extends the standard two-stage analysis used to combine results across different sub-groups or populations. The most straightforward application is for the meta-analysis of non-linear relationships, described for example by regression coefficients of splines or other functions, but the methodology easily generalizes to any setting where complex associations are described by multiple correlated parameters. The modelling framework of multivariate meta-analysis is implemented in the package mvmeta within the statistical environment R. As an illustrative example, we propose a two-stage analysis for investigating the non-linear exposure–response relationship between temperature and non-accidental mortality using time-series data from multiple cities. Multivariate meta-analysis represents a useful analytical tool for studying complex associations through a two-stage procedure. Copyright © 2012 John Wiley & Sons, Ltd. PMID:22807043

Risk factors for lower extremity injuries among half marathon and marathon runners of the Lage Landen Marathon Eindhoven 2012: A prospective cohort study in the Netherlands.

PubMed

van Poppel, D; de Koning, J; Verhagen, A P; Scholten-Peeters, G G M

2016-02-01

To determine risk factors for running injuries during the Lage Landen Marathon Eindhoven 2012. Prospective cohort study. Population-based study. This study included 943 runners. Running injuries after the Lage Landen Marathon. Sociodemographic and training-related factors as well as lifestyle factors were considered as potential risk factors and assessed in a questionnaire 1 month before the running event. The association between potential risk factors and injuries was determined, per running distance separately, using univariate and multivariate logistic regression analysis. In total, 154 respondents sustained a running injury. Among the marathon runners, in the univariate model, body mass index ≥ 26 kg/m(2), ≤ 5 years of running experience, and often performing interval training, were significantly associated with running injuries, whereas in the multivariate model only ≤ 5 years of running experience and not performing interval training on a regular basis were significantly associated with running injuries. Among marathon runners, no multivariate model could be created because of the low number of injuries and participants. This study indicates that interval training on a regular basis may be recommended to marathon runners to reduce the risk of injury. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Remote sensing and GIS-based landslide hazard analysis and cross-validation using multivariate logistic regression model on three test areas in Malaysia

NASA Astrophysics Data System (ADS)

Pradhan, Biswajeet

2010-05-01

This paper presents the results of the cross-validation of a multivariate logistic regression model using remote sensing data and GIS for landslide hazard analysis on the Penang, Cameron, and Selangor areas in Malaysia. Landslide locations in the study areas were identified by interpreting aerial photographs and satellite images, supported by field surveys. SPOT 5 and Landsat TM satellite imagery were used to map landcover and vegetation index, respectively. Maps of topography, soil type, lineaments and land cover were constructed from the spatial datasets. Ten factors which influence landslide occurrence, i.e., slope, aspect, curvature, distance from drainage, lithology, distance from lineaments, soil type, landcover, rainfall precipitation, and normalized difference vegetation index (ndvi), were extracted from the spatial database and the logistic regression coefficient of each factor was computed. Then the landslide hazard was analysed using the multivariate logistic regression coefficients derived not only from the data for the respective area but also using the logistic regression coefficients calculated from each of the other two areas (nine hazard maps in all) as a cross-validation of the model. For verification of the model, the results of the analyses were then compared with the field-verified landslide locations. Among the three cases of the application of logistic regression coefficient in the same study area, the case of Selangor based on the Selangor logistic regression coefficients showed the highest accuracy (94%), where as Penang based on the Penang coefficients showed the lowest accuracy (86%). Similarly, among the six cases from the cross application of logistic regression coefficient in other two areas, the case of Selangor based on logistic coefficient of Cameron showed highest (90%) prediction accuracy where as the case of Penang based on the Selangor logistic regression coefficients showed the lowest accuracy (79%). Qualitatively, the cross application model yields reasonable results which can be used for preliminary landslide hazard mapping.

Serum dehydroepiandrosterone sulphate, psychosocial factors and musculoskeletal pain in workers.

PubMed

Marinelli, A; Prodi, A; Pesel, G; Ronchese, F; Bovenzi, M; Negro, C; Larese Filon, F

2017-12-30

The serum level of dehydroepiandrosterone sulphate (DHEA-S) has been suggested as a biological marker of stress. To assess the association between serum DHEA-S, psychosocial factors and musculoskeletal (MS) pain in university workers. The study population included voluntary workers at the scientific departments of the University of Trieste (Italy) who underwent periodical health surveillance from January 2011 to June 2012. DHEA-S level was analysed in serum. The assessment tools included the General Health Questionnaire (GHQ) and a modified Nordic musculoskeletal symptoms questionnaire. The relation between DHEA-S, individual characteristics, pain perception and psychological factors was assessed by means of multivariable linear regression analysis. There were 189 study participants. The study population was characterized by high reward and low effort. Pain perception in the neck, shoulder, upper limbs, upper back and lower back was reported by 42, 32, 19, 29 and 43% of people, respectively. In multivariable regression analysis, gender, age and pain perception in the shoulder and upper limbs were significantly related to serum DHEA-S. Effort and overcommitment were related to shoulder and neck pain but not to DHEA-S. The GHQ score was associated with pain perception in different body sites and inversely to DHEA-S but significance was lost in multivariable regression analysis. DHEA-S was associated with age, gender and perception of MS pain, while effort-reward imbalance dimensions and GHQ score failed to reach the statistical significance in multivariable regression analysis. © The Author(s) 2017. Published by Oxford University Press on behalf of the Society of Occupational Medicine. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Independent Prognostic Factors for Acute Organophosphorus Pesticide Poisoning.

PubMed

Tang, Weidong; Ruan, Feng; Chen, Qi; Chen, Suping; Shao, Xuebo; Gao, Jianbo; Zhang, Mao

2016-07-01

Acute organophosphorus pesticide poisoning (AOPP) is becoming a significant problem and a potential cause of human mortality because of the abuse of organophosphate compounds. This study aims to determine the independent prognostic factors of AOPP by using multivariate logistic regression analysis. The clinical data for 71 subjects with AOPP admitted to our hospital were retrospectively analyzed. This information included the Acute Physiology and Chronic Health Evaluation II (APACHE II) scores, 6-h post-admission blood lactate levels, post-admission 6-h lactate clearance rates, admission blood cholinesterase levels, 6-h post-admission blood cholinesterase levels, cholinesterase activity, blood pH, and other factors. Univariate analysis and multivariate logistic regression analyses were conducted to identify all prognostic factors and independent prognostic factors, respectively. A receiver operating characteristic curve was plotted to analyze the testing power of independent prognostic factors. Twelve of 71 subjects died. Admission blood lactate levels, 6-h post-admission blood lactate levels, post-admission 6-h lactate clearance rates, blood pH, and APACHE II scores were identified as prognostic factors for AOPP according to the univariate analysis, whereas only 6-h post-admission blood lactate levels, post-admission 6-h lactate clearance rates, and blood pH were independent prognostic factors identified by multivariate logistic regression analysis. The receiver operating characteristic analysis suggested that post-admission 6-h lactate clearance rates were of moderate diagnostic value. High 6-h post-admission blood lactate levels, low blood pH, and low post-admission 6-h lactate clearance rates were independent prognostic factors identified by multivariate logistic regression analysis. Copyright © 2016 by Daedalus Enterprises.

Quality Reporting of Multivariable Regression Models in Observational Studies: Review of a Representative Sample of Articles Published in Biomedical Journals.

PubMed

Real, Jordi; Forné, Carles; Roso-Llorach, Albert; Martínez-Sánchez, Jose M

2016-05-01

Controlling for confounders is a crucial step in analytical observational studies, and multivariable models are widely used as statistical adjustment techniques. However, the validation of the assumptions of the multivariable regression models (MRMs) should be made clear in scientific reporting. The objective of this study is to review the quality of statistical reporting of the most commonly used MRMs (logistic, linear, and Cox regression) that were applied in analytical observational studies published between 2003 and 2014 by journals indexed in MEDLINE.Review of a representative sample of articles indexed in MEDLINE (n = 428) with observational design and use of MRMs (logistic, linear, and Cox regression). We assessed the quality of reporting about: model assumptions and goodness-of-fit, interactions, sensitivity analysis, crude and adjusted effect estimate, and specification of more than 1 adjusted model.The tests of underlying assumptions or goodness-of-fit of the MRMs used were described in 26.2% (95% CI: 22.0-30.3) of the articles and 18.5% (95% CI: 14.8-22.1) reported the interaction analysis. Reporting of all items assessed was higher in articles published in journals with a higher impact factor.A low percentage of articles indexed in MEDLINE that used multivariable techniques provided information demonstrating rigorous application of the model selected as an adjustment method. Given the importance of these methods to the final results and conclusions of observational studies, greater rigor is required in reporting the use of MRMs in the scientific literature.

MODELING SNAKE MICROHABITAT FROM RADIOTELEMETRY STUDIES USING POLYTOMOUS LOGISTIC REGRESSION

EPA Science Inventory

Multivariate analysis of snake microhabitat has historically used techniques that were derived under assumptions of normality and common covariance structure (e.g., discriminant function analysis, MANOVA). In this study, polytomous logistic regression (PLR which does not require ...

Mortality Prediction Model of Septic Shock Patients Based on Routinely Recorded Data

PubMed Central

Carrara, Marta; Baselli, Giuseppe; Ferrario, Manuela

2015-01-01

We studied the problem of mortality prediction in two datasets, the first composed of 23 septic shock patients and the second composed of 73 septic subjects selected from the public database MIMIC-II. For each patient we derived hemodynamic variables, laboratory results, and clinical information of the first 48 hours after shock onset and we performed univariate and multivariate analyses to predict mortality in the following 7 days. The results show interesting features that individually identify significant differences between survivors and nonsurvivors and features which gain importance only when considered together with the others in a multivariate regression model. This preliminary study on two small septic shock populations represents a novel contribution towards new personalized models for an integration of multiparameter patient information to improve critical care management of shock patients. PMID:26557154

Employer-sponsored insurance, health care cost growth, and the economic performance of U.S. Industries.

PubMed

Sood, Neeraj; Ghosh, Arkadipta; Escarce, José J

2009-10-01

To estimate the effect of growth in health care costs that outpaces gross domestic product (GDP) growth ("excess" growth in health care costs) on employment, gross output, and value added to GDP of U.S. industries. We analyzed data from 38 U.S. industries for the period 1987-2005. All data are publicly available from various government agencies. We estimated bivariate and multivariate regressions. To develop the regression models, we assumed that rapid growth in health care costs has a larger effect on economic performance for industries where large percentages of workers receive employer-sponsored health insurance (ESI). We used the estimated regression coefficients to simulate economic outcomes under alternative scenarios of health care cost inflation. Faster growth in health care costs had greater adverse effects on economic outcomes for industries with larger percentages of workers who had ESI. We found that a 10 percent increase in excess growth in health care costs would have resulted in 120,803 fewer jobs, US$28,022 million in lost gross output, and US$14,082 million in lost value added in 2005. These declines represent 0.17 to 0.18 percent of employment, gross output, and value added in 2005. Excess growth in health care costs is adversely affecting the economic performance of U.S. industries.

Hybrid ABC Optimized MARS-Based Modeling of the Milling Tool Wear from Milling Run Experimental Data

PubMed Central

García Nieto, Paulino José; García-Gonzalo, Esperanza; Ordóñez Galán, Celestino; Bernardo Sánchez, Antonio

2016-01-01

Milling cutters are important cutting tools used in milling machines to perform milling operations, which are prone to wear and subsequent failure. In this paper, a practical new hybrid model to predict the milling tool wear in a regular cut, as well as entry cut and exit cut, of a milling tool is proposed. The model was based on the optimization tool termed artificial bee colony (ABC) in combination with multivariate adaptive regression splines (MARS) technique. This optimization mechanism involved the parameter setting in the MARS training procedure, which significantly influences the regression accuracy. Therefore, an ABC–MARS-based model was successfully used here to predict the milling tool flank wear (output variable) as a function of the following input variables: the time duration of experiment, depth of cut, feed, type of material, etc. Regression with optimal hyperparameters was performed and a determination coefficient of 0.94 was obtained. The ABC–MARS-based model's goodness of fit to experimental data confirmed the good performance of this model. This new model also allowed us to ascertain the most influential parameters on the milling tool flank wear with a view to proposing milling machine's improvements. Finally, conclusions of this study are exposed. PMID:28787882

Hybrid ABC Optimized MARS-Based Modeling of the Milling Tool Wear from Milling Run Experimental Data.

PubMed

García Nieto, Paulino José; García-Gonzalo, Esperanza; Ordóñez Galán, Celestino; Bernardo Sánchez, Antonio

2016-01-28

Milling cutters are important cutting tools used in milling machines to perform milling operations, which are prone to wear and subsequent failure. In this paper, a practical new hybrid model to predict the milling tool wear in a regular cut, as well as entry cut and exit cut, of a milling tool is proposed. The model was based on the optimization tool termed artificial bee colony (ABC) in combination with multivariate adaptive regression splines (MARS) technique. This optimization mechanism involved the parameter setting in the MARS training procedure, which significantly influences the regression accuracy. Therefore, an ABC-MARS-based model was successfully used here to predict the milling tool flank wear (output variable) as a function of the following input variables: the time duration of experiment, depth of cut, feed, type of material, etc . Regression with optimal hyperparameters was performed and a determination coefficient of 0.94 was obtained. The ABC-MARS-based model's goodness of fit to experimental data confirmed the good performance of this model. This new model also allowed us to ascertain the most influential parameters on the milling tool flank wear with a view to proposing milling machine's improvements. Finally, conclusions of this study are exposed.

A cross-sectional study to estimate associations between education level and osteoporosis in a Chinese postmenopausal women sample.

PubMed

Piao, Hui-Hong; He, Jiajia; Zhang, Keqin; Tang, Zihui

2015-01-01

Our research aims to investigate the associations between education level and osteoporosis (OP) in Chinese postmenopausal women. A large-scale, community-based, cross-sectional study was conducted to examine the associations between education level and OP. A self-reported questionnaire was used to access the demographical information and medical history of the participants. A total of 1905 postmenopausal women were available for data analysis in this study. Multiple regression models controlling for confounding factors to include education level were performed to investigate the relationship with OP. The prevalence of OP was 28.29% in our study sample. Multivariate linear regression analyses adjusted for relevant potential confounding factors detected significant associations between education level and T-score (β = 0.025, P-value = 0.095, 95% CI: -0.004-0.055 for model 1; and β = 0.092, P-value = 0.032, 95% CI: 0.008-0.175 for model 2). Multivariate logistic regression analyses detected significant associations between education level and OP in model 1 (P-value = 0.070 for model 1, Table 5), while no significant associations was reported in model 2 (P value = 0.131). In participants with high education levels, the OR for OP was 0.914 (95% CI: 0.830-1.007). The findings indicated that education level was independently and significantly associated with OP. The prevalence of OP was more frequent in Chinese postmenopausal women with low educational status.

The extension of total gain (TG) statistic in survival models: properties and applications.

PubMed

Choodari-Oskooei, Babak; Royston, Patrick; Parmar, Mahesh K B

2015-07-01

The results of multivariable regression models are usually summarized in the form of parameter estimates for the covariates, goodness-of-fit statistics, and the relevant p-values. These statistics do not inform us about whether covariate information will lead to any substantial improvement in prediction. Predictive ability measures can be used for this purpose since they provide important information about the practical significance of prognostic factors. R (2)-type indices are the most familiar forms of such measures in survival models, but they all have limitations and none is widely used. In this paper, we extend the total gain (TG) measure, proposed for a logistic regression model, to survival models and explore its properties using simulations and real data. TG is based on the binary regression quantile plot, otherwise known as the predictiveness curve. Standardised TG ranges from 0 (no explanatory power) to 1 ('perfect' explanatory power). The results of our simulations show that unlike many of the other R (2)-type predictive ability measures, TG is independent of random censoring. It increases as the effect of a covariate increases and can be applied to different types of survival models, including models with time-dependent covariate effects. We also apply TG to quantify the predictive ability of multivariable prognostic models developed in several disease areas. Overall, TG performs well in our simulation studies and can be recommended as a measure to quantify the predictive ability in survival models.

Factors affecting choice between ureterostomy, ileal conduit and continent reservoir after radical cystectomy: Japanese series.

PubMed

Sugihara, Toru; Yasunaga, Hideo; Horiguchi, Hiromasa; Fujimura, Tetsuya; Fushimi, Kiyohide; Yu, Changhong; Kattan, Michael W; Homma, Yukio

2014-12-01

Little is known about the disparity of choices between three urinary diversions after radical cystectomy, focusing on patient and institutional factors. We identified urothelial carcinoma patients who received radical cystectomy with cutaneous ureterostomy, ileal conduit or continent reservoir using the Japanese Diagnosis Procedure Combination database from 2007 to 2012. Data comprised age, sex, comorbidities (converted into the Charlson index), TNM classification (converted into oncological stage), hospitals' academic status, hospital volume, bed volume and geographical region. Multivariate ordinal logistic regression analyses fitted with the proportional odds model were performed to analyze factors affecting urinary diversion choices. For dependent variables, the three diversions were converted into an ordinal variable in order of complexity: cutaneous ureterostomy (reference), ileal conduit and continent reservoir. Geographical variations were also examined by multivariate logistic regression models. A total of 4790 patients (1131 cutaneous ureterostomies [23.6 %], 2970 ileal conduits [62.0 %] and 689 continent reservoirs [14.4 %]) were included. Ordinal logistic regression analyses showed that male sex, lower age, lower Charlson index, early tumor stage, higher hospital volume (≥3.4 cases/year) and larger bed volume (≥450 beds) were significantly associated with the preference of more complex urinary diversion. Significant geographical disparity was also found. Good patient condition and early oncological status, as well as institutional factors, including high hospital volume, large bed volume and specific geographical regions, are independently related to the likelihood of choosing complex diversions. Recognizing this disparity would help reinforce the need for clinical practice uniformity.

Evaluation of the efficiency of continuous wavelet transform as processing and preprocessing algorithm for resolution of overlapped signals in univariate and multivariate regression analyses; an application to ternary and quaternary mixtures

NASA Astrophysics Data System (ADS)

Hegazy, Maha A.; Lotfy, Hayam M.; Mowaka, Shereen; Mohamed, Ekram Hany

2016-07-01

Wavelets have been adapted for a vast number of signal-processing applications due to the amount of information that can be extracted from a signal. In this work, a comparative study on the efficiency of continuous wavelet transform (CWT) as a signal processing tool in univariate regression and a pre-processing tool in multivariate analysis using partial least square (CWT-PLS) was conducted. These were applied to complex spectral signals of ternary and quaternary mixtures. CWT-PLS method succeeded in the simultaneous determination of a quaternary mixture of drotaverine (DRO), caffeine (CAF), paracetamol (PAR) and p-aminophenol (PAP, the major impurity of paracetamol). While, the univariate CWT failed to simultaneously determine the quaternary mixture components and was able to determine only PAR and PAP, the ternary mixtures of DRO, CAF, and PAR and CAF, PAR, and PAP. During the calculations of CWT, different wavelet families were tested. The univariate CWT method was validated according to the ICH guidelines. While for the development of the CWT-PLS model a calibration set was prepared by means of an orthogonal experimental design and their absorption spectra were recorded and processed by CWT. The CWT-PLS model was constructed by regression between the wavelet coefficients and concentration matrices and validation was performed by both cross validation and external validation sets. Both methods were successfully applied for determination of the studied drugs in pharmaceutical formulations.

Decomposing Racial/Ethnic Disparities in Influenza Vaccination among the Elderly

PubMed Central

Yoo, Byung-Kwang; Hasebe, Takuya; Szilagyi, Peter G.

2015-01-01

While persistent racial/ethnic disparities in influenza vaccination have been reported among the elderly, characteristics contributing to disparities are poorly understood. This study aimed to assess characteristics associated with racial/ethnic disparities in influenza vaccination using a nonlinear Oaxaca-Blinder decomposition method. We performed cross-sectional multivariable logistic regression analyses for which the dependent variable was self-reported receipt of influenza vaccine during the 2010–2011 season among community dwelling non-Hispanic African-American (AA), non-Hispanic White (W), English-speaking Hispanic (EH) and Spanish-speaking Hispanic (SH) elderly, enrolled in the 2011 Medicare Current Beneficiary Survey (MCBS) (un-weighted/weighted N= 6,095/19.2million). Using the nonlinear Oaxaca-Blinder decomposition method, we assessed the relative contribution of seventeen covariates—including socio-demographic characteristics, health status, insurance, access, preference regarding healthcare, and geographic regions —to disparities in influenza vaccination. Unadjusted racial/ethnic disparities in influenza vaccination were 14.1 percentage points (pp) (W-AA disparity, p<.001), 25.7 pp (W-SH disparity, p<.001) and 0.6 pp (W-EH disparity, p>.8). The Oaxaca-Blinder decomposition method estimated that the unadjusted W-AA and W-SH disparities in vaccination could be reduced by only 45% even if AA and SH groups become equivalent to Whites in all covariates in multivariable regression models. The remaining 55% of disparities were attributed to (a) racial/ethnic differences in the estimated coefficients (e.g., odds ratios) in the regression models and (b) characteristics not included in the regression models. Our analysis found that only about 45% of racial/ethnic disparities in influenza vaccination among the elderly could be reduced by equalizing recognized characteristics among racial/ethnic groups. Future studies are needed to identify additional modifiable characteristics causing disparities in influenza vaccination. PMID:25900133

The association between GGCX, miR-133 genetic polymorphisms and warfarin stable dosage in Han Chinese patients with mechanical heart valve replacement.

PubMed

Tang, X-Y; Zhang, J; Peng, J; Tan, S-L; Zhang, W; Song, G-B; Liu, L-M; Li, C-L; Ren, H; Zeng, L; Liu, Z-Q; Chen, X-P; Zhou, X-M; Zhou, H-H; Hu, J-X; Li, Z

2017-08-01

Warfarin is a widely used anticoagulant with a narrow therapeutic index. Polymorphisms in the VKORC1, CYP2C9 and CYP4F2 genes have been verified to correlate with warfarin stable dosage (WSD). Whether any other genes or variants affect the dosage is unknown. The aim of our study was to investigate the relationship between GGCX, miR-133 variants and the WSD in Han Chinese patients with mechanical heart valve replacement (MHVR). A total of 231 patients were enrolled in the study. Blood samples were collected for genotyping. The average WSD among subjects with different GGCX or miR-133 genotypes was compared. Regression analyses were performed to test for any association of genetic polymorphisms with WSD. The warfarin dosage in patients with the GGCX rs699664 TT and rs12714145 TT genotypes was 3.77±0.93 (95% CI: 3.35-4.19) mg/d and 3.70±1.00 (95% CI: 3.32-4.09) mg/d, respectively. The GGCX rs699664 and rs12714145 genotypes were significantly associated with WSD (P<.05). But they were ruled out in the multivariate regression analysis. There were no significant differences in the average warfarin stable dosage between subjects with MIR133B rs142410335 wild-type and variant genotypes (P>.05). The genotypes of GGCX rs699644 and rs12714145 were significantly associated with WSD (P<.05), but their contributions were not significant after accounting for other factors. MIR133B rs142410335 makes no significant contributions to warfarin stable dosage in Han Chinese patients with MHVR neither in univariate regression nor in multivariate regression analyses. © 2017 John Wiley & Sons Ltd.

Marital status and survival of patients with oral cavity squamous cell carcinoma: a population-based study.

PubMed

Shi, Xiao; Zhang, Ting-Ting; Hu, Wei-Ping; Ji, Qing-Hai

2017-04-25

The relationship between marital status and oral cavity squamous cell carcinoma (OCSCC) survival has not been explored. The objective of our study was to evaluate the impact of marital status on OCSCC survival and investigate the potential mechanisms. Married patients had better 5-year cancer-specific survival (CSS) (66.7% vs 54.9%) and 5-year overall survival (OS) (56.0% vs 41.1%). In multivariate Cox regression models, unmarried patients also showed higher mortality risk for both CSS (Hazard Ratio [HR]: 1.260, 95% confidence interval (CI): 1.187-1.339, P < 0.001) and OS (HR: 1.328, 95% CI: 1.266-1.392, P < 0.001). Multivariate logistic regression showed married patients were more likely to be diagnosed at earlier stage (P < 0.001) and receive surgery (P < 0.001). Married patients still demonstrated better prognosis in the 1:1 matched group analysis (CSS: 62.9% vs 60.8%, OS: 52.3% vs 46.5%). 11022 eligible OCSCC patients were identified from Surveillance, Epidemiology, and End Results (SEER) database, including 5902 married and 5120 unmarried individuals. Kaplan-Meier analysis, Log-rank test and Cox proportional hazards regression model were used to analyze survival and mortality risk. Influence of marital status on stage, age at diagnosis and selection of treatment was determined by binomial and multinomial logistic regression. Propensity score matching method was adopted to perform a 1:1 matched cohort. Marriage has an independently protective effect on OCSCC survival. Earlier diagnosis and more sufficient treatment are possible explanations. Besides, even after 1:1 matching, survival advantage of married group still exists, indicating that spousal support from other aspects may also play an important role.

Marital status and survival of patients with oral cavity squamous cell carcinoma: a population-based study

PubMed Central

Shi, Xiao; Zhang, Ting-ting; Hu, Wei-ping; Ji, Qing-hai

2017-01-01

Background The relationship between marital status and oral cavity squamous cell carcinoma (OCSCC) survival has not been explored. The objective of our study was to evaluate the impact of marital status on OCSCC survival and investigate the potential mechanisms. Results Married patients had better 5-year cancer-specific survival (CSS) (66.7% vs 54.9%) and 5-year overall survival (OS) (56.0% vs 41.1%). In multivariate Cox regression models, unmarried patients also showed higher mortality risk for both CSS (Hazard Ratio [HR]: 1.260, 95% confidence interval (CI): 1.187–1.339, P < 0.001) and OS (HR: 1.328, 95% CI: 1.266–1.392, P < 0.001). Multivariate logistic regression showed married patients were more likely to be diagnosed at earlier stage (P < 0.001) and receive surgery (P < 0.001). Married patients still demonstrated better prognosis in the 1:1 matched group analysis (CSS: 62.9% vs 60.8%, OS: 52.3% vs 46.5%). Materials and Methods 11022 eligible OCSCC patients were identified from Surveillance, Epidemiology, and End Results (SEER) database, including 5902 married and 5120 unmarried individuals. Kaplan-Meier analysis, Log-rank test and Cox proportional hazards regression model were used to analyze survival and mortality risk. Influence of marital status on stage, age at diagnosis and selection of treatment was determined by binomial and multinomial logistic regression. Propensity score matching method was adopted to perform a 1:1 matched cohort. Conclusions Marriage has an independently protective effect on OCSCC survival. Earlier diagnosis and more sufficient treatment are possible explanations. Besides, even after 1:1 matching, survival advantage of married group still exists, indicating that spousal support from other aspects may also play an important role. PMID:28415710

Duration of Mechanical Ventilation in the Emergency Department.

PubMed

Angotti, Lauren B; Richards, Jeremy B; Fisher, Daniel F; Sankoff, Jeffrey D; Seigel, Todd A; Al Ashry, Haitham S; Wilcox, Susan R

2017-08-01

Due to hospital crowding, mechanically ventilated patients are increasingly spending hours boarding in emergency departments (ED) before intensive care unit (ICU) admission. This study aims to evaluate the association between time ventilated in the ED and in-hospital mortality, duration of mechanical ventilation, ICU and hospital length of stay (LOS). This was a multi-center, prospective, observational study of patients ventilated in the ED, conducted at three academic Level I Trauma Centers from July 2011 to March 2013. All consecutive adult patients on invasive mechanical ventilation were eligible for enrollment. We performed a Cox regression to assess for a mortality effect for mechanically ventilated patients with each hour of increasing LOS in the ED and multivariable regression analyses to assess for independently significant contributors to in-hospital mortality. Our primary outcome was in-hospital mortality, with secondary outcomes of ventilator days, ICU LOS and hospital LOS. We further commented on use of lung protective ventilation and frequency of ventilator changes made in this cohort. We enrolled 535 patients, of whom 525 met all inclusion criteria. Altered mental status without respiratory pathology was the most common reason for intubation, followed by trauma and respiratory failure. Using iterated Cox regression, a mortality effect occurred at ED time of mechanical ventilation > 7 hours, and the longer ED stay was also associated with a longer total duration of intubation. However, adjusted multivariable regression analysis demonstrated only older age and admission to the neurosciences ICU as independently associated with increased mortality. Of interest, only 23.8% of patients ventilated in the ED for over seven hours had changes made to their ventilator. In a prospective observational study of patients mechanically ventilated in the ED, there was a significant mortality benefit to expedited transfer of patients into an appropriate ICU setting.

Why do some studies find that CPR fraction is not a predictor of survival?

PubMed

Wik, Lars; Olsen, Jan-Aage; Persse, David; Sterz, Fritz; Lozano, Michael; Brouwer, Marc A; Westfall, Mark; Souders, Chris M; Travis, David T; Herken, Ulrich R; Lerner, E Brooke

2016-07-01

An 80% chest compression fraction (CCF) during resuscitation is recommended. However, heterogeneous results in CCF studies were found during the 2015 Consensus on Science (CoS), which may be because chest compressions are stopped for a wide variety of reasons including providing lifesaving care, provider distraction, fatigue, confusion, and inability to perform lifesaving skills efficiently. The effect of confounding variables on CCF to predict cardiac arrest survival. A secondary analysis of emergency medical services (EMS) treated out-of-hospital cardiac arrest (OHCA) patients who received manual compressions. CCF (percent of time patients received compressions) was determined from electronic defibrillator files. Two Sample Wilcoxon Rank Sum or regression determined a statistical association between CCF and age, gender, bystander CPR, public location, witnessed arrest, shockable rhythm, resuscitation duration, study site, and number of shocks. Univariate and multivariate logistic regressions were used to determine CCF effect on survival. Of 2132 patients with manual compressions 1997 had complete data. Shockable rhythm (p<0.001), public location (p<0.004), treatment duration (p<0.001), and number of shocks (p<0.001) were associated with lower CCF. Univariate logistic regression found that CCF was inversely associated with survival (OR 0.07; 95% CI 0.01-0.36). Multivariate regression controlling for factors associated with survival and/or CCF found that increasing CCF was associated with survival (OR 6.34; 95% CI 1.02-39.5). CCF cannot be looked at in isolation as a predictor of survival, but in the context of other resuscitation activities. When controlling for the effects of other resuscitation activities, a higher CCF is predictive of survival. This may explain the heterogeneity of findings during the CoS review. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Impact of hyperglycemia on outcomes of patients with Pseudomonas aeruginosa bacteremia.

PubMed

Patel, Twisha S; Cottreau, Jessica M; Hirsch, Elizabeth B; Tam, Vincent H

2016-02-01

Bacteremia caused by Pseudomonas aeruginosa is associated with significant morbidity and mortality. In other bacterial infections, hyperglycemia has been identified as a risk factor for mortality in nondiabetic patients. The objective of this study was to determine the impact of early hyperglycemia on outcomes in diabetic and nondiabetic patients with P. aeruginosa bacteremia. A retrospective cohort study was performed in adult patients (≥18 years old) with P. aeruginosa bacteremia. Patients received at least 1 drug empirically to which the isolate was susceptible in vitro. Classification and regression tree analysis was used to determine the threshold breakpoint for average blood glucose concentration within 48 hours of positive blood culture (BG48). Logistic regression was used to explore independent risk factors for 30-day mortality. A total of 176 bacteremia episodes were identified; patients in 66 episodes were diabetic. Diabetic patients had higher BG48 (165.2±64.8 mg/dL versus 123.7±31.5 mg/dL, P<0.001) and lower 30-day mortality (10.7% versus 22.7%, P=0.046) than nondiabetic patients. Multivariate regression revealed 30-day mortality in nondiabetic patients was associated with Acute Physiology and Chronic Health Evaluation II score (odds ratio [OR] 1.1; 95% confidence interval [CI] 1.0-1.2) and BG48 >168 mg/dL (OR 6.3; 95% CI 1.7-23.3). However, blood glucose concentration was not identified as an independent risk factor for mortality in diabetic patients by multivariate regression analysis. Hyperglycemia did not appear to affect outcomes in diabetic patients, whereas nondiabetic patients had a higher risk of mortality from P. aeruginosa bacteremia. Prospective studies evaluating the impact of glycemic control in these patients are needed. Copyright © 2016 Elsevier Inc. All rights reserved.

Predicting volumes in four Hawaii hardwoods...first multivariate equations developed

Treesearch

David A. Sharpnack

1966-01-01

Multivariate regression equations were developed for predicting board-foot (Int. 1/ 4-inch log rule ) and cubic-foot volumes in each 8.15-foot section of trees of four Hawaii hardwood species. The species are koa (Acacia koa), ohia (Metrosideros polymorpha), robusta eucalyptus (Eucalyptus robusta), and...

A Multivariate Test of the Bott Hypothesis in an Urban Irish Setting

ERIC Educational Resources Information Center

Gordon, Michael; Downing, Helen

1978-01-01

Using a sample of 686 married Irish women in Cork City the Bott hypothesis was tested, and the results of a multivariate regression analysis revealed that neither network connectedness nor the strength of the respondent's emotional ties to the network had any explanatory power. (Author)

Gender-related needs and preferences in cancer care indicate the need for an individualized approach to cancer patients.

PubMed

Wessels, Hester; de Graeff, Alexander; Wynia, Klaske; de Heus, Miriam; Kruitwagen, Cas L J J; Woltjer, Gerda T G J; Teunissen, Saskia C C M; Voest, Emile E

2010-01-01

Improving quality of care for cancer patients requires insight into their specific wishes, needs, and preferences concerning cancer care. The aim of this study was to explore the impact of gender on cancer patients' needs and preferences. Data were obtained from 386 questionnaires assessing cancer patients' preferences for health care. Multivariate regression analyses were performed with data obtained from medical oncology patients treated in seven Dutch hospitals, using the scales of the questionnaire as dependent variables. Patients rated safety, expertise, performance, and attitude of physicians and nurses highest on their list of preferences. There were significant differences between male and female patients concerning preferences in health care in 15 of the 21 scales and in two of the eight single items. Without exception, women found the care aspects mentioned in these scales and items more important than men. Multivariate regression analysis showed that, of all the patient- and disease-related factors, gender was the most important independent predictor of patient preferences. Gender impacts cancer patients' needs and preferences and should be taken into account for optimal cancer care. Cancer care might be tailored toward gender, for example, with regard to the means and extent of communication, manner and extent of support, counseling and rehabilitation, consultation length, and physician assignment. The results of this study may guide health care professionals and organizations to develop a gender-specific health care approach to further improve cancer patient-centered care.

Gender-Related Needs and Preferences in Cancer Care Indicate the Need for an Individualized Approach to Cancer Patients

PubMed Central

Wessels, Hester; de Graeff, Alexander; Wynia, Klaske; de Heus, Miriam; Kruitwagen, Cas L.J.J.; Woltjer, Gerda T.G.J.; Teunissen, Saskia C.C.M.

2010-01-01

Aim. Improving quality of care for cancer patients requires insight into their specific wishes, needs, and preferences concerning cancer care. The aim of this study was to explore the impact of gender on cancer patients' needs and preferences. Patients and Methods. Data were obtained from 386 questionnaires assessing cancer patients' preferences for health care. Multivariate regression analyses were performed with data obtained from medical oncology patients treated in seven Dutch hospitals, using the scales of the questionnaire as dependent variables. Results. Patients rated safety, expertise, performance, and attitude of physicians and nurses highest on their list of preferences. There were significant differences between male and female patients concerning preferences in health care in 15 of the 21 scales and in two of the eight single items. Without exception, women found the care aspects mentioned in these scales and items more important than men. Multivariate regression analysis showed that, of all the patient- and disease-related factors, gender was the most important independent predictor of patient preferences. Conclusion. Gender impacts cancer patients' needs and preferences and should be taken into account for optimal cancer care. Cancer care might be tailored toward gender, for example, with regard to the means and extent of communication, manner and extent of support, counseling and rehabilitation, consultation length, and physician assignment. The results of this study may guide health care professionals and organizations to develop a gender-specific health care approach to further improve cancer patient–centered care. PMID:20507890

Red Blood Cell Transfusion Need for Elective Primary Posterior Lumbar Fusion in A High-Volume Center for Spine Surgery

PubMed Central

Ristagno, Giuseppe; Beluffi, Simonetta; Tanzi, Dario; Belloli, Federica; Carmagnini, Paola; Croci, Massimo; D’Aviri, Giuseppe; Menasce, Guido; Pastore, Juan C.; Pellanda, Armando; Pollini, Alberto; Savoia, Giorgio

2018-01-01

(1) Background: This study evaluated the perioperative red blood cell (RBC) transfusion need and determined predictors for transfusion in patients undergoing elective primary lumbar posterior spine fusion in a high-volume center for spine surgery. (2) Methods: Data from all patients undergoing spine surgery between 1 January 2014 and 31 December 2016 were reviewed. Patients’ demographics and comorbidities, perioperative laboratory results, and operative time were analyzed in relation to RBC transfusion. Multivariate logistic regression analysis was performed to identify the predictors of transfusion. (3) Results: A total of 874 elective surgeries for primary spine fusion were performed over the three years. Only 54 cases (6%) required RBC transfusion. Compared to the non-transfused patients, transfused patients were mainly female (p = 0.0008), significantly older, with a higher ASA grade (p = 0.0002), and with lower pre-surgery hemoglobin (HB) level and hematocrit (p < 0.0001). In the multivariate logistic regression, a lower pre-surgery HB (OR (95% CI) 2.84 (2.11–3.82)), a higher ASA class (1.77 (1.03–3.05)) and a longer operative time (1.02 (1.01–1.02)) were independently associated with RBC transfusion. (4) Conclusions: In the instance of elective surgery for primary posterior lumbar fusion in a high-volume center for spine surgery, the need for RBC transfusion is low. Factors anticipating transfusion should be taken into consideration in the patient’s pre-surgery preparation. PMID:29385760

Impact of Preoperative Anaemia and Blood Transfusion on Postoperative Outcomes in Gynaecological Surgery

PubMed Central

Richards, Toby; Musallam, Khaled M.; Nassif, Joseph; Ghazeeri, Ghina; Seoud, Muhieddine; Gurusamy, Kurinchi S.; Jamali, Faek R.

2015-01-01

Objective To evaluate the effect of preoperative anaemia and blood transfusion on 30-day postoperative morbidity and mortality in patients undergoing gynecological surgery. Study Design Data were analyzed from 12,836 women undergoing operation in the American College of Surgeons National Surgical Quality Improvement Program. Outcomes measured were; 30-day postoperative mortality, composite and specific morbidities (cardiac, respiratory, central nervous system, renal, wound, sepsis, venous thrombosis, or major bleeding). Multivariate logistic regression models were performed using adjusted odds ratios (ORadj) to assess the independent effects of preoperative anaemia (hematocrit <36.0%) on outcomes, effect estimates were performed before and after adjustment for perioperative transfusion requirement. Results The prevalence of preoperative anaemia was 23.9% (95%CI: 23.2–24.7). Adjusted for confounders by multivariate logistic regression; preoperative anaemia was independently and significantly associated with increased odds of 30-day mortality (OR: 2.40, 95%CI: 1.06–5.44) and composite morbidity (OR: 1.80, 95%CI: 1.45–2.24). This was reflected by significantly higher adjusted odds of almost all specific morbidities including; respiratory, central nervous system, renal, wound, sepsis, and venous thrombosis. Blood Transfusion increased the effect of preoperative anaemia on outcomes (61% of the effect on mortality and 16% of the composite morbidity). Conclusions Preoperative anaemia is associated with adverse post-operative outcomes in women undergoing gynecological surgery. This risk associated with preoperative anaemia did not appear to be corrected by use of perioperative transfusion. PMID:26147954

Risk Factors for Complications in Acute Appendicitis among Paediatric Population.

PubMed

Poudel, R; Bhandari, T R

2017-01-01

Appendicitis is one of the most common causes of acute abdomen in children. Patients who are diagnosed early and undergo an appendectomy before perforation have a good outcome. However, it is difficult to diagnose in young children because its clinical manifestations may be atypical. The aim of this study was to determine the risk factors for complications in acute appendicitis in paediatric population. We performed a cross sectional study on children (age ≤18 years) who underwent appendectomy for suspected appendicitis from January 2014 to December 2015. Medical records of patients who met inclusion criteria were reviewed. Preoperative, operative and post-operative data were analyzed. The main outcome measure was intraoperative confirmation of gangrenous or perforated appendicitis. Multivariate logistic regression analysis was performed, and the main predictors of interest were patient's age, duration of pain and total leucocyte count. Total 73 paediatric patients (46 males) with mean age 13±3.8 were studied. In multivariate logistic regression analysis, patients having pain duration more than 72 hours and patients with leucocyte count >15000/mm3 were more likely to have complicated appendicitis [(OR:14.6), (95% CI= 2.40 - 89.77), (P= 0.004)] and [(OR=16.38), (95% CI = 1.836-146), (P = 0.012)] respectively. However, the age of the patient is not independently associated with complicated appendicitis. Increase in total leucocyte count and duration of the presentation can be a good marker of complicated appendicitis.

Optical scatterometry of quarter-micron patterns using neural regression

NASA Astrophysics Data System (ADS)

Bischoff, Joerg; Bauer, Joachim J.; Haak, Ulrich; Hutschenreuther, Lutz; Truckenbrodt, Horst

1998-06-01

With shrinking dimensions and increasing chip areas, a rapid and non-destructive full wafer characterization after every patterning cycle is an inevitable necessity. In former publications it was shown that Optical Scatterometry (OS) has the potential to push the attainable feature limits of optical techniques from 0.8 . . . 0.5 microns for imaging methods down to 0.1 micron and below. Thus the demands of future metrology can be met. Basically being a nonimaging method, OS combines light scatter (or diffraction) measurements with modern data analysis schemes to solve the inverse scatter issue. For very fine patterns with lambda-to-pitch ratios grater than one, the specular reflected light versus the incidence angle is recorded. Usually, the data analysis comprises two steps -- a training cycle connected the a rigorous forward modeling and the prediction itself. Until now, two data analysis schemes are usually applied -- the multivariate regression based Partial Least Squares method (PLS) and a look-up-table technique which is also referred to as Minimum Mean Square Error approach (MMSE). Both methods are afflicted with serious drawbacks. On the one hand, the prediction accuracy of multivariate regression schemes degrades with larger parameter ranges due to the linearization properties of the method. On the other hand, look-up-table methods are rather time consuming during prediction thus prolonging the processing time and reducing the throughput. An alternate method is an Artificial Neural Network (ANN) based regression which combines the advantages of multivariate regression and MMSE. Due to the versatility of a neural network, not only can its structure be adapted more properly to the scatter problem, but also the nonlinearity of the neuronal transfer functions mimic the nonlinear behavior of optical diffraction processes more adequately. In spite of these pleasant properties, the prediction speed of ANN regression is comparable with that of the PLS-method. In this paper, the viability and performance of ANN-regression will be demonstrated with the example of sub-quarter-micron resist metrology. To this end, 0.25 micrometer line/space patterns have been printed in positive photoresist by means of DUV projection lithography. In order to evaluate the total metrology chain from light scatter measurement through data analysis, a thorough modeling has been performed. Assuming a trapezoidal shape of the developed resist profile, a training data set was generated by means of the Rigorous Coupled Wave Approach (RCWA). After training the model, a second data set was computed and deteriorated by Gaussian noise to imitate real measuring conditions. Then, these data have been fed into the models established before resulting in a Standard Error of Prediction (SEP) which corresponds to the measuring accuracy. Even with putting only little effort in the design of a back-propagation network, the ANN is clearly superior to the PLS-method. Depending on whether a network with one or two hidden layers was used, accuracy gains between 2 and 5 can be achieved compared with PLS regression. Furthermore, the ANN is less noise sensitive, for there is only a doubling of the SEP at 5% noise for ANN whereas for PLS the accuracy degrades rapidly with increasing noise. The accuracy gain also depends on the light polarization and on the measured parameters. Finally, these results have been proven experimentally, where the OS-results are in good accordance with the profiles obtained from cross- sectioning micrographs.

Effects of Flavor and Texture on the Sensory Perception of Gouda-Type Cheese Varieties during Ripening Using Multivariate Analysis.

PubMed

Shiota, Makoto; Iwasawa, Ai; Suzuki-Iwashima, Ai; Iida, Fumiko

2015-12-01

The impact of flavor composition, texture, and other factors on desirability of different commercial sources of Gouda-type cheese using multivariate analyses on the basis of sensory and instrumental analyses were investigated. Volatile aroma compounds were measured using headspace solid-phase microextraction gas chromatography/mass spectrometry (GC/MS) and steam distillation extraction (SDE)-GC/MS, and fatty acid composition, low-molecular-weight compounds, including amino acids, and organic acids, as well pH, texture, and color were measured to determine their relationship with sensory perception. Orthogonal partial least squares-discriminant analysis (OPLS-DA) was performed to discriminate between 2 different ripening periods in 7 sample sets, revealing that ethanol, ethyl acetate, hexanoic acid, and octanoic acid increased with increasing sensory attribute scores for sweetness, fruity, and sulfurous. A partial least squares (PLS) regression model was constructed to predict the desirability of cheese using these parameters. We showed that texture and buttery flavors are important factors affecting the desirability of Gouda-type cheeses for Japanese consumers using these multivariate analyses. © 2015 Institute of Food Technologists®

The role of objective cognitive dysfunction in subjective cognitive complaints after stroke.

PubMed

van Rijsbergen, M W A; Mark, R E; Kop, W J; de Kort, P L M; Sitskoorn, M M

2017-03-01

Objective cognitive performance (OCP) is often impaired in patients post-stroke but the consequences of OCP for patient-reported subjective cognitive complaints (SCC) are poorly understood. We performed a detailed analysis on the association between post-stroke OCP and SCC. Assessments of OCP and SCC were obtained in 208 patients 3 months after stroke. OCP was evaluated using conventional and ecologically valid neuropsychological tests. Levels of SCC were measured using the CheckList for Cognitive and Emotional (CLCE) consequences following stroke inventory. Multivariate hierarchical regression analyses were used to evaluate the association of OCP with CLCE scores adjusting for age, sex and intelligence quotient. Analyses were performed to examine the global extent of OCP dysfunction (based on the total number of impaired neuropsychological tests, i.e. objective cognitive impairment index) and for each OCP test separately using the raw neuropsychological (sub)test scores. The objective cognitive impairment index for global OCP was positively correlated with the CLCE score (Spearman's rho = 0.22, P = 0.003), which remained significant in multivariate adjusted models (β = 0.25, P = 0.01). Results for the separate neuropsychological tests indicated that only one task (the ecologically valid Rivermead Behavioural Memory Test) was independently associated with the CLCE in multivariate adjusted models (β = -0.34, P < 0.001). Objective neuropsychological test performance, as measured by the global dysfunction index or an ecologically valid memory task, was associated with SCC. These data suggest that cumulative deficits in multiple cognitive domains contribute to subjectively experienced poor cognitive abilities in daily life in patients post-stroke. © 2016 EAN.

Cerebellar contribution to motor and cognitive performance in multiple sclerosis: An MRI sub-regional volumetric analysis.

PubMed

D'Ambrosio, Alessandro; Pagani, Elisabetta; Riccitelli, Gianna C; Colombo, Bruno; Rodegher, Mariaemma; Falini, Andrea; Comi, Giancarlo; Filippi, Massimo; Rocca, Maria A

2017-08-01

To investigate the role of cerebellar sub-regions on motor and cognitive performance in multiple sclerosis (MS) patients. Whole and sub-regional cerebellar volumes, brain volumes, T2 hyperintense lesion volumes (LV), and motor performance scores were obtained from 95 relapse-onset MS patients and 32 healthy controls (HC). MS patients also underwent an evaluation of working memory and processing speed functions. Cerebellar anterior and posterior lobes were segmented using the Spatially Unbiased Infratentorial Toolbox (SUIT) from Statistical Parametric Mapping (SPM12). Multivariate linear regression models assessed the relationship between magnetic resonance imaging (MRI) measures and motor/cognitive scores. Compared to HC, only secondary progressive multiple sclerosis (SPMS) patients had lower cerebellar volumes (total and posterior cerebellum). In MS patients, lower anterior cerebellar volume and brain T2 LV predicted worse motor performance, whereas lower posterior cerebellar volume and brain T2 LV predicted poor cognitive performance. Global measures of brain volume and infratentorial T2 LV were not selected by the final multivariate models. Cerebellar volumetric abnormalities are likely to play an important contribution to explain motor and cognitive performance in MS patients. Consistently with functional mapping studies, cerebellar posterior-inferior volume accounted for variance in cognitive measures, whereas anterior cerebellar volume accounted for variance in motor performance, supporting the assessment of cerebellar damage at sub-regional level.

Multilevel covariance regression with correlated random effects in the mean and variance structure.

PubMed

Quintero, Adrian; Lesaffre, Emmanuel

2017-09-01

Multivariate regression methods generally assume a constant covariance matrix for the observations. In case a heteroscedastic model is needed, the parametric and nonparametric covariance regression approaches can be restrictive in the literature. We propose a multilevel regression model for the mean and covariance structure, including random intercepts in both components and allowing for correlation between them. The implied conditional covariance function can be different across clusters as a result of the random effect in the variance structure. In addition, allowing for correlation between the random intercepts in the mean and covariance makes the model convenient for skewedly distributed responses. Furthermore, it permits us to analyse directly the relation between the mean response level and the variability in each cluster. Parameter estimation is carried out via Gibbs sampling. We compare the performance of our model to other covariance modelling approaches in a simulation study. Finally, the proposed model is applied to the RN4CAST dataset to identify the variables that impact burnout of nurses in Belgium. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Identifying Interacting Genetic Variations by Fish-Swarm Logic Regression

PubMed Central

Yang, Aiyuan; Yan, Chunxia; Zhu, Feng; Zhao, Zhongmeng; Cao, Zhi

2013-01-01

Understanding associations between genotypes and complex traits is a fundamental problem in human genetics. A major open problem in mapping phenotypes is that of identifying a set of interacting genetic variants, which might contribute to complex traits. Logic regression (LR) is a powerful multivariant association tool. Several LR-based approaches have been successfully applied to different datasets. However, these approaches are not adequate with regard to accuracy and efficiency. In this paper, we propose a new LR-based approach, called fish-swarm logic regression (FSLR), which improves the logic regression process by incorporating swarm optimization. In our approach, a school of fish agents are conducted in parallel. Each fish agent holds a regression model, while the school searches for better models through various preset behaviors. A swarm algorithm improves the accuracy and the efficiency by speeding up the convergence and preventing it from dropping into local optimums. We apply our approach on a real screening dataset and a series of simulation scenarios. Compared to three existing LR-based approaches, our approach outperforms them by having lower type I and type II error rates, being able to identify more preset causal sites, and performing at faster speeds. PMID:23984382

Creation of mortality risk charts using 123I meta-iodobenzylguanidine heart-to-mediastinum ratio in patients with heart failure: 2- and 5-year risk models.

PubMed

Nakajima, Kenichi; Nakata, Tomoaki; Matsuo, Shinro; Jacobson, Arnold F

2016-10-01

(123)I meta-iodobenzylguanidine (MIBG) imaging has been extensively used for prognostication in patients with chronic heart failure (CHF). The purpose of this study was to create mortality risk charts for short-term (2 years) and long-term (5 years) prediction of cardiac mortality. Using a pooled database of 1322 CHF patients, multivariate analysis, including (123)I-MIBG late heart-to-mediastinum ratio (HMR), left ventricular ejection fraction (LVEF), and clinical factors, was performed to determine optimal variables for the prediction of 2- and 5-year mortality risk using subsets of the patients (n = 1280 and 933, respectively). Multivariate logistic regression analysis was performed to create risk charts. Cardiac mortality was 10 and 22% for the sub-population of 2- and 5-year analyses. A four-parameter multivariate logistic regression model including age, New York Heart Association (NYHA) functional class, LVEF, and HMR was used. Annualized mortality rate was <1% in patients with NYHA Class I-II and HMR ≥ 2.0, irrespective of age and LVEF. In patients with NYHA Class III-IV, mortality rate was 4-6 times higher for HMR < 1.40 compared with HMR ≥ 2.0 in all LVEF classes. Among the subset of patients with b-type natriuretic peptide (BNP) results (n = 491 and 359 for 2- and 5-year models, respectively), the 5-year model showed incremental value of HMR in addition to BNP. Both 2- and 5-year risk prediction models with (123)I-MIBG HMR can be used to identify low-risk as well as high-risk patients, which can be effective for further risk stratification of CHF patients even when BNP is available. © The Author 2015. Published by Oxford University Press on behalf of the European Society of Cardiology.

TIMP1 and MMP9 are predictors of mortality in septic patients in the emergency department and intensive care unit unlike MMP9/TIMP1 ratio: Multivariate model.

PubMed

Niño, Maria Eugenia; Serrano, Sergio Eduardo; Niño, Daniela Camila; McCosham, Diana Margarita; Cardenas, Maria Eugenia; Villareal, Vivian Poleth; Lopez, Marcos; Pazin-Filho, Antonio; Jaimes, Fabian Alberto; Cunha, Fernando; Schulz, Richard; Torres-Dueñas, Diego

2017-01-01

Matrix metalloproteinases and tissue inhibitors of metalloproteinases could be promising biomarkers for establishing prognosis during the development of sepsis. It is necessary to clarify the relationship between matrix metalloproteinases and their tissue inhibitors. We conducted a cohort study with 563 septic patients, in order to elucidate the biological role and significance of these inflammatory biomarkers and their relationship to the severity and mortality of patients with sepsis. A multicentric prospective cohort was performed. The sample was composed of patients who had sepsis as defined by the International Conference 2001. Serum procalcitonin, creatinine, urea nitrogen, C-Reactive protein, TIMP1, TIMP2, MMP2 and MMP9 were quantified; each patient was followed until death or up to 30 days. A descriptive analysis was performed by calculating the mean and the 95% confidence interval for continuous variables and proportions for categorical variables. A multivariate logistic regression model was constructed by the method of intentional selection of covariates with mortality at 30 days as dependent variable and all the other variables as predictors. Of the 563 patients, 68 patients (12.1%) died within the first 30 days of hospitalization in the ICU. The mean values for TIMP1, TIMP2 and MMP2 were lower in survivors, MMP9 was higher in survivors. Multivariate logistic regression showed that age, SOFA and Charlson scores, along with TIMP1 concentration, were statistically associated with mortality at 30 days of septic patients; serum MMP9 was not statistically associated with mortality of patients, but was a confounder of the TIMP1 variable. It could be argued that plasma levels of TIMP1 should be considered as a promising prognostic biomarker in the setting of sepsis. Additionally, this study, like other studies with large numbers of septic patients does not support the predictive value of TIMP1 / MMP9.

Combined high-field intraoperative magnetic resonance imaging and endoscopy increase extent of resection and progression-free survival for pituitary adenomas

PubMed Central

Sylvester, Peter T.; Evans, John A.; Zipfel, Gregory J.; Chole, Richard A.; Uppaluri, Ravindra; Haughey, Bruce H.; Getz, Anne E.; Silverstein, Julie; Rich, Keith M.; Kim, Albert H.; Dacey, Ralph G.

2014-01-01

Purpose The clinical benefit of combined intraoperative magnetic resonance imaging (iMRI) and endoscopy for transsphenoidal pituitary adenoma resection has not been completely characterized. This study assessed the impact of microscopy, endoscopy, and/or iMRI on progression-free survival, extent of resection status (gross-, near-, and subtotal resection), and operative complications. Methods Retrospective analyses were performed on 446 transsphenoidal pituitary adenoma surgeries at a single institution between 1998 and 2012. Multivariate analyses were used to control for baseline characteristics, differences during extent of resection status, and progression-free survival analysis. Results Additional surgery was performed after iMRI in 56/156 cases (35.9 %), which led to increased extent of resection status in 15/156 cases (9.6 %). Multivariate ordinal logistic regression revealed no increase in extent of resection status following iMRI or endoscopy alone; however, combining these modalities increased extent of resection status (odds ratio 2.05, 95 % CI 1.21–3.46) compared to conventional transsphenoidal microsurgery. Multivariate Cox regression revealed that reduced extent of resection status shortened progression-free survival for near- versus gross-total resection [hazard ratio (HR) 2.87, 95 % CI 1.24–6.65] and sub- versus near-total resection (HR 2.10; 95 % CI 1.00–4.40). Complication comparisons between microscopy, endoscopy, and iMRI revealed increased perioperative deaths for endoscopy versus microscopy (4/209 and 0/237, respectively), but this difference was non-significant considering multiple post hoc comparisons (Fisher exact, p = 0.24). Conclusions Combined use of endoscopy and iMRI increased pituitary adenoma extent of resection status compared to conventional transsphenoidal microsurgery, and increased extent of resection status was associated with longer progression-free survival. Treatment modality combination did not significantly impact complication rate. PMID:24599833

Application and validation of Cox regression models in a single-center series of double kidney transplantation.

PubMed

Santori, G; Fontana, I; Bertocchi, M; Gasloli, G; Magoni Rossi, A; Tagliamacco, A; Barocci, S; Nocera, A; Valente, U

2010-05-01

A useful approach to reduce the number of discarded marginal kidneys and to increase the nephron mass is double kidney transplantation (DKT). In this study, we retrospectively evaluated the potential predictors for patient and graft survival in a single-center series of 59 DKT procedures performed between April 21, 1999, and September 21, 2008. The kidney recipients of mean age 63.27 +/- 5.17 years included 16 women (27%) and 43 men (73%). The donors of mean age 69.54 +/- 7.48 years included 32 women (54%) and 27 men (46%). The mean posttransplant dialysis time was 2.37 +/- 3.61 days. The mean hospitalization was 20.12 +/- 13.65 days. Average serum creatinine (SCr) at discharge was 1.5 +/- 0.59 mg/dL. In view of the limited numbers of recipient deaths (n = 4) and graft losses (n = 8) that occurred in our series, the proportional hazards assumption for each Cox regression model with P < .05 was tested by using correlation coefficients between transformed survival times and scaled Schoenfeld residuals, and checked with smoothed plots of Schoenfeld residuals. For patient survival, the variables that reached statistical significance were donor SCr (P = .007), donor creatinine cleararance (P = .023), and recipient age (P = .047). Each significant model passed the Schoenfeld test. By entering these variables into a multivariate Cox model for patient survival, no further significance was observed. In the univariate Cox models performed for graft survival, statistical significance was noted for donor SCr (P = .027), SCr 3 months post-DKT (P = .043), and SCr 6 months post-DKT (P = .017). All significant univariate models for graft survival passed the Schoenfeld test. A final multivariate model retained SCr at 6 months (beta = 1.746, P = .042) and donor SCr (beta = .767, P = .090). In our analysis, SCr at 6 months seemed to emerge from both univariate and multivariate Cox models as a potential predictor of graft survival among DKT. Multicenter studies with larger recipient populations and more graft losses should be performed to confirm our findings. Copyright (c) 2010 Elsevier Inc. All rights reserved.

Ibrutinib versus previous standard of care: an adjusted comparison in patients with relapsed/refractory chronic lymphocytic leukaemia.

PubMed

Hansson, Lotta; Asklid, Anna; Diels, Joris; Eketorp-Sylvan, Sandra; Repits, Johanna; Søltoft, Frans; Jäger, Ulrich; Österborg, Anders

2017-10-01

This study explored the relative efficacy of ibrutinib versus previous standard-of-care treatments in relapsed/refractory patients with chronic lymphocytic leukaemia (CLL), using multivariate regression modelling to adjust for baseline prognostic factors. Individual patient data were collected from an observational Stockholm cohort of consecutive patients (n = 144) diagnosed with CLL between 2002 and 2013 who had received at least second-line treatment. Data were compared with results of the RESONATE clinical trial. A multivariate Cox proportional hazards regression model was used which estimated the hazard ratio (HR) of ibrutinib versus previous standard of care. The adjusted HR of ibrutinib versus the previous standard-of-care cohort was 0.15 (p < 0.0001) for progression-free survival (PFS) and 0.36 (p < 0.0001) for overall survival (OS). A similar difference was observed also when patients treated late in the period (2012-) were compared separately. Multivariate analysis showed that later line of therapy, male gender, older age and poor performance status were significant independent risk factors for worse PFS and OS. Our results suggest that PFS and OS with ibrutinib in the RESONATE study were significantly longer than with previous standard-of-care regimens used in second or later lines in routine healthcare. The approach used, which must be interpreted with caution, compares patient-level data from a clinical trial with outcomes observed in a daily clinical practice and may complement results from randomised trials or provide preliminary wider comparative information until phase 3 data exist.

Computation of nonlinear least squares estimator and maximum likelihood using principles in matrix calculus

NASA Astrophysics Data System (ADS)

Mahaboob, B.; Venkateswarlu, B.; Sankar, J. Ravi; Balasiddamuni, P.

2017-11-01

This paper uses matrix calculus techniques to obtain Nonlinear Least Squares Estimator (NLSE), Maximum Likelihood Estimator (MLE) and Linear Pseudo model for nonlinear regression model. David Pollard and Peter Radchenko [1] explained analytic techniques to compute the NLSE. However the present research paper introduces an innovative method to compute the NLSE using principles in multivariate calculus. This study is concerned with very new optimization techniques used to compute MLE and NLSE. Anh [2] derived NLSE and MLE of a heteroscedatistic regression model. Lemcoff [3] discussed a procedure to get linear pseudo model for nonlinear regression model. In this research article a new technique is developed to get the linear pseudo model for nonlinear regression model using multivariate calculus. The linear pseudo model of Edmond Malinvaud [4] has been explained in a very different way in this paper. David Pollard et.al used empirical process techniques to study the asymptotic of the LSE (Least-squares estimation) for the fitting of nonlinear regression function in 2006. In Jae Myung [13] provided a go conceptual for Maximum likelihood estimation in his work “Tutorial on maximum likelihood estimation

Rex fortran 4 system for combinatorial screening or conventional analysis of multivariate regressions

Treesearch

L.R. Grosenbaugh

1967-01-01

Describes an expansible computerized system that provides data needed in regression or covariance analysis of as many as 50 variables, 8 of which may be dependent. Alternatively, it can screen variously generated combinations of independent variables to find the regression with the smallest mean-squared-residual, which will be fitted if desired. The user can easily...

Multivariate logistic regression for predicting total culturable virus presence at the intake of a potable-water treatment plant: novel application of the atypical coliform/total coliform ratio.

PubMed

Black, L E; Brion, G M; Freitas, S J

2007-06-01

Predicting the presence of enteric viruses in surface waters is a complex modeling problem. Multiple water quality parameters that indicate the presence of human fecal material, the load of fecal material, and the amount of time fecal material has been in the environment are needed. This paper presents the results of a multiyear study of raw-water quality at the inlet of a potable-water plant that related 17 physical, chemical, and biological indices to the presence of enteric viruses as indicated by cytopathic changes in cell cultures. It was found that several simple, multivariate logistic regression models that could reliably identify observations of the presence or absence of total culturable virus could be fitted. The best models developed combined a fecal age indicator (the atypical coliform [AC]/total coliform [TC] ratio), the detectable presence of a human-associated sterol (epicoprostanol) to indicate the fecal source, and one of several fecal load indicators (the levels of Giardia species cysts, coliform bacteria, and coprostanol). The best fit to the data was found when the AC/TC ratio, the presence of epicoprostanol, and the density of fecal coliform bacteria were input into a simple, multivariate logistic regression equation, resulting in 84.5% and 78.6% accuracies for the identification of the presence and absence of total culturable virus, respectively. The AC/TC ratio was the most influential input variable in all of the models generated, but producing the best prediction required additional input related to the fecal source and the fecal load. The potential for replacing microbial indicators of fecal load with levels of coprostanol was proposed and evaluated by multivariate logistic regression modeling for the presence and absence of virus.

Reprint of: Relationship between cataract severity and socioeconomic status.

PubMed

Wesolosky, Jason D; Rudnisky, Christopher J

2015-06-01

To determine the relationship between cataract severity and socioeconomic status (SES). Retrospective, observational case series. A total of 1350 eyes underwent phacoemulsification cataract extraction by a single surgeon using an Alcon Infiniti system. Cataract severity was measured using phaco time in seconds. SES was measured using area-level aggregate census data: median income, education, proportion of common-law couples, and employment rate. Preoperative best corrected visual acuity was obtained and converted to logarithm of the minimum angle of resolution values. For patients undergoing bilateral surgery, the generalized estimating equation was used to account for the correlation between eyes. Univariate analyses were performed using simple regression, and multivariate analyses were performed to account for variables with significant relationships (p < 0.05) on univariate testing. Sensitivity analyses were performed to assess the effect of including patient age in the controlled analyses. Multivariate analyses demonstrated that cataracts were more severe when the median income was lower (p = 0.001) and the proportion of common-law couples living in a patient's community (p = 0.012) and the unemployment rate (p = 0.002) were higher. These associations persisted even when controlling for patient age. Patients of lower SES have more severe cataracts. Copyright © 2015. Published by Elsevier Inc.

Sensorimotor and postural control factors associated with driving safety in a community-dwelling older driver population.

PubMed

Lacherez, Philippe; Wood, Joanne M; Anstey, Kaarin J; Lord, Stephen R

2014-02-01

To establish whether sensorimotor function and balance are associated with on-road driving performance in older adults. The performance of 270 community-living adults aged 70-88 years recruited via the electoral roll was measured on a battery of peripheral sensation, strength, flexibility, reaction time, and balance tests and on a standardized measure of on-road driving performance. Forty-seven participants (17.4%) were classified as unsafe based on their driving assessment. Unsafe driving was associated with reduced peripheral sensation, lower limb weakness, reduced neck range of motion, slow reaction time, and poor balance in univariate analyses. Multivariate logistic regression analysis identified poor vibration sensitivity, reduced quadriceps strength, and increased sway on a foam surface with eyes closed as significant and independent risk factors for unsafe driving. These variables classified participants into safe and unsafe drivers with a sensitivity of 74% and specificity of 70%. A number of sensorimotor and balance measures were associated with driver safety and the multivariate model comprising measures of sensation, strength, and balance was highly predictive of unsafe driving in this sample. These findings highlight important determinants of driver safety and may assist in developing efficacious driver safety strategies for older drivers.

Third molar development: measurements versus scores as age predictor.

PubMed

Thevissen, P W; Fieuws, S; Willems, G

2011-10-01

Human third molar development is widely used to predict chronological age of sub adult individuals with unknown or doubted age. For these predictions, classically, the radiologically observed third molar growth and maturation is registered using a staging and related scoring technique. Measures of lengths and widths of the developing wisdom tooth and its adjacent second molar can be considered as an alternative registration. The aim of this study was to verify relations between mandibular third molar developmental stages or measurements of mandibular second molar and third molars and age. Age related performance of stages and measurements were compared to assess if measurements added information to age predictions from third molar formation stage. The sample was 340 orthopantomograms (170 females, 170 males) of individuals homogenously distributed in age between 7 and 24 years. Mandibular lower right, third and second molars, were staged following Gleiser and Hunt, length and width measurements were registered, and various ratios of these measurements were calculated. Univariable regression models with age as response and third molar stage, measurements and ratios of second and third molars as predictors, were considered. Multivariable regression models assessed if measurements or ratios added information to age prediction from third molar stage. Coefficients of determination (R(2)) and root mean squared errors (RMSE) obtained from all regression models were compared. The univariable regression model using stages as predictor yielded most accurate age predictions (males: R(2) 0.85, RMSE between 0.85 and 1.22 year; females: R(2) 0.77, RMSE between 1.19 and 2.11 year) compared to all models including measurements and ratios. The multivariable regression models indicated that measurements and ratios added no clinical relevant information to the age prediction from third molar stage. Ratios and measurements of second and third molars are less accurate age predictors than stages of developing third molars. Copyright © 2011 Elsevier Ltd. All rights reserved.

Comparing statistical and machine learning classifiers: alternatives for predictive modeling in human factors research.

PubMed

Carnahan, Brian; Meyer, Gérard; Kuntz, Lois-Ann

2003-01-01

Multivariate classification models play an increasingly important role in human factors research. In the past, these models have been based primarily on discriminant analysis and logistic regression. Models developed from machine learning research offer the human factors professional a viable alternative to these traditional statistical classification methods. To illustrate this point, two machine learning approaches--genetic programming and decision tree induction--were used to construct classification models designed to predict whether or not a student truck driver would pass his or her commercial driver license (CDL) examination. The models were developed and validated using the curriculum scores and CDL exam performances of 37 student truck drivers who had completed a 320-hr driver training course. Results indicated that the machine learning classification models were superior to discriminant analysis and logistic regression in terms of predictive accuracy. Actual or potential applications of this research include the creation of models that more accurately predict human performance outcomes.

Random forests on Hadoop for genome-wide association studies of multivariate neuroimaging phenotypes

PubMed Central

2013-01-01

Motivation Multivariate quantitative traits arise naturally in recent neuroimaging genetics studies, in which both structural and functional variability of the human brain is measured non-invasively through techniques such as magnetic resonance imaging (MRI). There is growing interest in detecting genetic variants associated with such multivariate traits, especially in genome-wide studies. Random forests (RFs) classifiers, which are ensembles of decision trees, are amongst the best performing machine learning algorithms and have been successfully employed for the prioritisation of genetic variants in case-control studies. RFs can also be applied to produce gene rankings in association studies with multivariate quantitative traits, and to estimate genetic similarities measures that are predictive of the trait. However, in studies involving hundreds of thousands of SNPs and high-dimensional traits, a very large ensemble of trees must be inferred from the data in order to obtain reliable rankings, which makes the application of these algorithms computationally prohibitive. Results We have developed a parallel version of the RF algorithm for regression and genetic similarity learning tasks in large-scale population genetic association studies involving multivariate traits, called PaRFR (Parallel Random Forest Regression). Our implementation takes advantage of the MapReduce programming model and is deployed on Hadoop, an open-source software framework that supports data-intensive distributed applications. Notable speed-ups are obtained by introducing a distance-based criterion for node splitting in the tree estimation process. PaRFR has been applied to a genome-wide association study on Alzheimer's disease (AD) in which the quantitative trait consists of a high-dimensional neuroimaging phenotype describing longitudinal changes in the human brain structure. PaRFR provides a ranking of SNPs associated to this trait, and produces pair-wise measures of genetic proximity that can be directly compared to pair-wise measures of phenotypic proximity. Several known AD-related variants have been identified, including APOE4 and TOMM40. We also present experimental evidence supporting the hypothesis of a linear relationship between the number of top-ranked mutated states, or frequent mutation patterns, and an indicator of disease severity. Availability The Java codes are freely available at http://www2.imperial.ac.uk/~gmontana. PMID:24564704

Random forests on Hadoop for genome-wide association studies of multivariate neuroimaging phenotypes.

PubMed

Wang, Yue; Goh, Wilson; Wong, Limsoon; Montana, Giovanni

2013-01-01

Multivariate quantitative traits arise naturally in recent neuroimaging genetics studies, in which both structural and functional variability of the human brain is measured non-invasively through techniques such as magnetic resonance imaging (MRI). There is growing interest in detecting genetic variants associated with such multivariate traits, especially in genome-wide studies. Random forests (RFs) classifiers, which are ensembles of decision trees, are amongst the best performing machine learning algorithms and have been successfully employed for the prioritisation of genetic variants in case-control studies. RFs can also be applied to produce gene rankings in association studies with multivariate quantitative traits, and to estimate genetic similarities measures that are predictive of the trait. However, in studies involving hundreds of thousands of SNPs and high-dimensional traits, a very large ensemble of trees must be inferred from the data in order to obtain reliable rankings, which makes the application of these algorithms computationally prohibitive. We have developed a parallel version of the RF algorithm for regression and genetic similarity learning tasks in large-scale population genetic association studies involving multivariate traits, called PaRFR (Parallel Random Forest Regression). Our implementation takes advantage of the MapReduce programming model and is deployed on Hadoop, an open-source software framework that supports data-intensive distributed applications. Notable speed-ups are obtained by introducing a distance-based criterion for node splitting in the tree estimation process. PaRFR has been applied to a genome-wide association study on Alzheimer's disease (AD) in which the quantitative trait consists of a high-dimensional neuroimaging phenotype describing longitudinal changes in the human brain structure. PaRFR provides a ranking of SNPs associated to this trait, and produces pair-wise measures of genetic proximity that can be directly compared to pair-wise measures of phenotypic proximity. Several known AD-related variants have been identified, including APOE4 and TOMM40. We also present experimental evidence supporting the hypothesis of a linear relationship between the number of top-ranked mutated states, or frequent mutation patterns, and an indicator of disease severity. The Java codes are freely available at http://www2.imperial.ac.uk/~gmontana.

Groundwater potential mapping using C5.0, random forest, and multivariate adaptive regression spline models in GIS.

PubMed

Golkarian, Ali; Naghibi, Seyed Amir; Kalantar, Bahareh; Pradhan, Biswajeet

2018-02-17

Ever increasing demand for water resources for different purposes makes it essential to have better understanding and knowledge about water resources. As known, groundwater resources are one of the main water resources especially in countries with arid climatic condition. Thus, this study seeks to provide groundwater potential maps (GPMs) employing new algorithms. Accordingly, this study aims to validate the performance of C5.0, random forest (RF), and multivariate adaptive regression splines (MARS) algorithms for generating GPMs in the eastern part of Mashhad Plain, Iran. For this purpose, a dataset was produced consisting of spring locations as indicator and groundwater-conditioning factors (GCFs) as input. In this research, 13 GCFs were selected including altitude, slope aspect, slope angle, plan curvature, profile curvature, topographic wetness index (TWI), slope length, distance from rivers and faults, rivers and faults density, land use, and lithology. The mentioned dataset was divided into two classes of training and validation with 70 and 30% of the springs, respectively. Then, C5.0, RF, and MARS algorithms were employed using R statistical software, and the final values were transformed into GPMs. Finally, two evaluation criteria including Kappa and area under receiver operating characteristics curve (AUC-ROC) were calculated. According to the findings of this research, MARS had the best performance with AUC-ROC of 84.2%, followed by RF and C5.0 algorithms with AUC-ROC values of 79.7 and 77.3%, respectively. The results indicated that AUC-ROC values for the employed models are more than 70% which shows their acceptable performance. As a conclusion, the produced methodology could be used in other geographical areas. GPMs could be used by water resource managers and related organizations to accelerate and facilitate water resource exploitation.

Comparison of Various Anthropometric and Body Fat Indices in Identifying Cardiometabolic Disturbances in Chinese Men and Women

PubMed Central

Zhang, Zhe-qing; Deng, Juan; He, Li-ping; Ling, Wen-hua; Su, Yi-xiang; Chen, Yu-ming

2013-01-01

Background Although many adiposity indices may be used to predict obesity-related health risks, uncertainty remains over which of them performs best. Objective This study compared the predictive capability of direct and indirect adiposity measures in identifying people at higher risk of metabolic abnormalities. Methods This population-based cross-sectional study recruited 2780 women and 1160 men. Body weight and height, waist circumference (WC), and hip circumference (HC) were measured and body mass index (BMI), waist-to-hip ratio (WHR), and waist-to-height ratio (WHtR) were calculated. Body fat (and percentage of fat) over the whole body and the trunk were determined by bioelectrical impedance analysis (BIA). Blood pressure, fasting lipid profiles, and glucose and urine acid levels were assessed. Results In women, the ROC and the multivariate logistic regression analyses both showed that WHtR consistently had the best performance in identifying hypertension, dyslipidemia, hyperuricemia, diabetes/IFG, and metabolic syndrome (MetS). In men, the ROC analysis showed that WHtR was the best predictor of hypertension, WHtR and WC were equally good predictors of dyslipidemia and MetS, and WHtR was the second-best predictor of hyperuricemia and diabetes/IFG. The multivariate logistic regression also found WHtR to be superior in discriminating between MetS, diabetes/IFG, and dyslipidemia while BMI performed better in predicting hypertension and hyperuricemia in men. The BIA-derived indices were the second-worst predictors for all of the endpoints, and HC was the worst. Conclusion WHtR was the best predictor of various metabolic abnormalities. BMI may be used as an alternative measure of obesity for identifying hypertension in both sexes. PMID:23951031

Body mass index in ambulatory cerebral palsy patients.

PubMed

Feeley, Brian T; Gollapudi, Kiran; Otsuka, Norman Y

2007-05-01

Malnutrition is a common problem in children with cerebral palsy. Although malnutrition is often recognized in patients with severe cerebral palsy, it can be unrecognized in less severely affected patients. The consequences of malnutrition are serious, and include decreased muscle strength, poor immune status, and depressed cerebral functioning. Low body mass index has been used as a marker for malnutrition. The purpose of this study was to determine which patients in an ambulatory cerebral palsy patient population were at risk for low body mass index. A retrospective chart review was performed on 75 patients. Age, sex, height, weight, type of cerebral palsy, and functional status [gross motor functional classification system (GMFCS) level] was recorded from the chart. Descriptive statistics with bivariate and multivariate regression analyses were performed. Thirty-eight boys and 37 girls with an average age of 8.11 years were included in the study. Unique to our patient population, all cerebral palsy patients were independent ambulators. Patients with quadriplegic cerebral palsy had a significantly lower body mass index than those with diplegic and hemiplegic cerebral palsy. Patients with a GMFCS III had significantly lower body mass index than those with GMFCS I and II. When multivariate regression analysis to control for age and sex was performed, low body mass index remained associated with quadriplegic cerebral palsy and GMFCS III. Malnutrition is a common health problem in patients with cerebral palsy, leading to significant morbidity in multiple organ systems. We found that in an ambulatory cerebral palsy population, patients with lower functional status or quadriplegia had significantly lower body mass index, suggesting that even highly functioning ambulatory cerebral palsy patients are at risk for malnutrition.

Clinical predictors of the optimal spectacle correction for comfort performing desktop tasks.

PubMed

Leffler, Christopher T; Davenport, Byrd; Rentz, Jodi; Miller, Amy; Benson, William

2008-11-01

The best strategy for spectacle correction of presbyopia for near tasks has not been determined. Thirty volunteers over the age of 40 years were tested for subjective accommodative amplitude, pupillary size, fusional vergence, interpupillary distance, arm length, preferred working distance, near and far visual acuity and preferred reading correction in the phoropter and trial frames. Subjects performed near tasks (reading, writing and counting change) using various spectacle correction strengths. Predictors of the correction maximising near task comfort were determined by multivariable linear regression. The mean age was 54.9 years (range 43 to 71) and 40 per cent had diabetes. Significant predictors of the most comfortable addition in univariate analyses were age (p<0.001), interpupillary distance (p=0.02), fusional vergence amplitude (p=0.02), distance visual acuity in the worse eye (p=0.01), vision at 40 cm in the worse eye with distance correction (p=0.01), duration of diabetes (p=0.01), and the preferred correction to read at 40 cm with the phoropter (p=0.002) or trial frames (p<0.001). Target distance selected wearing trial frames (in dioptres), arm length, and accommodative amplitude were not significant predictors (p>0.15). The preferred addition wearing trial frames holding a reading target at a distance selected by the patient was the only independent predictor. Excluding this variable, distance visual acuity was predictive independent of age or near vision wearing distance correction. The distance selected for task performance was predicted by vision wearing distance correction at near and at distance. Multivariable linear regression can be used to generate tables based on distance visual acuity and age or near vision wearing distance correction to determine tentative near spectacle addition. Final spectacle correction for desktop tasks can be estimated by subjective refraction with trial frames.

Does midwife experience affect the rate of severe perineal tears?

PubMed

Mizrachi, Yossi; Leytes, Sophia; Levy, Michal; Hiaev, Zvia; Ginath, Shimon; Bar, Jacob; Kovo, Michal

2017-06-01

Our aim was to study whether midwife experience affects the rate of severe perineal tears (3rd and 4th degree). A retrospective cohort study of all women with term vertex singleton pregnancies, who underwent normal vaginal deliveries, in a single tertiary hospital, between 2011 and 2015, was performed. Exclusion criteria were instrumental deliveries and stillbirth. All midwives used a "hands on" technique for protecting the perineum. The midwife experience at each delivery was calculated as the time interval between her first delivery and current delivery. A comparison was performed between deliveries in which midwife experience was less than 2 years (inexperienced), between 2 and 10 years (moderately experienced), and more than 10 years (highly experienced). A multivariate regression analysis was performed to assess the association between midwife experience and the incidence of severe perineal tears, after controlling for confounders. Overall, 15 146 deliveries were included. Severe perineal tears were diagnosed in 51 (0.33%) deliveries. Women delivered by inexperienced midwives had a higher rate of severe perineal tears compared with women delivered by highly experienced midwives (0.5% vs 0.2%, respectively, P=.024). On multivariate regression analysis, midwife experience was independently associated with a lower rate of severe perineal tears, after controlling for confounding factors. Each additional year of experience was associated with a 4.7% decrease in the risk of severe perineal tears (adjusted OR 0.95 [95% CI 0.91-0.99, P=.03). More experienced midwives had a lower rate of severe perineal tears, and may be preferred for managing deliveries of women at high risk for such tears. © 2017 Wiley Periodicals, Inc.

Enhanced ID Pit Sizing Using Multivariate Regression Algorithm

NASA Astrophysics Data System (ADS)

Krzywosz, Kenji

2007-03-01

EPRI is funding a program to enhance and improve the reliability of inside diameter (ID) pit sizing for balance-of plant heat exchangers, such as condensers and component cooling water heat exchangers. More traditional approaches to ID pit sizing involve the use of frequency-specific amplitude or phase angles. The enhanced multivariate regression algorithm for ID pit depth sizing incorporates three simultaneous input parameters of frequency, amplitude, and phase angle. A set of calibration data sets consisting of machined pits of various rounded and elongated shapes and depths was acquired in the frequency range of 100 kHz to 1 MHz for stainless steel tubing having nominal wall thickness of 0.028 inch. To add noise to the acquired data set, each test sample was rotated and test data acquired at 3, 6, 9, and 12 o'clock positions. The ID pit depths were estimated using a second order and fourth order regression functions by relying on normalized amplitude and phase angle information from multiple frequencies. Due to unique damage morphology associated with the microbiologically-influenced ID pits, it was necessary to modify the elongated calibration standard-based algorithms by relying on the algorithm developed solely from the destructive sectioning results. This paper presents the use of transformed multivariate regression algorithm to estimate ID pit depths and compare the results with the traditional univariate phase angle analysis. Both estimates were then compared with the destructive sectioning results.

Determination of boiling point of petrochemicals by gas chromatography-mass spectrometry and multivariate regression analysis of structural activity relationship.

PubMed

Fakayode, Sayo O; Mitchell, Breanna S; Pollard, David A

2014-08-01

Accurate understanding of analyte boiling points (BP) is of critical importance in gas chromatographic (GC) separation and crude oil refinery operation in petrochemical industries. This study reported the first combined use of GC separation and partial-least-square (PLS1) multivariate regression analysis of petrochemical structural activity relationship (SAR) for accurate BP determination of two commercially available (D3710 and MA VHP) calibration gas mix samples. The results of the BP determination using PLS1 multivariate regression were further compared with the results of traditional simulated distillation method of BP determination. The developed PLS1 regression was able to correctly predict analytes BP in D3710 and MA VHP calibration gas mix samples, with a root-mean-square-%-relative-error (RMS%RE) of 6.4%, and 10.8% respectively. In contrast, the overall RMS%RE of 32.9% and 40.4%, respectively obtained for BP determination in D3710 and MA VHP using a traditional simulated distillation method were approximately four times larger than the corresponding RMS%RE of BP prediction using MRA, demonstrating the better predictive ability of MRA. The reported method is rapid, robust, and promising, and can be potentially used routinely for fast analysis, pattern recognition, and analyte BP determination in petrochemical industries. Copyright © 2014 Elsevier B.V. All rights reserved.

Inference for multivariate regression model based on multiply imputed synthetic data generated via posterior predictive sampling

NASA Astrophysics Data System (ADS)

Moura, Ricardo; Sinha, Bimal; Coelho, Carlos A.

2017-06-01

The recent popularity of the use of synthetic data as a Statistical Disclosure Control technique has enabled the development of several methods of generating and analyzing such data, but almost always relying in asymptotic distributions and in consequence being not adequate for small sample datasets. Thus, a likelihood-based exact inference procedure is derived for the matrix of regression coefficients of the multivariate regression model, for multiply imputed synthetic data generated via Posterior Predictive Sampling. Since it is based in exact distributions this procedure may even be used in small sample datasets. Simulation studies compare the results obtained from the proposed exact inferential procedure with the results obtained from an adaptation of Reiters combination rule to multiply imputed synthetic datasets and an application to the 2000 Current Population Survey is discussed.

High prevalence of suicide risk in people living with HIV: who is at higher risk?

PubMed

Passos, Susane Müller Klug; Souza, Luciano Dias de Mattos; Spessato, Bárbara Coiro

2014-01-01

A cross-sectional study was developed to evaluate suicide risk and associated factors in HIV/AIDS patients at a regional reference center for the treatment of HIV/AIDS in southern Brazil. We assessed 211 patients in regard to suicide risk, clinical and sociodemographic characteristics, drug use, depression, and anxiety. Suicide risk was assessed with Mini International Neuropsychiatric Interview, Module C. Multivariate analysis was performed using Poisson regression. Of the total sample, 34.1% were at risk of suicide. In the multivariate analysis, the following variables were independently associated with suicide risk: female gender; age up to 47 years; unemployment; indicative of anxiety; indicative of depression; and abuse or addiction on psychoactive substances. Suicide risk is high in this population. Psychosocial factors should be included in the physical and clinical evaluation, given their strong association with suicide risk.

Higher clinical performance during a surgical clerkship is independently associated with matriculation of medical students into general surgery.

PubMed

Daly, Shaun C; Deal, Rebecca A; Rinewalt, Daniel E; Francescatti, Amanda B; Luu, Minh B; Millikan, Keith W; Anderson, Mary C; Myers, Jonathan A

2014-04-01

The purpose of our study was to determine the predictive impact of individual academic measures for the matriculation of senior medical students into a general surgery residency. Academic records were evaluated for third-year medical students (n = 781) at a single institution between 2004 and 2011. Cohorts were defined by student matriculation into either a general surgery residency program (n = 58) or a non-general surgery residency program (n = 723). Multivariate logistic regression was performed to evaluate independently significant academic measures. Clinical evaluation raw scores were predictive of general surgery matriculation (P = .014). In addition, multivariate modeling showed lower United States Medical Licensing Examination Step 1 scores to be independently associated with matriculation into general surgery (P = .007). Superior clinical aptitude is independently associated with general surgical matriculation. This is in contrast to the negative correlation United States Medical Licensing Examination Step 1 scores have on general surgery matriculation. Recognizing this, surgical clerkship directors can offer opportunities for continued surgical education to students showing high clinical aptitude, increasing their likelihood of surgical matriculation. Copyright © 2014 Elsevier Inc. All rights reserved.

Racial differences in circulating natriuretic peptide levels: the atherosclerosis risk in communities study.

PubMed

Gupta, Deepak K; Claggett, Brian; Wells, Quinn; Cheng, Susan; Li, Man; Maruthur, Nisa; Selvin, Elizabeth; Coresh, Josef; Konety, Suma; Butler, Kenneth R; Mosley, Thomas; Boerwinkle, Eric; Hoogeveen, Ron; Ballantyne, Christie M; Solomon, Scott D

2015-05-21

Natriuretic peptides promote natriuresis, diuresis, and vasodilation. Experimental deficiency of natriuretic peptides leads to hypertension (HTN) and cardiac hypertrophy, conditions more common among African Americans. Hospital-based studies suggest that African Americans may have reduced circulating natriuretic peptides, as compared to Caucasians, but definitive data from community-based cohorts are lacking. We examined plasma N-terminal pro B-type natriuretic peptide (NTproBNP) levels according to race in 9137 Atherosclerosis Risk in Communities (ARIC) Study participants (22% African American) without prevalent cardiovascular disease at visit 4 (1996-1998). Multivariable linear and logistic regression analyses were performed adjusting for clinical covariates. Among African Americans, percent European ancestry was determined from genetic ancestry informative markers and then examined in relation to NTproBNP levels in multivariable linear regression analysis. NTproBNP levels were significantly lower in African Americans (median, 43 pg/mL; interquartile range [IQR], 18, 88) than Caucasians (median, 68 pg/mL; IQR, 36, 124; P<0.0001). In multivariable models, adjusted log NTproBNP levels were 40% lower (95% confidence interval [CI], -43, -36) in African Americans, compared to Caucasians, which was consistent across subgroups of age, gender, HTN, diabetes, insulin resistance, and obesity. African-American race was also significantly associated with having nondetectable NTproBNP (adjusted OR, 5.74; 95% CI, 4.22, 7.80). In multivariable analyses in African Americans, a 10% increase in genetic European ancestry was associated with a 7% (95% CI, 1, 13) increase in adjusted log NTproBNP. African Americans have lower levels of plasma NTproBNP than Caucasians, which may be partially owing to genetic variation. Low natriuretic peptide levels in African Americans may contribute to the greater risk for HTN and its sequalae in this population. © 2015 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley Blackwell.

Predictive factors for rebleeding and death in alcoholic cirrhotic patients with acute variceal bleeding: a multivariate analysis.

PubMed

Krige, Jake E J; Kotze, Urda K; Distiller, Greg; Shaw, John M; Bornman, Philippus C

2009-10-01

Bleeding from esophageal varices is a leading cause of death in alcoholic cirrhotic patients. The aim of the present single-center study was to identify risk factors predictive of variceal rebleeding and death within 6 weeks of initial treatment. Univariate and multivariate analyses were performed on 310 prospectively documented alcoholic cirrhotic patients with acute variceal hemorrhage (AVH) who underwent 786 endoscopic variceal injection treatments between January 1984 and December 2006. All injections were administered during the first 6 weeks after the patients were treated for their first variceal bleed. Seventy-five (24.2%) patients experienced a rebleed, 38 within 5 days of the initial treatment and 37 within 6 weeks of their initial treatment. Of the 15 variables studied and included in a multivariate analysis using a logistic regression model, a bilirubin level >51 mmol/l and transfusion of >6 units of blood during the initial hospital admission were predictors of variceal rebleeding within the first 6 weeks. Seventy-seven (24.8%) patients died, 29 (9.3%) within 5 days and 48 (15.4%) between 6 and 42 days after the initial treatment. Stepwise multivariate logistic regression analysis showed that six variables were predictors of death within the first 6 weeks: encephalopathy, ascites, bilirubin level >51 mmol/l, international normalized ratio (INR) >2.3, albumin <25 g/l, and the need for balloon tube tamponade. Survival was influenced by the severity of liver failure, with most deaths occurring in Child-Pugh grade C patients. Patients with AVH and encephalopathy, ascites, bilirubin levels >51 mmol/l, INR >2.3, albumin <25 g/l and who require balloon tube tamponade are at increased risk of dying within the first 6 weeks. Bilirubin levels >51 mmol/l and transfusion of >6 units of blood were predictors of variceal rebleeding.

Predictors of Parent-Adolescent Communication in Post-apartheid South Africa: A Protective Factor in Adolescent Sexual and Reproductive Health

PubMed Central

Coetzee, Jenny; Dietrich, Janan; Otwombe, Kennedy; Nkala, Busi; Khunwane, Mamakiri; van der Watt, Martin; Sikkema, Kathleen J; Gray, Glenda E

2014-01-01

In the HIV context, risky sexual behaviours can be reduced through effective parent-adolescent communication. This study used the Parent Adolescent Communication Scale to determine parent-adolescent communication by ethnicity and identify predictors of high parent-adolescent communication amongst South African adolescents post-apartheid. A cross-sectional interviewer-administered survey was administered to 822 adolescents from Johannesburg, South Africa. Backward stepwise multivariate regressions were performed. The sample was predominantly Black African (62%, n=506) and female (57%, n=469). Of the participants, 57% (n=471) reported high parent-adolescent communication. Multivariate regression showed that gender was a significant predictor of high parent-adolescent communication (Black African OR:1.47,CI:1.0-2.17, Indian OR:2.67,CI:1.05-6.77, White OR:2.96,CI:1.21-7.18). Female-headed households were predictors of high parent-adolescent communication amongst Black Africans (OR:1.49,CI:1.01-2.20), but of low parent-adolescent communication amongst Whites (OR:0.36,CI: 0.15-0.89). Overall levels of parent-adolescent communication in South Africa are low. HIV prevention programmes for South African adolescents should include information and skills regarding effective parent-adolescent communication. PMID:24636691

High Maternal Blood Mercury Level Is Associated with Low Verbal IQ in Children.

PubMed

Jeong, Kyoung Sook; Park, Hyewon; Ha, Eunhee; Shin, Jiyoung; Hong, Yun Chul; Ha, Mina; Park, Hyesook; Kim, Bung Nyun; Lee, Boeun; Lee, Soo Jeong; Lee, Kyung Yeon; Kim, Ja Hyeong; Kim, Yangho

2017-07-01

The objective of the present study was to investigate the relationship of IQ in children with maternal blood mercury concentration during late pregnancy. The present study is a component of the Mothers and Children's Environmental Health (MOCEH) study, a multi-center birth cohort project in Korea that began in 2006. The study cohort consisted of 553 children whose mothers underwent testing for blood mercury during late pregnancy. The children were given the Korean language version of the Wechsler Preschool and Primary Scale of Intelligence, revised edition (WPPSI-R) at 60 months of age. Multivariate linear regression analysis, with adjustment for covariates, was used to assess the relationship between verbal, performance, and total IQ in children and blood mercury concentration of mothers during late pregnancy. The results of multivariate linear regression analysis indicated that a doubling of blood mercury was associated with the decrease in verbal and total IQ by 2.482 (95% confidence interval [CI], 0.749-4.214) and 2.402 (95% CI, 0.526-4.279), respectively, after adjustment. This inverse association remained after further adjustment for blood lead concentration. Fish intake is an effect modifier of child IQ. In conclusion, high maternal blood mercury level is associated with low verbal IQ in children. © 2017 The Korean Academy of Medical Sciences.

Nomogram Prediction of Overall Survival After Curative Irradiation for Uterine Cervical Cancer

DOE Office of Scientific and Technical Information (OSTI.GOV)

Seo, YoungSeok; Yoo, Seong Yul; Kim, Mi-Sook

Purpose: The purpose of this study was to develop a nomogram capable of predicting the probability of 5-year survival after radical radiotherapy (RT) without chemotherapy for uterine cervical cancer. Methods and Materials: We retrospectively analyzed 549 patients that underwent radical RT for uterine cervical cancer between March 1994 and April 2002 at our institution. Multivariate analysis using Cox proportional hazards regression was performed and this Cox model was used as the basis for the devised nomogram. The model was internally validated for discrimination and calibration by bootstrap resampling. Results: By multivariate regression analysis, the model showed that age, hemoglobin levelmore » before RT, Federation Internationale de Gynecologie Obstetrique (FIGO) stage, maximal tumor diameter, lymph node status, and RT dose at Point A significantly predicted overall survival. The survival prediction model demonstrated good calibration and discrimination. The bootstrap-corrected concordance index was 0.67. The predictive ability of the nomogram proved to be superior to FIGO stage (p = 0.01). Conclusions: The devised nomogram offers a significantly better level of discrimination than the FIGO staging system. In particular, it improves predictions of survival probability and could be useful for counseling patients, choosing treatment modalities and schedules, and designing clinical trials. However, before this nomogram is used clinically, it should be externally validated.« less

Impact of preoperative antithrombotic therapy on blood management after implantation of primary total knee arthroplasty

PubMed Central

Leitner, Lukas; Musser, Ewald; Kastner, Norbert; Friesenbichler, Jörg; Hirzberger, Daniela; Radl, Roman; Leithner, Andreas; Sadoghi, Patrick

2016-01-01

Red blood cell concentrates (RCC) substitution after total knee arthroplasty (TKA) is correlated with multifold of complications and an independent predictor for higher postoperative mortality. TKA is mainly performed in elderly patients with pre-existing polymorbidity, often requiring permanent preoperative antithrombotic therapy (PAT). The aim of this retrospective analysis was to investigate the impact of demand for PAT on inpatient blood management in patients undergoing TKA. In this study 200 patients were retrospectively evaluated after TKA for differences between PAT and non-PAT regarding demographic parameters, preoperative ASA score > 2, duration of operation, pre-, and intraoperative hemoglobin level, and postoperative parameters including amount of wound drainage, RCC requirement, and inpatient time. In a multivariate logistic regression analysis the independent influences of PAT, demographic parameters, ASA score > 2, and duration of the operation on RCC demand following TKA were analyzed. Patients with PAT were significantly older, more often had an ASA > 2 at surgery, needed a higher number of RCCs units and more frequently and had lower perioperative hemoglobin levels. Multivariate logistic regression revealed PAT was an independent predictor for RCC requirement. PAT patients are more likely to require RCC following TKA and should be accurately monitored with respect to postoperative blood loss. PMID:27488941

Impact of preoperative antithrombotic therapy on blood management after implantation of primary total knee arthroplasty.

PubMed

Leitner, Lukas; Musser, Ewald; Kastner, Norbert; Friesenbichler, Jörg; Hirzberger, Daniela; Radl, Roman; Leithner, Andreas; Sadoghi, Patrick

2016-08-04

Red blood cell concentrates (RCC) substitution after total knee arthroplasty (TKA) is correlated with multifold of complications and an independent predictor for higher postoperative mortality. TKA is mainly performed in elderly patients with pre-existing polymorbidity, often requiring permanent preoperative antithrombotic therapy (PAT). The aim of this retrospective analysis was to investigate the impact of demand for PAT on inpatient blood management in patients undergoing TKA. In this study 200 patients were retrospectively evaluated after TKA for differences between PAT and non-PAT regarding demographic parameters, preoperative ASA score > 2, duration of operation, pre-, and intraoperative hemoglobin level, and postoperative parameters including amount of wound drainage, RCC requirement, and inpatient time. In a multivariate logistic regression analysis the independent influences of PAT, demographic parameters, ASA score > 2, and duration of the operation on RCC demand following TKA were analyzed. Patients with PAT were significantly older, more often had an ASA > 2 at surgery, needed a higher number of RCCs units and more frequently and had lower perioperative hemoglobin levels. Multivariate logistic regression revealed PAT was an independent predictor for RCC requirement. PAT patients are more likely to require RCC following TKA and should be accurately monitored with respect to postoperative blood loss.

[Risk factors for elevated serum total bile acid in preterm infants].

PubMed

Song, Yan-Ting; Wang, Yong-Qin; Zhao, Yue-Hua; Zhu, Hai-Ling; Liu, Qian; Zhang, Xiao; Gao, Yi-Wen; Zhang, Wei-Ye; Sang, Yu-Tong

2018-03-01

To study the risk factors for elevated serum total bile acid (TBA) in preterm infants. A retrospective analysis was performed for the clinical data of 216 preterm infants who were admitted to the neonatal intensive care unit. According to the presence or absence of elevated TBA (TBA >24.8 μmol/L), the preterm infants were divided into elevated TBA group with 53 infants and non-elevated TBA group with 163 infants. A univariate analysis and an unconditional multivariate logistic regression analysis were used to investigate the risk factors for elevated TBA. The univariate analysis showed that there were significant differences between the elevated TBA group and the non-elevated TBA group in gestational age at birth, birth weight, proportion of small-for-gestational-age infants, proportion of infants undergoing ventilator-assisted ventilation, fasting time, parenteral nutrition time, and incidence of neonatal respiratory failure and sepsis (P<0.05). The unconditional multivariate logistic regression analysis showed that low birth weight (OR=3.84, 95%CI: 1.53-9.64) and neonatal sepsis (OR=2.56, 95%CI: 1.01-6.47) were independent risk factors for elevated TBA in preterm infants. Low birth weight and neonatal sepsis may lead to elevated TBA in preterm infants.

Uterine fibroids at routine second-trimester ultrasound survey and risk of sonographic short cervix.

PubMed

Blitz, Matthew J; Rochelson, Burton; Augustine, Stephanie; Greenberg, Meir; Sison, Cristina P; Vohra, Nidhi

2016-11-01

To determine whether women with sonographically identified uterine fibroids are at higher risk for a short cervix. This retrospective cohort study evaluated all women with singleton gestations who had a routine second-trimester ultrasound at 17-23 weeks gestational age from 2010 to 2013. When fibroids were noted, their presence, number, location and size were recorded. Exclusion criteria included a history of cervical conization or loop electrosurgical excision procedure (LEEP), uterine anomalies, maternal age greater than 40 years, and a previously placed cerclage. The primary variable of interest was short cervix (<25 mm). Secondary variables of interest included gestational age at delivery, mode of delivery, indication for cesarean, malpresentation, birth weight, and Apgar scores. A multivariable logistic regression analysis was performed. Fibroids were identified in 522/10 314 patients (5.1%). In the final multivariable logistic regression model, short cervix was increased in women with fibroids (OR 2.29, 95% CI: 1.40, 3.74). The number of fibroids did not affect the frequency of short cervix. Fibroids were significantly associated with preterm delivery (<37 weeks), primary cesarean, breech presentation, lower birth weight infants, and lower Apgar scores. Women with uterine fibroids may be at higher risk for a short cervix. Fibroids are also associated with several adverse obstetric and neonatal outcomes.

[Risk factors for anorexia in children].

PubMed

Liu, Wei-Xiao; Lang, Jun-Feng; Zhang, Qin-Feng

2016-11-01

To investigate the risk factors for anorexia in children, and to reduce the prevalence of anorexia in children. A questionnaire survey and a case-control study were used to collect the general information of 150 children with anorexia (case group) and 150 normal children (control group). Univariate analysis and multivariate logistic stepwise regression analysis were performed to identify the risk factors for anorexia in children. The results of the univariate analysis showed significant differences between the case and control groups in the age in months when supplementary food were added, feeding pattern, whether they liked meat, vegetables and salty food, whether they often took snacks and beverages, whether they liked to play while eating, and whether their parents asked them to eat food on time (P<0.05). The results of the multivariate logistic regression analysis showed that late addition of supplementary food (OR=5.408), high frequency of taking snacks and/or drinks (OR=11.813), and eating while playing (OR=6.654) were major risk factors for anorexia in children. Liking of meat (OR=0.093) and vegetables (OR=0.272) and eating on time required by parents (OR=0.079) were protective factors against anorexia in children. Timely addition of supplementary food, a proper diet, and development of children's proper eating and living habits can reduce the incidence of anorexia in children.

Does tip-of-the-tongue for proper names discriminate amnestic mild cognitive impairment?

PubMed

Juncos-Rabadán, Onésimo; Facal, David; Lojo-Seoane, Cristina; Pereiro, Arturo X

2013-04-01

Difficulty in retrieving people's names is very common in the early stages of Alzheimer's disease and mild cognitive impairment. Such difficulty is often observed as the tip-of-the-tongue (TOT) phenomenon. The main aim of this study was to explore whether a famous people's naming task that elicited the TOT state can be used to discriminate between amnestic mild cognitive impairment (aMCI) patients and normal controls. Eighty-four patients with aMCI and 106 normal controls aged over 50 years performed a task involving naming 50 famous people shown in pictures. Univariate and multivariate regression analyses were used to study the relationships between aMCI and semantic and phonological measures in the TOT paradigm. Univariate regression analyses revealed that all TOT measures significantly predicted aMCI. Multivariate analysis of all these measures correctly classified 70% of controls (specificity) and 71.6% of aMCI patients (sensitivity), with an AUC (area under curve ROC) value of 0.74, but only the phonological measure remained significant. This classification value was similar to that obtained with the Semantic verbal fluency test. TOTs for proper names may effectively discriminate aMCI patients from normal controls through measures that represent one of the naming processes affected, that is, phonological access.

Learning investment indicators through data extension

NASA Astrophysics Data System (ADS)

Dvořák, Marek

2017-07-01

Stock prices in the form of time series were analysed using single and multivariate statistical methods. After simple data preprocessing in the form of logarithmic differences, we augmented this single variate time series to a multivariate representation. This method makes use of sliding windows to calculate several dozen of new variables using simple statistic tools like first and second moments as well as more complicated statistic, like auto-regression coefficients and residual analysis, followed by an optional quadratic transformation that was further used for data extension. These were used as a explanatory variables in a regularized logistic LASSO regression which tried to estimate Buy-Sell Index (BSI) from real stock market data.

Advanced statistics: linear regression, part II: multiple linear regression.

PubMed

Marill, Keith A

2004-01-01

The applications of simple linear regression in medical research are limited, because in most situations, there are multiple relevant predictor variables. Univariate statistical techniques such as simple linear regression use a single predictor variable, and they often may be mathematically correct but clinically misleading. Multiple linear regression is a mathematical technique used to model the relationship between multiple independent predictor variables and a single dependent outcome variable. It is used in medical research to model observational data, as well as in diagnostic and therapeutic studies in which the outcome is dependent on more than one factor. Although the technique generally is limited to data that can be expressed with a linear function, it benefits from a well-developed mathematical framework that yields unique solutions and exact confidence intervals for regression coefficients. Building on Part I of this series, this article acquaints the reader with some of the important concepts in multiple regression analysis. These include multicollinearity, interaction effects, and an expansion of the discussion of inference testing, leverage, and variable transformations to multivariate models. Examples from the first article in this series are expanded on using a primarily graphic, rather than mathematical, approach. The importance of the relationships among the predictor variables and the dependence of the multivariate model coefficients on the choice of these variables are stressed. Finally, concepts in regression model building are discussed.

Pan evaporation modeling using six different heuristic computing methods in different climates of China

NASA Astrophysics Data System (ADS)

Wang, Lunche; Kisi, Ozgur; Zounemat-Kermani, Mohammad; Li, Hui

2017-01-01

Pan evaporation (Ep) plays important roles in agricultural water resources management. One of the basic challenges is modeling Ep using limited climatic parameters because there are a number of factors affecting the evaporation rate. This study investigated the abilities of six different soft computing methods, multi-layer perceptron (MLP), generalized regression neural network (GRNN), fuzzy genetic (FG), least square support vector machine (LSSVM), multivariate adaptive regression spline (MARS), adaptive neuro-fuzzy inference systems with grid partition (ANFIS-GP), and two regression methods, multiple linear regression (MLR) and Stephens and Stewart model (SS) in predicting monthly Ep. Long-term climatic data at various sites crossing a wide range of climates during 1961-2000 are used for model development and validation. The results showed that the models have different accuracies in different climates and the MLP model performed superior to the other models in predicting monthly Ep at most stations using local input combinations (for example, the MAE (mean absolute errors), RMSE (root mean square errors), and determination coefficient (R2) are 0.314 mm/day, 0.405 mm/day and 0.988, respectively for HEB station), while GRNN model performed better in Tibetan Plateau (MAE, RMSE and R2 are 0.459 mm/day, 0.592 mm/day and 0.932, respectively). The accuracies of above models ranked as: MLP, GRNN, LSSVM, FG, ANFIS-GP, MARS and MLR. The overall results indicated that the soft computing techniques generally performed better than the regression methods, but MLR and SS models can be more preferred at some climatic zones instead of complex nonlinear models, for example, the BJ (Beijing), CQ (Chongqing) and HK (Haikou) stations. Therefore, it can be concluded that Ep could be successfully predicted using above models in hydrological modeling studies.

The microbiological profile and presence of bloodstream infection influence mortality rates in necrotizing fasciitis

PubMed Central

2011-01-01

Introduction Necrotizing fasciitis (NF) is a life threatening infectious disease with a high mortality rate. We carried out a microbiological characterization of the causative pathogens. We investigated the correlation of mortality in NF with bloodstream infection and with the presence of co-morbidities. Methods In this retrospective study, we analyzed 323 patients who presented with necrotizing fasciitis at two different institutions. Bloodstream infection (BSI) was defined as a positive blood culture result. The patients were categorized as survivors and non-survivors. Eleven clinically important variables which were statistically significant by univariate analysis were selected for multivariate regression analysis and a stepwise logistic regression model was developed to determine the association between BSI and mortality. Results Univariate logistic regression analysis showed that patients with hypotension, heart disease, liver disease, presence of Vibrio spp. in wound cultures, presence of fungus in wound cultures, and presence of Streptococcus group A, Aeromonas spp. or Vibrio spp. in blood cultures, had a significantly higher risk of in-hospital mortality. Our multivariate logistic regression analysis showed a higher risk of mortality in patients with pre-existing conditions like hypotension, heart disease, and liver disease. Multivariate logistic regression analysis also showed that presence of Vibrio spp in wound cultures, and presence of Streptococcus Group A in blood cultures were associated with a high risk of mortality while debridement > = 3 was associated with improved survival. Conclusions Mortality in patients with necrotizing fasciitis was significantly associated with the presence of Vibrio in wound cultures and Streptococcus group A in blood cultures. PMID:21693053

Evaluation of the Bitterness of Traditional Chinese Medicines using an E-Tongue Coupled with a Robust Partial Least Squares Regression Method.

PubMed

Lin, Zhaozhou; Zhang, Qiao; Liu, Ruixin; Gao, Xiaojie; Zhang, Lu; Kang, Bingya; Shi, Junhan; Wu, Zidan; Gui, Xinjing; Li, Xuelin

2016-01-25

To accurately, safely, and efficiently evaluate the bitterness of Traditional Chinese Medicines (TCMs), a robust predictor was developed using robust partial least squares (RPLS) regression method based on data obtained from an electronic tongue (e-tongue) system. The data quality was verified by the Grubb's test. Moreover, potential outliers were detected based on both the standardized residual and score distance calculated for each sample. The performance of RPLS on the dataset before and after outlier detection was compared to other state-of-the-art methods including multivariate linear regression, least squares support vector machine, and the plain partial least squares regression. Both R² and root-mean-squares error (RMSE) of cross-validation (CV) were recorded for each model. With four latent variables, a robust RMSECV value of 0.3916 with bitterness values ranging from 0.63 to 4.78 were obtained for the RPLS model that was constructed based on the dataset including outliers. Meanwhile, the RMSECV, which was calculated using the models constructed by other methods, was larger than that of the RPLS model. After six outliers were excluded, the performance of all benchmark methods markedly improved, but the difference between the RPLS model constructed before and after outlier exclusion was negligible. In conclusion, the bitterness of TCM decoctions can be accurately evaluated with the RPLS model constructed using e-tongue data.

Using Logistic Regression To Predict the Probability of Debris Flows Occurring in Areas Recently Burned By Wildland Fires

USGS Publications Warehouse

Rupert, Michael G.; Cannon, Susan H.; Gartner, Joseph E.

2003-01-01

Logistic regression was used to predict the probability of debris flows occurring in areas recently burned by wildland fires. Multiple logistic regression is conceptually similar to multiple linear regression because statistical relations between one dependent variable and several independent variables are evaluated. In logistic regression, however, the dependent variable is transformed to a binary variable (debris flow did or did not occur), and the actual probability of the debris flow occurring is statistically modeled. Data from 399 basins located within 15 wildland fires that burned during 2000-2002 in Colorado, Idaho, Montana, and New Mexico were evaluated. More than 35 independent variables describing the burn severity, geology, land surface gradient, rainfall, and soil properties were evaluated. The models were developed as follows: (1) Basins that did and did not produce debris flows were delineated from National Elevation Data using a Geographic Information System (GIS). (2) Data describing the burn severity, geology, land surface gradient, rainfall, and soil properties were determined for each basin. These data were then downloaded to a statistics software package for analysis using logistic regression. (3) Relations between the occurrence/non-occurrence of debris flows and burn severity, geology, land surface gradient, rainfall, and soil properties were evaluated and several preliminary multivariate logistic regression models were constructed. All possible combinations of independent variables were evaluated to determine which combination produced the most effective model. The multivariate model that best predicted the occurrence of debris flows was selected. (4) The multivariate logistic regression model was entered into a GIS, and a map showing the probability of debris flows was constructed. The most effective model incorporates the percentage of each basin with slope greater than 30 percent, percentage of land burned at medium and high burn severity in each basin, particle size sorting, average storm intensity (millimeters per hour), soil organic matter content, soil permeability, and soil drainage. The results of this study demonstrate that logistic regression is a valuable tool for predicting the probability of debris flows occurring in recently-burned landscapes.

Use of complementary and alternative medicine for physical performance, energy, immune function, and general health among older women and men in the United States.

PubMed

Tait, Elizabeth M; Laditka, Sarah B; Laditka, James N; Nies, Mary A; Racine, Elizabeth F

2012-01-01

We examined use of complementary and alternative medicine (CAM) for health and well-being by older women and men. Data were from the 2007 National Health Interview Survey, representing 89.5 million Americans ages 50+. Multivariate logistic regression accounted for the survey design. For general health, 52 million people used CAM. The numbers for immune function, physical performance, and energy were 21.6, 15.9, and 10.1 million respectively. In adjusted results, women were much more likely than men to use CAM for all four reasons, especially energy. Older adults, particularly women, could benefit from research on CAM benefits and risks.

Multivariate regression model for partitioning tree volume of white oak into round-product classes

Treesearch

Daniel A. Yaussy; David L. Sonderman

1984-01-01

Describes the development of multivariate equations that predict the expected cubic volume of four round-product classes from independent variables composed of individual tree-quality characteristics. Although the model has limited application at this time, it does demonstrate the feasibility of partitioning total tree cubic volume into round-product classes based on...

Nutritional variables predict chances of returning home and activities of daily living in post-acute geriatric care

PubMed Central

Maeda, Keisuke; Koga, Takayuki; Akagi, Junji

2018-01-01

Background Little is known about the association between malnutrition and the chances of returning home from post-acute facilities in older adult patients. This study aimed to understand whether malnutrition and malnutrition-related factors would be determinants for returning home and activities of daily living (ADL) at discharge after post-acute care. Methods Patients aged ≥65 years living at home before the onset of an acute disease and admitted to a post-acute ward were enrolled (n=207) in this prospective observational study. Malnutrition was defined based on the criteria of the European Society for Clinical Nutrition and Metabolism. Nutritional parameters included the nutritional intake at the time of admission and oral conditions evaluated by the Oral Health Assessment Tool (OHAT). The Barthel Index was used to assess daily activities. A Cox regression analysis of the length of stay was performed. Multivariable linear regression analyses to determine associations between malnutrition, returning home, and ADL at discharge were performed, after adjusting the variables of acute care setting. Results The mean patient age was 84.7±6.7 years; 38% were men. European Society for Clinical Nutrition and Metabolism-defined malnutrition was observed in 129 (62.3%) patients, and 118 (57.0%) of all patients returned home. Multivariable regression analyses showed that malnutrition was a negative predictor of returning home (hazard ratio: 0.517 [0.351–0.761], p=0.001), and an increase in the nutritional intake (kcal/kg/d) was a positive predictor of the Barthel Index at discharge (coefficient: 0.34±0.15, p=0.021). The OHAT was not associated with returning home and ADL. Conclusion Malnutrition and nutritional intake are associated with returning home and ADL at discharge, respectively, after post-acute care. Further studies investigating the effects of a nutritional intervention for post-acute patients would be necessary. PMID:29416323

A hybrid PCA-CART-MARS-based prognostic approach of the remaining useful life for aircraft engines.

PubMed

Sánchez Lasheras, Fernando; García Nieto, Paulino José; de Cos Juez, Francisco Javier; Mayo Bayón, Ricardo; González Suárez, Victor Manuel

2015-03-23

Prognostics is an engineering discipline that predicts the future health of a system. In this research work, a data-driven approach for prognostics is proposed. Indeed, the present paper describes a data-driven hybrid model for the successful prediction of the remaining useful life of aircraft engines. The approach combines the multivariate adaptive regression splines (MARS) technique with the principal component analysis (PCA), dendrograms and classification and regression trees (CARTs). Elements extracted from sensor signals are used to train this hybrid model, representing different levels of health for aircraft engines. In this way, this hybrid algorithm is used to predict the trends of these elements. Based on this fitting, one can determine the future health state of a system and estimate its remaining useful life (RUL) with accuracy. To evaluate the proposed approach, a test was carried out using aircraft engine signals collected from physical sensors (temperature, pressure, speed, fuel flow, etc.). Simulation results show that the PCA-CART-MARS-based approach can forecast faults long before they occur and can predict the RUL. The proposed hybrid model presents as its main advantage the fact that it does not require information about the previous operation states of the input variables of the engine. The performance of this model was compared with those obtained by other benchmark models (multivariate linear regression and artificial neural networks) also applied in recent years for the modeling of remaining useful life. Therefore, the PCA-CART-MARS-based approach is very promising in the field of prognostics of the RUL for aircraft engines.

Development and validation of a risk calculator predicting exercise-induced ventricular arrhythmia in patients with cardiovascular disease.

PubMed

Hermes, Ilarraza-Lomelí; Marianna, García-Saldivia; Jessica, Rojano-Castillo; Carlos, Barrera-Ramírez; Rafael, Chávez-Domínguez; María Dolores, Rius-Suárez; Pedro, Iturralde

2016-10-01

Mortality due to cardiovascular disease is often associated with ventricular arrhythmias. Nowadays, patients with cardiovascular disease are more encouraged to take part in physical training programs. Nevertheless, high-intensity exercise is associated to a higher risk for sudden death, even in apparently healthy people. During an exercise testing (ET), health care professionals provide patients, in a controlled scenario, an intense physiological stimulus that could precipitate cardiac arrhythmia in high risk individuals. There is still no clinical or statistical tool to predict this incidence. The aim of this study was to develop a statistical model to predict the incidence of exercise-induced potentially life-threatening ventricular arrhythmia (PLVA) during high intensity exercise. 6415 patients underwent a symptom-limited ET with a Balke ramp protocol. A multivariate logistic regression model where the primary outcome was PLVA was performed. Incidence of PLVA was 548 cases (8.5%). After a bivariate model, thirty one clinical or ergometric variables were statistically associated with PLVA and were included in the regression model. In the multivariate model, 13 of these variables were found to be statistically significant. A regression model (G) with a X(2) of 283.987 and a p<0.001, was constructed. Significant variables included: heart failure, antiarrhythmic drugs, myocardial lower-VD, age and use of digoxin, nitrates, among others. This study allows clinicians to identify patients at risk of ventricular tachycardia or couplets during exercise, and to take preventive measures or appropriate supervision. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Factors related to treatment refusal in Taiwanese cancer patients.

PubMed

Chiang, Ting-Yu; Wang, Chao-Hui; Lin, Yu-Fen; Chou, Shu-Lan; Wang, Ching-Ting; Juang, Hsiao-Ting; Lin, Yung-Chang; Lin, Mei-Hsiang

2015-01-01

Incidence and mortality rates for cancer have increased dramatically in the recent 30 years in Taiwan. However, not all patients receive treatment. Treatment refusal might impair patient survival and life quality. In order to improve this situation, we proposed this study to evaluate factors that are related to refusal of treatment in cancer patients via a cancer case manager system. This study analysed data from a case management system during the period from 2010 to 2012 at a medical center in Northern Taiwan. We enrolled a total of 14,974 patients who were diagnosed with cancer. Using the PRECEDE Model as a framework, we conducted logistic regression analysis to identify independent variables that are significantly associated with refusal of therapy in cancer patients. A multivariate logistic regression model was also applied to estimate adjusted the odds ratios (ORs) with 95% confidence intervals (95%CI). A total of 253 patients (1.69%) refused treatment. The multivariate logistic regression result showed that the high risk factors for refusal of treatment in cancer patient included: concerns about adverse effects (p<0.001), poor performance(p<0.001), changes in medical condition (p<0.001), timing of case manager contact (p=.026), the methods by which case manager contact patients (p<0.001) and the frequency that case managers contact patients (≥10times) (p=0.016). Cancer patients who refuse treatment have poor survival. The present study provides evidence of factors that are related to refusal of therapy and might be helpful for further application and improvement of cancer care.

A Hybrid PCA-CART-MARS-Based Prognostic Approach of the Remaining Useful Life for Aircraft Engines

PubMed Central

Lasheras, Fernando Sánchez; Nieto, Paulino José García; de Cos Juez, Francisco Javier; Bayón, Ricardo Mayo; Suárez, Victor Manuel González

2015-01-01

Prognostics is an engineering discipline that predicts the future health of a system. In this research work, a data-driven approach for prognostics is proposed. Indeed, the present paper describes a data-driven hybrid model for the successful prediction of the remaining useful life of aircraft engines. The approach combines the multivariate adaptive regression splines (MARS) technique with the principal component analysis (PCA), dendrograms and classification and regression trees (CARTs). Elements extracted from sensor signals are used to train this hybrid model, representing different levels of health for aircraft engines. In this way, this hybrid algorithm is used to predict the trends of these elements. Based on this fitting, one can determine the future health state of a system and estimate its remaining useful life (RUL) with accuracy. To evaluate the proposed approach, a test was carried out using aircraft engine signals collected from physical sensors (temperature, pressure, speed, fuel flow, etc.). Simulation results show that the PCA-CART-MARS-based approach can forecast faults long before they occur and can predict the RUL. The proposed hybrid model presents as its main advantage the fact that it does not require information about the previous operation states of the input variables of the engine. The performance of this model was compared with those obtained by other benchmark models (multivariate linear regression and artificial neural networks) also applied in recent years for the modeling of remaining useful life. Therefore, the PCA-CART-MARS-based approach is very promising in the field of prognostics of the RUL for aircraft engines. PMID:25806876

Applying quantitative adiposity feature analysis models to predict benefit of bevacizumab-based chemotherapy in ovarian cancer patients

NASA Astrophysics Data System (ADS)

Wang, Yunzhi; Qiu, Yuchen; Thai, Theresa; More, Kathleen; Ding, Kai; Liu, Hong; Zheng, Bin

2016-03-01

How to rationally identify epithelial ovarian cancer (EOC) patients who will benefit from bevacizumab or other antiangiogenic therapies is a critical issue in EOC treatments. The motivation of this study is to quantitatively measure adiposity features from CT images and investigate the feasibility of predicting potential benefit of EOC patients with or without receiving bevacizumab-based chemotherapy treatment using multivariate statistical models built based on quantitative adiposity image features. A dataset involving CT images from 59 advanced EOC patients were included. Among them, 32 patients received maintenance bevacizumab after primary chemotherapy and the remaining 27 patients did not. We developed a computer-aided detection (CAD) scheme to automatically segment subcutaneous fat areas (VFA) and visceral fat areas (SFA) and then extracted 7 adiposity-related quantitative features. Three multivariate data analysis models (linear regression, logistic regression and Cox proportional hazards regression) were performed respectively to investigate the potential association between the model-generated prediction results and the patients' progression-free survival (PFS) and overall survival (OS). The results show that using all 3 statistical models, a statistically significant association was detected between the model-generated results and both of the two clinical outcomes in the group of patients receiving maintenance bevacizumab (p<0.01), while there were no significant association for both PFS and OS in the group of patients without receiving maintenance bevacizumab. Therefore, this study demonstrated the feasibility of using quantitative adiposity-related CT image features based statistical prediction models to generate a new clinical marker and predict the clinical outcome of EOC patients receiving maintenance bevacizumab-based chemotherapy.

Evaluation of the efficiency of continuous wavelet transform as processing and preprocessing algorithm for resolution of overlapped signals in univariate and multivariate regression analyses; an application to ternary and quaternary mixtures.

PubMed

Hegazy, Maha A; Lotfy, Hayam M; Mowaka, Shereen; Mohamed, Ekram Hany

2016-07-05

Wavelets have been adapted for a vast number of signal-processing applications due to the amount of information that can be extracted from a signal. In this work, a comparative study on the efficiency of continuous wavelet transform (CWT) as a signal processing tool in univariate regression and a pre-processing tool in multivariate analysis using partial least square (CWT-PLS) was conducted. These were applied to complex spectral signals of ternary and quaternary mixtures. CWT-PLS method succeeded in the simultaneous determination of a quaternary mixture of drotaverine (DRO), caffeine (CAF), paracetamol (PAR) and p-aminophenol (PAP, the major impurity of paracetamol). While, the univariate CWT failed to simultaneously determine the quaternary mixture components and was able to determine only PAR and PAP, the ternary mixtures of DRO, CAF, and PAR and CAF, PAR, and PAP. During the calculations of CWT, different wavelet families were tested. The univariate CWT method was validated according to the ICH guidelines. While for the development of the CWT-PLS model a calibration set was prepared by means of an orthogonal experimental design and their absorption spectra were recorded and processed by CWT. The CWT-PLS model was constructed by regression between the wavelet coefficients and concentration matrices and validation was performed by both cross validation and external validation sets. Both methods were successfully applied for determination of the studied drugs in pharmaceutical formulations. Copyright © 2016 Elsevier B.V. All rights reserved.

Preoperative Fasting C-Peptide Predicts Type 2 Diabetes Mellitus Remission in Low-BMI Chinese Patients After Roux-en-Y Gastric Bypass.

PubMed

Zhao, Lei; Li, Weizheng; Su, Zhihong; Liu, Yong; Zhu, Liyong; Zhu, Shaihong

2018-05-29

This study investigated the role of preoperative fasting C-peptide (FCP) levels in predicting diabetic outcomes in low-BMI Chinese patients following Roux-en-Y gastric bypass (RYGB) by comparing the metabolic outcomes of patients with FCP > 1 ng/ml versus FCP ≤ 1 ng/ml. The study sample included 78 type 2 diabetes mellitus patients with an average BMI < 30 kg/m 2 at baseline. Patients' parameters were analyzed before and after surgery, with a 2-year follow-up. A univariate logistic regression analysis and multivariate analysis of variance between the remission and improvement group were performed to determine factors that were associated with type 2 diabetes remission after RYGB. Linear correlation analyses between FCP and metabolic parameters were performed. Patients were divided into two groups: FCP > 1 ng/ml and FCP ≤ 1 ng/ml, with measured parameters compared between the groups. Patients' fasting plasma glucose, 2-h postprandial plasma glucose, FCP, and HbA1c improved significantly after surgery (p < 0.05). Factors associated with type 2 diabetes remission were BMI, 2hINS, and FCP at the univariate logistic regression analysis (p < 0.05). Multivariate logistic regression analysis was performed then showed the results were more related to FCP (OR = 2.39). FCP showed a significant linear correlation with fasting insulin and BMI (p < 0.05). There was a significant difference in remission rate between the FCP > 1 ng/ml and FCP ≤ 1 ng/ml groups (p = 0.01). The parameters of patients with FCP > 1 ng/ml, including BMI, plasma glucose, HbA1c, and plasma insulin, decreased markedly after surgery (p < 0.05). FCP level is a significant predictor of diabetes outcomes after RYGB in low-BMI Chinese patients. An FCP level of 1 ng/ml may be a useful threshold for predicting surgical prognosis, with FCP > 1 ng/ml predicting better clinical outcomes following RYGB.

ACTN3 genotype and physical function and frailty in an elderly Chinese population: the Rugao Longevity and Ageing Study.

PubMed

Ma, Teng; Lu, Deyi; Zhu, Yin-Sheng; Chu, Xue-Feng; Wang, Yong; Shi, Guo-Ping; Wang, Zheng-Dong; Yu, Li; Jiang, Xiao-Yan; Wang, Xiao-Feng

2018-05-01

To examine the associations of the actinin alpha 3 gene (ACTN3) R577X polymorphism with physical performance and frailty in an older Chinese population. Data from 1,463 individuals (57.8% female) aged 70-87 years from the Rugao Longevity and Ageing Study were used. The associations between R577X and timed 5-m walk, grip strength, timed Up and Go test, and frailty index (FI) based on deficits of 23 laboratory tests (FI-Lab) were examined. Analysis of variance and linear regression models were used to evaluate the genetic effects of ACTN3 R577X on physical performance and FI-Lab. The XX and RX genotypes of the ACTN3 R557X polymorphism accounted for 17.1 and 46.9%, respectively. Multivariate regression analysis revealed that in men aged 70-79 years, the ACTN3 577X allele was significantly associated with physical performance (5-m walk time, regression coefficient (β) = 0.258, P = 0.006; grip strength, β = -1.062, P = 0.012; Up and Go test time β = 0.368, P = 0.019). In women aged 70-79 years, a significant association between the ACTN3 577X allele and the FI-Lab score was observed, with a regression coefficient of β = 0.019 (P = 0.003). These findings suggest an age- and gender-specific X-additive model of R577X for 5-m walk time, grip strength, Up and Go Test time, and FI-Lab score. The ACTN3 577X allele is associated with an age- and sex-specific decrease in physical performance and an increase in frailty in an older population.

Employer-Sponsored Insurance, Health Care Cost Growth, and the Economic Performance of U.S. Industries

PubMed Central

Sood, Neeraj; Ghosh, Arkadipta; Escarce, José J

2009-01-01

Objective To estimate the effect of growth in health care costs that outpaces gross domestic product (GDP) growth (“excess” growth in health care costs) on employment, gross output, and value added to GDP of U.S. industries. Study Setting We analyzed data from 38 U.S. industries for the period 1987–2005. All data are publicly available from various government agencies. Study Design We estimated bivariate and multivariate regressions. To develop the regression models, we assumed that rapid growth in health care costs has a larger effect on economic performance for industries where large percentages of workers receive employer-sponsored health insurance (ESI). We used the estimated regression coefficients to simulate economic outcomes under alternative scenarios of health care cost inflation. Results Faster growth in health care costs had greater adverse effects on economic outcomes for industries with larger percentages of workers who had ESI. We found that a 10 percent increase in excess growth in health care costs would have resulted in 120,803 fewer jobs, US$28,022 million in lost gross output, and US$14,082 million in lost value added in 2005. These declines represent 0.17 to 0.18 percent of employment, gross output, and value added in 2005. Conclusion Excess growth in health care costs is adversely affecting the economic performance of U.S. industries. PMID:19500165

Stata Modules for Calculating Novel Predictive Performance Indices for Logistic Models.

PubMed

Barkhordari, Mahnaz; Padyab, Mojgan; Hadaegh, Farzad; Azizi, Fereidoun; Bozorgmanesh, Mohammadreza

2016-01-01

Prediction is a fundamental part of prevention of cardiovascular diseases (CVD). The development of prediction algorithms based on the multivariate regression models loomed several decades ago. Parallel with predictive models development, biomarker researches emerged in an impressively great scale. The key question is how best to assess and quantify the improvement in risk prediction offered by new biomarkers or more basically how to assess the performance of a risk prediction model. Discrimination, calibration, and added predictive value have been recently suggested to be used while comparing the predictive performances of the predictive models' with and without novel biomarkers. Lack of user-friendly statistical software has restricted implementation of novel model assessment methods while examining novel biomarkers. We intended, thus, to develop a user-friendly software that could be used by researchers with few programming skills. We have written a Stata command that is intended to help researchers obtain cut point-free and cut point-based net reclassification improvement index and (NRI) and relative and absolute Integrated discriminatory improvement index (IDI) for logistic-based regression analyses.We applied the commands to a real data on women participating the Tehran lipid and glucose study (TLGS) to examine if information of a family history of premature CVD, waist circumference, and fasting plasma glucose can improve predictive performance of the Framingham's "general CVD risk" algorithm. The command is addpred for logistic regression models. The Stata package provided herein can encourage the use of novel methods in examining predictive capacity of ever-emerging plethora of novel biomarkers.

Comparison of Muscle Mass Indices Using Computed Tomography or Dual X-Ray Absorptiometry for Predicting Physical Performance in Hemodialysis Patients.

PubMed

Kang, Seok Hui; Lee, Hyun Seok; Lee, Sukyung; Cho, Ji-Hyung; Kim, Jun Chul

2017-01-01

Our study aims to evaluate the association between thigh muscle cross-sectional area (TMA) using computed tomography (CT), or appendicular skeletal muscle mass (ASM) using dual energy X-ray absorptiometry (DEXA), and physical performance levels in hemodialysis (HD) patients. Patients were included if they were on HD for ≥6 months (n = 84). ASM and TMA were adjusted to body weight (BW, kg) or height2 (Ht2, m2). Each participant performed a short physical performance battery test (SPPB), a sit-to-stand for 30 second test (STS30), a 6-minute walk test (6-MWT), a timed up and go test (TUG), and hand grip strength (HGS) test. Correlation coefficients for SPPB, GS, 5STS, STS30, 6-MWT, and TUG were highest in TMA/BW. Results from partial correlation or linear regression analyses displayed similar trends to those derived from Pearson's correlation analyses. An increase in TMA/BW or TMA/Ht2 was associated with a decreased odds ratio of low SPPB, GS, or HGS in multivariate analyses. Indices using DEXA were associated with a decreased odds ratio of a low HGS only in multivariate analysis. TMA indices using CT may be more valuable in predicting physical performance or strength in HD patients. © 2017 The Author(s). Published by S. Karger AG, Basel.

Bayesian inference for multivariate meta-analysis Box-Cox transformation models for individual patient data with applications to evaluation of cholesterol lowering drugs

PubMed Central

Kim, Sungduk; Chen, Ming-Hui; Ibrahim, Joseph G.; Shah, Arvind K.; Lin, Jianxin

2013-01-01

In this paper, we propose a class of Box-Cox transformation regression models with multidimensional random effects for analyzing multivariate responses for individual patient data (IPD) in meta-analysis. Our modeling formulation uses a multivariate normal response meta-analysis model with multivariate random effects, in which each response is allowed to have its own Box-Cox transformation. Prior distributions are specified for the Box-Cox transformation parameters as well as the regression coefficients in this complex model, and the Deviance Information Criterion (DIC) is used to select the best transformation model. Since the model is quite complex, a novel Monte Carlo Markov chain (MCMC) sampling scheme is developed to sample from the joint posterior of the parameters. This model is motivated by a very rich dataset comprising 26 clinical trials involving cholesterol lowering drugs where the goal is to jointly model the three dimensional response consisting of Low Density Lipoprotein Cholesterol (LDL-C), High Density Lipoprotein Cholesterol (HDL-C), and Triglycerides (TG) (LDL-C, HDL-C, TG). Since the joint distribution of (LDL-C, HDL-C, TG) is not multivariate normal and in fact quite skewed, a Box-Cox transformation is needed to achieve normality. In the clinical literature, these three variables are usually analyzed univariately: however, a multivariate approach would be more appropriate since these variables are correlated with each other. A detailed analysis of these data is carried out using the proposed methodology. PMID:23580436

Bayesian inference for multivariate meta-analysis Box-Cox transformation models for individual patient data with applications to evaluation of cholesterol-lowering drugs.

PubMed

Kim, Sungduk; Chen, Ming-Hui; Ibrahim, Joseph G; Shah, Arvind K; Lin, Jianxin

2013-10-15

In this paper, we propose a class of Box-Cox transformation regression models with multidimensional random effects for analyzing multivariate responses for individual patient data in meta-analysis. Our modeling formulation uses a multivariate normal response meta-analysis model with multivariate random effects, in which each response is allowed to have its own Box-Cox transformation. Prior distributions are specified for the Box-Cox transformation parameters as well as the regression coefficients in this complex model, and the deviance information criterion is used to select the best transformation model. Because the model is quite complex, we develop a novel Monte Carlo Markov chain sampling scheme to sample from the joint posterior of the parameters. This model is motivated by a very rich dataset comprising 26 clinical trials involving cholesterol-lowering drugs where the goal is to jointly model the three-dimensional response consisting of low density lipoprotein cholesterol (LDL-C), high density lipoprotein cholesterol (HDL-C), and triglycerides (TG) (LDL-C, HDL-C, TG). Because the joint distribution of (LDL-C, HDL-C, TG) is not multivariate normal and in fact quite skewed, a Box-Cox transformation is needed to achieve normality. In the clinical literature, these three variables are usually analyzed univariately; however, a multivariate approach would be more appropriate because these variables are correlated with each other. We carry out a detailed analysis of these data by using the proposed methodology. Copyright © 2013 John Wiley & Sons, Ltd.

Predictors of in-hospital vs postdischarge mortality in pneumonia.

PubMed

Metersky, Mark L; Waterer, Grant; Nsa, Wato; Bratzler, Dale W

2012-08-01

Many patients who die within 30 days of admission to the hospital for pneumonia die after discharge. Recently, 30-day mortality for patients with pneumonia became a publicly reported performance measure, meaning that hospitals are, in part, being measured based on how the patient fares after discharge from the hospital. This study was undertaken to determine which factors predict in-hospital vs postdischarge mortality in patients with pneumonia. This was a retrospective analysis of a database of 21,223 patients on Medicare aged 65 years and older admitted to the hospital between 2000 and 2001. Multivariate logistic regression analyses were performed to determine the association between 26 patient characteristics and the timing of death (in-hospital vs postdischarge) among those patients who died within 30 days of hospital admission. Among the 21,223 patients, 2,561 (12.1%) died within 30 days of admission: 1,343 (52.4%) during the hospital stay, and 1,218 (47.6%) after discharge. Multivariate logistic regression demonstrated that seven factors were significantly associated with death prior to discharge: systolic BP < 90 mm Hg, respiration rate > 30/min, bacteremia, arterial pH < 7.35, BUN level > 11 mmol/L, arterial Po(2) < 60 mm Hg or arterial oxygen saturation < 90%, and need for mechanical ventilation. Some underlying comorbidities were associated with a nonstatistically significant trend toward death after discharge. Of elderly patients dying within 30 days of admission to the hospital, approximately one-half die after discharge from the hospital. Comorbidities, in general, were equally associated with death in the hospital and death after discharge

Spatial variation of pneumonia hospitalization risk in Twin Cities metro area, Minnesota.

PubMed

Iroh Tam, P Y; Krzyzanowski, B; Oakes, J M; Kne, L; Manson, S

2017-11-01

Fine resolution spatial variability in pneumonia hospitalization may identify correlates with socioeconomic, demographic and environmental factors. We performed a retrospective study within the Fairview Health System network of Minnesota. Patients 2 months of age and older hospitalized with pneumonia between 2011 and 2015 were geocoded to their census block group, and pneumonia hospitalization risk was analyzed in relation to socioeconomic, demographic and environmental factors. Spatial analyses were performed using Esri's ArcGIS software, and multivariate Poisson regression was used. Hospital encounters of 17 840 patients were included in the analysis. Multivariate Poisson regression identified several significant associations, including a 40% increased risk of pneumonia hospitalization among census block groups with large, compared with small, populations of ⩾65 years, a 56% increased risk among census block groups in the bottom (first) quartile of median household income compared to the top (fourth) quartile, a 44% higher risk in the fourth quartile of average nitrogen dioxide emissions compared with the first quartile, and a 47% higher risk in the fourth quartile of average annual solar insolation compared to the first quartile. After adjusting for income, moving from the first to the second quartile of the race/ethnic diversity index resulted in a 21% significantly increased risk of pneumonia hospitalization. In conclusion, the risk of pneumonia hospitalization at the census-block level is associated with age, income, race/ethnic diversity index, air quality, and solar insolation, and varies by region-specific factors. Identifying correlates using fine spatial analysis provides opportunities for targeted prevention and control.

Technical performance score is associated with outcomes after the Norwood procedure.

PubMed

Nathan, Meena; Sleeper, Lynn A; Ohye, Richard G; Frommelt, Peter C; Caldarone, Christopher A; Tweddell, James S; Lu, Minmin; Pearson, Gail D; Gaynor, J William; Pizarro, Christian; Williams, Ismee A; Colan, Steven D; Dunbar-Masterson, Carolyn; Gruber, Peter J; Hill, Kevin; Hirsch-Romano, Jennifer; Jacobs, Jeffrey P; Kaltman, Jonathan R; Kumar, S Ram; Morales, David; Bradley, Scott M; Kanter, Kirk; Newburger, Jane W

2014-11-01

The technical performance score (TPS) has been reported in a single center study to predict the outcomes after congenital cardiac surgery. We sought to determine the association of the TPS with outcomes in patients undergoing the Norwood procedure in the Single Ventricle Reconstruction trial. We calculated the TPS (class 1, optimal; class 2, adequate; class 3, inadequate) according to the predischarge echocardiograms analyzed in a core laboratory and unplanned reinterventions that occurred before discharge from the Norwood hospitalization. Multivariable regression examined the association of the TPS with interval to first extubation, Norwood length of stay, death or transplantation, unplanned postdischarge reinterventions, and neurodevelopment at 14 months old. Of 549 patients undergoing a Norwood procedure, 356 (65%) had an echocardiogram adequate to assess atrial septal restriction or arch obstruction or an unplanned reintervention, enabling calculation of the TPS. On multivariable regression, adjusting for preoperative variables, a better TPS was an independent predictor of a shorter interval to first extubation (P=.019), better transplant-free survival before Norwood discharge (P<.001; odds ratio, 9.1 for inadequate vs optimal), shorter hospital length of stay (P<.001), fewer unplanned reinterventions between Norwood discharge and stage II (P=.004), and a higher Bayley II psychomotor development index at 14 months (P=.031). The TPS was not associated with transplant-free survival after Norwood discharge, unplanned reinterventions after stage II, or the Bayley II mental development index at 14 months. TPS is an independent predictor of important outcomes after Norwood and could serve as a tool for quality improvement. Copyright © 2014 The American Association for Thoracic Surgery. All rights reserved.

Low-grade inflammation is associated with lower haemoglobin levels in healthy individuals: results from the Danish blood donor study.

PubMed

Kotzé, S R; Pedersen, O B; Petersen, M S; Sørensen, E; Thørner, L W; Sørensen, C J; Rigas, A S; Hjalgrim, H; Rostgaard, K; Ullum, H; Erikstrup, C

2016-08-01

Chronic inflammation can lead to anaemia of chronic disease due to the sequestration of iron caused by inflammatory cytokines and the protein hepcidin. However, the effect of low-grade inflammation (LGI) on haemoglobin among healthy individuals is not known. This study examines the effect of LGI on haemoglobin among Danish blood donors. We performed multivariable linear regression to assess the effect of LGI (i.e. high-sensitivity C-reactive protein above 3 mg/l but below 10 mg/l) on haemoglobin in 17 322 Danish blood donors. We also performed multivariable logistic regression to evaluate the effect of LGI on the risk of having low haemoglobin (below the 10th percentile among men and women, respectively). We adjusted for donation activity, age, sex, low ferritin, oral contraceptives and menopause. All analyses were stratified by current smoking status. LGI was associated with lower haemoglobin (0·08 mm lower [0·12 g/dl], 95% confidence interval (CI): -0·11-0·05) and increased risk of low haemoglobin (OR = 1·22, 95% CI: 1·05-1·43) in non-smokers. Conversely, LGI was associated with higher haemoglobin in smokers (0·12 mm [0·19 g/dl], 95% CI: 0·06-0·18). In this first study of LGI and haemoglobin in healthy individuals, there was a negative association between LGI and haemoglobin in non-smokers. The association was positive in smokers, probably because smoking leads to both increased inflammation and increased haemoglobin through CO exposure. © 2016 International Society of Blood Transfusion.

National Trends of Simple Prostatectomy for Benign Prostatic Hyperplasia With an Analysis of Risk Factors for Adverse Perioperative Outcomes.

PubMed

Pariser, Joseph J; Pearce, Shane M; Patel, Sanjay G; Bales, Gregory T

2015-10-01

To examine the national trends of simple prostatectomy (SP) for benign prostatic hyperplasia (BPH) focusing on perioperative outcomes and risk factors for complications. The National Inpatient Sample (2002-2012) was utilized to identify patients with BPH undergoing SP. Analysis included demographics, hospital details, associated procedures, and operative approach (open, robotic, or laparoscopic). Outcomes included complications, length of stay, charges, and mortality. Multivariate logistic regression was used to determine the risk factors for perioperative complications. Linear regression was used to assess the trends in the national annual utilization of SP. The study population included 35,171 patients. Median length of stay was 4 days (interquartile range 3-6). Cystolithotomy was performed concurrently in 6041 patients (17%). The overall complication rate was 28%, with bleeding occurring most commonly. In total, 148 (0.4%) patients experienced in-hospital mortality. On multivariate analysis, older age, black race, and overall comorbidity were associated with greater risk of complications while the use of a minimally invasive approach and concurrent cystolithotomy had a decreased risk. Over the study period, the national use of simple prostatectomy decreased, on average, by 145 cases per year (P = .002). By 2012, 135/2580 procedures (5%) were performed using a minimally invasive approach. The nationwide utilization of SP for BPH has decreased. Bleeding complications are common, but perioperative mortality is low. Patients who are older, black race, or have multiple comorbidities are at higher risk of complications. Minimally invasive approaches, which are becoming increasingly utilized, may reduce perioperative morbidity. Copyright © 2015 Elsevier Inc. All rights reserved.

A comparison of model-based imputation methods for handling missing predictor values in a linear regression model: A simulation study

NASA Astrophysics Data System (ADS)

Hasan, Haliza; Ahmad, Sanizah; Osman, Balkish Mohd; Sapri, Shamsiah; Othman, Nadirah

2017-08-01

In regression analysis, missing covariate data has been a common problem. Many researchers use ad hoc methods to overcome this problem due to the ease of implementation. However, these methods require assumptions about the data that rarely hold in practice. Model-based methods such as Maximum Likelihood (ML) using the expectation maximization (EM) algorithm and Multiple Imputation (MI) are more promising when dealing with difficulties caused by missing data. Then again, inappropriate methods of missing value imputation can lead to serious bias that severely affects the parameter estimates. The main objective of this study is to provide a better understanding regarding missing data concept that can assist the researcher to select the appropriate missing data imputation methods. A simulation study was performed to assess the effects of different missing data techniques on the performance of a regression model. The covariate data were generated using an underlying multivariate normal distribution and the dependent variable was generated as a combination of explanatory variables. Missing values in covariate were simulated using a mechanism called missing at random (MAR). Four levels of missingness (10%, 20%, 30% and 40%) were imposed. ML and MI techniques available within SAS software were investigated. A linear regression analysis was fitted and the model performance measures; MSE, and R-Squared were obtained. Results of the analysis showed that MI is superior in handling missing data with highest R-Squared and lowest MSE when percent of missingness is less than 30%. Both methods are unable to handle larger than 30% level of missingness.

The impact of lungs from diabetic donors on lung transplant recipients†.

PubMed

Ambur, Vishnu; Taghavi, Sharven; Jayarajan, Senthil; Kadakia, Sagar; Zhao, Huaqing; Gomez-Abraham, Jesus; Toyoda, Yoshiya

2017-02-01

We attempted to determine if transplants of lungs from diabetic donors (DDs) is associated with increased mortality of recipients in the modern era of the lung allocation score (LAS). The United Network for Organ Sharing (UNOS) database was queried for all adult lung transplant recipients from 2006 to 2014. Patients receiving a lung from a DD were compared to those receiving a transplant from a non-DD. Multivariate Cox regression analysis using variables associated with mortality was used to examine survival. A total of 13 159 adult lung transplants were performed between January 2006 and June 2014: 4278 (32.5%) were single-lung transplants (SLT) and 8881 (67.5%) were double-lung transplants (DLT). The log-rank test demonstrated a lower median survival in the DD group (5.6 vs 5.0 years, P = 0.003). We performed additional analysis by dividing this initial cohort into two cohorts by transplant type. On multivariate analysis, receiving an SLT from a DD was associated with increased mortality (HR 1.28, 95% CI 1.07–1.54, P = 0.011). Interestingly, multivariate analysis demonstrated no difference in mortality rates for patients receiving a DLT from a DD (HR 1.12, 95% CI 0.97–1.30, P = 0.14). DLT with DDs can be performed safely without increased mortality, but SLT using DDs results in worse survival and post-transplant outcomes. Preference should be given to DLT when using lungs from donors with diabetes. © The Author 2016. Published by Oxford University Press on behalf of the European Association for Cardio-Thoracic Surgery. All rights reserved.

Impact of Depression, Fatigue, and Global Measure of Cortical Volume on Cognitive Impairment in Multiple Sclerosis

PubMed Central

De Cola, Maria Cristina; D'Aleo, Giangaetano; Sessa, Edoardo; Marino, Silvia

2015-01-01

Objective. To investigate the influence of demographic and clinical variables, such as depression, fatigue, and quantitative MRI marker on cognitive performances in a sample of patients affected by multiple sclerosis (MS). Methods. 60 MS patients (52 relapsing remitting and 8 primary progressive) underwent neuropsychological assessments using Rao's Brief Repeatable Battery of Neuropsychological Tests (BRB-N), the Beck Depression Inventory-second edition (BDI-II), and the Fatigue Severity Scale (FSS). We performed magnetic resonance imaging to all subjects using a 3 T scanner and obtained tissue-specific volumes (normalized brain volume and cortical brain volume). We used Student's t-test to compare depressed and nondepressed MS patients. Finally, we performed a multivariate regression analysis in order to assess possible predictors of patients' cognitive outcome among demographic and clinical variables. Results. 27.12% of the sample (16/59) was cognitively impaired, especially in tasks requiring attention and information processing speed. From between group comparison, we find that depressed patients had worse performances on BRB-N score, greater disability and disease duration, and brain volume decrease. According to multiple regression analysis, the BDI-II score was a significant predictor for most of the neuropsychological tests. Conclusions. Our findings suggest that the presence of depressive symptoms is an important determinant of cognitive performance in MS patients. PMID:25861633

A spline-based regression parameter set for creating customized DARTEL MRI brain templates from infancy to old age.

PubMed

Wilke, Marko

2018-02-01

This dataset contains the regression parameters derived by analyzing segmented brain MRI images (gray matter and white matter) from a large population of healthy subjects, using a multivariate adaptive regression splines approach. A total of 1919 MRI datasets ranging in age from 1-75 years from four publicly available datasets (NIH, C-MIND, fCONN, and IXI) were segmented using the CAT12 segmentation framework, writing out gray matter and white matter images normalized using an affine-only spatial normalization approach. These images were then subjected to a six-step DARTEL procedure, employing an iterative non-linear registration approach and yielding increasingly crisp intermediate images. The resulting six datasets per tissue class were then analyzed using multivariate adaptive regression splines, using the CerebroMatic toolbox. This approach allows for flexibly modelling smoothly varying trajectories while taking into account demographic (age, gender) as well as technical (field strength, data quality) predictors. The resulting regression parameters described here can be used to generate matched DARTEL or SHOOT templates for a given population under study, from infancy to old age. The dataset and the algorithm used to generate it are publicly available at https://irc.cchmc.org/software/cerebromatic.php.

[Influences of environmental factors and interaction of several chemokines gene-environmental on systemic lupus erythematosus].

PubMed

Ye, Dong-qing; Hu, Yi-song; Li, Xiang-pei; Huang, Fen; Yang, Shi-gui; Hao, Jia-hu; Yin, Jing; Zhang, Guo-qing; Liu, Hui-hui

2004-11-01

To explore the impact of environmental factors, daily lifestyle, psycho-social factors and the interactions between environmental factors and chemokines genes on systemic lupus erythematosus (SLE). Case-control study was carried out and environmental factors for SLE were analyzed by univariate and multivariate unconditional logistic regression. Interactions between environmental factors and chemokines polymorphism contributing to systemic lupus erythematosus were also analyzed by logistic regression model. There were nineteen factors associated with SLE when univariate unconditional logistic regression was used. However, when multivariate unconditional logistic regression was used, only five factors showed having impacts on the disease, in which drinking well water (OR=0.099) was protective factor for SLE, and multiple drug allergy (OR=8.174), over-exposure to sunshine (OR=18.339), taking antibiotics (OR=9.630) and oral contraceptives were risk factors for SLE. When unconditional logistic regression model was used, results showed that there was interaction between eating irritable food and -2518MCP-1G/G genotype (OR=4.387). No interaction between environmental factors was found that contributing to SLE in this study. Many environmental factors were related to SLE, and there was an interaction between -2518MCP-1G/G genotype and eating irritable food.

Bootstrap Enhanced Penalized Regression for Variable Selection with Neuroimaging Data.

PubMed

Abram, Samantha V; Helwig, Nathaniel E; Moodie, Craig A; DeYoung, Colin G; MacDonald, Angus W; Waller, Niels G

2016-01-01

Recent advances in fMRI research highlight the use of multivariate methods for examining whole-brain connectivity. Complementary data-driven methods are needed for determining the subset of predictors related to individual differences. Although commonly used for this purpose, ordinary least squares (OLS) regression may not be ideal due to multi-collinearity and over-fitting issues. Penalized regression is a promising and underutilized alternative to OLS regression. In this paper, we propose a nonparametric bootstrap quantile (QNT) approach for variable selection with neuroimaging data. We use real and simulated data, as well as annotated R code, to demonstrate the benefits of our proposed method. Our results illustrate the practical potential of our proposed bootstrap QNT approach. Our real data example demonstrates how our method can be used to relate individual differences in neural network connectivity with an externalizing personality measure. Also, our simulation results reveal that the QNT method is effective under a variety of data conditions. Penalized regression yields more stable estimates and sparser models than OLS regression in situations with large numbers of highly correlated neural predictors. Our results demonstrate that penalized regression is a promising method for examining associations between neural predictors and clinically relevant traits or behaviors. These findings have important implications for the growing field of functional connectivity research, where multivariate methods produce numerous, highly correlated brain networks.

Bootstrap Enhanced Penalized Regression for Variable Selection with Neuroimaging Data

PubMed Central

Abram, Samantha V.; Helwig, Nathaniel E.; Moodie, Craig A.; DeYoung, Colin G.; MacDonald, Angus W.; Waller, Niels G.

2016-01-01

Recent advances in fMRI research highlight the use of multivariate methods for examining whole-brain connectivity. Complementary data-driven methods are needed for determining the subset of predictors related to individual differences. Although commonly used for this purpose, ordinary least squares (OLS) regression may not be ideal due to multi-collinearity and over-fitting issues. Penalized regression is a promising and underutilized alternative to OLS regression. In this paper, we propose a nonparametric bootstrap quantile (QNT) approach for variable selection with neuroimaging data. We use real and simulated data, as well as annotated R code, to demonstrate the benefits of our proposed method. Our results illustrate the practical potential of our proposed bootstrap QNT approach. Our real data example demonstrates how our method can be used to relate individual differences in neural network connectivity with an externalizing personality measure. Also, our simulation results reveal that the QNT method is effective under a variety of data conditions. Penalized regression yields more stable estimates and sparser models than OLS regression in situations with large numbers of highly correlated neural predictors. Our results demonstrate that penalized regression is a promising method for examining associations between neural predictors and clinically relevant traits or behaviors. These findings have important implications for the growing field of functional connectivity research, where multivariate methods produce numerous, highly correlated brain networks. PMID:27516732

Risk factor assessment to anticipate performance in the National Developmental Screening Test in children from a disadvantaged area.

PubMed

Montes, Alejandro; Pazos, Gustavo

2016-02-01

Identifying children at risk of failing the National Developmental Screening Test by combining prevalences of children suspected of having inapparent developmental disorders (IDDs) and associated risk factors (RFs) would allow to save resources. 1. To estimate the prevalence of children suspected of having IDDs. 2. To identify associated RFs. 3. To assess three methods developed based on observed RFs and propose a pre-screening procedure. The National Developmental Screening Test was administered to 60 randomly selected children aged between 2 and 4 years old from a socioeconomically disadvantaged area from Puerto Madryn. Twenty-four biological and socioenvironmental outcome measures were assessed in order to identify potential RFs using bivariate and multivariate analyses. The likelihood of failing the screening test was estimated as follows: 1. a multivariate logistic regression model was developed; 2. a relationship was established between the number of RFs present in each child and the percentage of children who failed the test; 3. these two methods were combined. The prevalence of children suspected of having IDDs was 55.0% (95% confidence interval: 42.4%-67.6%). Six RFs were initially identified using the bivariate approach. Three of them (maternal education, number of health checkups and Z scores for height-for-age, and maternal age) were included in the logistic regression model, which has a greater explanatory power. The third method included in the assessment showed greater sensitivity and specificity (85% and 79%, respectively). The estimated prevalence of children suspected of having IDDs was four times higher than the national standards. Seven RFs were identified. Combining the analysis of risk factor accumulation and a multivariate model provides a firm basis for developing a sensitive, specific and practical pre-screening procedure for socioeconomically disadvantaged areas. Sociedad Argentina de Pediatría.

Urinary bladder cancer treated with radical cystectomy: perioperative parameters and early complications prospectively registered in a national population-based database.

PubMed

Jerlström, Tomas; Gårdmark, Truls; Carringer, Malcolm; Holmäng, Sten; Liedberg, Fredrik; Hosseini, Abolfazl; Malmström, Per-Uno; Ljungberg, Börje; Hagberg, Oskar; Jahnson, Staffan

2014-08-01

Cystectomy combined with pelvic lymph-node dissection and urinary diversion entails high morbidity and mortality. Improvements are needed, and a first step is to collect information on the current situation. In 2011, this group took the initiative to start a population-based database in Sweden (population 9.5 million in 2011) with prospective registration of patients and complications until 90 days after cystectomy. This article reports findings from the first year of registration. Participation was voluntary, and data were reported by local urologists or research nurses. Perioperative parameters and early complications classified according to the modified Clavien system were registered, and selected variables of possible importance for complications were analysed by univariate and multivariate logistic regression. During 2011, 285 (65%) of 435 cystectomies performed in Sweden were registered in the database, the majority reported by the seven academic centres. Median blood loss was 1000 ml, operating time 318 min, and length of hospital stay 15 days. Any complications were registered for 103 patients (36%). Clavien grades 1-2 and 3-5 were noted in 19% and 15%, respectively. Thirty-seven patients (13%) were reoperated on at least once. In logistic regression analysis elevated risk of complications was significantly associated with operating time exceeding 318 min in both univariate and multivariate analysis, and with age 76-89 years only in multivariate analysis. It was feasible to start a national population-based registry of radical cystectomies for bladder cancer. The evaluation of the first year shows an increased risk of complications in patients with longer operating time and higher age. The results agree with some previously published series but should be interpreted with caution considering the relatively low coverage, which is expected to be higher in the future.

The association between tranexamic acid and convulsive seizures after cardiac surgery: a multivariate analysis in 11 529 patients.

PubMed

Sharma, V; Katznelson, R; Jerath, A; Garrido-Olivares, L; Carroll, J; Rao, V; Wasowicz, M; Djaiani, G

2014-02-01

Because of a lack of contemporary data regarding seizures after cardiac surgery, we undertook a retrospective analysis of prospectively collected data from 11 529 patients in whom cardiopulmonary bypass was used from January 2004 to December 2010. A convulsive seizure was defined as a transient episode of disturbed brain function characterised by abnormal involuntary motor movements. Multivariate regression analysis was performed to identify independent predictors of postoperative seizures. A total of 100 (0.9%) patients developed postoperative convulsive seizures. Generalised and focal seizures were identified in 68 and 32 patients, respectively. The median (IQR [range]) time after surgery when the seizure occurred was 7 (6-12 [1-216]) h and 8 (6-11 [4-18]) h, respectively. Epileptiform findings on electroencephalography were seen in 19 patients. Independent predictors of postoperative seizures included age, female sex, redo cardiac surgery, calcification of ascending aorta, congestive heart failure, deep hypothermic circulatory arrest, duration of aortic cross-clamp and tranexamic acid. When tested in a multivariate regression analysis, tranexamic acid was a strong independent predictor of seizures (OR 14.3, 95% CI 5.5-36.7; p < 0.001). Patients with convulsive seizures had 2.5 times higher in-hospital mortality rates and twice the length of hospital stay compared with patients without convulsive seizures. Mean (IQR [range]) length of stay in the intensive care unit was 115 (49-228 [32-481]) h in patients with convulsive seizures compared with 26 (22-69 [14-1080]) h in patients without seizures (p < 0.001). Convulsive seizures are a serious postoperative complication after cardiac surgery. As tranexamic acid is the only modifiable factor, its administration, particularly in doses exceeding 80 mg.kg(-1), should be weighed against the risk of postoperative seizures.

Time to antibiotics and outcomes in cancer patients with febrile neutropenia

PubMed Central

2014-01-01

Background Febrile neutropenia is an oncologic emergency. The timing of antibiotics administration in patients with febrile neutropenia may result in adverse outcomes. Our study aims to determine time-to- antibiotic administration in patients with febrile neutropenia, and its relationship with length of hospital stay, intensive care unit monitoring, and hospital mortality. Methods The study population was comprised of adult cancer patients with febrile neutropenia who were hospitalized, at a tertiary care hospital, between January 2010 and December 2011. Using Multination Association of Supportive Care in Cancer (MASCC) risk score, the study cohort was divided into high and low risk groups. A multivariate regression analysis was performed to assess relationship between time-to- antibiotic administration and various outcome variables. Results One hundred and five eligible patients with median age of 60 years (range: 18–89) and M:F of 43:62 were identified. Thirty-seven (35%) patients were in MASCC high risk group. Median time-to- antibiotic administration was 2.5 hrs (range: 0.03-50) and median length of hospital stay was 6 days (range: 1–57). In the multivariate analysis time-to- antibiotic administration (regression coefficient [RC]: 0.31 days [95% CI: 0.13-0.48]), known source of fever (RC: 4.1 days [95% CI: 0.76-7.5]), and MASCC high risk group (RC: 4 days [95% CI: 1.1-7.0]) were significantly correlated with longer hospital stay. Of 105 patients, 5 (4.7%) died & or required ICU monitoring. In multivariate analysis no variables significantly correlated with mortality or ICU monitoring. Conclusions Our study revealed that delay in antibiotics administration has been associated with a longer hospital stay. PMID:24716604

Predictive equations for the estimation of body size in seals and sea lions (Carnivora: Pinnipedia)

PubMed Central

Churchill, Morgan; Clementz, Mark T; Kohno, Naoki

2014-01-01

Body size plays an important role in pinniped ecology and life history. However, body size data is often absent for historical, archaeological, and fossil specimens. To estimate the body size of pinnipeds (seals, sea lions, and walruses) for today and the past, we used 14 commonly preserved cranial measurements to develop sets of single variable and multivariate predictive equations for pinniped body mass and total length. Principal components analysis (PCA) was used to test whether separate family specific regressions were more appropriate than single predictive equations for Pinnipedia. The influence of phylogeny was tested with phylogenetic independent contrasts (PIC). The accuracy of these regressions was then assessed using a combination of coefficient of determination, percent prediction error, and standard error of estimation. Three different methods of multivariate analysis were examined: bidirectional stepwise model selection using Akaike information criteria; all-subsets model selection using Bayesian information criteria (BIC); and partial least squares regression. The PCA showed clear discrimination between Otariidae (fur seals and sea lions) and Phocidae (earless seals) for the 14 measurements, indicating the need for family-specific regression equations. The PIC analysis found that phylogeny had a minor influence on relationship between morphological variables and body size. The regressions for total length were more accurate than those for body mass, and equations specific to Otariidae were more accurate than those for Phocidae. Of the three multivariate methods, the all-subsets approach required the fewest number of variables to estimate body size accurately. We then used the single variable predictive equations and the all-subsets approach to estimate the body size of two recently extinct pinniped taxa, the Caribbean monk seal (Monachus tropicalis) and the Japanese sea lion (Zalophus japonicus). Body size estimates using single variable regressions generally under or over-estimated body size; however, the all-subset regression produced body size estimates that were close to historically recorded body length for these two species. This indicates that the all-subset regression equations developed in this study can estimate body size accurately. PMID:24916814

[Correlation between gaseous exchange rate, body temperature, and mitochondrial protein content in the liver of mice].

PubMed

Muradian, Kh K; Utko, N O; Mozzhukhina, T H; Pishel', I M; Litoshenko, O Ia; Bezrukov, V V; Fraĭfel'd, V E

2002-01-01

Correlative and regressive relations between the gaseous exchange, thermoregulation and mitochondrial protein content were analyzed by two- and three-dimensional statistics in mice. It has been shown that the pair wise linear methods of analysis did not reveal any significant correlation between the parameters under exploration. However, it became evident at three-dimensional and non-linear plotting for which the coefficients of multivariable correlation reached and even exceeded 0.7-0.8. The calculations based on partial differentiation of the multivariable regression equations allow to conclude that at certain values of VO2, VCO2 and body temperature negative relations between the systems of gaseous exchange and thermoregulation become dominating.

Multi-Target Regression via Robust Low-Rank Learning.

PubMed

Zhen, Xiantong; Yu, Mengyang; He, Xiaofei; Li, Shuo

2018-02-01

Multi-target regression has recently regained great popularity due to its capability of simultaneously learning multiple relevant regression tasks and its wide applications in data mining, computer vision and medical image analysis, while great challenges arise from jointly handling inter-target correlations and input-output relationships. In this paper, we propose Multi-layer Multi-target Regression (MMR) which enables simultaneously modeling intrinsic inter-target correlations and nonlinear input-output relationships in a general framework via robust low-rank learning. Specifically, the MMR can explicitly encode inter-target correlations in a structure matrix by matrix elastic nets (MEN); the MMR can work in conjunction with the kernel trick to effectively disentangle highly complex nonlinear input-output relationships; the MMR can be efficiently solved by a new alternating optimization algorithm with guaranteed convergence. The MMR leverages the strength of kernel methods for nonlinear feature learning and the structural advantage of multi-layer learning architectures for inter-target correlation modeling. More importantly, it offers a new multi-layer learning paradigm for multi-target regression which is endowed with high generality, flexibility and expressive ability. Extensive experimental evaluation on 18 diverse real-world datasets demonstrates that our MMR can achieve consistently high performance and outperforms representative state-of-the-art algorithms, which shows its great effectiveness and generality for multivariate prediction.

Utility of Intermediate-Delay Washout CT Images for Differentiation of Malignant and Benign Adrenal Lesions: A Multivariate Analysis.

PubMed

Ng, Chaan S; Altinmakas, Emre; Wei, Wei; Ghosh, Payel; Li, Xiao; Grubbs, Elizabeth G; Perrier, Nancy D; Lee, Jeffrey E; Prieto, Victor G; Hobbs, Brian P

2018-06-27

The objective of this study was to identify features that impact the diagnostic performance of intermediate-delay washout CT for distinguishing malignant from benign adrenal lesions. This retrospective study evaluated 127 pathologically proven adrenal lesions (82 malignant, 45 benign) in 126 patients who had undergone portal venous phase and intermediate-delay washout CT (1-3 minutes after portal venous phase) with or without unenhanced images. Unenhanced images were available for 103 lesions. Quantitatively, lesion CT attenuation on unenhanced (UA) and delayed (DL) images, absolute and relative percentage of enhancement washout (APEW and RPEW, respectively), descriptive CT features (lesion size, margin characteristics, heterogeneity or homogeneity, fat, calcification), patient demographics, and medical history were evaluated for association with lesion status using multiple logistic regression with stepwise model selection. Area under the ROC curve (A z ) was calculated from both univariate and multivariate analyses. The predictive diagnostic performance of multivariate evaluations was ascertained through cross-validation. A z for DL, APEW, RPEW, and UA was 0.751, 0.795, 0.829, and 0.839, respectively. Multivariate analyses yielded the following significant CT quantitative features and associated A z when combined: RPEW and DL (A z = 0.861) when unenhanced images were not available and APEW and UA (A z = 0.889) when unenhanced images were available. Patient demographics and presence of a prior malignancy were additional significant factors, increasing A z to 0.903 and 0.927, respectively. The combined predictive classifier, without and with UA available, yielded 85.7% and 87.3% accuracies with cross-validation, respectively. When appropriately combined with other CT features, washout derived from intermediate-delay CT with or without additional clinical data has potential utility in differentiating malignant from benign adrenal lesions.

Obstructive urination problems after high-dose-rate brachytherapy boost treatment for prostate cancer are avoidable.

PubMed

Kragelj, Borut

2016-03-01

Aiming at improving treatment individualization in patients with prostate cancer treated with combination of external beam radiotherapy and high-dose-rate brachytherapy to boost the dose to prostate (HDRB-B), the objective was to evaluate factors that have potential impact on obstructive urination problems (OUP) after HDRB-B. In the follow-up study 88 patients consecutively treated with HDRB-B at the Institute of Oncology Ljubljana in the period 2006-2011 were included. The observed outcome was deterioration of OUP (DOUP) during the follow-up period longer than 1 year. Univariate and multivariate relationship analysis between DOUP and potential risk factors (treatment factors, patients' characteristics) was carried out by using binary logistic regression. ROC curve was constructed on predicted values and the area under the curve (AUC) calculated to assess the performance of the multivariate model. Analysis was carried out on 71 patients who completed 3 years of follow-up. DOUP was noted in 13/71 (18.3%) of them. The results of multivariate analysis showed statistically significant relationship between DOUP and anti-coagulation treatment (OR 4.86, 95% C.I. limits: 1.21-19.61, p = 0.026). Also minimal dose received by 90% of the urethra volume was close to statistical significance (OR = 1.23; 95% C.I. limits: 0.98-1.07, p = 0.099). The value of AUC was 0.755. The study emphasized the relationship between DOUP and anticoagulation treatment, and suggested the multivariate model with fair predictive performance. This model potentially enables a reduction of DOUP after HDRB-B. It supports the belief that further research should be focused on urethral sphincter as a critical structure for OUP.

Multivariate time series analysis of neuroscience data: some challenges and opportunities.

PubMed

Pourahmadi, Mohsen; Noorbaloochi, Siamak

2016-04-01

Neuroimaging data may be viewed as high-dimensional multivariate time series, and analyzed using techniques from regression analysis, time series analysis and spatiotemporal analysis. We discuss issues related to data quality, model specification, estimation, interpretation, dimensionality and causality. Some recent research areas addressing aspects of some recurring challenges are introduced. Copyright © 2015 Elsevier Ltd. All rights reserved.

Experimental variability and data pre-processing as factors affecting the discrimination power of some chemometric approaches (PCA, CA and a new algorithm based on linear regression) applied to (+/-)ESI/MS and RPLC/UV data: Application on green tea extracts.

PubMed

Iorgulescu, E; Voicu, V A; Sârbu, C; Tache, F; Albu, F; Medvedovici, A

2016-08-01

The influence of the experimental variability (instrumental repeatability, instrumental intermediate precision and sample preparation variability) and data pre-processing (normalization, peak alignment, background subtraction) on the discrimination power of multivariate data analysis methods (Principal Component Analysis -PCA- and Cluster Analysis -CA-) as well as a new algorithm based on linear regression was studied. Data used in the study were obtained through positive or negative ion monitoring electrospray mass spectrometry (+/-ESI/MS) and reversed phase liquid chromatography/UV spectrometric detection (RPLC/UV) applied to green tea extracts. Extractions in ethanol and heated water infusion were used as sample preparation procedures. The multivariate methods were directly applied to mass spectra and chromatograms, involving strictly a holistic comparison of shapes, without assignment of any structural identity to compounds. An alternative data interpretation based on linear regression analysis mutually applied to data series is also discussed. Slopes, intercepts and correlation coefficients produced by the linear regression analysis applied on pairs of very large experimental data series successfully retain information resulting from high frequency instrumental acquisition rates, obviously better defining the profiles being compared. Consequently, each type of sample or comparison between samples produces in the Cartesian space an ellipsoidal volume defined by the normal variation intervals of the slope, intercept and correlation coefficient. Distances between volumes graphically illustrates (dis)similarities between compared data. The instrumental intermediate precision had the major effect on the discrimination power of the multivariate data analysis methods. Mass spectra produced through ionization from liquid state in atmospheric pressure conditions of bulk complex mixtures resulting from extracted materials of natural origins provided an excellent data basis for multivariate analysis methods, equivalent to data resulting from chromatographic separations. The alternative evaluation of very large data series based on linear regression analysis produced information equivalent to results obtained through application of PCA an CA. Copyright © 2016 Elsevier B.V. All rights reserved.

Gap-filling a spatially explicit plant trait database: comparing imputation methods and different levels of environmental information

NASA Astrophysics Data System (ADS)

Poyatos, Rafael; Sus, Oliver; Badiella, Llorenç; Mencuccini, Maurizio; Martínez-Vilalta, Jordi

2018-05-01

The ubiquity of missing data in plant trait databases may hinder trait-based analyses of ecological patterns and processes. Spatially explicit datasets with information on intraspecific trait variability are rare but offer great promise in improving our understanding of functional biogeography. At the same time, they offer specific challenges in terms of data imputation. Here we compare statistical imputation approaches, using varying levels of environmental information, for five plant traits (leaf biomass to sapwood area ratio, leaf nitrogen content, maximum tree height, leaf mass per area and wood density) in a spatially explicit plant trait dataset of temperate and Mediterranean tree species (Ecological and Forest Inventory of Catalonia, IEFC, dataset for Catalonia, north-east Iberian Peninsula, 31 900 km2). We simulated gaps at different missingness levels (10-80 %) in a complete trait matrix, and we used overall trait means, species means, k nearest neighbours (kNN), ordinary and regression kriging, and multivariate imputation using chained equations (MICE) to impute missing trait values. We assessed these methods in terms of their accuracy and of their ability to preserve trait distributions, multi-trait correlation structure and bivariate trait relationships. The relatively good performance of mean and species mean imputations in terms of accuracy masked a poor representation of trait distributions and multivariate trait structure. Species identity improved MICE imputations for all traits, whereas forest structure and topography improved imputations for some traits. No method performed best consistently for the five studied traits, but, considering all traits and performance metrics, MICE informed by relevant ecological variables gave the best results. However, at higher missingness (> 30 %), species mean imputations and regression kriging tended to outperform MICE for some traits. MICE informed by relevant ecological variables allowed us to fill the gaps in the IEFC incomplete dataset (5495 plots) and quantify imputation uncertainty. Resulting spatial patterns of the studied traits in Catalan forests were broadly similar when using species means, regression kriging or the best-performing MICE application, but some important discrepancies were observed at the local level. Our results highlight the need to assess imputation quality beyond just imputation accuracy and show that including environmental information in statistical imputation approaches yields more plausible imputations in spatially explicit plant trait datasets.

A Comparison of Conventional Linear Regression Methods and Neural Networks for Forecasting Educational Spending.

ERIC Educational Resources Information Center

Baker, Bruce D.; Richards, Craig E.

1999-01-01

Applies neural network methods for forecasting 1991-95 per-pupil expenditures in U.S. public elementary and secondary schools. Forecasting models included the National Center for Education Statistics' multivariate regression model and three neural architectures. Regarding prediction accuracy, neural network results were comparable or superior to…

"Let Me Count the Ways:" Fostering Reasons for Living among Low-Income, Suicidal, African American Women

ERIC Educational Resources Information Center

West, Lindsey M.; Davis, Telsie A.; Thompson, Martie P.; Kaslow, Nadine J.

2011-01-01

Protective factors for fostering reasons for living were examined among low-income, suicidal, African American women. Bivariate logistic regressions revealed that higher levels of optimism, spiritual well-being, and family social support predicted reasons for living. Multivariate logistic regressions indicated that spiritual well-being showed…

Predicting exposure-response associations of ambient particulate matter with mortality in 73 Chinese cities.

PubMed

Madaniyazi, Lina; Guo, Yuming; Chen, Renjie; Kan, Haidong; Tong, Shilu

2016-01-01

Estimating the burden of mortality associated with particulates requires knowledge of exposure-response associations. However, the evidence on exposure-response associations is limited in many cities, especially in developing countries. In this study, we predicted associations of particulates smaller than 10 μm in aerodynamic diameter (PM10) with mortality in 73 Chinese cities. The meta-regression model was used to test and quantify which city-specific characteristics contributed significantly to the heterogeneity of PM10-mortality associations for 16 Chinese cities. Then, those city-specific characteristics with statistically significant regression coefficients were treated as independent variables to build multivariate meta-regression models. The model with the best fitness was used to predict PM10-mortality associations in 73 Chinese cities in 2010. Mean temperature, PM10 concentration and green space per capita could best explain the heterogeneity in PM10-mortality associations. Based on city-specific characteristics, we were able to develop multivariate meta-regression models to predict associations between air pollutants and health outcomes reasonably well. Copyright © 2015 Elsevier Ltd. All rights reserved.

Hierarchical Bayesian spatial models for predicting multiple forest variables using waveform LiDAR, hyperspectral imagery, and large inventory datasets

USGS Publications Warehouse

Finley, Andrew O.; Banerjee, Sudipto; Cook, Bruce D.; Bradford, John B.

2013-01-01

In this paper we detail a multivariate spatial regression model that couples LiDAR, hyperspectral and forest inventory data to predict forest outcome variables at a high spatial resolution. The proposed model is used to analyze forest inventory data collected on the US Forest Service Penobscot Experimental Forest (PEF), ME, USA. In addition to helping meet the regression model's assumptions, results from the PEF analysis suggest that the addition of multivariate spatial random effects improves model fit and predictive ability, compared with two commonly applied modeling approaches. This improvement results from explicitly modeling the covariation among forest outcome variables and spatial dependence among observations through the random effects. Direct application of such multivariate models to even moderately large datasets is often computationally infeasible because of cubic order matrix algorithms involved in estimation. We apply a spatial dimension reduction technique to help overcome this computational hurdle without sacrificing richness in modeling.

Relationship between cataract severity and socioeconomic status.

PubMed

Wesolosky, Jason D; Rudnisky, Christopher J

2013-12-01

To determine the relationship between cataract severity and socioeconomic status (SES). Retrospective, observational case series. A total of 1350 eyes underwent phacoemulsification cataract extraction by a single surgeon using an Alcon Infiniti system. Cataract severity was measured using phaco time in seconds. SES was measured using area-level aggregate census data: median income, education, proportion of common-law couples, and employment rate. Preoperative best corrected visual acuity was obtained and converted to logarithm of the minimum angle of resolution values. For patients undergoing bilateral surgery, the generalized estimating equation was used to account for the correlation between eyes. Univariate analyses were performed using simple regression, and multivariate analyses were performed to account for variables with significant relationships (p < 0.05) on univariate testing. Sensitivity analyses were performed to assess the effect of including patient age in the controlled analyses. Multivariate analyses demonstrated that cataracts were more severe when the median income was lower (p = 0.001) and the proportion of common-law couples living in a patient's community (p = 0.012) and the unemployment rate (p = 0.002) were higher. These associations persisted even when controlling for patient age. Patients of lower SES have more severe cataracts. Copyright © 2013 Canadian Ophthalmological Society. Published by Elsevier Inc. All rights reserved.

Prognostic value of inflammation-based scores in patients with osteosarcoma

PubMed Central

Liu, Bangjian; Huang, Yujing; Sun, Yuanjue; Zhang, Jianjun; Yao, Yang; Shen, Zan; Xiang, Dongxi; He, Aina

2016-01-01

Systemic inflammation responses have been associated with cancer development and progression. C-reactive protein (CRP), Glasgow prognostic score (GPS), neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), lymphocyte-monocyte ratio (LMR), and neutrophil-platelet score (NPS) have been shown to be independent risk factors in various types of malignant tumors. This retrospective analysis of 162 osteosarcoma cases was performed to estimate their predictive value of survival in osteosarcoma. All statistical analyses were performed by SPSS statistical software. Receiver operating characteristic (ROC) analysis was generated to set optimal thresholds; area under the curve (AUC) was used to show the discriminatory abilities of inflammation-based scores; Kaplan-Meier analysis was performed to plot the survival curve; cox regression models were employed to determine the independent prognostic factors. The optimal cut-off points of NLR, PLR, and LMR were 2.57, 123.5 and 4.73, respectively. GPS and NLR had a markedly larger AUC than CRP, PLR and LMR. High levels of CRP, GPS, NLR, PLR, and low level of LMR were significantly associated with adverse prognosis (P < 0.05). Multivariate Cox regression analyses revealed that GPS, NLR, and occurrence of metastasis were top risk factors associated with death of osteosarcoma patients. PMID:28008988

Using Time Series Analysis to Predict Cardiac Arrest in a PICU.

PubMed

Kennedy, Curtis E; Aoki, Noriaki; Mariscalco, Michele; Turley, James P

2015-11-01

To build and test cardiac arrest prediction models in a PICU, using time series analysis as input, and to measure changes in prediction accuracy attributable to different classes of time series data. Retrospective cohort study. Thirty-one bed academic PICU that provides care for medical and general surgical (not congenital heart surgery) patients. Patients experiencing a cardiac arrest in the PICU and requiring external cardiac massage for at least 2 minutes. None. One hundred three cases of cardiac arrest and 109 control cases were used to prepare a baseline dataset that consisted of 1,025 variables in four data classes: multivariate, raw time series, clinical calculations, and time series trend analysis. We trained 20 arrest prediction models using a matrix of five feature sets (combinations of data classes) with four modeling algorithms: linear regression, decision tree, neural network, and support vector machine. The reference model (multivariate data with regression algorithm) had an accuracy of 78% and 87% area under the receiver operating characteristic curve. The best model (multivariate + trend analysis data with support vector machine algorithm) had an accuracy of 94% and 98% area under the receiver operating characteristic curve. Cardiac arrest predictions based on a traditional model built with multivariate data and a regression algorithm misclassified cases 3.7 times more frequently than predictions that included time series trend analysis and built with a support vector machine algorithm. Although the final model lacks the specificity necessary for clinical application, we have demonstrated how information from time series data can be used to increase the accuracy of clinical prediction models.

Weight, socio-demographics, and health behaviour related correlates of academic performance in first year university students.

PubMed

Deliens, Tom; Clarys, Peter; De Bourdeaudhuij, Ilse; Deforche, Benedicte

2013-12-17

This study aimed to examine differences in socio-demographics and health behaviour between Belgian first year university students who attended all final course exams and those who did not. Secondly, this study aimed to identify weight and health behaviour related correlates of academic performance in those students who attended all course exams. Anthropometrics of 101 first year university students were measured at both the beginning of the first (T1) and second (T2) semester of the academic year. An on-line health behaviour questionnaire was filled out at T2. As a measure of academic performance student end-of-year Grade Point Averages (GPA) were obtained from the university's registration office. Independent samples t-tests and chi2-tests were executed to compare students who attended all course exams during the first year of university and students who did not carry through. Uni- and multivariate linear regression analyses were conducted to identify correlates of academic performance in students who attended all course exams during the first year of university. Students who did not attend all course exams were predominantly male, showed higher increases in waist circumference during the first semester and consumed more French fries than those who attended all final course exams. Being male, lower secondary school grades, increases in weight, Body Mass Index and waist circumference over the first semester, more gaming on weekdays, being on a diet, eating at the student restaurant more frequently, higher soda and French fries consumption, and higher frequency of alcohol use predicted lower GPA's in first year university students. When controlled for each other, being on a diet and higher frequency of alcohol use remained significant in the multivariate regression model, with frequency of alcohol use being the strongest correlate of GPA. This study, conducted in Belgian first year university students, showed that academic performance is associated with a wide range of weight and health related behaviours. Future studies should investigate whether interventions aiming at promoting healthy behaviours among students could also have a positive impact on academic performance.

Better functional mobility in community-dwelling elderly is related to D-hormone serum levels and to daily calcium intake.

PubMed

Dukas, L; Staehelin, H B; Schacht, E; Bischoff, H A

2005-01-01

The influence of calcitropic hormones on functional mobility has been studied in vitamin D (calcidiol) deficient elderly or elderly with a history of falls, however, data in community-dwelling independent vitamin D replete elderly are missing. We therefore assessed in an observational survey the association of calcidiol (25(OH)D3) and calcitriol (D-hormone / 1,25(OH)2D3) status as well as of daily calcium intake on functional mobility in older subjects We evaluated 192 women and 188 men, aged superior 70 years and living independently. Average Timed-up and go test (TUG-test) in seconds was taken as measure of functional mobility. Calcidiol and D-hormone serum concentrations and daily calcium intake were studied in multivariate controlled linear regression models with TUG-test performance as the dependent variable and/or as dichotomous variables (deficient vs. non-deficient, above vs. below the median, respectively). Subjects with low D-hormone serum concentrations took significantly more time to perform the TUG-test (low = 7.70s +/- 2.52 SD ; high = 6.70s +/- 1.29 SD; p = 0.004). In the linear multivariate controlled regression model increased D-hormone serum concentrations predicted better TUG-test performance (estimate -0.0007, p = 0.044). Participants with a calcium intake of > or =512 mg/day were significantly faster to perform the TUG-test than participants with a daily calcium intake of <512 mg/day (estimate:-0.43, p = 0.007). Other significant predictors of better TUG-test performance in both models were: male gender, less comorbid conditions, younger age, lower BMI, iPTH serum levels and creatinine clearance. Calcidiol serum levels were not associated with TUG-test performance. Higher D-hormone status and a calcium intake of > or =512 mg/day in community-dwelling independent older persons are significant determinants of better functional mobility. Therefore, to ensure optimal functional mobility, the care of older persons should address correction of D-hormone deficiency and increasing daily calcium intake.

Weight, socio-demographics, and health behaviour related correlates of academic performance in first year university students

PubMed Central

2013-01-01

Background This study aimed to examine differences in socio-demographics and health behaviour between Belgian first year university students who attended all final course exams and those who did not. Secondly, this study aimed to identify weight and health behaviour related correlates of academic performance in those students who attended all course exams. Methods Anthropometrics of 101 first year university students were measured at both the beginning of the first (T1) and second (T2) semester of the academic year. An on-line health behaviour questionnaire was filled out at T2. As a measure of academic performance student end-of-year Grade Point Averages (GPA) were obtained from the university’s registration office. Independent samples t-tests and chi 2 -tests were executed to compare students who attended all course exams during the first year of university and students who did not carry through. Uni- and multivariate linear regression analyses were conducted to identify correlates of academic performance in students who attended all course exams during the first year of university. Results Students who did not attend all course exams were predominantly male, showed higher increases in waist circumference during the first semester and consumed more French fries than those who attended all final course exams. Being male, lower secondary school grades, increases in weight, Body Mass Index and waist circumference over the first semester, more gaming on weekdays, being on a diet, eating at the student restaurant more frequently, higher soda and French fries consumption, and higher frequency of alcohol use predicted lower GPA’s in first year university students. When controlled for each other, being on a diet and higher frequency of alcohol use remained significant in the multivariate regression model, with frequency of alcohol use being the strongest correlate of GPA. Conclusions This study, conducted in Belgian first year university students, showed that academic performance is associated with a wide range of weight and health related behaviours. Future studies should investigate whether interventions aiming at promoting healthy behaviours among students could also have a positive impact on academic performance. PMID:24344995

Logistic regression analysis of factors associated with avascular necrosis of the femoral head following femoral neck fractures in middle-aged and elderly patients.

PubMed

Ai, Zi-Sheng; Gao, You-Shui; Sun, Yuan; Liu, Yue; Zhang, Chang-Qing; Jiang, Cheng-Hua

2013-03-01

Risk factors for femoral neck fracture-induced avascular necrosis of the femoral head have not been elucidated clearly in middle-aged and elderly patients. Moreover, the high incidence of screw removal in China and its effect on the fate of the involved femoral head require statistical methods to reflect their intrinsic relationship. Ninety-nine patients older than 45 years with femoral neck fracture were treated by internal fixation between May 1999 and April 2004. Descriptive analysis, interaction analysis between associated factors, single factor logistic regression, multivariate logistic regression, and detailed interaction analysis were employed to explore potential relationships among associated factors. Avascular necrosis of the femoral head was found in 15 cases (15.2 %). Age × the status of implants (removal vs. maintenance) and gender × the timing of reduction were interactive according to two-factor interactive analysis. Age, the displacement of fractures, the quality of reduction, and the status of implants were found to be significant factors in single factor logistic regression analysis. Age, age × the status of implants, and the quality of reduction were found to be significant factors in multivariate logistic regression analysis. In fine interaction analysis after multivariate logistic regression analysis, implant removal was the most important risk factor for avascular necrosis in 56-to-85-year-old patients, with a risk ratio of 26.00 (95 % CI = 3.076-219.747). The middle-aged and elderly have less incidence of avascular necrosis of the femoral head following femoral neck fractures treated by cannulated screws. The removal of cannulated screws can induce a significantly high incidence of avascular necrosis of the femoral head in elderly patients, while a high-quality reduction is helpful to reduce avascular necrosis.

Multivariate adaptive regression splines analysis to predict biomarkers of spontaneous preterm birth.

PubMed

Menon, Ramkumar; Bhat, Geeta; Saade, George R; Spratt, Heidi

2014-04-01

To develop classification models of demographic/clinical factors and biomarker data from spontaneous preterm birth in African Americans and Caucasians. Secondary analysis of biomarker data using multivariate adaptive regression splines (MARS), a supervised machine learning algorithm method. Analysis of data on 36 biomarkers from 191 women was reduced by MARS to develop predictive models for preterm birth in African Americans and Caucasians. Maternal plasma, cord plasma collected at admission for preterm or term labor and amniotic fluid at delivery. Data were partitioned into training and testing sets. Variable importance, a relative indicator (0-100%) and area under the receiver operating characteristic curve (AUC) characterized results. Multivariate adaptive regression splines generated models for combined and racially stratified biomarker data. Clinical and demographic data did not contribute to the model. Racial stratification of data produced distinct models in all three compartments. In African Americans maternal plasma samples IL-1RA, TNF-α, angiopoietin 2, TNFRI, IL-5, MIP1α, IL-1β and TGF-α modeled preterm birth (AUC train: 0.98, AUC test: 0.86). In Caucasians TNFR1, ICAM-1 and IL-1RA contributed to the model (AUC train: 0.84, AUC test: 0.68). African Americans cord plasma samples produced IL-12P70, IL-8 (AUC train: 0.82, AUC test: 0.66). Cord plasma in Caucasians modeled IGFII, PDGFBB, TGF-β1 , IL-12P70, and TIMP1 (AUC train: 0.99, AUC test: 0.82). Amniotic fluid in African Americans modeled FasL, TNFRII, RANTES, KGF, IGFI (AUC train: 0.95, AUC test: 0.89) and in Caucasians, TNF-α, MCP3, TGF-β3 , TNFR1 and angiopoietin 2 (AUC train: 0.94 AUC test: 0.79). Multivariate adaptive regression splines models multiple biomarkers associated with preterm birth and demonstrated racial disparity. © 2014 Nordic Federation of Societies of Obstetrics and Gynecology.

Statistical performance of image cytometry for DNA, lipids, cytokeratin, & CD45 in a model system for circulation tumor cell detection.

PubMed

Futia, Gregory L; Schlaepfer, Isabel R; Qamar, Lubna; Behbakht, Kian; Gibson, Emily A

2017-07-01

Detection of circulating tumor cells (CTCs) in a blood sample is limited by the sensitivity and specificity of the biomarker panel used to identify CTCs over other blood cells. In this work, we present Bayesian theory that shows how test sensitivity and specificity set the rarity of cell that a test can detect. We perform our calculation of sensitivity and specificity on our image cytometry biomarker panel by testing on pure disease positive (D + ) populations (MCF7 cells) and pure disease negative populations (D - ) (leukocytes). In this system, we performed multi-channel confocal fluorescence microscopy to image biomarkers of DNA, lipids, CD45, and Cytokeratin. Using custom software, we segmented our confocal images into regions of interest consisting of individual cells and computed the image metrics of total signal, second spatial moment, spatial frequency second moment, and the product of the spatial-spatial frequency moments. We present our analysis of these 16 features. The best performing of the 16 features produced an average separation of three standard deviations between D + and D - and an average detectable rarity of ∼1 in 200. We performed multivariable regression and feature selection to combine multiple features for increased performance and showed an average separation of seven standard deviations between the D + and D - populations making our average detectable rarity of ∼1 in 480. Histograms and receiver operating characteristics (ROC) curves for these features and regressions are presented. We conclude that simple regression analysis holds promise to further improve the separation of rare cells in cytometry applications. © 2017 International Society for Advancement of Cytometry. © 2017 International Society for Advancement of Cytometry.

Early Transcatheter Aortic Valve Function With and Without Therapeutic Anticoagulation.

PubMed

Hiremath, Pranoti G; Kearney, Kathleen; Smith, Bryn; Don, Creighton; Dvir, Danny; Aldea, Gabriel; Reisman, Mark; McCabe, James M

2017-11-01

Prosthetic leaflet thrombosis is a growing concern in transcatheter aortic valve replacement (TAVR). Given the uncertainty of best practices for antiplatelet and anticoagulation therapies in the post-TAVR period, additional evidence regarding the impact of anticoagulation on prosthetic valve function after TAVR is needed. Patients undergoing native-valve TAVR at a single academic institution between 2012 and 2015 were analyzed based on any anticoagulant use at hospital discharge post TAVR. Changes in prosthetic valve peak velocity and mean gradient were assessed based on transthoracic echocardiograms performed immediately following valve implant and at 4-week follow-up. Multivariate regression analyses were performed to explore the impact of anticoagulation status on early TAVR valve performance. For 403 patients, there were no available data to analyze. Of those, 29.6% were discharged on anticoagulation. Following TAVR, the average mean prosthetic valve gradient was 11.8 ± 5.6 mm Hg and peak velocity was 2.33 ± 0.52 m/s. There were no significant differences between anticoagulated and non-anticoagulated groups in the mean or peak gradients or velocity immediately following implant or at 4 weeks, which remained true following multivariate adjustment (P=.80 for delta mean gradient; P=.91 for delta peak velocity). Our data suggest that the absence of anticoagulation is not associated with short-term degradation in TAVR performance and do not support the routine use of anticoagulation following native-valve TAVR.

Predictors of neonatal outcome in women with severe preeclampsia or eclampsia between 24 and 33 weeks' gestation.

PubMed

Witlin, A G; Saade, G R; Mattar, F; Sibai, B M

2000-03-01

We sought to characterize predictors of neonatal outcome in women with severe preeclampsia or eclampsia who were delivered of their infants preterm. We performed a retrospective analysis of 195 pregnancies delivered between 24 and 33 weeks' gestation because of severe preeclampsia or eclampsia. Multiple logistic regression and univariate chi(2) analysis were performed for the dependent outcome variables of survival and respiratory distress syndrome by use of independent fetal and maternal variables. A P value of <.05 was considered significant. In the multivariate analysis, respiratory distress syndrome was inversely related to gestational age at delivery (P =.0018) and directly related to cesarean delivery (P =.02), whereas survival was directly related to birth weight (P =.00025). There was no correlation in the multivariate analysis between respiratory distress syndrome or survival and corticosteroid use, composite neonatal morbidity, mean arterial pressure, eclampsia, or abruptio placentae. In the univariate analysis respiratory distress syndrome was associated with cesarean delivery (odds ratio, 7.19; 95% confidence interval, 2. 91-18.32). The incidence of intrauterine growth restriction increased as gestational age advanced. Furthermore, intrauterine growth restriction decreased survival in both the multivariate (P =. 038; odds ratio, 13.2; 95% confidence interval, 1.16-151.8) and univariate (P =.001; odds ratio, 5.88; 95% confidence interval, 1. 81-19.26) analyses. The presence of intrauterine growth restriction adversely affected survival independently of other variables. Presumed intrauterine stress, as reflected by the severity of maternal disease, did not improve neonatal outcome.

External Validation of the European Hernia Society Classification for Postoperative Complications after Incisional Hernia Repair: A Cohort Study of 2,191 Patients.

PubMed

Kroese, Leonard F; Kleinrensink, Gert-Jan; Lange, Johan F; Gillion, Jean-Francois

2018-03-01

Incisional hernia is a frequent complication after midline laparotomy. Surgical hernia repair is associated with complications, but no clear predictive risk factors have been identified. The European Hernia Society (EHS) classification offers a structured framework to describe hernias and to analyze postoperative complications. Because of its structured nature, it might prove to be useful for preoperative patient or treatment classification. The objective of this study was to investigate the EHS classification as a predictor for postoperative complications after incisional hernia surgery. An analysis was performed using a registry-based, large-scale, prospective cohort study, including all patients undergoing incisional hernia surgery between September 1, 2011 and February 29, 2016. Univariate analyses and multivariable logistic regression analysis were performed to identify risk factors for postoperative complications. A total of 2,191 patients were included, of whom 323 (15%) had 1 or more complications. Factors associated with complications in univariate analyses (p < 0.20) and clinically relevant factors were included in the multivariable analysis. In the multivariable analysis, EHS width class, incarceration, open surgery, duration of surgery, Altemeier wound class, and therapeutic antibiotic treatment were independent risk factors for postoperative complications. Third recurrence and emergency surgery were associated with fewer complications. Incisional hernia repair is associated with a 15% complication rate. The EHS width classification is associated with postoperative complications. To identify patients at risk for complications, the EHS classification is useful. Copyright © 2017. Published by Elsevier Inc.

[Analysis of risk factors for dry eye syndrome in visual display terminal workers].

PubMed

Zhu, Yong; Yu, Wen-lan; Xu, Ming; Han, Lei; Cao, Wen-dong; Zhang, Hong-bing; Zhang, Heng-dong

2013-08-01

To analyze the risk factors for dry eye syndrome in visual display terminal (VDT) workers and to provide a scientific basis for protecting the eye health of VDT workers. Questionnaire survey, Schirmer I test, tear break-up time test, and workshop microenvironment evaluation were performed in 185 VDT workers. Multivariate logistic regression analysis was performed to determine the risk factors for dry eye syndrome in VDT workers after adjustment for confounding factors. In the logistic regression model, the regression coefficients of daily mean time of exposure to screen, daily mean time of watching TV, parallel screen-eye angle, upward screen-eye angle, eye-screen distance of less than 20 cm, irregular breaks during screen-exposed work, age, and female gender on the results of Schirmer I test were 0.153, 0.548, 0.400, 0.796, 0.234, 0.516, 0.559, and -0.685, respectively; the regression coefficients of daily mean time of exposure to screen, parallel screen-eye angle, upward screen-eye angle, age, working years, and female gender on tear break-up time were 0.021, 0.625, 2.652, 0.749, 0.403, and 1.481, respectively. Daily mean time of exposure to screen, daily mean time of watching TV, parallel screen-eye angle, upward screen-eye angle, eye-screen distance of less than 20 cm, irregular breaks during screen-exposed work, age, and working years are risk factors for dry eye syndrome in VDT workers.

Evaluation of the Bitterness of Traditional Chinese Medicines using an E-Tongue Coupled with a Robust Partial Least Squares Regression Method

PubMed Central

Lin, Zhaozhou; Zhang, Qiao; Liu, Ruixin; Gao, Xiaojie; Zhang, Lu; Kang, Bingya; Shi, Junhan; Wu, Zidan; Gui, Xinjing; Li, Xuelin

2016-01-01

To accurately, safely, and efficiently evaluate the bitterness of Traditional Chinese Medicines (TCMs), a robust predictor was developed using robust partial least squares (RPLS) regression method based on data obtained from an electronic tongue (e-tongue) system. The data quality was verified by the Grubb’s test. Moreover, potential outliers were detected based on both the standardized residual and score distance calculated for each sample. The performance of RPLS on the dataset before and after outlier detection was compared to other state-of-the-art methods including multivariate linear regression, least squares support vector machine, and the plain partial least squares regression. Both R2 and root-mean-squares error (RMSE) of cross-validation (CV) were recorded for each model. With four latent variables, a robust RMSECV value of 0.3916 with bitterness values ranging from 0.63 to 4.78 were obtained for the RPLS model that was constructed based on the dataset including outliers. Meanwhile, the RMSECV, which was calculated using the models constructed by other methods, was larger than that of the RPLS model. After six outliers were excluded, the performance of all benchmark methods markedly improved, but the difference between the RPLS model constructed before and after outlier exclusion was negligible. In conclusion, the bitterness of TCM decoctions can be accurately evaluated with the RPLS model constructed using e-tongue data. PMID:26821026

Performances on the CogState and standard neuropsychological batteries among HIV patients without dementia.

PubMed

Overton, Edgar Turner; Kauwe, John S K; Paul, Robert; Tashima, Karen; Tate, David F; Patel, Pragna; Carpenter, Charles C J; Patty, David; Brooks, John T; Clifford, David B

2011-11-01

HIV-associated neurocognitive disorders remain prevalent but challenging to diagnose particularly among non-demented individuals. To determine whether a brief computerized battery correlates with formal neurocognitive testing, we identified 46 HIV-infected persons who had undergone both formal neurocognitive testing and a brief computerized battery. Simple detection tests correlated best with formal neuropsychological testing. By multivariable regression model, 53% of the variance in the composite Global Deficit Score was accounted for by elements from the brief computerized tool (P < 0.01). These data confirm previous correlation data with the computerized battery. Using the five significant parameters from the regression model in a Receiver Operating Characteristic curve, 90% of persons were accurately classified as being cognitively impaired or not. The test battery requires additional evaluation, specifically for identifying persons with mild impairment, a state upon which interventions may be effective.

The relationship between perceived social capital and the health promotion willingness of companies: a systematic telephone survey with chief executive officers in the information and communication technology sector.

PubMed

Jung, Julia; Nitzsche, Anika; Ernstmann, Nicole; Driller, Elke; Wasem, Jürgen; Stieler-Lorenz, Brigitte; Pfaff, Holger

2011-03-01

This study examines the association between perceived social capital and health promotion willingness (HPW) of companies from a chief executive officer's perspective. Data for the cross-sectional study were collected through telephone interviews with one chief executive officer from randomly selected companies within the German information and communication technology sector. A hierarchical multivariate logistic regression analysis was performed. Results of the logistic regression analysis of data from a total of n = 522 interviews suggest that higher values of perceived social capital are associated with pronounced HPW in companies (odds ratio = 3.78; 95% confidence intervals, 2.24 to 6.37). Our findings suggest that characteristics of high social capital, such as an established environment of trust as well as a feeling of common values and convictions could help promote HPW.

Factors associated with obstructive sleep apnea among commercial motor vehicle drivers.

PubMed

Xie, Wen; Chakrabarty, Sangita; Levine, Robert; Johnson, Roy; Talmage, James B

2011-02-01

Identify factors associated with obstructive sleep apnea (OSA) risk during commercial driver medical examinations. A case-control study was conducted at an occupational health clinic by reviewing the commercial driver medical examinations medical records performed from January 2007 to December 2008. The magnitude of association with OSA was estimated with logistic regression. Among 1890 commercial motor vehicle drivers, 51 were confirmed positive for OSA by polysomnography after initial screening by Joint Task Force guidelines, yielding estimated positive predictive values of 78.5% for the screening criteria. Multivariable logistic regression showed that body mass index ≥ 30 (odds ratio: 26.86), hypertension (odds ratio: 2.57), and diabetes (odds ratio: 2.03) were independently associated with OSA. Medical examiners' use of objectively measurable risk factors, such as obesity, history of hypertension, and/or diabetes, rather than symptoms, may be more effective in identifying undiagnosed OSA in commercial drivers during the commercial driver medical examinations.

A comparison of different strategies in multivariate regression models for the direct determination of Mn, Cr, and Ni in steel samples using laser-induced breakdown spectroscopy

NASA Astrophysics Data System (ADS)

Luna, Aderval S.; Gonzaga, Fabiano B.; da Rocha, Werickson F. C.; Lima, Igor C. A.

2018-01-01

Laser-induced breakdown spectroscopy (LIBS) analysis was carried out on eleven steel samples to quantify the concentrations of chromium, nickel, and manganese. LIBS spectral data were correlated to known concentrations of the samples using different strategies in partial least squares (PLS) regression models. For the PLS analysis, one predictive model was separately generated for each element, while different approaches were used for the selection of variables (VIP: variable importance in projection and iPLS: interval partial least squares) in the PLS model to quantify the contents of the elements. The comparison of the performance of the models showed that there was no significant statistical difference using the Wilcoxon signed rank test. The elliptical joint confidence region (EJCR) did not detect systematic errors in these proposed methodologies for each metal.

Association of tRNA methyltransferase NSUN2/IGF-II molecular signature with ovarian cancer survival.

PubMed

Yang, Jia-Cheng; Risch, Eric; Zhang, Meiqin; Huang, Chan; Huang, Huatian; Lu, Lingeng

2017-09-01

To investigate the association between NSUN2/IGF-II signature and ovarian cancer survival. Using a publicly accessible dataset of RNA sequencing and clinical follow-up data, we performed Classification and Regression Tree and survival analyses. Patients with NSUN2 high IGF-II low had significantly superior overall and disease progression-free survival, followed by NSUN2 low IGF-II low , NSUN2 high IGF-II high and NSUN2 low IGF-II high (p < 0.0001 for overall, p = 0.0024 for progression-free survival, respectively). The associations of NSUN2/IGF-II signature with the risks of death and relapse remained significant in multivariate Cox regression models. Random-effects meta-analyses show the upregulated NSUN2 and IGF-II expression in ovarian cancer versus normal tissues. The NSUN2/IGF-II signature associates with heterogeneous outcome and may have clinical implications in managing ovarian cancer.

Comparison of Logistic Regression and Random Forests techniques for shallow landslide susceptibility assessment in Giampilieri (NE Sicily, Italy)

NASA Astrophysics Data System (ADS)

Trigila, Alessandro; Iadanza, Carla; Esposito, Carlo; Scarascia-Mugnozza, Gabriele

2015-11-01

The aim of this work is to define reliable susceptibility models for shallow landslides using Logistic Regression and Random Forests multivariate statistical techniques. The study area, located in North-East Sicily, was hit on October 1st 2009 by a severe rainstorm (225 mm of cumulative rainfall in 7 h) which caused flash floods and more than 1000 landslides. Several small villages, such as Giampilieri, were hit with 31 fatalities, 6 missing persons and damage to buildings and transportation infrastructures. Landslides, mainly types such as earth and debris translational slides evolving into debris flows, were triggered on steep slopes and involved colluvium and regolith materials which cover the underlying metamorphic bedrock. The work has been carried out with the following steps: i) realization of a detailed event landslide inventory map through field surveys coupled with observation of high resolution aerial colour orthophoto; ii) identification of landslide source areas; iii) data preparation of landslide controlling factors and descriptive statistics based on a bivariate method (Frequency Ratio) to get an initial overview on existing relationships between causative factors and shallow landslide source areas; iv) choice of criteria for the selection and sizing of the mapping unit; v) implementation of 5 multivariate statistical susceptibility models based on Logistic Regression and Random Forests techniques and focused on landslide source areas; vi) evaluation of the influence of sample size and type of sampling on results and performance of the models; vii) evaluation of the predictive capabilities of the models using ROC curve, AUC and contingency tables; viii) comparison of model results and obtained susceptibility maps; and ix) analysis of temporal variation of landslide susceptibility related to input parameter changes. Models based on Logistic Regression and Random Forests have demonstrated excellent predictive capabilities. Land use and wildfire variables were found to have a strong control on the occurrence of very rapid shallow landslides.

The relationship between hemoglobin level and the type 1 diabetic nephropathy in Anhui Han's patients.

PubMed

Jiang, Jun; Lei, Lan; Zhou, Xiaowan; Li, Peng; Wei, Ren

2018-02-20

Recent studies have shown that low hemoglobin (Hb) level promote the progression of chronic kidney disease. This study assessed the relationship between Hb level and type 1 diabetic nephropathy (DN) in Anhui Han's patients. There were a total of 236 patients diagnosed with type 1 diabetes mellitus and (T1DM) seen between January 2014 and December 2016 in our centre. Hemoglobin levels in patients with DN were compared with those without DN. The relationship between Hb level and the urinary albumin-creatinine ratio (ACR) was examined by Spearman's correlational analysis and multiple stepwise regression analysis. The binary logistic multivariate regression analysis was performed to analyze the correlated factors for type 1 DN, calculate the Odds Ratio (OR) and 95%confidence interval (CI). The predicting value of Hb level for DN was evaluated by area under receiver operation characteristic curve (AUROC) for discrimination and Hosmer-Lemeshow goodness-of-fit test for calibration. The average Hb levels in the DN group (116.1 ± 20.8 g/L) were significantly lower than the non-DN group (131.9 ± 14.4 g/L) , P < 0.001. Hb levels were independently correlated with the urinary ACR in multiple stepwise regression analysis. The logistic multivariate regression analysis showed that the Hb level (OR: 0.936, 95% CI: 0.910 to 0.963, P < 0.001) was inversely correlated with DN in patients with T1DM. In sub-analysis, low Hb level (Hb < 120g/L in female, Hb < 130g/L in male) was still negatively associated with DN in patients with T1DM. The AUROC was 0.721 (95% CI: 0.655 to 0.787) in assessing the discrimination of the Hb level for DN. The value of P was 0.593 in Hosmer-Lemeshow goodness-of-fit test. In Anhui Han's patients with T1DM, the Hb level is inversely correlated with urinary ACR and DN. This article is protected by copyright. All rights reserved.

Association between metabolic syndrome and intravesical prostatic protrusion in patients with benign prostatic enlargement and lower urinary tract symptoms (MIPS Study).

PubMed

Russo, Giorgio I; Regis, Federica; Spatafora, Pietro; Frizzi, Jacopo; Urzì, Daniele; Cimino, Sebastiano; Serni, Sergio; Carini, Marco; Gacci, Mauro; Morgia, Giuseppe

2018-05-01

To investigate the association between metabolic syndrome (MetS) and morphological features of benign prostatic enlargement (BPE), including total prostate volume (TPV), transitional zone volume (TZV) and intravesical prostatic protrusion (IPP). Between January 2015 and January 2017, 224 consecutive men aged >50 years presenting with lower urinary tract symptoms (LUTS) suggestive of BPE were recruited to this multicentre cross-sectional study. MetS was defined according to International Diabetes Federation criteria. Multivariate linear and logistic regression models were performed to verify factors associated with IPP, TZV and TPV. Patients with MetS were observed to have a significant increase in IPP (P < 0.01), TPV (P < 0.01) and TZV (P = 0.02). On linear regression analysis, adjusted for age and metabolic factors of MetS, we found that high-density lipoprotein (HDL) cholesterol was negatively associated with IPP (r = -0.17), TPV (r = -0.19) and TZV (r = -0.17), while hypertension was positively associated with IPP (r = 0.16), TPV (r = 0.19) and TZV (r = 0.16). On multivariate logistic regression analysis adjusted for age and factors of MetS, hypertension (categorical; odds ratio [OR] 2.95), HDL cholesterol (OR 0.94) and triglycerides (OR 1.01) were independent predictors of TPV ≥ 40 mL. We also found that HDL cholesterol (OR 0.86), hypertension (OR 2.0) and waist circumference (OR 1.09) were significantly associated with TZV ≥ 20 mL. On age-adjusted logistic regression analysis, MetS was significantly associated with IPP ≥ 10 mm (OR 34.0; P < 0.01), TZV ≥ 20 mL (OR 4.40; P < 0.01) and TPV ≥ 40 mL (OR 5.89; P = 0.03). We found an association between MetS and BPE, demonstrating a relationship with IPP. © 2017 The Authors BJU International © 2017 BJU International Published by John Wiley & Sons Ltd.

An Investigation of Multivariate Adaptive Regression Splines for Modeling and Analysis of Univariate and Semi-Multivariate Time Series Systems

DTIC Science & Technology

1991-09-01

However, there is no guarantee that this would work; for instance if the data were generated by an ARCH model (Tong, 1990 pp. 116-117) then a simple...Hill, R., Griffiths, W., Lutkepohl, H., and Lee, T., Introduction to the Theory and Practice of Econometrics , 2th ed., Wiley, 1985. Kendall, M., Stuart

Nonparametric regression applied to quantitative structure-activity relationships

PubMed

Constans; Hirst

2000-03-01

Several nonparametric regressors have been applied to modeling quantitative structure-activity relationship (QSAR) data. The simplest regressor, the Nadaraya-Watson, was assessed in a genuine multivariate setting. Other regressors, the local linear and the shifted Nadaraya-Watson, were implemented within additive models--a computationally more expedient approach, better suited for low-density designs. Performances were benchmarked against the nonlinear method of smoothing splines. A linear reference point was provided by multilinear regression (MLR). Variable selection was explored using systematic combinations of different variables and combinations of principal components. For the data set examined, 47 inhibitors of dopamine beta-hydroxylase, the additive nonparametric regressors have greater predictive accuracy (as measured by the mean absolute error of the predictions or the Pearson correlation in cross-validation trails) than MLR. The use of principal components did not improve the performance of the nonparametric regressors over use of the original descriptors, since the original descriptors are not strongly correlated. It remains to be seen if the nonparametric regressors can be successfully coupled with better variable selection and dimensionality reduction in the context of high-dimensional QSARs.

Impact of national income and inequality on sugar and caries relationship.

PubMed

Masood, M; Masood, Y; Newton, T

2012-01-01

The aim of this study was to examine the impact that national income and income inequality in high and low income countries have on the relationship between dental caries and sugar consumption. An ecological study design was used in this study of 73 countries. The mean decayed, missing, or filled permanent teeth (DMFT) for 12-year-old children were obtained from the WHO Oral Health Country/Area Profile Programme. United Nations Food and Agricultural Organization data were used for per capita sugar consumption. Gross national incomes per capita based on purchasing power parity and the Gini coefficient were obtained from World Bank data. Bivariate and multivariate linear regression analysis was performed to estimate the associations between mean DMFT and per capita sugar consumption in different income and income inequality countries. Bivariate and multivariate regression analysis showed that countries with a high national income and low income inequality have a strong negative association between sugar consumption and caries (B = -2.80, R2 = 0.17), whereas countries with a low income and high income inequality have a strong positive relationship between DMFT and per capita sugar consumption (B = -0.89, R2 = 0.20). The relationship between per capita consumption of sugar and dental caries is modified by the absolute level of income of the country, but not by the level of income inequality within a country. Copyright © 2012 S. Karger AG, Basel.

Subclinical Hypothyroidism after 131I-Treatment of Graves' Disease: A Risk Factor for Depression?

PubMed

Yu, Jing; Tian, Ai-Juan; Yuan, Xin; Cheng, Xiao-Xin

2016-01-01

Although it is well accepted that there is a close relationship between hypothyroidism and depression, previous studies provided inconsistent or even opposite results in whether subclinical hypothyroidism (SCH) increased the risk of depression. One possible reason is that the etiology of SCH in these studies was not clearly distinguished. We therefore investigated the relationship between SCH resulting from 131I treatment of Graves' disease and depression. The incidence of depression among 95 patients with SCH and 121 euthyroid patients following 131I treatment of Graves' disease was studied. The risk factors of depression were determined with multivariate logistic regression analysis. Thyroid hormone replacement therapy was performed in patients with thyroid-stimulating hormone (TSH) levels exceeding 10 mIU/L. Patients with SCH had significantly higher Hamilton Depression Scale scores, serum TSH and thyroid peroxidase antibody (TPOAb) levels compared with euthyroid patients. Multivariate logistic regression analysis revealed SCH, Graves' eye syndrome and high serum TPO antibody level as risk factors for depression. L-thyroxine treatment is beneficial for SCH patients with serum TSH levels exceeding 10 mIU/L. The results of the present study demonstrated that SCH is prevalent among 131I treated Graves' patients. SCH might increase the risk of developing depression. L-thyroxine replacement therapy helps to resolve depressive disorders in SCH patients with TSH > 10mIU/L. These data provide insight into the relationship between SCH and depression.

PTSD, cyberbullying and peer violence: prevalence and correlates among adolescent emergency department patients.

PubMed

Ranney, Megan L; Patena, John V; Nugent, Nicole; Spirito, Anthony; Boyer, Edward; Zatzick, Douglas; Cunningham, Rebecca

2016-01-01

Posttraumatic stress disorder (PTSD) is often underdiagnosed and undertreated among adolescents. The objective of this analysis was to describe the prevalence and correlates of symptoms consistent with PTSD among adolescents presenting to an urban emergency department (ED). A cross-sectional survey of adolescents aged 13-17 years presenting to the ED for any reason was conducted between August 2013 and March 2014. Validated self-report measures were used to measure mental health symptoms, violence exposure and risky behaviors. Multivariate logistic regression analysis was performed to determine adjusted differences in associations between symptoms consistent with PTSD and predicted correlates. Of 353 adolescents, 23.2% reported current symptoms consistent with PTSD, 13.9% had moderate or higher depressive symptoms and 11.3% reported past-year suicidal ideation. Adolescents commonly reported physical peer violence (46.5%), cyberbullying (46.7%) and exposure to community violence (58.9%). On multivariate logistic regression, physical peer violence, cyberbullying victimization, exposure to community violence, female gender and alcohol or other drug use positively correlated with symptoms consistent with PTSD. Among adolescents presenting to the ED for any reason, symptoms consistent with PTSD, depressive symptoms, physical peer violence, cyberbullying and community violence exposure are common and interrelated. Greater attention to PTSD, both disorder and symptom levels, and its cooccurring risk factors is needed. Copyright © 2016 Elsevier Inc. All rights reserved.

A generalized multivariate regression model for modelling ocean wave heights

NASA Astrophysics Data System (ADS)

Wang, X. L.; Feng, Y.; Swail, V. R.

2012-04-01

In this study, a generalized multivariate linear regression model is developed to represent the relationship between 6-hourly ocean significant wave heights (Hs) and the corresponding 6-hourly mean sea level pressure (MSLP) fields. The model is calibrated using the ERA-Interim reanalysis of Hs and MSLP fields for 1981-2000, and is validated using the ERA-Interim reanalysis for 2001-2010 and ERA40 reanalysis of Hs and MSLP for 1958-2001. The performance of the fitted model is evaluated in terms of Pierce skill score, frequency bias index, and correlation skill score. Being not normally distributed, wave heights are subjected to a data adaptive Box-Cox transformation before being used in the model fitting. Also, since 6-hourly data are being modelled, lag-1 autocorrelation must be and is accounted for. The models with and without Box-Cox transformation, and with and without accounting for autocorrelation, are inter-compared in terms of their prediction skills. The fitted MSLP-Hs relationship is then used to reconstruct historical wave height climate from the 6-hourly MSLP fields taken from the Twentieth Century Reanalysis (20CR, Compo et al. 2011), and to project possible future wave height climates using CMIP5 model simulations of MSLP fields. The reconstructed and projected wave heights, both seasonal means and maxima, are subject to a trend analysis that allows for non-linear (polynomial) trends.

A novel practical scoring for early diagnosis of traumatic bowel injury without obvious solid organ injury in hemodynamically stable patients.

PubMed

Zarour, Ahmad; El-Menyar, Ayman; Khattabi, Mazen; Tayyem, Raed; Hamed, Osama; Mahmood, Ismail; Abdelrahman, Husham; Chiu, William; Al-Thani, Hassan

2014-01-01

To develop a scoring tool based on clinical and radiological findings for early diagnosis and intervention in hemodynamically stable patients with traumatic bowel and mesenteric injury (TBMI) without obvious solid organ injury (SOI). A retrospective analysis was conducted for all traumatic abdominal injury patients in Qatar from 2008 to 2011. Data included demographics and clinical, radiological and operative findings. Multivariate logistic regression was performed to analyze the predictors for the need of therapeutic laparotomy. A total of 105 patients met the inclusion criteria with a mean age of 33 ± 15. Motor Vehicle Crashes (58%) and fall (21%) were the major MOI. Using Receiver operating characteristic curve, Z-score of >9 was the cutoff point (AUC = 0.98) for high probability of the presence of TBMI requiring surgical intervention. Z-Score >9 was found to have sensitivity (96.7%), specificity (97.4%), PPV (93.5%) and NPV (98.7%). Multivariate regression analysis found Z-score (>9) to be an independent predictor for the need of exploratory laparotomy (OR7.0; 95% CI: 2.46-19.78, p = 0.001). This novel tool for early diagnosis of TBMI is found to be simple and helpful in selecting stable patients with free intra-abdominal fluid without SOI for exploratory Laparotomy. However, further prospective studies are warranted. Copyright © 2014 Surgical Associates Ltd. Published by Elsevier Ltd. All rights reserved.

Predictors of parent-adolescent communication in post-apartheid South Africa: a protective factor in adolescent sexual and reproductive health.

PubMed

Coetzee, Jenny; Dietrich, Janan; Otwombe, Kennedy; Nkala, Busi; Khunwane, Mamakiri; van der Watt, Martin; Sikkema, Kathleen J; Gray, Glenda E

2014-04-01

In the HIV context, risky sexual behaviours can be reduced through effective parent-adolescent communication. This study used the Parent Adolescent Communication Scale to determine parent-adolescent communication by ethnicity and identify predictors of high parent-adolescent communication amongst South African adolescents post-apartheid. A cross-sectional interviewer-administered survey was administered to 822 adolescents from Johannesburg, South Africa. Backward stepwise multivariate regressions were performed. The sample was predominantly Black African (62%, n = 506) and female (57%, n = 469). Of the participants, 57% (n = 471) reported high parent-adolescent communication. Multivariate regression showed that gender was a significant predictor of high parent-adolescent communication (Black African OR:1.47, CI: 1.0-2.17, Indian OR: 2.67, CI: 1.05-6.77, White OR: 2.96, CI: 1.21-7.18). Female-headed households were predictors of high parent-adolescent communication amongst Black Africans (OR:1.49, CI: 1.01-2.20), but of low parent-adolescent communication amongst Whites (OR:0.36, CI: 0.15-0.89). Overall levels of parent-adolescent communication in South Africa are low. HIV prevention programmes for South African adolescents should include information and skills regarding effective parent-adolescent communication. Copyright © 2014 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

The impact of early hyperglycaemia on children with traumatic brain injury.

PubMed

Fu, Yue-Qiang; Chong, Shu-Ling; Lee, Jan Hau; Liu, Cheng-Jun; Fu, Sheng; Loh, Tsee Foong; Ng, Kee Chong; Xu, Feng

2017-01-01

Hyperglycaemia is common amongst children with traumatic brain injury (TBI). We aim to investigate the association between early hyperglycaemia and poor clinical outcomes in children with moderate to severe TBI. We performed a retrospective study in a tertiary paediatric hospital between May 2012 and October 2014 of all patients with TBI who were aged <16 years with a Glasgow Coma Scale (GCS) of ≤13. The primary outcome was death. Secondary outcomes were 14 ventilation-free, 14 paediatric intensive care unit (PICU)-free and 28 hospital-free days. We defined hyperglycaemia as glucose >11.1 mmol/L (200 mg/dL). There were 109 patients with a median age of 54 months [inter-quartile range (IQR): 17-82]. Median glucose on arrival was 6.1 mmol/L (IQR: 5.2-9.8). Median GCS in our cohort was 8 (IQR: 6-12). Multivariate logistic regression demonstrated that initial hyperglycaemia [odds ratio (OR): 15.23; 95% confidence interval (CI): 3.74-62.00; P < 0.001], and GCS <8 (OR: 13.02; 95% CI: 2.31-73.33; P = 0.004) were risk factors for mortality. Multivariate linear regression showed that initial hyperglycaemia was a risk factor for reduced ventilation-free, PICU-free and hospital-free days. Early hyperglycaemia predicts for in-hospital mortality, reduced ventilation-free, PICU-free and hospital-free days in children with moderate to severe TBI.

Analysis of association of clinical aspects and IL1B tagSNPs with severe preeclampsia.

PubMed

Leme Galvão, Larissa Paes; Menezes, Filipe Emanuel; Mendonca, Caio; Barreto, Ikaro; Alvim-Pereira, Claudia; Alvim-Pereira, Fabiano; Gurgel, Ricardo

2016-01-01

This study investigates the association between IL1B genotypes using a tag SNP (single polymorphism) approach, maternal and environmental factors in Brazilian women with severe preeclampsia. A case-control study with a total of 456 patients (169 preeclamptic women and 287 controls) was conducted in the two reference maternity hospitals of Sergipe state, Northeast Brazil. A questionnaire was administered and DNA was extracted to genotype the population for four tag SNPs of the IL1Beta: rs 1143643, rs 1143633, rs 1143634 and rs 1143630. Haplotype association analysis and p-values were calculated using the THESIAS test. Odds ratio (OR) estimation, confidence interval (CI) and multivariate logistic regression were performed. High pregestational body mass index (pre-BMI), first gestation, cesarean section, more than six medical visits, low level of consciousness on admission and TC and TT genotype in rs1143630 of IL1Beta showed association with the preeclamptic group in univariate analysis. After multivariate logistic regression pre-BMI, first gestation and low level of consciousness on admission remained associated. We identified an association between clinical variables and preeclampsia. Univariate analysis suggested that inflammatory process-related genes, such as IL1B, may be involved and should be targeted in further studies. The identification of the genetic background involved in preeclampsia host response modulation is mandatory in order to understand the preeclampsia process.

Factors Affecting Discharge to Home of Geriatric Intermediate Care Facility Residents in Japan.

PubMed

Morita, Kojiro; Ono, Sachiko; Ishimaru, Miho; Matsui, Hiroki; Naruse, Takashi; Yasunaga, Hideo

2018-04-01

To investigate factors associated with lower likelihood of discharge to home from geriatric intermediate care facilities in Japan. Retrospective cohort study. We used data from the nationwide long-term care (LTC) insurance claims database (April 2012-March 2014). Study participants were 342,758 individuals newly admitted to 3,459 geriatric intermediate care facilities during the study period. The primary outcome was discharge to home. We performed a multivariable competing-risk Cox regression with adjustment for resident-, facility-, and region-level characteristics. Resident level of care needs and several medical conditions were included as time-varying covariates. Death, admission to a hospital, and admission to another LTC facility were treated as competing risks. During the 2-year follow-up period, 19% of participants were discharged to home. In the multivariable competing-risk Cox regression, the following factors were significantly associated with lower likelihood of discharge to home: older age, higher level of care need, having several medical conditions, private ownership of the facility, more beds in the facility, and more LTC facility beds per 1,000 adults aged 65 and older in the region. Only 19% of residents were discharged to home. Our results are useful for policy-makers to promote discharge to home of older adults in geriatric intermediate care facilities. © 2018, Copyright the Authors Journal compilation © 2018, The American Geriatrics Society.

Influence of education on HIV infection among pregnant women attending their antenatal care in Sekondi-Takoradi metropolis, Ghana.

PubMed

Orish, Verner N; Onyeabor, Onyekachi S; Boampong, Johnson N; Afoakwah, Richmond; Nwaefuna, Ekene; Acquah, Samuel; Orish, Esther O; Sanyaolu, Adekunle O; Iriemenam, Nnaemeka C

2014-08-01

This study investigated the influence of the level of education on HIV infection among pregnant women attending antenatal care in Sekondi-Takoradi, Ghana. A cross-sectional study was conducted at four hospitals in the Sekondi-Takoradi metropolis. The study group comprised 885 consenting pregnant women attending antenatal care clinics. Questionnaires were administered and venous blood samples were screened for HIV and other parameters. Multivariable logistic regression analyses were performed to determine the association between the level of education attained by the pregnant women and their HIV statuses. The data showed that 9.83% (87/885) of the pregnant women were HIV seropositive while 90.17% (798/885) were HIV seronegative. There were significant differences in mean age (years) between the HIV seropositive women (27.45 ± 5.5) and their HIV seronegative (26.02 ± 5.6) counterparts (p = .026) but the inference disappeared after adjustment (p = .22). Multivariable logistic regression analysis revealed that pregnant women with secondary/tertiary education were less likely to have HIV infection compared with those with none/primary education (adjusted OR, 0.53; 95% CI, 0.30-0.91; p = .022). Our data showed an association with higher level of education and HIV statuses of the pregnant women. It is imperative to encourage formal education among pregnant women in this region.

Accession medical waivers and deployment duration in the U.S. Army.

PubMed

Gubata, Marlene E; Oetting, Alexis A; Niebuhr, David W; Cowan, David N

2013-06-01

To examine the performance of active duty U.S. Army personnel with accession medical waivers during combat deployments, the deployment duration and likelihood of early return from theater for medically waived (n = 18,093) and medically qualified (n = 250,209) personnel deploying between September 2001 and March 2011 were determined. The mean and median deployment duration for waived men (309.4 ± 107.5 and 346) and for waived women (291.8 ± 115.3 and 341) was not shorter than for medically qualified men (304.6 ± 112.1 and 346) and women (289.5 ± 116.3 and 337). When adjusted for other accession factors in a multivariate linear regression model, neither waived men (p = 1.00) nor women (p = 0.7713) had significantly shorter deployments. In a case-control analysis, 24,369 men and 3,094 women were defined as having a short deployment. Multivariate logistic regression found that medically waived men (odds ratio [OR] = 0.87, 95% confidence interval [CI] = 0.82-0.92) and women (OR = 1.02, 95% CI = 0.87-1.19) were not more likely to have shorter deployments compared to medically qualified individuals. These findings show that those with an accession medical waiver were not more likely to have shorter deployments or more likely to return early from deployment than those without waivers. Reprint & Copyright © 2013 Association of Military Surgeons of the U.S.

Relationship between alcohol intake, body fat, and physical activity – a population-based study

PubMed Central

Liangpunsakul, Suthat; Crabb, David W.; Qi, Rong

2010-01-01

Objectives Aside from fat, ethanol is the macronutrient with the highest energy density. Whether the energy derived from ethanol affects the body composition and fat mass is debatable. We investigated the relationship between alcohol intake, body composition, and physical activity in the US population using the third National Health and Nutrition Examination Survey (NHANES III). Methods Ten thousand five hundred and fifty subjects met eligible criteria and constituted our study cohort. Estimated percent body fat and resting metabolic rate were calculated based on the sum of the skinfolds. Multivariate regression analyses were performed accounting for the study sampling weight. Results In both genders, moderate and hazardous alcohol drinkers were younger (p<0.05), had significantly lower BMI (P<0.01) and body weight (p<0.01) than controls, non drinkers. Those with hazardous alcohol consumption had significantly less physical activity compared to those with no alcohol use and moderate drinkers in both genders. Female had significantly higher percent body fat than males. In the multivariate linear regression analyses, the levels of alcohol consumption were found to be an independent predictor associated with lower percent body fat only in male subjects. Conclusions Our results showed that alcoholics are habitually less active and that alcohol drinking is an independent predictor of lower percent body fat especially in male alcoholics. PMID:20696406

Predictors of free flap loss in the head and neck region: A four-year retrospective study with 451 microvascular transplants at a single centre.

PubMed

Mücke, Thomas; Ritschl, Lucas M; Roth, Maximilian; Güll, Florian D; Rau, Andrea; Grill, Sonja; Kesting, Marco R; Wolff, Klaus-Dietrich; Loeffelbein, Denys J

2016-09-01

Microvascular free flaps have become an essential part of reconstructive surgery following head and neck tumour ablation. The authors' aim was to investigate the influence of cardiovascular risk factors, preoperative irradiation, previous operations and metabolically active medication on free flap loss in order to predict patients at risk and to improve their therapy. All patients who underwent reconstructive surgery with microvascular free flaps in the head and neck region between 2009 and 2013 were retrospectively analysed. Uni- and multivariate logistic regressions were performed to determine the association between possible predictor variables for free flap loss. We included 451 patients in our analysis. The overall free flap failure rate was 4.0%. Multivariate regression analysis revealed significantly increased risks of free flap failure depending on prior attempts at microvascular transplants (p < 0.001, OR = 14.21) and length of hospitalisation (p = 0.007, OR = 1.05). With consistently low rates of flap failure, microvascular reconstruction of defects in the head and neck region has proven to be highly reliable, even in patients with comorbidities. The expertise of the operating team seems to remain the main factor affecting flap success. The only discerned independent predictor was previously failed attempts at microvascular reconstruction. Copyright © 2016 European Association for Cranio-Maxillo-Facial Surgery. Published by Elsevier Ltd. All rights reserved.

Risk factors for indications of intraoperative blood transfusion among patients undergoing surgical treatment for colorectal adenocarcinoma.

PubMed

Gonçalves, Iara; Linhares, Marcelo; Bordin, Jose; Matos, Delcio

2009-01-01

Identification of risk factors for requiring transfusions during surgery for colorectal cancer may lead to preventive actions or alternative measures, towards decreasing the use of blood components in these procedures, and also rationalization of resources use in hemotherapy services. This was a retrospective case-control study using data from 383 patients who were treated surgically for colorectal adenocarcinoma at 'Fundação Pio XII', in Barretos-SP, Brazil, between 1999 and 2003. To recognize significant risk factors for requiring intraoperative blood transfusion in colorectal cancer surgical procedures. Univariate analyses were performed using Fisher's exact test or the chi-squared test for dichotomous variables and Student's t test for continuous variables, followed by multivariate analysis using multiple logistic regression. In the univariate analyses, height (P = 0.06), glycemia (P = 0.05), previous abdominal or pelvic surgery (P = 0.031), abdominoperineal surgery (P<0.001), extended surgery (P<0.001) and intervention with radical intent (P<0.001) were considered significant. In the multivariate analysis using logistic regression, intervention with radical intent (OR = 10.249, P<0.001, 95% CI = 3.071-34.212) and abdominoperineal amputation (OR = 3.096, P = 0.04, 95% CI = 1.445-6.623) were considered to be independently significant. This investigation allows the conclusion that radical intervention and the abdominoperineal procedure in the surgical treatment of colorectal adenocarcinoma are risk factors for requiring intraoperative blood transfusion.

Cerebrovascular risk factors for patients with cerebral watershed infarction: A case-control study based on computed tomography angiography in a population from Southwest China.

PubMed

Dong, Mei-Xue; Hu, Ling; Huang, Yuan-Jun; Xu, Xiao-Min; Liu, Yang; Wei, You-Dong

2017-07-01

To determine cerebrovascular risk factors for patients with cerebral watershed infarction (CWI) from Southwest China.Patients suffering from acute ischemic stroke were categorized into internal CWI (I-CWI), external CWI (E-CWI), or non-CWI (patients without CWI) groups. Clinical data were collected and degrees of steno-occlusion of all cerebral arteries were scored. Arteries associated with the circle of Willis were also assessed. Data were compared using Pearson chi-squared tests for categorical data and 1-way analysis of variance with Bonferroni post hoc tests for continuous data, as appropriate. Multivariate binary logistic regression analysis was performed to determine independent cerebrovascular risk factors for CWI.Compared with non-CWI, I-CWI had higher degrees of steno-occlusion of the ipsilateral middle cerebral artery, ipsilateral carotid artery, and contralateral middle cerebral artery. E-CWI showed no significant differences. All the 3 arteries were independent cerebrovascular risk factors for I-CWI confirmed by multivariate binary logistic regression analysis. I-CWI had higher degrees of steno-occlusion of the ipsilateral middle cerebral artery compared with E-CWI. No significant differences were found among arteries associated with the circle of Willis.The ipsilateral middle cerebral artery, carotid artery, and contralateral middle cerebral artery were independent cerebrovascular risk factors for I-CWI. No cerebrovascular risk factor was identified for E-CWI.

ARMS2 variants may predict the 3-year outcome of photodynamic therapy for wet age-related macular degeneration

PubMed Central

Nakai, Shunichiro; Matsumiya, Wataru; Miki, Akiko; Nakamura, Makoto

2017-01-01

Purpose To determine the association of age-related maculopathy susceptibility 2 (ARMS2) gene polymorphisms with the 3-year outcomes of photodynamic therapy (PDT) in wet age-related macular degeneration (wet AMD). Methods The single nucleotide polymorphism (SNP) at rs10490924 in the ARMS2 gene of 65 patients with wet AMD who underwent PDT was genotyped using the TaqMan assay. The clinical characteristics and the outcomes of PDT were compared among the three genotypes at rs10490924. A multivariate regression analysis was performed to evaluate the influence of the clinical cofactors on the association of rs10490924 with the visual outcome at 36 months after the first PDT. Results A significant difference was found among the genotypes in the age and the baseline lesion size. The patients with the GG genotype showed a significant improvement in vision, and the patients with the TT genotype showed a significant worsening of vision at all time points measured after the initial PDT. In the multivariate regression analysis, the number of the G allele at rs10490924 was associated with a significantly greater improvement in the baseline best-corrected visual acuity (BCVA) at 36 months after the first PDT. Conclusions ARMS2 variants are likely associated with the 3-year outcomes of PDT in patients with wet AMD. PMID:28761324

Symptoms and signs associated with benign and malignant proximal fibular tumors: a clinicopathological analysis of 52 cases.

PubMed

Sun, Tao; Wang, Lingxiang; Guo, Changzhi; Zhang, Guochuan; Hu, Wenhai

2017-05-02

Malignant tumors in the proximal fibula are rare but life-threatening; however, biopsy is not routine due to the high risk of peroneal nerve injury. Our aim was to determine preoperative clinical indicators of malignancy. Between 2004 and 2016, 52 consecutive patients with proximal fibular tumors were retrospectively reviewed. Details of the clinicopathological characteristics including age, gender, location of tumors, the presenting symptoms, the duration of symptoms, and pathological diagnosis were collected. Descriptive statistics were calculated, and univariate and multivariate regression were performed. Of these 52 patients, 84.6% had benign tumors and 15.4% malignant tumors. The most common benign tumors were osteochondromas (46.2%), followed by enchondromas (13.5%) and giant cell tumors (13.5%). The most common malignancy was osteosarcomas (11.5%). The most common presenting symptoms were a palpable mass (52.0%) and pain (46.2%). Pain was the most sensitive (100%) and fourth specific (64%); both high skin temperature and peroneal nerve compression had the highest specificity (98%) and third sensitivity (64%); change in symptoms had the second highest specificity (89%) while 50% sensitivity. Using multivariate regression, palpable pain, high skin temperature, and peroneal nerve compression symptoms were predictors of malignancy. Most tumors in the proximal fibula are benign, and the malignancy is rare. Palpable pain, peroneal nerve compression symptoms, and high skin temperature were specific in predicting malignancy.

The association of corporate work environment factors, health risks, and medical conditions with presenteeism among Australian employees.

PubMed

Musich, Shirley; Hook, Dan; Baaner, Stephanie; Spooner, Michelle; Edington, Dee W

2006-01-01

To investigate the impact of selected corporate environment factors, health risks, and medical conditions on job performance using a self-reported measure of presenteeism. A cross-sectional survey utilizing health risk appraisal (HRA) data merging presenteeism with corporate environment factors, health risks, and medical conditions. Approximately 8000 employees across ten diverse Australian corporations. Employees (N = 1523; participation rate, 19%) who completed an HRA questionnaire. Self-reported HRA data were used to test associations of defined adverse corporate environment factors with presenteeism. Stepwise multivariate logistic regression modeling assessed the relative associations of corporate environment factors, health risks, and medical conditions with increased odds of any presenteeism. Increased presenteeism was significantly associated with poor working conditions, ineffective management/leadership, and work/life imbalance (adjusting for age, gender, health risks, and medical conditions). In multivariate logistic regression models, work/life imbalance, poor working conditions, life dissatisfaction, high stress, back pain, allergies, and younger age were significantly associated with presenteeism. Although the study has some limitations, including a possible response bias caused by the relatively low participation rate across the corporations, the study does demonstrate significant associations between corporate environment factors, health risks, and medical conditions and self-reported presenteeism. The study provides initial evidence that health management programming may benefit on-the-job productivity outcomes if expanded to include interventions targeting work environments.

Prediction of adherent placenta in pregnancy with placenta previa using ultrasonography and magnetic resonance imaging.

PubMed

Tanimura, Kenji; Yamasaki, Yui; Ebina, Yasuhiko; Deguchi, Masashi; Ueno, Yoshiko; Kitajima, Kazuhiro; Yamada, Hideto

2015-04-01

Adherent placenta is a life-threatening condition in pregnancy, and is often complicated by placenta previa. The aim of this prospective study was to determine prenatal imaging findings that predict the presence of adherent placenta in pregnancies with placenta previa. The study included 58 consecutive pregnant women with placenta previa who underwent both ultrasonography and magnetic resonance imaging prenatally. Ultrasonographic findings of anterior placental location, grade 2 or higher placental lacunae (PL≥G2), loss of retroplacental hypoechoic clear zone (LCZ) and the presence of turbulent blood flow in the arteries were evaluated, in addition to MRI findings. Forty-three women underwent cesarean section alone; 15 women with adherent placenta underwent cesarean section followed by hysterectomy with pathological examination. To determine imaging findings that predict adherent placenta, univariate and multivariate logistic regression analyses were performed. Univariate logistic regression analyses demonstrated that anterior placental location, PL≥G2, LCZ, and MRI were associated with the presence of adherent placenta. Multivariate analyses revealed that LCZ (p<0.01, odds ratio 15.6, 95%CI 2.1-114.6) was a single significant predictor of adherent placenta in women with placenta previa. This prospective study demonstrated for the first time that US findings, especially LCZ, might be useful for identifying patients at high risk for adherent placenta among pregnant women with placenta previa. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Geographic inequities in liver allograft supply and demand: does it affect patient outcomes?

PubMed

Rana, Abbas; Kaplan, Bruce; Riaz, Irbaz B; Porubsky, Marian; Habib, Shahid; Rilo, Horacio; Gruessner, Angelika C; Gruessner, Rainer W G

2015-03-01

Significant geographic inequities mar the distribution of liver allografts for transplantation. We analyzed the effect of geographic inequities on patient outcomes. During our study period (January 1 through December 31, 2010), 11,244 adult candidates were listed for liver transplantation: 5,285 adult liver allografts became available, and 5,471 adult recipients underwent transplantation. We obtained population data from the 2010 United States Census. To determine the effect of regional supply and demand disparities on patient outcomes, we performed linear regression and multivariate Cox regression analyses. Our proposed disparity metric, the ratio of listed candidates to liver allografts available varied from 1.3 (region 11) to 3.4 (region 1). When that ratio was used as the explanatory variable, the R(2) values for outcome measures were as follows: 1-year waitlist mortality, 0.23 and 1-year posttransplant survival, 0.27. According to our multivariate analysis, the ratio of listed candidates to liver allografts available had a significant effect on waitlist survival (hazards ratio, 1.21; 95% confidence interval, 1.04-1.40) but was not a significant risk factor for posttransplant survival. We found significant differences in liver allograft supply and demand--but these differences had only a modest effect on patient outcomes. Redistricting and allocation-sharing schemes should seek to equalize regional supply and demand rather than attempting to equalize patient outcomes.

Multivariate methods for indoor PM10 and PM2.5 modelling in naturally ventilated schools buildings

NASA Astrophysics Data System (ADS)

Elbayoumi, Maher; Ramli, Nor Azam; Md Yusof, Noor Faizah Fitri; Yahaya, Ahmad Shukri Bin; Al Madhoun, Wesam; Ul-Saufie, Ahmed Zia

2014-09-01

In this study the concentrations of PM10, PM2.5, CO and CO2 concentrations and meteorological variables (wind speed, air temperature, and relative humidity) were employed to predict the annual and seasonal indoor concentration of PM10 and PM2.5 using multivariate statistical methods. The data have been collected in twelve naturally ventilated schools in Gaza Strip (Palestine) from October 2011 to May 2012 (academic year). The bivariate correlation analysis showed that the indoor PM10 and PM2.5 were highly positive correlated with outdoor concentration of PM10 and PM2.5. Further, Multiple linear regression (MLR) was used for modelling and R2 values for indoor PM10 were determined as 0.62 and 0.84 for PM10 and PM2.5 respectively. The Performance indicators of MLR models indicated that the prediction for PM10 and PM2.5 annual models were better than seasonal models. In order to reduce the number of input variables, principal component analysis (PCA) and principal component regression (PCR) were applied by using annual data. The predicted R2 were 0.40 and 0.73 for PM10 and PM2.5, respectively. PM10 models (MLR and PCR) show the tendency to underestimate indoor PM10 concentrations as it does not take into account the occupant's activities which highly affect the indoor concentrations during the class hours.

Coexisting infectious diseases on admission as a risk factor for mechanical ventilation in patients with Guillain-Barré syndrome.

PubMed

Kobori, Shinichiro; Kubo, Tatsuhiko; Otani, Makoto; Muramatsu, Keiji; Fujino, Yoshihisa; Adachi, Hiroaki; Horiguchi, Hiromasa; Fushimi, Kiyohide; Matsuda, Shinya

2017-07-01

The aim of this study was to investigate patient characteristics on admission to hospital that increase the risk of subsequent mechanical ventilation (MV) use for patients with Guillain-Barré syndrome (GBS). We extracted data from the Japanese Diagnosis Procedure Combination (DPC) database for 4132 GBS patients admitted to hospital. Clinical characteristics of GBS patients with and without MV were compared. Multivariate logistic regression analyses were performed to estimate the odds ratios (ORs) and 95% confidence intervals (CIs) for the associations of requirement for MV with coexisting infectious diseases, after adjustment for potential confounding variables, age, sex, hospital type, and ambulance transportation. In total, 281 patients required MV, and 493 patients had coexisting respiratory diseases on admission. After adjustment for covariates and stratification by coexisting respiratory diseases, multivariate logistic regression analysis revealed that coexisting cytomegaloviral (CMV) disease (OR 8.81; 95% CI, 2.34-33.1) and herpes simplex viral (HSV) infections (OR 4.83; 95% CI, 1.16-20.1) were significantly associated with the requirement for MV in the group without coexisting respiratory diseases. Our findings suggest that coexisting CMV and HSV infections on admission might be significantly associated with increased risk of respiratory failure in GBS patients. Copyright © 2017 The Authors. Production and hosting by Elsevier B.V. All rights reserved.

Awareness and attitude of the public toward personalized medicine in Korea

PubMed Central

Lee, Iyn-Hyang; Kang, Hye-Young; Suh, Hae Sun; Lee, Sukhyang; Oh, Eun Sil

2018-01-01

Objectives As personalized medicine (PM) is expected to greatly improve health outcomes, efforts have recently been made for its clinical implementation in Korea. We aimed to evaluate public awareness and attitude regarding PM. Methods We performed a self-administered questionnaire survey to 703 adults, who participated in the survey on a voluntary basis. The primary outcome measures included public knowledge, attitude, and acceptance of PM. We conducted multinomial multivariate logistic analysis for outcome variables with three response categories and performed multivariate logistic regression analyses for dichotomous outcome variables. Results Only 28% of participants had knowledge that genetic factors can contribute to inter-individual variations in drug response and the definition of PM (199 out of 702). Higher family income was correlated with greater knowledge concerning PM (OR = 3.76, p = 0.034). A majority of respondents preferred integrated pharmacogenomic testing over drug-specific testing and agreed to inclusion of pharmacogenomic testing in the national health examination (64% and 77%, respectively), but only 51% were willing to pay for it. Discussion Our results identify the urgent need for public education as well as the potential health disparities in access to PM. This study helps to frame policies for implementing PM in clinical practice. PMID:29451916

Multivariate Quantification of the Solid State Phase Composition of Co-Amorphous Naproxen-Indomethacin.

PubMed

Beyer, Andreas; Grohganz, Holger; Löbmann, Korbinian; Rades, Thomas; Leopold, Claudia S

2015-10-27

To benefit from the optimized dissolution properties of active pharmaceutical ingredients in their amorphous forms, co-amorphisation as a viable tool to stabilize these amorphous phases is of both academic and industrial interest. Reports dealing with the physical stability and recrystallization behavior of co-amorphous systems are however limited to qualitative evaluations based on the corresponding X-ray powder diffractograms. Therefore, the objective of the study was to develop a quantification model based on X-ray powder diffractometry (XRPD), followed by a multivariate partial least squares regression approach that enables the simultaneous determination of up to four solid state fractions: crystalline naproxen, γ-indomethacin, α-indomethacin as well as co-amorphous naproxen-indomethacin. For this purpose, a calibration set that covers the whole range of possible combinations of the four components was prepared and analyzed by XRPD. In order to test the model performances, leave-one-out cross validation was performed and revealed root mean square errors of validation between 3.11% and 3.45% for the crystalline molar fractions and 5.57% for the co-amorphous molar fraction. In summary, even four solid state phases, involving one co-amorphous phase, can be quantified with this XRPD data-based approach.

[Predictive factors of complications during CT-guided transthoracic biopsy].

PubMed

Fontaine-Delaruelle, C; Souquet, P-J; Gamondes, D; Pradat, E; de Leusse, A; Ferretti, G R; Couraud, S

2017-04-01

CT-guided transthoracic core-needle biopsy (TTNB) is frequently used for the diagnosis of lung nodules. The aim of this study is to describe TTNBs' complications and to investigate predictive factors of complications. All consecutive TTNBs performed in three centers between 2006 and 2012 were included. Binary logistic regression was used for multivariate analysis. Overall, 970 TTNBs were performed in 929 patients. The complication rate was 34% (life-threatening complication in 6%). The most frequent complications were pneumothorax (29% included 4% which required chest-tube) and hemoptysis (5%). The mortality rate was 0.1% (n=1). In multivariate analysis, predictive factor for a complication was small target size (AOR=0.984; 95% CI [0.976-0.992]; P<0.001). This predictive factor was also found for occurrence of life-threatening complication (AOR=0.982; [0.965-0.999]; P=0.037), of pneumothorax (AOR=0.987; [0.978-0.995]; P=0.002) and of hemoptysis (AOR=0.973; [0.951-0.997]; P=0.024). One complication occurred in one-third of TTNBs. The proportion of life-threatening complication was 6%. A small lesion size was predictive of complication occurrence. Copyright © 2016 Elsevier Masson SAS. All rights reserved.

Discrimination and characterization of strawberry juice based on electronic nose and tongue: comparison of different juice processing approaches by LDA, PLSR, RF, and SVM.

PubMed

Qiu, Shanshan; Wang, Jun; Gao, Liping

2014-07-09

An electronic nose (E-nose) and an electronic tongue (E-tongue) have been used to characterize five types of strawberry juices based on processing approaches (i.e., microwave pasteurization, steam blanching, high temperature short time pasteurization, frozen-thawed, and freshly squeezed). Juice quality parameters (vitamin C, pH, total soluble solid, total acid, and sugar/acid ratio) were detected by traditional measuring methods. Multivariate statistical methods (linear discriminant analysis (LDA) and partial least squares regression (PLSR)) and neural networks (Random Forest (RF) and Support Vector Machines) were employed to qualitative classification and quantitative regression. E-tongue system reached higher accuracy rates than E-nose did, and the simultaneous utilization did have an advantage in LDA classification and PLSR regression. According to cross-validation, RF has shown outstanding and indisputable performances in the qualitative and quantitative analysis. This work indicates that the simultaneous utilization of E-nose and E-tongue can discriminate processed fruit juices and predict quality parameters successfully for the beverage industry.

Comparison of a Classical and Quantum Based Restricted Boltzmann Machine (RBM) for Application to Non-linear Multivariate Regression.

NASA Astrophysics Data System (ADS)

Dorband, J. E.; Tilak, N.; Radov, A.

2016-12-01

In this paper, a classical computer implementation of RBM is compared to a quantum annealing based RBM running on a D-Wave 2X (an adiabatic quantum computer). The codes for both are essentially identical. Only a flag is set to change the activation function from a classically computed logistic function to the D-Wave. To obtain greater understanding of the behavior of the D-Wave, a study of the stochastic properties of a virtual qubit (a 12 qubit chain) and a cell of qubits (an 8 qubit cell) was performed. We will present the results of comparing the D-Wave implementation with a theoretically errorless adiabatic quantum computer. The main purpose of this study is to develop a generic RBM regression tool in order to infer CO2 fluxes from the NASA satellite OCO-2 observed CO2 concentrations and predicted atmospheric states using regression models. The carbon fluxes will then be assimilated into a land surface model to predict the Net Ecosystem Exchange at globally distributed regional sites.

Statistical-learning strategies generate only modestly performing predictive models for urinary symptoms following external beam radiotherapy of the prostate: A comparison of conventional and machine-learning methods

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yahya, Noorazrul, E-mail: noorazrul.yahya@research.uwa.edu.au; Ebert, Martin A.; Bulsara, Max

Purpose: Given the paucity of available data concerning radiotherapy-induced urinary toxicity, it is important to ensure derivation of the most robust models with superior predictive performance. This work explores multiple statistical-learning strategies for prediction of urinary symptoms following external beam radiotherapy of the prostate. Methods: The performance of logistic regression, elastic-net, support-vector machine, random forest, neural network, and multivariate adaptive regression splines (MARS) to predict urinary symptoms was analyzed using data from 754 participants accrued by TROG03.04-RADAR. Predictive features included dose-surface data, comorbidities, and medication-intake. Four symptoms were analyzed: dysuria, haematuria, incontinence, and frequency, each with three definitions (grade ≥more » 1, grade ≥ 2 and longitudinal) with event rate between 2.3% and 76.1%. Repeated cross-validations producing matched models were implemented. A synthetic minority oversampling technique was utilized in endpoints with rare events. Parameter optimization was performed on the training data. Area under the receiver operating characteristic curve (AUROC) was used to compare performance using sample size to detect differences of ≥0.05 at the 95% confidence level. Results: Logistic regression, elastic-net, random forest, MARS, and support-vector machine were the highest-performing statistical-learning strategies in 3, 3, 3, 2, and 1 endpoints, respectively. Logistic regression, MARS, elastic-net, random forest, neural network, and support-vector machine were the best, or were not significantly worse than the best, in 7, 7, 5, 5, 3, and 1 endpoints. The best-performing statistical model was for dysuria grade ≥ 1 with AUROC ± standard deviation of 0.649 ± 0.074 using MARS. For longitudinal frequency and dysuria grade ≥ 1, all strategies produced AUROC>0.6 while all haematuria endpoints and longitudinal incontinence models produced AUROC<0.6. Conclusions: Logistic regression and MARS were most likely to be the best-performing strategy for the prediction of urinary symptoms with elastic-net and random forest producing competitive results. The predictive power of the models was modest and endpoint-dependent. New features, including spatial dose maps, may be necessary to achieve better models.« less

The Role of Auxiliary Variables in Deterministic and Deterministic-Stochastic Spatial Models of Air Temperature in Poland

NASA Astrophysics Data System (ADS)

Szymanowski, Mariusz; Kryza, Maciej

2017-02-01

Our study examines the role of auxiliary variables in the process of spatial modelling and mapping of climatological elements, with air temperature in Poland used as an example. The multivariable algorithms are the most frequently applied for spatialization of air temperature, and their results in many studies are proved to be better in comparison to those obtained by various one-dimensional techniques. In most of the previous studies, two main strategies were used to perform multidimensional spatial interpolation of air temperature. First, it was accepted that all variables significantly correlated with air temperature should be incorporated into the model. Second, it was assumed that the more spatial variation of air temperature was deterministically explained, the better was the quality of spatial interpolation. The main goal of the paper was to examine both above-mentioned assumptions. The analysis was performed using data from 250 meteorological stations and for 69 air temperature cases aggregated on different levels: from daily means to 10-year annual mean. Two cases were considered for detailed analysis. The set of potential auxiliary variables covered 11 environmental predictors of air temperature. Another purpose of the study was to compare the results of interpolation given by various multivariable methods using the same set of explanatory variables. Two regression models: multiple linear (MLR) and geographically weighted (GWR) method, as well as their extensions to the regression-kriging form, MLRK and GWRK, respectively, were examined. Stepwise regression was used to select variables for the individual models and the cross-validation method was used to validate the results with a special attention paid to statistically significant improvement of the model using the mean absolute error (MAE) criterion. The main results of this study led to rejection of both assumptions considered. Usually, including more than two or three of the most significantly correlated auxiliary variables does not improve the quality of the spatial model. The effects of introduction of certain variables into the model were not climatologically justified and were seen on maps as unexpected and undesired artefacts. The results confirm, in accordance with previous studies, that in the case of air temperature distribution, the spatial process is non-stationary; thus, the local GWR model performs better than the global MLR if they are specified using the same set of auxiliary variables. If only GWR residuals are autocorrelated, the geographically weighted regression-kriging (GWRK) model seems to be optimal for air temperature spatial interpolation.

Effects of Social Class and School Conditions on Educational Enrollment and Achievement of Boys and Girls in Rural Viet Nam

ERIC Educational Resources Information Center

Nguyen, Phuong L.

2006-01-01

This study examines the effects of parental SES, school quality, and community factors on children's enrollment and achievement in rural areas in Viet Nam, using logistic regression and ordered logistic regression. Multivariate analysis reveals significant differences in educational enrollment and outcomes by level of household expenditures and…

Procedures for using signals from one sensor as substitutes for signals of another

NASA Technical Reports Server (NTRS)

Suits, G.; Malila, W.; Weller, T.

1988-01-01

Long-term monitoring of surface conditions may require a transfer from using data from one satellite sensor to data from a different sensor having different spectral characteristics. Two general procedures for spectral signal substitution are described in this paper, a principal-components procedure and a complete multivariate regression procedure. They are evaluated through a simulation study of five satellite sensors (MSS, TM, AVHRR, CZCS, and HRV). For illustration, they are compared to another recently described procedure for relating AVHRR and MSS signals. The multivariate regression procedure is shown to be best. TM can accurately emulate the other sensors, but they, on the other hand, have difficulty in accurately emulating its shortwave infrared bands (TM5 and TM7).

Predictive Utility of Marketed Volumetric Software Tools in Subjects at Risk for Alzheimer's: Do Regions Outside the Hippocampus Matter?

PubMed Central

Tanpitukpongse, Teerath P.; Mazurowski, Maciej A.; Ikhena, John; Petrella, Jeffrey R.

2016-01-01

Background and Purpose To assess prognostic efficacy of individual versus combined regional volumetrics in two commercially-available brain volumetric software packages for predicting conversion of patients with mild cognitive impairment to Alzheimer's disease. Materials and Methods Data was obtained through the Alzheimer's Disease Neuroimaging Initiative. 192 subjects (mean age 74.8 years, 39% female) diagnosed with mild cognitive impairment at baseline were studied. All had T1WI MRI sequences at baseline and 3-year clinical follow-up. Analysis was performed with NeuroQuant® and Neuroreader™. Receiver operating characteristic curves assessing the prognostic efficacy of each software package were generated using a univariable approach employing individual regional brain volumes, as well as two multivariable approaches (multiple regression and random forest), combining multiple volumes. Results On univariable analysis of 11 NeuroQuant® and 11 Neuroreader™ regional volumes, hippocampal volume had the highest area under the curve for both software packages (0.69 NeuroQuant®, 0.68 Neuroreader™), and was not significantly different (p > 0.05) between packages. Multivariable analysis did not increase the area under the curve for either package (0.63 logistic regression, 0.60 random forest NeuroQuant®; 0.65 logistic regression, 0.62 random forest Neuroreader™). Conclusion Of the multiple regional volume measures available in FDA-cleared brain volumetric software packages, hippocampal volume remains the best single predictor of conversion of mild cognitive impairment to Alzheimer's disease at 3-year follow-up. Combining volumetrics did not add additional prognostic efficacy. Therefore, future prognostic studies in MCI, combining such tools with demographic and other biomarker measures, are justified in using hippocampal volume as the only volumetric biomarker. PMID:28057634

Prehospital helicopter transport and survival of patients with traumatic brain injury.

PubMed

Bekelis, Kimon; Missios, Symeon; Mackenzie, Todd A

2015-03-01

To investigate the association of helicopter transport with survival of patients with traumatic brain injury (TBI), in comparison with ground emergency medical services (EMS). Helicopter utilization and its effect on the outcomes of TBI remain controversial. We performed a retrospective cohort study involving patients with TBI who were registered in the National Trauma Data Bank between 2009 and 2011. Regression techniques with propensity score matching were used to investigate the association of helicopter transport with survival of patients with TBI, in comparison with ground EMS. During the study period, there were 209,529 patients with TBI who were registered in the National Trauma Data Bank and met the inclusion criteria. Of these patients, 35,334 were transported via helicopters and 174,195 via ground EMS. For patients transported to level I trauma centers, 2797 deaths (12%) were recorded after helicopter transport and 8161 (7.8%) after ground EMS. Multivariable logistic regression analysis demonstrated an association of helicopter transport with increased survival [OR (odds ratio), 1.95; 95% confidence interval (CI), 1.81-2.10; absolute risk reduction (ARR), 6.37%]. This persisted after propensity score matching (OR, 1.88; 95% CI, 1.74-2.03; ARR, 5.93%). For patients transported to level II trauma centers, 1282 deaths (10.6%) were recorded after helicopter transport and 5097 (7.3%) after ground EMS. Multivariable logistic regression analysis demonstrated an association of helicopter transport with increased survival (OR, 1.81; 95% CI, 1.64-2.00; ARR 5.17%). This again persisted after propensity score matching (OR, 1.73; 95% CI, 1.55-1.94; ARR, 4.69). Helicopter transport of patients with TBI to level I and II trauma centers was associated with improved survival, in comparison with ground EMS.

Prehospital Helicopter Transport and Survival of Patients With Traumatic Brain Injury

PubMed Central

Mackenzie, Todd A.

2015-01-01

Objective To investigate the association of helicopter transport with survival of patients with traumatic brain injury (TBI), in comparison with ground emergency medical services (EMS). Background Helicopter utilization and its effect on the outcomes of TBI remain controversial. Methods We performed a retrospective cohort study involving patients with TBI who were registered in the National Trauma Data Bank between 2009 and 2011. Regression techniques with propensity score matching were used to investigate the association of helicopter transport with survival of patients with TBI, in comparison with ground EMS. Results During the study period, there were 209,529 patients with TBI who were registered in the National Trauma Data Bank and met the inclusion criteria. Of these patients, 35,334 were transported via helicopters and 174,195 via ground EMS. For patients transported to level I trauma centers, 2797 deaths (12%) were recorded after helicopter transport and 8161 (7.8%) after ground EMS. Multivariable logistic regression analysis demonstrated an association of helicopter transport with increased survival [OR (odds ratio), 1.95; 95% confidence interval (CI), 1.81–2.10; absolute risk reduction (ARR), 6.37%]. This persisted after propensity score matching (OR, 1.88; 95% CI, 1.74–2.03; ARR, 5.93%). For patients transported to level II trauma centers, 1282 deaths (10.6%) were recorded after helicopter transport and 5097 (7.3%) after ground EMS. Multivariable logistic regression analysis demonstrated an association of helicopter transport with increased survival (OR, 1.81; 95% CI, 1.64–2.00; ARR 5.17%). This again persisted after propensity score matching (OR, 1.73; 95% CI, 1.55–1.94; ARR, 4.69). Conclusions Helicopter transport of patients with TBI to level I and II trauma centers was associated with improved survival, in comparison with ground EMS. PMID:24743624

The six-minute walk test predicts cardiorespiratory fitness in individuals with aneurysmal subarachnoid hemorrhage.

PubMed

Harmsen, Wouter J; Ribbers, Gerard M; Slaman, Jorrit; Heijenbrok-Kal, Majanka H; Khajeh, Ladbon; van Kooten, Fop; Neggers, Sebastiaan J C M M; van den Berg-Emons, Rita J

2017-05-01

Peak oxygen uptake (VO 2peak ) established during progressive cardiopulmonary exercise testing (CPET) is the "gold-standard" for cardiorespiratory fitness. However, CPET measurements may be limited in patients with aneurysmal subarachnoid hemorrhage (a-SAH) by disease-related complaints, such as cardiovascular health-risks or anxiety. Furthermore, CPET with gas-exchange analyses require specialized knowledge and infrastructure with limited availability in most rehabilitation facilities. To determine whether an easy-to-administer six-minute walk test (6MWT) is a valid clinical alternative to progressive CPET in order to predict VO 2peak in individuals with a-SAH. Twenty-seven patients performed the 6MWT and CPET with gas-exchange analyses on a cycle ergometer. Univariate and multivariate regression models were made to investigate the predictability of VO 2peak from the six-minute walk distance (6MWD). Univariate regression showed that the 6MWD was strongly related to VO 2peak (r = 0.75, p < 0.001), with an explained variance of 56% and a prediction error of 4.12 ml/kg/min, representing 18% of mean VO 2peak . Adding age and sex to an extended multivariate regression model improved this relationship (r = 0.82, p < 0.001), with an explained variance of 67% and a prediction error of 3.67 ml/kg/min corresponding to 16% of mean VO 2peak . The 6MWT is an easy-to-administer submaximal exercise test that can be selected to estimate cardiorespiratory fitness at an aggregated level, in groups of patients with a-SAH, which may help to evaluate interventions in a clinical or research setting. However, the relatively large prediction error does not allow for an accurate prediction in individual patients.

Outcomes of deliveries by family physicians or obstetricians: a population-based cohort study using an instrumental variable

PubMed Central

Aubrey-Bassler, Kris; Cullen, Richard M.; Simms, Alvin; Asghari, Shabnam; Crane, Joan; Wang, Peizhong Peter; Godwin, Marshall

2015-01-01

Background: Previous research has suggested that obstetric outcomes are similar for deliveries by family physicians and obstetricians, but many of these studies were small, and none of them adjusted for unmeasured selection bias. We compared obstetric outcomes between these provider types using an econometric method designed to adjust for unobserved confounding. Methods: We performed a retrospective population-based cohort study of all Canadian (except Quebec) hospital births with delivery by family physicians and obstetricians at more than 20 weeks gestational age, with birth weight greater than 500 g, between Apr. 1, 2006, and Mar. 31, 2009. The primary outcomes were the relative risks of in-hospital perinatal death and a composite of maternal mortality and major morbidity assessed with multivariable logistic regression and instrumental variable–adjusted multivariable regression. Results: After exclusions, there were 3600 perinatal deaths and 14 394 cases of maternal morbidity among 799 823 infants and 793 053 mothers at 390 hospitals. For deliveries by family physicians v. obstetricians, the relative risk of perinatal mortality was 0.98 (95% confidence interval [CI] 0.85–1.14) and of maternal morbidity was 0.81 (95% CI 0.70–0.94) according to logistic regression. The respective relative risks were 0.97 (95% CI 0.58–1.64) and 1.13 (95% CI 0.65–1.95) according to instrumental variable methods. Interpretation: After adjusting for both observed and unobserved confounders, we found a similar risk of perinatal mortality and adverse maternal outcome for obstetric deliveries by family physicians and obstetricians. Whether there are differences between these groups for other outcomes remains to be seen. PMID:26303244

Prevalence of difficult venous access and associated risk factors in highly complex hospitalised patients.

PubMed

Armenteros-Yeguas, Victoria; Gárate-Echenique, Lucía; Tomás-López, Maria Aranzazu; Cristóbal-Domínguez, Estíbaliz; Moreno-de Gusmão, Breno; Miranda-Serrano, Erika; Moraza-Dulanto, Maria Inmaculada

2017-12-01

To estimate the prevalence of difficult venous access in complex patients with multimorbidity and to identify associated risk factors. In highly complex patients, factors like ageing, the need for frequent use of irritant medication and multiple venous catheterisations to complete treatment could contribute to exhaustion of venous access. A cross-sectional study was conducted. 'Highly complex' patients (n = 135) were recruited from March 2013-November 2013. The main study variable was the prevalence of difficult venous access, assessed using one of the following criteria: (1) a history of difficulties obtaining venous access based on more than two attempts to insert an intravenous line and (2) no visible or palpable veins. Other factors potentially associated with the risk of difficult access were also measured (age, gender and chronic illnesses). Univariate analysis was performed for each potential risk factor. Factors with p < 0·2 were then included in multivariable logistic regression analysis. Odds ratios were also calculated. The prevalence of difficult venous access was 59·3%. The univariate logistic regression analysis indicated that gender, a history of vascular access complications and osteoarticular disease were significantly associated with difficult venous access. The multivariable logistic regression showed that only gender was an independent risk factor and the odds ratios was 2·85. The prevalence of difficult venous access is high in this population. Gender (female) is the only independent risk factor associated with this. Previous history of several attempts at catheter insertion is an important criterion in the assessment of difficult venous access. The prevalence of difficult venous access in complex patients is 59·3%. Significant risk factors include being female and a history of complications related to vascular access. © 2017 John Wiley & Sons Ltd.

Intraoperative factors associated with delayed recovery of liver function after hepatectomy: analysis of 1969 living donors.

PubMed

Choi, S-S; Cho, S-S; Ha, T-Y; Hwang, S; Lee, S-G; Kim, Y-K

2016-02-01

The safety of healthy living donors who are undergoing hepatic resection is a primary concern. We aimed to identify intraoperative anaesthetic and surgical factors associated with delayed recovery of liver function after hepatectomy in living donors. We retrospectively analysed 1969 living donors who underwent hepatectomy for living donor liver transplantation. Delayed recovery of hepatic function was defined by increases in international normalised ratio of prothrombin time and concomitant hyperbilirubinaemia on or after post-operative day 5. Univariate and multivariate logistic regression analyses were performed to determine the factors associated with delayed recovery of hepatic function after living donor hepatectomy. Delayed recovery of liver function after donor hepatectomy was observed in 213 (10.8%) donors. Univariate logistic regression analysis showed that sevoflurane anaesthesia, synthetic colloid, donor age, body mass index, fatty change and remnant liver volume were significant factors for prediction of delayed recovery of hepatic function. Multivariate logistic regression analysis showed that independent factors significantly associated with delayed recovery of liver function after donor hepatectomy were sevoflurane anaesthesia (odds ratio = 3.514, P < 0.001), synthetic colloid (odds ratio = 1.045, P = 0.033), donor age (odds ratio = 0.970, P = 0.003), female gender (odds ratio = 1.512, P = 0.014) and remnant liver volume (odds ratio = 0.963, P < 0.001). Anaesthesia with sevoflurane was an independent factor in predicting delayed recovery of hepatic function after donor hepatectomy. Although synthetic colloid may be associated with delayed recovery of hepatic function after donor hepatectomy, further study is required. These results can provide useful information on perioperative management of living liver donors. © 2015 The Acta Anaesthesiologica Scandinavica Foundation. Published by John Wiley & Sons Ltd.

Retinal nerve fibre layer thinning is associated with drug resistance in epilepsy

PubMed Central

Balestrini, Simona; Clayton, Lisa M S; Bartmann, Ana P; Chinthapalli, Krishna; Novy, Jan; Coppola, Antonietta; Wandschneider, Britta; Stern, William M; Acheson, James; Bell, Gail S; Sander, Josemir W; Sisodiya, Sanjay M

2016-01-01

Objective Retinal nerve fibre layer (RNFL) thickness is related to the axonal anterior visual pathway and is considered a marker of overall white matter ‘integrity’. We hypothesised that RNFL changes would occur in people with epilepsy, independently of vigabatrin exposure, and be related to clinical characteristics of epilepsy. Methods Three hundred people with epilepsy attending specialist clinics and 90 healthy controls were included in this cross-sectional cohort study. RNFL imaging was performed using spectral-domain optical coherence tomography (OCT). Drug resistance was defined as failure of adequate trials of two antiepileptic drugs to achieve sustained seizure freedom. Results The average RNFL thickness and the thickness of each of the 90° quadrants were significantly thinner in people with epilepsy than healthy controls (p<0.001, t test). In a multivariate logistic regression model, drug resistance was the only significant predictor of abnormal RNFL thinning (OR=2.09, 95% CI 1.09 to 4.01, p=0.03). Duration of epilepsy (coefficient −0.16, p=0.004) and presence of intellectual disability (coefficient −4.0, p=0.044) also showed a significant relationship with RNFL thinning in a multivariate linear regression model. Conclusions Our results suggest that people with epilepsy with no previous exposure to vigabatrin have a significantly thinner RNFL than healthy participants. Drug resistance emerged as a significant independent predictor of RNFL borderline attenuation or abnormal thinning in a logistic regression model. As this is easily assessed by OCT, RNFL thickness might be used to better understand the mechanisms underlying drug resistance, and possibly severity. Longitudinal studies are needed to confirm our findings. PMID:25886782

Hospital of diagnosis and probability of having surgical treatment for resectable gastric cancer.

PubMed

van Putten, M; Verhoeven, R H A; van Sandick, J W; Plukker, J T M; Lemmens, V E P P; Wijnhoven, B P L; Nieuwenhuijzen, G A P

2016-02-01

Gastric cancer surgery is increasingly being centralized in the Netherlands, whereas the diagnosis is often made in hospitals where gastric cancer surgery is not performed. The aim of this study was to assess whether hospital of diagnosis affects the probability of undergoing surgery and its impact on overall survival. All patients with potentially curable gastric cancer according to stage (cT1/1b-4a, cN0-2, cM0) diagnosed between 2005 and 2013 were selected from The Netherlands Cancer Registry. Multilevel logistic regression was used to examine the probability of undergoing surgery according to hospital of diagnosis. The effect of variation in probability of undergoing surgery among hospitals of diagnosis on overall survival during the intervals 2005-2009 and 2010-2013 was examined by using Cox regression analysis. A total of 5620 patients with potentially curable gastric cancer, diagnosed in 91 hospitals, were included. The proportion of patients who underwent surgery ranged from 53.1 to 83.9 per cent according to hospital of diagnosis (P < 0.001); after multivariable adjustment for patient and tumour characteristics it ranged from 57.0 to 78.2 per cent (P < 0.001). Multivariable Cox regression showed that patients diagnosed between 2010 and 2013 in hospitals with a low probability of patients undergoing curative treatment had worse overall survival (hazard ratio 1.21; P < 0.001). The large variation in probability of receiving surgery for gastric cancer between hospitals of diagnosis and its impact on overall survival indicates that gastric cancer decision-making is suboptimal. © 2015 BJS Society Ltd Published by John Wiley & Sons Ltd.

A retrospective study: Multivariate logistic regression analysis of the outcomes after pressure sores reconstruction with fasciocutaneous, myocutaneous, and perforator flaps.

PubMed

Chiu, Yu-Jen; Liao, Wen-Chieh; Wang, Tien-Hsiang; Shih, Yu-Chung; Ma, Hsu; Lin, Chih-Hsun; Wu, Szu-Hsien; Perng, Cherng-Kang

2017-08-01

Despite significant advances in medical care and surgical techniques, pressure sore reconstruction is still prone to elevated rates of complication and recurrence. We conducted a retrospective study to investigate not only complication and recurrence rates following pressure sore reconstruction but also preoperative risk stratification. This study included 181 ulcers underwent flap operations between January 2002 and December 2013 were included in the study. We performed a multivariable logistic regression model, which offers a regression-based method accounting for the within-patient correlation of the success or failure of each flap. The overall complication and recurrence rates for all flaps were 46.4% and 16.0%, respectively, with a mean follow-up period of 55.4 ± 38.0 months. No statistically significant differences of complication and recurrence rates were observed among three different reconstruction methods. In subsequent analysis, albumin ≤3.0 g/dl and paraplegia were significantly associated with higher postoperative complication. The anatomic factor, ischial wound location, significantly trended toward the development of ulcer recurrence. In the fasciocutaneous group, paraplegia had significant correlation to higher complication and recurrence rates. In the musculocutaneous flap group, variables had no significant correlation to complication and recurrence rates. In the free-style perforator group, ischial wound location and malnourished status correlated with significantly higher complication rates; ischial wound location also correlated with significantly higher recurrence rate. Ultimately, our review of a noteworthy cohort with lengthy follow-up helped identify and confirm certain risk factors that can facilitate a more informed and thoughtful pre- and postoperative decision-making process for patients with pressure ulcers. Copyright © 2017 British Association of Plastic, Reconstructive and Aesthetic Surgeons. Published by Elsevier Ltd. All rights reserved.

Suspicion of respiratory tract infection with multidrug-resistant Enterobacteriaceae: epidemiology and risk factors from a Paediatric Intensive Care Unit.

PubMed

Renk, Hanna; Stoll, Lenja; Neunhoeffer, Felix; Hölzl, Florian; Kumpf, Matthias; Hofbeck, Michael; Hartl, Dominik

2017-02-21

Multidrug-resistant (MDR) infections are a serious concern for children admitted to the Paediatric Intensive Care Unit (PICU). Tracheal colonization with MDR Enterobacteriaceae predisposes to respiratory infection, but underlying risk factors are poorly understood. This study aims to determine the incidence of children with suspected infection during mechanical ventilation and analyses risk factors for the finding of MDR Enterobacteriaceae in tracheal aspirates. A retrospective single-centre analysis of Enterobacteriaceae isolates from the lower respiratory tract of ventilated PICU patients from 2005 to 2014 was performed. Resistance status was determined and clinical records were reviewed for potential risk factors. A classification and regression tree (CRT) to predict risk factors for infection with MDR Enterobacteriaceae was employed. The model was validated by simple and multivariable logistic regression. One hundred sixty-seven Enterobacteriaceae isolates in 123 children were identified. The most frequent isolates were Enterobacter spp., Klebsiella spp. and E.coli. Among these, 116 (69%) isolates were susceptible and 51 (31%) were MDR. In the CRT analysis, antibiotic exposure for ≥ 7 days and presence of gastrointestinal comorbidity were the most relevant predictors for an MDR isolate. Antibiotic exposure for ≥ 7 days was confirmed as a significant risk factor for infection with MDR Enterobacteriaceae by a multivariable logistic regression model. This study shows that critically-ill children with tracheal Enterobacteriaceae infection are at risk of carrying MDR isolates. Prior use of antibiotics for ≥ 7 days significantly increased the risk of finding MDR organisms in ventilated PICU patients with suspected infection. Our results imply that early identification of patients at risk, rapid microbiological diagnostics and tailored antibiotic therapy are essential to improve management of critically ill children infected with Enterobacteriaceae.

Increased left ventricular mass index is present in patients with type 2 diabetes without ischemic heart disease.

PubMed

Seferovic, Jelena P; Tesic, Milorad; Seferovic, Petar M; Lalic, Katarina; Jotic, Aleksandra; Biering-Sørensen, Tor; Giga, Vojislav; Stankovic, Sanja; Milic, Natasa; Lukic, Ljiljana; Milicic, Tanja; Macesic, Marija; Gajovic, Jelena Stanarcic; Lalic, Nebojsa M

2018-01-17

Left ventricular mass index (LVMI) increase has been described in hypertension (HTN), but less is known about its association with type 2 diabetes (T2DM). As these conditions frequently co-exist, we investigated the association of T2DM, HTN and both with echocardiographic parameters, and hypothesized that patients with both had highest LVMI, followed by patients with only T2DM or HTN. Study population included 101 T2DM patients, 62 patients with HTN and no T2DM, and 76 patients with T2DM and HTN, excluded for ischemic heart disease. Demographic and clinical data, biochemical measurements, stress echocardiography, transthoracic 2D Doppler and tissue Doppler echocardiography were performed. Multivariable logistic regression was used to determine the independent association with T2DM. Linear regression models and Pearson's correlation were used to assess the correlations between LVMI and other parameters. Patients with only T2DM had significantly greater LVMI (84.9 ± 20.3 g/m 2 ) compared to patients with T2DM and HTN (77.9 ± 16 g/m 2 ) and only HTN (69.8 ± 12.4 g/m 2 ). In multivariate logistic regression analysis, T2DM was associated with LVMI (OR 1.033, 95%CI 1.003-1.065, p = 0.029). A positive correlation of LVMI was found with fasting glucose (p < 0.001) and HbA1c (p = 0.0003). Increased LVMI could be a potential, pre-symptomatic marker of myocardial structural change in T2DM.

Seminal Plasma HIV-1 RNA Concentration Is Strongly Associated with Altered Levels of Seminal Plasma Interferon-γ, Interleukin-17, and Interleukin-5

PubMed Central

Hoffman, Jennifer C.; Anton, Peter A.; Baldwin, Gayle Cocita; Elliott, Julie; Anisman-Posner, Deborah; Tanner, Karen; Grogan, Tristan; Elashoff, David; Sugar, Catherine; Yang, Otto O.

2014-01-01

Abstract Seminal plasma HIV-1 RNA level is an important determinant of the risk of HIV-1 sexual transmission. We investigated potential associations between seminal plasma cytokine levels and viral concentration in the seminal plasma of HIV-1-infected men. This was a prospective, observational study of paired blood and semen samples from 18 HIV-1 chronically infected men off antiretroviral therapy. HIV-1 RNA levels and cytokine levels in seminal plasma and blood plasma were measured and analyzed using simple linear regressions to screen for associations between cytokines and seminal plasma HIV-1 levels. Forward stepwise regression was performed to construct the final multivariate model. The median HIV-1 RNA concentrations were 4.42 log10 copies/ml (IQR 2.98, 4.70) and 2.96 log10 copies/ml (IQR 2, 4.18) in blood and seminal plasma, respectively. In stepwise multivariate linear regression analysis, blood HIV-1 RNA level (p<0.0001) was most strongly associated with seminal plasma HIV-1 RNA level. After controlling for blood HIV-1 RNA level, seminal plasma HIV-1 RNA level was positively associated with interferon (IFN)-γ (p=0.03) and interleukin (IL)-17 (p=0.03) and negatively associated with IL-5 (p=0.0007) in seminal plasma. In addition to blood HIV-1 RNA level, cytokine profiles in the male genital tract are associated with HIV-1 RNA levels in semen. The Th1 and Th17 cytokines IFN-γ and IL-17 are associated with increased seminal plasma HIV-1 RNA, while the Th2 cytokine IL-5 is associated with decreased seminal plasma HIV-1 RNA. These results support the importance of genital tract immunomodulation in HIV-1 transmission. PMID:25209674

The Association Between Immigration Status and Office-based Medical Provider Visits for Cancer Patients in the United States.

PubMed

Wang, Yang; Wilson, Fernando A; Chen, Li-Wu

2017-06-01

We examined differences in cancer-related office-based provider visits associated with immigration status in the United States. Data from the 2007-2012 Medical Expenditure Panel Survey and National Health Interview Survey included adult patients diagnosed with cancer. Univariate analyses described distributions of cancer-related office-based provider visits received, expenditures, visit characteristics, as well as demographic, socioeconomic, and health covariates, across immigration groups. We measured the relationships of immigrant status to number of visits and associated expenditure within the past 12 months, adjusting for age, sex, educational attainment, race/ethnicity, self-reported health status, time since cancer diagnosis, cancer remission status, marital status, poverty status, insurance status, and usual source of care. We finally performed sensitivity analyses for regression results by using the propensity score matching method to adjust for potential selection bias. Noncitizens had about 2 fewer visits in a 12-month period in comparison to US-born citizens (4.0 vs. 5.9). Total expenditure per patient was higher for US-born citizens than immigrants (not statistically significant). Noncitizens (88.3%) were more likely than US-born citizens (76.6%) to be seen by a medical doctor during a visit. Multivariate regression results showed that noncitizens had 42% lower number of visiting medical providers at office-based settings for cancer care than US-born citizens, after adjusting for all the other covariates. There were no significant differences in expenditures across immigration groups. The propensity score matching results were largely consistent with those in multivariate-adjusted regressions. Results suggest targeted interventions are needed to reduce disparities in utilization between immigrants and US-born citizen cancer patients.

Sex differences in the effect of aging on dry eye disease.

PubMed

Ahn, Jong Ho; Choi, Yoon-Hyeong; Paik, Hae Jung; Kim, Mee Kum; Wee, Won Ryang; Kim, Dong Hyun

2017-01-01

Aging is a major risk factor in dry eye disease (DED), and understanding sexual differences is very important in biomedical research. However, there is little information about sex differences in the effect of aging on DED. We investigated sex differences in the effect of aging and other risk factors for DED. This study included data of 16,824 adults from the Korea National Health and Nutrition Examination Survey (2010-2012), which is a population-based cross-sectional survey. DED was defined as the presence of frequent ocular dryness or a previous diagnosis by an ophthalmologist. Basic sociodemographic factors and previously known risk factors for DED were included in the analyses. Linear regression modeling and multivariate logistic regression modeling were used to compare the sex differences in the effect of risk factors for DED; we additionally performed tests for interactions between sex and other risk factors for DED in logistic regression models. In our linear regression models, the prevalence of DED symptoms in men increased with age ( R =0.311, P =0.012); however, there was no association between aging and DED in women ( P >0.05). Multivariate logistic regression analyses showed that aging in men was not associated with DED (DED symptoms/diagnosis: odds ratio [OR] =1.01/1.04, each P >0.05), while aging in women was protectively associated with DED (DED symptoms/diagnosis: OR =0.94/0.91, P =0.011/0.003). Previous ocular surgery was significantly associated with DED in both men and women (men/women: OR =2.45/1.77 [DED symptoms] and 3.17/2.05 [DED diagnosis], each P <0.001). Tests for interactions of sex revealed significantly different aging × sex and previous ocular surgery × sex interactions ( P for interaction of sex: DED symptoms/diagnosis - 0.044/0.011 [age] and 0.012/0.006 [previous ocular surgery]). There were distinct sex differences in the effect of aging on DED in the Korean population. DED following ocular surgery also showed sexually different patterns. Age matching and sex matching are strongly recommended in further studies about DED, especially DED following ocular surgery.

Pyrogenic carbon distribution in mineral topsoils of the northeastern United States

USGS Publications Warehouse

Jauss, Verena; Sullivan, Patrick J.; Sanderman, Jonathan; Smith, David; Lehmann, Johannes

2017-01-01

Due to its slow turnover rates in soil, pyrogenic carbon (PyC) is considered an important C pool and relevant to climate change processes. Therefore, the amounts of soil PyC were compared to environmental covariates over an area of 327,757 km2 in the northeastern United States in order to understand the controls on PyC distribution over large areas. Topsoil (defined as the soil A horizon, after removal of any organic horizons) samples were collected at 165 field sites in a generalised random tessellation stratified design that corresponded to approximately 1 site per 1600 km2 and PyC was estimated from diffuse reflectance mid-infrared spectroscopy measurements using a partial least-squares regression analysis in conjunction with a large database of PyC measurements based on a solid-state 13C nuclear magnetic resonance spectroscopy technique. Three spatial models were applied to the data in order to relate critical environmental covariates to the changes in spatial density of PyC over the landscape. Regional mean density estimates of PyC were 11.0 g kg− 1 (0.84 Gg km− 2) for Ordinary Kriging, 25.8 g kg− 1(12.2 Gg km− 2) for Multivariate Linear Regression, and 26.1 g kg− 1 (12.4 Gg km− 2) for Bayesian Regression Kriging. Akaike Information Criterion (AIC) indicated that the Multivariate Linear Regression model performed best (AIC = 842.6; n = 165) compared to Ordinary Kriging (AIC = 982.4) and Bayesian Regression Kriging (AIC = 979.2). Soil PyC concentrations correlated well with total soil sulphur (P < 0.001; n = 165), plant tissue lignin (P = 0.003), and drainage class (P = 0.008). This suggests the opportunity of including related environmental parameters in the spatial assessment of PyC in soils. Better estimates of the contribution of PyC to the global carbon cycle will thus also require more accurate assessments of these covariates.

Coordination patterns related to high clinical performance in a simulated anesthetic crisis.

PubMed

Manser, Tanja; Harrison, Thomas Kyle; Gaba, David M; Howard, Steven K

2009-05-01

Teamwork is an integral component in the delivery of safe patient care. Several studies highlight the importance of effective teamwork and the need for teams to respond dynamically to changing task requirements, for example, during crisis situations. In this study, we address one of the many facets of "effective teamwork" in medical teams by investigating coordination patterns related to high performance in the management of a simulated malignant hyperthermia (MH) scenario. We hypothesized that (a) anesthesia crews dynamically adapt their work and coordination patterns to the occurrence of a simulated MH crisis and that (b) crews with higher clinical performance scores (based on a time-based scoring system for critical MH treatment steps) exhibit different coordination patterns. This observational study investigated differences in work and coordination patterns of 24 two-person anesthesia crews in a simulated MH scenario. Clinical and coordination behavior were coded using a structured observation system consisting of 36 mutually exclusive observation categories for clinical activities, coordination activities, teaching, and other communication. Clinical performance scores for treating the simulated episode of MH were calculated using a time-based scoring system for critical treatment steps. Coordination patterns in response to the occurrence of a crisis situation were analyzed using multivariate analysis of variance and the relationship between coordination patterns and clinical performance was investigated using hierarchical regression analyses. Qualitative analyses of the three highest and lowest performing crews were conducted to complement the quantitative analysis. First, a multivariate analysis of variance revealed statistically significant changes in the proportion of time spent on clinical and coordination activities once the MH crisis was declared (F [5,19] = 162.81, P < 0.001, eta(p)(2) = 0.98). Second, hierarchical regression analyses controlling for the effects of cognitive aid use showed that higher performing anesthesia crews exhibit statistically significant less task distribution (beta = -0.539, P < 0.01) and significantly more situation assessment (beta = 0.569, P < 0.05). Additional qualitative video analysis revealed, for example, that lower scoring crews were more likely to split into subcrews (i.e., both anesthesiologists worked with other members of the perioperative team without maintaining a shared plan among the two-person anesthesia crew). Our results of the relationship of coordination patterns and clinical performance will inform future research on adaptive coordination in medical teams and support the development of specific training to improve team coordination and performance.

Transformational leadership and workplace injury and absenteeism: analysis of a National Nursing Assistant Survey.

PubMed

Lee, Doohee; Coustasse, Alberto; Sikula, Andrew

2011-01-01

Transformational leadership (TL) has long been popular among management scholars and health services researchers, but no research studies have empirically tested the association of TL with workplace injuries and absenteeism among nursing assistants (NAs). This cross-sectional study seeks to explore whether TL is associated with workplace injuries and absenteeism among NAs. We analyzed the 2004 National Nursing Assistant Survey data (n = 2,882). A multivariate logistic regression analysis was performed to test the role of TL in the context of workplace performances. Results reveal that the TL model was positively linked to workplace injury in the level of NAs. Injury-related absenteeism was also associated with the TL style, indicating that TL behaviors may help address workplace absence among NAs. Findings suggest that introducing TL practices may benefit NAs in improving workplace performances.

Stata Modules for Calculating Novel Predictive Performance Indices for Logistic Models

PubMed Central

Barkhordari, Mahnaz; Padyab, Mojgan; Hadaegh, Farzad; Azizi, Fereidoun; Bozorgmanesh, Mohammadreza

2016-01-01

Background Prediction is a fundamental part of prevention of cardiovascular diseases (CVD). The development of prediction algorithms based on the multivariate regression models loomed several decades ago. Parallel with predictive models development, biomarker researches emerged in an impressively great scale. The key question is how best to assess and quantify the improvement in risk prediction offered by new biomarkers or more basically how to assess the performance of a risk prediction model. Discrimination, calibration, and added predictive value have been recently suggested to be used while comparing the predictive performances of the predictive models’ with and without novel biomarkers. Objectives Lack of user-friendly statistical software has restricted implementation of novel model assessment methods while examining novel biomarkers. We intended, thus, to develop a user-friendly software that could be used by researchers with few programming skills. Materials and Methods We have written a Stata command that is intended to help researchers obtain cut point-free and cut point-based net reclassification improvement index and (NRI) and relative and absolute Integrated discriminatory improvement index (IDI) for logistic-based regression analyses.We applied the commands to a real data on women participating the Tehran lipid and glucose study (TLGS) to examine if information of a family history of premature CVD, waist circumference, and fasting plasma glucose can improve predictive performance of the Framingham’s “general CVD risk” algorithm. Results The command is addpred for logistic regression models. Conclusions The Stata package provided herein can encourage the use of novel methods in examining predictive capacity of ever-emerging plethora of novel biomarkers. PMID:27279830

Institutional and Economic Determinants of Public Health System Performance

PubMed Central

Mays, Glen P.; McHugh, Megan C.; Shim, Kyumin; Perry, Natalie; Lenaway, Dennis; Halverson, Paul K.; Moonesinghe, Ramal

2006-01-01

Objectives. Although a growing body of evidence demonstrates that availability and quality of essential public health services vary widely across communities, relatively little is known about the factors that give rise to these variations. We examined the association of institutional, financial, and community characteristics of local public health delivery systems and the performance of essential services. Methods. Performance measures were collected from local public health systems in 7 states and combined with secondary data sources. Multivariate, linear, and nonlinear regression models were used to estimate associations between system characteristics and the performance of essential services. Results. Performance varied significantly with the size, financial resources, and organizational structure of local public health systems, with some public health services appearing more sensitive to these characteristics than others. Staffing levels and community characteristics also appeared to be related to the performance of selected services. Conclusions. Reconfiguring the organization and financing of public health systems in some communities—such as through consolidation and enhanced intergovernmental coordination—may hold promise for improving the performance of essential services. PMID:16449584

Neuropsychologic predictors of competency in Alzheimer's disease using a rational reasons legal standard.

PubMed

Marson, D C; Cody, H A; Ingram, K K; Harrell, L E

1995-10-01

To identify neuropsychologic predictors of competency performance and status in Alzheimer's disease (AD) using a specific legal standard (LS). This study is a follow-up to the competency assessment research reported in this issue of the archives. Univariate and multivariate analyses of independent neuropsychologic test measures with a dependent measure of competency to consent to treatment. University medical center. Fifteen normal older control subjects and 29 patients with probable AD. Subjects were administered a battery of neuropsychologic measures theoretically linked to competency function, as well as two clinical vignettes testing their capacity to consent to medical treatment under five different LSs. The present study focused on one specific LS: the capacity to provide "rational reasons" for a treatment choice (LS4). Neuropsychologic test scores were correlated with scores on LS4 for the normal control group and the AD group. The resulting univariate predictors were then analyzed using stepwise regression and discriminant function to identify the key multivariate predictors of competency performance and status under LS4. Measures of word fluency predicted the LS4 scores of controls (R2 = .33) and the AD group (R2 = .36). A word fluency measure also emerged as the best single predictor of competency status for the full subject sample (n = 44), correctly classifying 82% of cases. Dementia severity (Mini-Mental State Examination score) did not emerge as a multivariate predictor of competency performance or status. Interestingly, measures of verbal reasoning and memory were not strongly associated with LS4. Word fluency measures predicted the normative performance and intact competency status of older control subjects and the declining performance and compromised competency status of patients with AD on a "rational reasons" standard of competency to consent to treatment. Cognitive capacities related to frontal lobe function appear to underlie the capacity to formulate rational reasons for a treatment choice. Neuropsychologic studies of competency function have important theoretical and clinical value.

Why credit risk markets are predestined for exhibiting log-periodic power law structures

NASA Astrophysics Data System (ADS)

Wosnitza, Jan Henrik; Leker, Jens

2014-01-01

Recent research has established the existence of log-periodic power law (LPPL) patterns in financial institutions’ credit default swap (CDS) spreads. The main purpose of this paper is to clarify why credit risk markets are predestined for exhibiting LPPL structures. To this end, the credit risk prediction of two variants of logistic regression, i.e. polynomial logistic regression (PLR) and kernel logistic regression (KLR), are firstly compared to the standard logistic regression (SLR). In doing so, the question whether the performances of rating systems based on balance sheet ratios can be improved by nonlinear transformations of the explanatory variables is resolved. Building on the result that nonlinear balance sheet ratio transformations hardly improve the SLR’s predictive power in our case, we secondly compare the classification performance of a multivariate SLR to the discriminative powers of probabilities of default derived from three different capital market data, namely bonds, CDSs, and stocks. Benefiting from the prompt inclusion of relevant information, the capital market data in general and CDSs in particular increasingly outperform the SLR while approaching the time of the credit event. Due to the higher classification performances, it seems plausible for creditors to align their investment decisions with capital market-based default indicators, i.e., to imitate the aggregate opinion of the market participants. Since imitation is considered to be the source of LPPL structures in financial time series, it is highly plausible to scan CDS spread developments for LPPL patterns. By establishing LPPL patterns in governmental CDS spread trajectories of some European crisis countries, the LPPL’s application to credit risk markets is extended. This novel piece of evidence further strengthens the claim that credit risk markets are adequate breeding grounds for LPPL patterns.

Multivariate generalized hidden Markov regression models with random covariates: Physical exercise in an elderly population.

PubMed

Punzo, Antonio; Ingrassia, Salvatore; Maruotti, Antonello

2018-04-22

A time-varying latent variable model is proposed to jointly analyze multivariate mixed-support longitudinal data. The proposal can be viewed as an extension of hidden Markov regression models with fixed covariates (HMRMFCs), which is the state of the art for modelling longitudinal data, with a special focus on the underlying clustering structure. HMRMFCs are inadequate for applications in which a clustering structure can be identified in the distribution of the covariates, as the clustering is independent from the covariates distribution. Here, hidden Markov regression models with random covariates are introduced by explicitly specifying state-specific distributions for the covariates, with the aim of improving the recovering of the clusters in the data with respect to a fixed covariates paradigm. The hidden Markov regression models with random covariates class is defined focusing on the exponential family, in a generalized linear model framework. Model identifiability conditions are sketched, an expectation-maximization algorithm is outlined for parameter estimation, and various implementation and operational issues are discussed. Properties of the estimators of the regression coefficients, as well as of the hidden path parameters, are evaluated through simulation experiments and compared with those of HMRMFCs. The method is applied to physical activity data. Copyright © 2018 John Wiley & Sons, Ltd.

A novel strategy for forensic age prediction by DNA methylation and support vector regression model

PubMed Central

Xu, Cheng; Qu, Hongzhu; Wang, Guangyu; Xie, Bingbing; Shi, Yi; Yang, Yaran; Zhao, Zhao; Hu, Lan; Fang, Xiangdong; Yan, Jiangwei; Feng, Lei

2015-01-01

High deviations resulting from prediction model, gender and population difference have limited age estimation application of DNA methylation markers. Here we identified 2,957 novel age-associated DNA methylation sites (P < 0.01 and R2 > 0.5) in blood of eight pairs of Chinese Han female monozygotic twins. Among them, nine novel sites (false discovery rate < 0.01), along with three other reported sites, were further validated in 49 unrelated female volunteers with ages of 20–80 years by Sequenom Massarray. A total of 95 CpGs were covered in the PCR products and 11 of them were built the age prediction models. After comparing four different models including, multivariate linear regression, multivariate nonlinear regression, back propagation neural network and support vector regression, SVR was identified as the most robust model with the least mean absolute deviation from real chronological age (2.8 years) and an average accuracy of 4.7 years predicted by only six loci from the 11 loci, as well as an less cross-validated error compared with linear regression model. Our novel strategy provides an accurate measurement that is highly useful in estimating the individual age in forensic practice as well as in tracking the aging process in other related applications. PMID:26635134

Analysis of Multivariate Experimental Data Using A Simplified Regression Model Search Algorithm

NASA Technical Reports Server (NTRS)

Ulbrich, Norbert Manfred

2013-01-01

A new regression model search algorithm was developed in 2011 that may be used to analyze both general multivariate experimental data sets and wind tunnel strain-gage balance calibration data. The new algorithm is a simplified version of a more complex search algorithm that was originally developed at the NASA Ames Balance Calibration Laboratory. The new algorithm has the advantage that it needs only about one tenth of the original algorithm's CPU time for the completion of a search. In addition, extensive testing showed that the prediction accuracy of math models obtained from the simplified algorithm is similar to the prediction accuracy of math models obtained from the original algorithm. The simplified algorithm, however, cannot guarantee that search constraints related to a set of statistical quality requirements are always satisfied in the optimized regression models. Therefore, the simplified search algorithm is not intended to replace the original search algorithm. Instead, it may be used to generate an alternate optimized regression model of experimental data whenever the application of the original search algorithm either fails or requires too much CPU time. Data from a machine calibration of NASA's MK40 force balance is used to illustrate the application of the new regression model search algorithm.

Comprehensive ripeness-index for prediction of ripening level in mangoes by multivariate modelling of ripening behaviour

NASA Astrophysics Data System (ADS)

Eyarkai Nambi, Vijayaram; Thangavel, Kuladaisamy; Manickavasagan, Annamalai; Shahir, Sultan

2017-01-01

Prediction of ripeness level in climacteric fruits is essential for post-harvest handling. An index capable of predicting ripening level with minimum inputs would be highly beneficial to the handlers, processors and researchers in fruit industry. A study was conducted with Indian mango cultivars to develop a ripeness index and associated model. Changes in physicochemical, colour and textural properties were measured throughout the ripening period and the period was classified into five stages (unripe, early ripe, partially ripe, ripe and over ripe). Multivariate regression techniques like partial least square regression, principal component regression and multi linear regression were compared and evaluated for its prediction. Multi linear regression model with 12 parameters was found more suitable in ripening prediction. Scientific variable reduction method was adopted to simplify the developed model. Better prediction was achieved with either 2 or 3 variables (total soluble solids, colour and acidity). Cross validation was done to increase the robustness and it was found that proposed ripening index was more effective in prediction of ripening stages. Three-variable model would be suitable for commercial applications where reasonable accuracies are sufficient. However, 12-variable model can be used to obtain more precise results in research and development applications.

Information transfer and information modification to identify the structure of cardiovascular and cardiorespiratory networks.

PubMed

Faes, Luca; Nollo, Giandomenico; Krohova, Jana; Czippelova, Barbora; Turianikova, Zuzana; Javorka, Michal

2017-07-01

To fully elucidate the complex physiological mechanisms underlying the short-term autonomic regulation of heart period (H), systolic and diastolic arterial pressure (S, D) and respiratory (R) variability, the joint dynamics of these variables need to be explored using multivariate time series analysis. This study proposes the utilization of information-theoretic measures to measure causal interactions between nodes of the cardiovascular/cardiorespiratory network and to assess the nature (synergistic or redundant) of these directed interactions. Indexes of information transfer and information modification are extracted from the H, S, D and R series measured from healthy subjects in a resting state and during postural stress. Computations are performed in the framework of multivariate linear regression, using bootstrap techniques to assess on a single-subject basis the statistical significance of each measure and of its transitions across conditions. We find patterns of information transfer and modification which are related to specific cardiovascular and cardiorespiratory mechanisms in resting conditions and to their modification induced by the orthostatic stress.

Predictors of in-hospital mortality amongst octogenarians undergoing emergency general surgery: a retrospective cohort study.

PubMed

Wilson, Iain; Paul Barrett, Michael; Sinha, Ashish; Chan, Shirley

2014-11-01

Elderly patients are often judged to be fit for emergency surgery based on age alone. This study identified risk factors predictive of in-hospital mortality amongst octogenarians undergoing emergency general surgery. A retrospective review of octogenarians undergoing emergency general surgery over 3 years was performed. Parametric survival analysis using Cox multivariate regression model was used to identify risk factors predictive of in-hospital mortality. Hazard ratios (HR) and corresponding 95% confidence interval were calculated. Seventy-three patients with a median age of 84 years were identified. Twenty-eight (38%) patients died post-operatively. Multivariate analysis identified ASA grade (ASA 5 HR 23.4 95% CI 2.38-230, p = 0.007) and chronic obstructive pulmonary disease (COPD) (HR 3.35 95% CI 1.15-9.69, p = 0.026) to be the only significant predictors of in-hospital mortality. Identification of high risk surgical patients should be based on physiological fitness for surgery rather than chronological age. Crown Copyright © 2014. Published by Elsevier Ltd. All rights reserved.

Hypothyroidism among SLE patients: Case-control study.

PubMed

Watad, Abdulla; Mahroum, Naim; Whitby, Aaron; Gertel, Smadar; Comaneshter, Doron; Cohen, Arnon D; Amital, Howard

2016-05-01

The prevalence of hypothyroidism in SLE patients varies considerably and early reports were mainly based on small cohorts. To investigate the association between SLE and hypothyroidism. Patients with SLE were compared with age and sex-matched controls regarding the proportion of hypothyroidism in a case-control study. Chi-square and t-tests were used for univariate analysis and a logistic regression model was used for multivariate analysis. The study was performed utilizing the medical database of Clalit Health Services. The study included 5018 patients with SLE and 25,090 age and sex-matched controls. The proportion of hypothyroidism in patients with SLE was increased compared with the prevalence in controls (15.58% and 5.75%, respectively, P<0.001). In a multivariate analysis, SLE was associated with hypothyroidism (odds ratio 2.644, 95% confidence interval 2.405-2.908). Patients with SLE have a greater proportion of hypothyroidism than matched controls. Therefore, physicians treating patients with SLE should be aware of the possibility of thyroid dysfunction. Copyright © 2016 Elsevier B.V. All rights reserved.

A national cohort study of parental socioeconomic status and non-fatal suicidal behaviour-the mediating role of school performance

PubMed Central

2012-01-01

Background A link between low parental socioeconomic status and mental health problems in offspring is well established in previous research. The mechanisms that explain this link are largely unknown. The present study investigated whether school performance was a mediating and/or moderating factor in the path between parental socioeconomic status and the risk of hospital admission for non-fatal suicidal behaviour. Methods A national cohort of 447 929 children born during 1973-1977 was followed prospectively in the National Patient Discharge Register from the end of their ninth and final year of compulsory school until 2001. Multivariate Cox proportional hazards and linear regression analyses were performed to test whether the association between parental socioeconomic status and non-fatal suicidal behaviour was mediated or moderated by school performance. Results The results of a series of multiple regression analyses, adjusted for demographic variables, revealed that school performance was as an important mediator in the relationship between parental socioeconomic status and risk of non-fatal suicidal behaviour, accounting for 60% of the variance. The hypothesized moderation of parental socioeconomic status-non-fatal suicidal behaviour relationship by school performance was not supported. Conclusions School performance is an important mediator through which parental socioeconomic status translates into a risk for non-fatal suicidal behaviour. Prevention efforts aimed to reduce socioeconomic inequalities in non-fatal suicidal behaviour among young people will need to consider socioeconomic inequalities in school performance. PMID:22230577

Concomitant Mediastinoscopy Increases the Risk of Postoperative Pneumonia After Pulmonary Lobectomy.

PubMed

Yendamuri, Sai; Battoo, Athar; Attwood, Kris; Dhillon, Samjot Singh; Dy, Grace K; Hennon, Mark; Picone, Anthony; Nwogu, Chukwumere; Demmy, Todd; Dexter, Elisabeth

2018-05-01

Mediastinoscopy is considered the gold standard for preresectional staging of lung cancer. We sought to examine the effect of concomitant mediastinoscopy on postoperative pneumonia (POP) in patients undergoing lobectomy. All patients in our institutional database (2008-2015) undergoing lobectomy who did not receive neoadjuvant therapy were included in our study. The relationship between mediastinoscopy and POP was examined using univariate (Chi square) and multivariate analyses (binary logistic regression). In order to validate our institutional findings, lobectomy data in the National Surgical Quality Improvement Program (NSQIP) from 2005 to 2014 were analyzed for these associations. Of 810 patients who underwent a lobectomy at our institution, 741 (91.5%) surgeries were performed by video-assisted thoracic surgery (VATS) and 487 (60.1%) patients underwent concomitant mediastinoscopy. Univariate analysis demonstrated an association between mediastinoscopy and POP in patients undergoing VATS [odds ratio (OR) 1.80; p = 0.003], but not open lobectomy. Multivariate analysis retained mediastinoscopy as a variable, although the relationship showed only a trend (OR 1.64; p = 0.1). In the NSQIP cohort (N = 12,562), concomitant mediastinoscopy was performed in 9.0% of patients, with 44.5% of all the lobectomies performed by VATS. Mediastinoscopy was associated with POP in patients having both open (OR1.69; p < 0.001) and VATS lobectomy (OR 1.72; p = 0.002). This effect remained in multivariate analysis in both the open and VATS lobectomy groups (OR 1.46, p = 0.003; and 1.53, p = 0.02, respectively). Mediastinoscopy may be associated with an increased risk of POP after pulmonary lobectomy. This observation should be examined in other datasets as it potentially impacts preresectional staging algorithms for patients with lung cancer.

Neighborhood poverty rate and mortality in patients receiving critical care in the academic medical center setting.

PubMed

Zager, Sam; Mendu, Mallika L; Chang, Domingo; Bazick, Heidi S; Braun, Andrea B; Gibbons, Fiona K; Christopher, Kenneth B

2011-06-01

Poverty is associated with increased risk of chronic illness but its contribution to critical care outcome is not well defined. We performed a multicenter observational study of 38,917 patients, aged ≥ 18 years, who received critical care between 1997 and 2007. The patients were treated in two academic medical centers in Boston, Massachusetts. Data sources included 1990 US census and hospital administrative data. The exposure of interest was neighborhood poverty rate, categorized as < 5%, 5% to 10%, 10% to 20%, 20% to 40% and > 40%. Neighborhood poverty rate is the percentage of residents below the federal poverty line. Census tracts were used as the geographic units of analysis. Logistic regression examined death by days 30, 90, and 365 post-critical care initiation and in-hospital mortality. Adjusted ORs were estimated by multivariable logistic regression models. Sensitivity analysis was performed for 1-year postdischarge mortality among patients discharged to home. Following multivariable adjustment, neighborhood poverty rate was not associated with all-cause 30-day mortality: 5% to 10% OR, 1.05 (95% CI, 0.98-1.14; P = .2); 10% to 20% OR, 0.96 (95% CI, 0.87-1.06; P = .5); 20% to 40% OR, 1.08 (95% CI, 0.96-1.22; P = .2); > 40% OR, 1.20 (95% CI, 0.90-1.60; P = .2); referent in each is < 5%. Similar nonsignificant associations were noted at 90-day and 365-day mortality post-critical care initiation and in-hospital mortality. Among patients discharged to home, neighborhood poverty rate was not associated with 1-year-postdischarge mortality. Our study suggests that there is no relationship between the neighborhood poverty rate and mortality up to 1 year following critical care at academic medical centers.

The Impact of Metastatic Spinal Tumor Location on 30-Day Perioperative Mortality and Morbidity After Surgical Decompression.

PubMed

Hussain, Awais K; Vig, Khushdeep S; Cheung, Zoe B; Phan, Kevin; Lima, Mauricio C; Kim, Jun S; Kaji, Deepak A; Arvind, Varun; Cho, Samuel Kang-Wook

2018-06-01

A retrospective cohort study from 2011 to 2014 was performed using the American College of Surgeons National Surgical Quality Improvement Program database. The purpose of this study was to assess the impact of tumor location in the cervical, thoracic, or lumbosacral spine on 30-day perioperative mortality and morbidity after surgical decompression of metastatic extradural spinal tumors. Operative treatment of metastatic spinal tumors involves extensive procedures that are associated with significant complication rates and healthcare costs. Past studies have examined various risk factors for poor clinical outcomes after surgical decompression procedures for spinal tumors, but few studies have specifically investigated the impact of tumor location on perioperative mortality and morbidity. We identified 2238 patients in the American College of Surgeons National Surgical Quality Improvement Program database who underwent laminectomy for excision of metastatic extradural tumors in the cervical, thoracic, or lumbosacral spine. Baseline patient characteristics were collected from the database. Univariate and multivariate regression analyses were performed to examine the association between spinal tumor location and 30-day perioperative mortality and morbidity. On univariate analysis, cervical spinal tumors were associated with the highest rate of pulmonary complications. Multivariate regression analysis demonstrated that cervical spinal tumors had the highest odds of multiple perioperative complications. However, thoracic spinal tumors were associated with the highest risk of intra- or postoperative blood transfusion. In contrast, patients with metastatic tumors in the lumbosacral spine had lower odds of perioperative mortality, pulmonary complications, and sepsis. Tumor location is an independent risk factor for perioperative mortality and morbidity after surgical decompression of metastatic spinal tumors. The addition of tumor location to existing prognostic scoring systems may help to improve their predictive accuracy. 3.

Role of macular hole angle in macular hole closure.

PubMed

Chhablani, Jay; Khodani, Mitali; Hussein, Abdullah; Bondalapati, Sailaja; Rao, Harsha B; Narayanan, Raja; Sudhalkar, Aditya

2015-12-01

To evaluate correlation of various spectral-domain optical coherence tomography (SD-OCT) parameters including macular hole angle as well as various indices with anatomical and visual outcomes after idiopathic macular hole repair surgery. Retrospective study of 137 eyes of 137 patients who underwent idiopathic macular hole repair surgery between January 2008 and January 2014 was performed. Various qualitative parameters such as presence of vitreomacular traction, epiretinal membrane and cystic edges at the macular hole as well as quantitative parameters such as maximum diameter on the apex of the hole, minimum diameter between edges, nasal and temporal vertical height, longest base diameter and macular hole angle between the retinal edge and the retinal pigment epithelium were noted. Indices including hole form factor, Macular Hole Index (MHI), Diameter Hole Index and Tractional Hole Index (THI) were calculated. Univariate and multivariate regression analysis was performed separately for final visual acuity (VA) and type of closure as dependent variable in relation to SD-OCT parameters as independent variables. On multivariate regression only minimum diameter between edges (p≤0.01) and longest base diameter (p≤0.03) were correlated significantly with both, type 1 closure and final VA. Among the indices, significant correlation of MHI (p=0.009) was noted with type of closure and that of THI with final VA (p=0.017). Our study shows no significant correlation between macular hole angle and hole closure. Minimum diameter between the edges and longest diameter of the hole are best predictors of hole closure and postoperative VA. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Treatment of esophageal anastomotic leakage with self-expanding metal stents: analysis of risk factors for treatment failure

PubMed Central

Persson, Saga; Rouvelas, Ioannis; Kumagai, Koshi; Song, Huan; Lindblad, Mats; Lundell, Lars; Nilsson, Magnus; Tsai, Jon A.

2016-01-01

Background and study aim: The endoscopic placement of self-expandable metallic esophageal stents (SEMS) has become the preferred primary treatment for esophageal anastomotic leakage in many institutions. The aim of this study was to investigate possible risk factors for failure of SEMS-based therapy in patients with esophageal anastomotic leakage. Patients and methods: Beginning in 2003, all patients with an esophageal leak were initially approached and assessed for temporary closure with a SEMS. Until 2014, all patients at the Karolinska University Hospital with a leak from an esophagogastric or esophagojejunal anastomosis were identified. Data regarding the characteristics of the patients and leaks and the treatment outcomes were compiled. Failure of the SEMS treatment strategy was defined as death due to the leak or a major change in management strategy. The risk factors for treatment failure were analyzed with simple and multivariable logistic regression statistics. Results: A total of 447 patients with an esophagogastric or esophagojejunal anastomosis were identified. Of these patients, 80 (18 %) had an anastomotic leak, of whom 46 (58 %) received a stent as first-line treatment. In 29 of these 46 patients, the leak healed without any major change in treatment strategy. Continuous leakage after the application of a stent, decreased physical performance preoperatively, and concomitant esophagotracheal fistula were identified as independent risk factors for failure with multivariable logistic regression analysis. Conclusion: Stent treatment for esophageal anastomotic leakage is successful in the majority of cases. Continuous leakage after initial stent insertion, decreased physical performance preoperatively, and the development of an esophagotracheal fistula decrease the probability of successful treatment. PMID:27092321

Determinants of brain-derived neurotrophic factor (BDNF) in umbilical cord and maternal serum.

PubMed

Flöck, A; Weber, S K; Ferrari, N; Fietz, C; Graf, C; Fimmers, R; Gembruch, U; Merz, W M

2016-01-01

Brain-derived neurotrophic factor (BDNF) plays a fundamental role in brain development; additionally, it is involved in various aspects of cerebral function, including neurodegenerative and psychiatric diseases. Involvement of BDNF in parturition has not been investigated. The aim of our study was to analyze determinants of umbilical cord BDNF (UC-BDNF) concentrations of healthy, term newborns and their respective mothers. This cross-sectional prospective study was performed at a tertiary referral center. Maternal venous blood samples were taken on admission to labor ward; newborn venous blood samples were drawn from the umbilical cord (UC), before delivery of the placenta. Analysis was performed with a commercially available immunoassay. Univariate analyses and stepwise multivariate regression models were applied. 120 patients were recruited. UC-BDNF levels were lower than maternal serum concentrations (median 641 ng/mL, IQR 506 vs. median 780 ng/mL, IQR 602). Correlation between UC- and maternal BDNF was low (R=0.251, p=0.01). In univariate analysis, mode of delivery (MoD), gestational age (GA), body mass index at delivery, and gestational diabetes were determinants of UC-BDNF (MoD and smoking for maternal BDNF, respectively). Stepwise multivariate regression analysis revealed a model with MoD and GA as determinants for UC-BDNF (MoD for maternal BDNF). MoD and GA at delivery are determinants of circulating BDNF in the mother and newborn. We hypothesize that BDNF, like other neuroendocrine factors, is involved in the neuroendocrine cascade of delivery. Timing and mode of delivery may exert BDNF-induced effects on the cerebral function of newborns and their mothers. Copyright © 2015 Elsevier Ltd. All rights reserved.

Study for Updated Gout Classification Criteria (SUGAR): identification of features to classify gout

PubMed Central

Taylor, William J.; Fransen, Jaap; Jansen, Tim L.; Dalbeth, Nicola; Schumacher, H. Ralph; Brown, Melanie; Louthrenoo, Worawit; Vazquez-Mellado, Janitzia; Eliseev, Maxim; McCarthy, Geraldine; Stamp, Lisa K.; Perez-Ruiz, Fernando; Sivera, Francisca; Ea, Hang-Korng; Gerritsen, Martijn; Scire, Carlo; Cavagna, Lorenzo; Lin, Chingtsai; Chou, Yin-Yi; Tausche, Anne-Kathrin; Vargas-Santos, Ana Beatriz; Janssen, Matthijs; Chen, Jiunn-Horng; Slot, Ole; Cimmino, Marco A.; Uhlig, Till; Neogi, Tuhina

2015-01-01

Objective To determine which clinical, laboratory and imaging features most accurately distinguished gout from non-gout. Methods A cross-sectional study of consecutive rheumatology clinic patients with at least one swollen joint or subcutaneous tophus. Gout was defined by synovial fluid or tophus aspirate microscopy by certified examiners in all patients. The sample was randomly divided into a model development (2/3) and test sample (1/3). Univariate and multivariate association between clinical features and MSU-defined gout was determined using logistic regression modelling. Shrinkage of regression weights was performed to prevent over-fitting of the final model. Latent class analysis was conducted to identify patterns of joint involvement. Results In total, 983 patients were included. Gout was present in 509 (52%). In the development sample (n=653), these features were selected for the final model (multivariate OR) joint erythema (2.13), difficulty walking (7.34), time to maximal pain < 24 hours (1.32), resolution by 2 weeks (3.58), tophus (7.29), MTP1 ever involved (2.30), location of currently tender joints: Other foot/ankle (2.28), MTP1 (2.82), serum urate level > 6 mg/dl (0.36 mmol/l) (3.35), ultrasound double contour sign (7.23), Xray erosion or cyst (2.49). The final model performed adequately in the test set with no evidence of misfit, high discrimination and predictive ability. MTP1 involvement was the most common joint pattern (39.4%) in gout cases. Conclusion Ten key discriminating features have been identified for further evaluation for new gout classification criteria. Ultrasound findings and degree of uricemia add discriminating value, and will significantly contribute to more accurate classification criteria. PMID:25777045

Susceptible genes of restless legs syndrome in migraine.

PubMed

Fuh, Jong-Ling; Chung, Ming-Yi; Yao, Shu-Chih; Chen, Ping-Kun; Liao, Yi-Chu; Hsu, Chia-Lin; Wang, Po-Jen; Wang, Yen-Feng; Chen, Shih-Pin; Fann, Cathy S-J; Kao, Lung-Sen; Wang, Shuu-Jiun

2016-10-01

Objective Several genetic variants have been found to increase the risk of restless legs syndrome (RLS). The aim of the present study was to determine if these genetic variants were also associated with the comorbidity of RLS and migraine in patients. Methods Thirteen single-nucleotide polymorphisms (SNPs) at six RLS risk loci ( MEIS1, BTBD9, MAP2K5, PTPRD, TOX3, and an intergenic region on chromosome 2p14) were genotyped in 211 migraine patients with RLS and 781 migraine patients without RLS. Association analyses were performed for the overall cohort, as well as for the subgroups of patients who experienced migraines with and without aura and episodic migraines (EMs) vs. chronic migraines (CMs). In order to verify which genetic markers were potentially related to the incidence of RLS in migraine patients, multivariate regression analyses were also performed. Results Among the six tested loci, only MEIS1 was significantly associated with RLS. The most significant SNP of MEIS1, rs2300478, increased the risk of RLS by 1.42-fold in the overall cohort ( p = 0.0047). In the subgroup analyses, MEIS1 augmented the risk of RLS only in the patients who experienced EMs (odds ratio (OR) = 1.99, p = 0.0004) and not those experiencing CMs. Multivariate regression analyses further showed that rs2300478 in MEIS1 (OR = 1.39, p = 0.018), a CM diagnosis (OR = 1.52, p = 0.022), and depression (OR = 1.86, p = 0.005) were independent predictors of RLS in migraine. Conclusions MEIS1 variants were associated with an increased risk of RLS in migraine patients. It is possible that an imbalance in iron homeostasis and the dopaminergic system may represent a link between RLS incidence and migraines.

Cytologic anaplasia is a prognostic factor in osteosarcoma biopsies, but mitotic rate or extent of spontaneous tumor necrosis are not: a critique of the College of American Pathologists Bone Biopsy template.

PubMed

Cates, Justin Mm; Dupont, William D

2017-01-01

The current College of American Pathologists cancer template for reporting biopsies of bone tumors recommends including information that is of unproven prognostic significance for osteosarcoma, such as the presence of spontaneous tumor necrosis and mitotic rate. Conversely, the degree of cytologic anaplasia (degree of differentiation) is not reported in this template. This retrospective cohort study of 125 patients with high-grade osteosarcoma was performed to evaluate the prognostic impact of these factors in diagnostic biopsy specimens in predicting the clinical outcome and response to neoadjuvant chemotherapy. Multivariate Cox regression was performed to adjust survival analyses for well-established prognostic factors. Multivariate logistic regression was used to determine odds ratios for good chemotherapy response (≥90% tumor necrosis). Osteosarcomas with severe anaplasia were independently associated with increased overall and disease-free survival, but mitotic rate and spontaneous necrosis had no prognostic impact after controlling for other confounding factors. Mitotic rate showed a trend towards increased odds of a good histologic response, but this effect was diminished after controlling for other predictive factors. Neither spontaneous necrosis nor the degree of cytologic anaplasia observed in biopsy specimens was predictive of a good response to chemotherapy. Mitotic rate and spontaneous tumor necrosis observed in pretreatment biopsy specimens of high-grade osteosarcoma are not strong independent prognostic factors for clinical outcome or predictors of response to neoadjuvant chemotherapy. Therefore, reporting these parameters for osteosarcoma, as recommended in the College of American Pathologists Bone Biopsy template, does not appear to have clinical utility. In contrast, histologic grading schemes for osteosarcoma based on the degree of cytologic anaplasia may have independent prognostic value and should continue to be evaluated.

MWA Versus RFA for Perivascular and Peribiliary CRLM: A Retrospective Patient- and Lesion-Based Analysis of Two Historical Cohorts

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tilborg, Aukje A. J. M. van, E-mail: a.vantilborg@vumc.nl; Scheffer, Hester J.; Jong, Marcus C. de

2016-10-15

PurposeTo retrospectively analyse the safety and efficacy of radiofrequency ablation (RFA) versus microwave ablation (MWA) in the treatment of unresectable colorectal liver metastases (CRLM) in proximity to large vessels and/or major bile ducts.Method and MaterialsA database search was performed to include patients with unresectable histologically proven and/or {sup 18}F–FDG–PET avid CRLM who were treated with RFA or MWA between January 2001 and September 2014 in a single centre. All lesions that were considered to have a peribiliary and/or perivascular location were included. Univariate logistic regression analysis was performed to assess the distribution of patient, tumour and procedure characteristics. Multivariate logisticmore » regression was used to correct for potential confounders.ResultsTwo hundred and forty-three patients with 774 unresectable CRLM were ablated. One hundred and twenty-two patients (78 males; 44 females) had at least one perivascular or peribiliary lesion (n = 199). Primary efficacy rate of RFA was superior to MWA after 3 and 12 months of follow-up (P = 0.010 and P = 0.022); however, after multivariate analysis this difference was non-significant at 12 months (P = 0.078) and vanished after repeat ablations (P = 0.39). More CTCAE grade III complications occurred after MWA versus RFA (18.8 vs. 7.9 %; P = 0.094); biliary complications were especially common after peribiliary MWA (P = 0.002).ConclusionFor perivascular CRLM, RFA and MWA are both safe treatment options that appear equally effective. For peribiliary CRLM, MWA has a higher complication rate than RFA, with similar efficacy. Based on these results, it is advised to use RFA for lesions in the proximity of major bile ducts.« less

Prevalence of kidney stones and associated risk factors in the Shunyi District of Beijing, China.

PubMed

Jiang, Y G; He, L H; Luo, G T; Zhang, X D

2017-10-01

Kidney stone formation is a multifactorial condition that involves interaction of environmental and genetic factors. Presence of kidney stones is strongly related to other diseases, which may result in a heavy economic and social burden. Clinical data on the prevalence and influencing factors in kidney stone disease in the north of China are scarce. In this study, we explored the prevalence of kidney stone and potentially associated risk factors in the Shunyi District of Beijing, China. A population-based cross-sectional study was conducted from December 2011 to November 2012 in a northern area of China. Participants were interviewed in randomly selected towns. Univariate analysis of continuous and categorical variables was first performed by calculation of Spearman's correlation coefficient and Pearson Chi squared value, respectively. Variables with statistical significance were further analysed by multivariate logistic regression to explore the potential influencing factors. A total of 3350 participants (1091 males and 2259 females) completed the survey and the response rate was 99.67%. Among the participants, 3.61% were diagnosed with kidney stone. Univariate analysis showed that significant differences were evident in 31 variables. Blood and urine tests were performed in 100 randomly selected patients with kidney stone and 100 healthy controls. Serum creatinine, calcium, and uric acid were significantly different between the patients with kidney stone and healthy controls. Multivariate logistic regression revealed that being male (odds ratio=102.681; 95% confidence interval, 1.062-9925.797), daily intake of white spirits (6.331; 1.204-33.282), and a history of urolithiasis (1797.775; 24.228-133 396.982) were factors potentially associated with kidney stone prevalence. Male gender, drinking white spirits, and a history of urolithiasis are potentially associated with kidney stone formation.

Acute care surgery: defining mortality in emergency general surgery in the state of Maryland.

PubMed

Narayan, Mayur; Tesoriero, Ronald; Bruns, Brandon R; Klyushnenkova, Elena N; Chen, Hegang; Diaz, Jose J

2015-04-01

Emergency general surgery (EGS) is a major component of acute care surgery, however, limited data exist on mortality with respect to trauma center (TC) designation. We hypothesized that mortality would be lower for EGS patients treated at a TC vs non-TC (NTC). A retrospective review of the Maryland Health Services Cost Review Commission database from 2009 to 2013 was performed. The American Association for the Surgery of Trauma EGS ICD-9 codes were used to identify EGS patients. Data collected included demographics, TC designation, emergency department admissions, and All Patients Refined Severity of Illness (APR_SOI). Trauma center designation was used as a marker of a formal acute care surgery program. Primary outcomes included in-hospital mortality. Multivariable logistic regression analysis was performed controlling for age. There were 817,942 EGS encounters. Mean ± SD age of patients was 60.1 ± 18.7 years, 46.5% were males; 71.1% of encounters were at NTCs; and 75.8% were emergency department admissions. Overall mortality was 4.05%. Mortality was calculated based on TC designation controlling for age across APR_SOI strata. Multivariable logistic regression analysis did not show statistically significant differences in mortality between hospital levels for minor APR_SOI. For moderate APR_SOI, mortality was significantly lower for TCs compared with NTCs (p < 0.001). Among TCs, the effect was strongest for Level I TC (odds ratio = 0.34). For extreme APR_SOI, mortality was higher at TCs vs NTCs (p < 0.001). Emergency general surgery patients treated at TCs had lower mortality for moderate APR_SOI, but increased mortality for extreme APR_SOI when compared with NTCs. Additional investigation is required to better evaluate this unexpected finding. Copyright © 2015 American College of Surgeons. Published by Elsevier Inc. All rights reserved.

Pattern of oral-maxillofacial trauma from violence against women and its associated factors.

PubMed

da Nóbrega, Lorena Marques; Bernardino, Ítalo de Macedo; Barbosa, Kevan Guilherme Nóbrega; E Silva, Jéssica Antoniana Lira; Massoni, Andreza Cristina de Lima Targino; d'Avila, Sérgio

2017-06-01

Violence against women is a global public health problem. The aim of this study was to characterize the profile of women victims of violence and identify factors associated with maxillofacial injuries. A cross-sectional study was performed based on an evaluation of 884 medico-legal and social records of women victims of physical aggression treated at the Center of Forensic Medicine and Dentistry in Brazil. The variables investigated were related to the sociodemographic characteristics of victims, circumstances of aggressions, and patterns of trauma. Descriptive and multivariate statistics using decision tree analysis by the Chi-squared automatic interaction detector (CHAID) algorithm, as well as univariate and multivariate Poisson regression analyses were performed. The occurrence of maxillofacial trauma was 46.4%. The mean age of victims was 29.38 (SD=12.55 years). Based on decision tree, the profile of violence against women can be explained by the aggressor's gender (P<.001) and sociodemographic characteristics of victims, such as marital status (P=.001), place of residence (P=.019), and educational level (P=.014). Based on the final Poisson regression model, women living in suburban areas were more likely to suffer maxillofacial trauma (PR=1.752; CI 95%=1.153-2.662; P=.009) compared to those living in rural areas. Moreover, aggression using a weapon resulted in a lower occurrence of maxillofacial trauma (PR=0.476; CI 95%=0.284-0.799; P=.005) compared to cases of aggression using physical force. The prevalence of oral-maxillofacial trauma was high, and the main associated factors were place of residence and mechanism of aggression. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Undernutrition as independent predictor of early mortality in elderly cancer patients.

PubMed

Martucci, Renata B; Barbosa, Mariana V; D'Almeida, Cristiane A; Rodrigues, Viviane D; Bergmann, Anke; de Pinho, Nivaldo B; Thuler, Luiz Claudio S

2017-02-01

The aim of this study was to evaluate the 1-y survival of elderly patients with cancer and the association between undernutrition and mortality. This was a cohort study with elderly patients ages ≥65 y admitted between September and October 2014. A nutritionist performed a Mini Nutritional Assessment-Short Form (MNA-SF) assessment during 48 h of hospital admission and collected data about potential confounding variables (comorbidities, stage of cancer, treatment in the previous 3 mo, and reason for hospitalization). Vital status was determined from the medical records or public records office. Overall survival was estimated using the Kaplan-Meier method. Cox regression was performed to estimate unadjusted hazard ratios. Variables with P < 0.20 by univariate analysis were selected for multivariate analysis. P < 0.05 was considered statistically significant. Of the 136 patients (mean age, 73.1 y; 52.2% men), 29.4%, 41.2%, and 29.4% were classified as normal, at risk for undernutrition, and undernutrition, respectively, according to the MNA-SF. The mortality rate was 31.6% after 12 mo. One-year mortality was higher among the undernourished patients, followed by patients at risk for undernutrition. After adjustment for confounding variables, the multivariate regression Cox model showed that being undernourished according to the MNA-SF increased the risk for death at 1 y (hazard ratio, 5.59; 95% confidence interval, 1.8-17.3; P < 0.001). The results showed that the MNA-SF can be a useful tool in identifying elderly patients at higher risk for 1-y mortality. Copyright © 2016 Elsevier Inc. All rights reserved.

[Relationship between alcohol dependence and new detected hypertension in adult residents of Xuzhou city].

PubMed

Dong, Zongmei; Lou, Pei'an; Zhang, Pan; Chen, Peipei; Qiao, Cheng; Li, Ting

2015-12-01

To observe the relationship between alcohol dependence and new detected hypertension in adult residents of Xuzhou city. Participants were sampled by stratified multi-stage randomly cluster sampling method from February 2013 to June 2013 among permanent residents aged 18 and more in Xuzhou city. The alcohol dependence was defined with Michigan Alcoholism Screening Test (MAST). Other information was obtained by questionnaire. Spearman correlation analysis and multivariate logistic regression analysis were performed to identify the relationship between alcohol dependence and new detected hypertension. The alcohol dependence rate was 11.56% on the whole cohort (n=36 157), and 22.02%(3 854/17 501) for male and 1.74%(324/18 656) for female(P<0.01). The new detected hypertension rate was 9.46%(3 422/36 157) in the whole cohort. The new detected hypertension rate increased in proportion with the severity of alcohol dependence (P<0.01). Spearman correlation analysis showed that alcohol dependence was positively correlated with systemic blood pressure(r=0.071, P<0.01) and diastolic blood pressure (r=0.077, P<0.01). After adjusting for gender, age, marital status, body mass index, smoking status, physical activity level, educational level, income level and region, multivariate logistic regression analysis showed that alcohol dependence was an independent risk factor for hypertension (low alcohol dependence: OR=1.44, 95%CI 1.14-1.81, P<0.01; light alcohol dependence: OR=1.35, 95%CI 1.11-1.64, P<0.01; medium alcohol dependence: OR=1.83, 95%CI 1.40-2.41, P<0.01). The alcohol dependence is an independent risk factor for new detected hypertension in adult residents of Xuzhou city. Intensive hypertension prevention and treatment strategies should be performed on this population based on our results.

High Serum Phosphorus Level Is Associated with Left Ventricular Diastolic Dysfunction in Peritoneal Dialysis Patients.

PubMed

Ye, Min; Tian, Na; Liu, Yanqiu; Li, Wei; Lin, Hong; Fan, Rui; Li, Cuiling; Liu, Donghong; Yao, Fengjuan

We initiated this study to explore the relationships of serum phosphorus level with left ventricular ultrasound features and diastolic function in peritoneal dialysis (PD) patients. 174 patients with end-stage renal disease (ESRD) receiving PD were enrolled in this retrospective observational study. Conventional echocardiography examination and tissue Doppler imaging (TDI) were performed in each patient. Clinical information and laboratory data were also collected. Analyses of echocardiographic features were performed according to phosphorus quartiles groups. And multivariate regression models were used to determine the association between serum phosphorus and Left ventricular diastolic dysfunction (LVDD). With the increase of serum phosphorus levels, patients on PD showed an increased tissue Doppler-derived E/e' ratio of lateral wall (P < 0.001), indicating a deterioration of left ventricular diastolic function. Steady growths of left atrium and left ventricular diameters as well as increase of left ventricular muscle mass were also observed across the increasing quartiles of phosphorus, while left ventricular ejection fraction remained normal. In a multivariate analysis, the regression coefficient for E/e' ratio in the highest phosphorus quartile was almost threefold higher relative to those in the lowest quartile group. And compared with patients in the lowest phosphorus quartile (<1.34 mmol/L) those in the highest phosphorus quartile (>1.95 mmol/L) had a more than fivefold increased odds of E/e' ratio >15. Our study showed an early impairment of left ventricular diastolic function in peritoneal dialysis patients. High serum phosphorus level was independently associated with greater risk of LVDD in these patients. Whether serum phosphorus will be a useful target for prevention or improvement of LVDD remains to be proved by further studies.

The relevance of timing in nonconvulsive status epilepticus: A series of 38 cases.

PubMed

Gutiérrez-Viedma, Álvaro; Parejo-Carbonell, Beatriz; Cuadrado, María-Luz; Serrano-García, Irene; Abarrategui, Belén; García-Morales, Irene

2018-05-01

Timing in the management of nonconvulsive status epilepticus (NCSE) seems to be one of the most important modifiable prognostic factors. We aimed to determine the precise relationship between timing in NCSE management and its outcome. We performed a cross-sectional study in which clinical data were prospectively obtained from all consecutive adults with NCSE admitted to our hospital from 2014 to 2016. Univariate and multivariable regression analyses were performed to identify clinical and timing variables associated with NCSE prognosis. Among 38 NCSE cases, 59.9% were women, and 39.5% had prior epilepsy history. The median time to treatment (TTT) initiation and the median time to assessment by a neurologist (TTN) were 5h, and the median time to first electroencephalography assessment was 18.5h; in the cases with out-of-hospital onset (n=24), the median time to hospital (TTH) arrival was 2.8h. The median time to NCSE control (TTC) was 16.5h, and it positively correlated with both the TTH (Spearman's rho: 0.439) and the TTT (Spearman's rho: 0.683). In the multivariable regression analyses, the TTC was extended 1.7h for each hour of hospital arrival delay (p=0.01) and 2.7h for each hour of treatment delay (p<0.001). Recognition delay was more common in the episodes with in-hospital onset, which also had longer TTN and TTC, and increased morbidity. There were pervasive delays in all phases of NCSE management. Delays in hospital arrival or treatment initiation may result in prolonged TTC. Recognition of in-hospital episodes may be more delayed, which may lead to poorer prognosis in these cases. Copyright © 2018 Elsevier Inc. All rights reserved.

Predictors of hospital re-admissions among Hispanics with hepatitis C-related cirrhosis.

PubMed

Atla, Pradeep R; Sheikh, Muhammad Y; Gill, Firdose; Kundu, Rabindra; Choudhury, Jayanta

2016-01-01

Hospital re-admissions in decompensated cirrhosis are associated with worse patient outcomes. Hispanics have a disproportionately high prevalence of hepatitis C virus (HCV)-related morbidity and mortality. The goal of this study was to evaluate the factors affecting re-admission rates among Hispanics with HCV-related cirrhosis. A total of 292 consecutive HCV-related cirrhosis admissions (Hispanics 189, non-Hispanics 103) from January 2009 to December 2012 were retrospectively reviewed; 132 were cirrhosis-related re-admissions. The statistical analysis was performed using STATA version 11.1. Chi-square/Fisher's exact and Student's t-tests were used to compare categorical and continuous variables, respectively. Multivariate logistic regression analysis was performed to identify predictors for hospital readmissions. Among the 132 cirrhosis-related readmissions, 71% were Hispanics while 29% were non-Hispanics (P=0.035). Hepatic encephalopathy (HE) and esophageal variceal hemorrhage were the most frequent causes of the first and subsequent readmissions. Hispanics with readmissions had a higher Child-Turcotte-Pugh (CTP) class (B and C) and higher model for end-stage liver disease (MELD) scores (≥15), as well as a higher incidence of alcohol use, HE, spontaneous bacterial peritonitis, hepatocellular carcinoma, and varices (P<0.05). The majority of the study patients (81%) had MELD scores <15. Multivariate regression analysis identified alcohol use (OR 2.63; 95%CI 1.1-6.4), HE (OR 5.5; 95%CI 2-15.3), varices (OR 3.2; 95%CI 1.3-8.2), and CTP class (OR 3.3; 95%CI 1.4-8.1) as predictors for readmissions among Hispanics. CTP classes B and C, among other factors, were the major predictors for hospital readmissions in Hispanics with HCV-related cirrhosis. The majority of these readmissions were due to HE and variceal hemorrhage.

Are We Cutting Enough? A Five-Year Audit of Melanoma Excision Margins in the South East of Ireland.

PubMed

Cronin, Catherine Tracy; Allen, Jack; Patterson, Ken; O'Donoghue, Gerrard

2018-01-05

Malignant melanoma is the fifth commonest invasive cancer in Ireland. The British Association of Dermatology (BAD) guidelines are currently the recognized standard for melanoma related surgery. The aim was to examine adherence to BAD guidelines and establish contributing factors resulting in non-adherence to guidelines in a group of melanoma patients in the South East Region of Ireland. A retrospective review of a prospectively maintained melanoma registry of all patients undergoing surgery in the South East Region of Ireland from January 2011 to 2016 was performed. Data were analyzed using SPSS statistical software. Univariate analysis using logistic regression was performed to examine factors associated with not meeting the BAD margin excision guidelines Data with a p < 0.05 was analyzed using a multivariate logistic regression model. 459 patients underwent surgery for invasive cutaneous melanoma. 314 (68.4%) surgeries had excision margins adequately recorded and of these 234(74.5%) fulfilled the BAD guidelines. 267(58.2%) patients (2011-2016 inclusive) qualified for sentinel lymph node biopsy (SNLB) with a cancer staging of pT1b or higher. Of these patients 100(37%) agreed to proceed to a SNLB following informed discussion. 33 had a positive sentinel node. On multivariate analysis inadequate margins were independently associated with tumor thickness 2.01-4.00 mm (p = 0.0001) and >4.00 mm (p = 0.0001) and head and neck location (p < 0.0001). Adherence to BAD guidelines in the South East is good but requires optimization since centralization of melanoma treatment in 2013 to a single specialized center. It is important that Clinicians are fully aware of the implications of not achieving adequate excision margins in surgery. Improvements in melanoma data management is needed to fully evaluate current practices in Ireland.

High prevalence of anemia in 10-month-old Japanese infants with breastfeeding.

PubMed

Kimura, Masahiko; Kurozawa, Youichi; Saito, Yumi; Watanabe, Hiroshi; Kobayashi, Ayame; Taketani, Takeshi

2018-05-05

Anemia in infancy is still prevalent in developing countries. Commercial iron-fortified complementary foods or iron drops are not available in Japan and breastfed infants have a higher risk of anemia. We studied anemia screening in infants in 10-month old infants and evaluated whether breastfeeding is a risk factor for anemia. Anemia screening was performed during a regular health check of 10-month children at four local pediatric clinics in Shimane prefecture, Japan. Venous blood was obtained for complete blood count. The clinical characteristics of each child were obtained through a questionnaire. Anemia was defined as a hemoglobin level < 11.0 g/dL. Children were categorized into anemia and no-anemia and univariate analyses were conducted to compare with clinical variables. Multivariate logistic regression analyses for anemia were performed to adjust for several clinical variables. We analyzed data in 325 children. In the univariate analyses, anemia was associated with breastfeeding, monthly body weight gain and gestational week. Multivariate logistic regression analyses revealed that anemia was associated with feeding type and gestational week, where the odds ratio (OR) of partial breastfeeding and formula feeding was 0.446 (95% confidential interval [CI], 0.208-0.957) and 0.223 ([CI], 0.075-0.660) respectively, compared to exclusive breastfeeding, in which the OR was taken as 1.0 and the OR of gestational week was 0.753 ([CI], 0583-0.972). Breastfeeding was an important factor for anemia in 10-month-old Japanese infants. Breastfed infants after 6 months of age may need sufficient iron sources such as iron supplements or iron fortified complimentary foods. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Assessing landslide susceptibility by statistical data analysis and GIS: the case of Daunia (Apulian Apennines, Italy)

NASA Astrophysics Data System (ADS)

Ceppi, C.; Mancini, F.; Ritrovato, G.

2009-04-01

This study aim at the landslide susceptibility mapping within an area of the Daunia (Apulian Apennines, Italy) by a multivariate statistical method and data manipulation in a Geographical Information System (GIS) environment. Among the variety of existing statistical data analysis techniques, the logistic regression was chosen to produce a susceptibility map all over an area where small settlements are historically threatened by landslide phenomena. By logistic regression a best fitting between the presence or absence of landslide (dependent variable) and the set of independent variables is performed on the basis of a maximum likelihood criterion, bringing to the estimation of regression coefficients. The reliability of such analysis is therefore due to the ability to quantify the proneness to landslide occurrences by the probability level produced by the analysis. The inventory of dependent and independent variables were managed in a GIS, where geometric properties and attributes have been translated into raster cells in order to proceed with the logistic regression by means of SPSS (Statistical Package for the Social Sciences) package. A landslide inventory was used to produce the bivariate dependent variable whereas the independent set of variable concerned with slope, aspect, elevation, curvature, drained area, lithology and land use after their reductions to dummy variables. The effect of independent parameters on landslide occurrence was assessed by the corresponding coefficient in the logistic regression function, highlighting a major role played by the land use variable in determining occurrence and distribution of phenomena. Once the outcomes of the logistic regression are determined, data are re-introduced in the GIS to produce a map reporting the proneness to landslide as predicted level of probability. As validation of results and regression model a cell-by-cell comparison between the susceptibility map and the initial inventory of landslide events was performed and an agreement at 75% level achieved.

TG study of the Li0.4Fe2.4Zn0.2O4 ferrite synthesis

NASA Astrophysics Data System (ADS)

Lysenko, E. N.; Nikolaev, E. V.; Surzhikov, A. P.

2016-02-01

In this paper, the kinetic analysis of Li-Zn ferrite synthesis was studied using thermogravimetry (TG) method through the simultaneous application of non-linear regression to several measurements run at different heating rates (multivariate non-linear regression). Using TG-curves obtained for the four heating rates and Netzsch Thermokinetics software package, the kinetic models with minimal adjustable parameters were selected to quantitatively describe the reaction of Li-Zn ferrite synthesis. It was shown that the experimental TG-curves clearly suggest a two-step process for the ferrite synthesis and therefore a model-fitting kinetic analysis based on multivariate non-linear regressions was conducted. The complex reaction was described by a two-step reaction scheme consisting of sequential reaction steps. It is established that the best results were obtained using the Yander three-dimensional diffusion model at the first stage and Ginstling-Bronstein model at the second step. The kinetic parameters for lithium-zinc ferrite synthesis reaction were found and discussed.

Cost analysis of iliac stenting performed in the operating room and the catheterization lab: A case-control study.

PubMed

Kim, Sooyeon; Kramer, Sage P; Dugan, Adam J; Minion, David J; Gurley, John C; Davenport, Daniel L; Ferraris, Victor A; Saha, Sibu P

2016-12-01

Iliac arterial stenting is performed both in the operating room (OR) and the catheterization lab (CL). To date, no analysis has compared resource utilization between these locations. Consecutive patients (n = 105) treated at a single center were retrospectively analyzed. Patients included adults with chronic, symptomatic iliac artery stenosis with a minimum Rutherford classification (RC) of 3, treated with stents. Exclusion criteria were prior stenting, acute ischemia, or major concomitant procedures. Immediate and two-year outcomes were observed. Patient demographics, perioperative details, physician billings, and hospital costs were recorded. Multivariable regression was used to adjust costs by patient and perioperative cost drivers. Fifty-one procedures (49%) were performed in the OR and 54 (51%) in the CL. Mean age was 57, and 44% were female. Severe cases were more often performed in the OR (RC ≥ 4; 42% vs. 11%, P < 0.001) and were associated with increased total costs (P < 0.01). OR procedures more often utilized additional stents (stents ≥ 2; 61% vs. 46%, P = 0.214), thrombolysis (12% vs. 0%, P = 0.011), cut-down approach (8% vs. 0%, P = 0.052), and general anesthesia (80% vs. 0%, P < 0.001): these were all associated with increased costs (P < 0.05). After multivariable regression, location was not a predictor of procedure room or total costs but was associated with increased professional fees. Same-stay (5%) and post-discharge reintervention (33%) did not vary by location. The OR was associated with increased length of stay, more ICU admissions, and increased total costs. However, OR patients had more severe disease and therefore often required more aggressive intervention. After controlling for these differences, procedure venue per se was not associated with increased costs, but OR cases incurred increased professional fees due to dual-provider charges. Given the similar clinical results between venues, it seems reasonable to perform most stenting in the CL or utilize conscious sedation in the OR. Copyright © 2016 IJS Publishing Group Ltd. Published by Elsevier Ltd. All rights reserved.

Multivariate statistical analysis: Principles and applications to coorbital streams of meteorite falls

NASA Technical Reports Server (NTRS)

Wolf, S. F.; Lipschutz, M. E.

1993-01-01

Multivariate statistical analysis techniques (linear discriminant analysis and logistic regression) can provide powerful discrimination tools which are generally unfamiliar to the planetary science community. Fall parameters were used to identify a group of 17 H chondrites (Cluster 1) that were part of a coorbital stream which intersected Earth's orbit in May, from 1855 - 1895, and can be distinguished from all other H chondrite falls. Using multivariate statistical techniques, it was demonstrated that a totally different criterion, labile trace element contents - hence thermal histories - or 13 Cluster 1 meteorites are distinguishable from those of 45 non-Cluster 1 H chondrites. Here, we focus upon the principles of multivariate statistical techniques and illustrate their application using non-meteoritic and meteoritic examples.

Multivariate analysis of cytokine profiles in pregnancy complications.

PubMed

Azizieh, Fawaz; Dingle, Kamaludin; Raghupathy, Raj; Johnson, Kjell; VanderPlas, Jacob; Ansari, Ali

2018-03-01

The immunoregulation to tolerate the semiallogeneic fetus during pregnancy includes a harmonious dynamic balance between anti- and pro-inflammatory cytokines. Several earlier studies reported significantly different levels and/or ratios of several cytokines in complicated pregnancy as compared to normal pregnancy. However, as cytokines operate in networks with potentially complex interactions, it is also interesting to compare groups with multi-cytokine data sets, with multivariate analysis. Such analysis will further examine how great the differences are, and which cytokines are more different than others. Various multivariate statistical tools, such as Cramer test, classification and regression trees, partial least squares regression figures, 2-dimensional Kolmogorov-Smirmov test, principal component analysis and gap statistic, were used to compare cytokine data of normal vs anomalous groups of different pregnancy complications. Multivariate analysis assisted in examining if the groups were different, how strongly they differed, in what ways they differed and further reported evidence for subgroups in 1 group (pregnancy-induced hypertension), possibly indicating multiple causes for the complication. This work contributes to a better understanding of cytokines interaction and may have important implications on targeting cytokine balance modulation or design of future medications or interventions that best direct management or prevention from an immunological approach. © 2018 The Authors. American Journal of Reproductive Immunology Published by John Wiley & Sons Ltd.

Post-treatment cognitive dysfunction in women treated with thyroidectomy for papillary thyroid carcinoma.

PubMed

Jung, Mi Sook; Visovatti, Moira

2017-03-01

The purpose of the study is to assess cognitive function in papillary thyroid cancer, one type of differentiated thyroid cancer, and to identify factors associated with cognitive dysfunction. Korean women treated with papillary thyroid cancer post thyroidectomy (n = 90) and healthy women similar in age and educational level (n = 90) performed attention and working memory tests and completed self-report questionnaires on cognitive complaints, psychological distress, symptom distress, and cultural characteristics. Comparative and multivariable regression analyses were performed to determine differences in cognitive function and possible predictors of neurocognitive performance and cognitive complaints. Thyroid cancer survivors performed and perceived their function to be significantly worse on tests of attention and working memory compared to individuals without thyroid cancer. Regression analyses found that having thyroid cancer, older age, and lower educational level were associated with worse neurocognitive performance, while greater fatigue, more sleep problems, and higher levels of childrearing burden but not having thyroid cancer were associated with lower perceived effectiveness in cognitive functioning. Findings suggest that women receiving thyroid hormone replacement therapy after thyroidectomy for papillary thyroid cancer are at risk for attention and working memory problems. Coexisting symptoms and culture-related women's burden affected perceived cognitive dysfunction. Health care providers should assess for cognitive problems in women with thyroid cancer and intervene to reduce distress and improve quality of life.

Explaining Match Outcome During The Men’s Basketball Tournament at The Olympic Games

PubMed Central

Leicht, Anthony S.; Gómez, Miguel A.; Woods, Carl T.

2017-01-01

In preparation for the Olympics, there is a limited opportunity for coaches and athletes to interact regularly with team performance indicators providing important guidance to coaches for enhanced match success at the elite level. This study examined the relationship between match outcome and team performance indicators during men’s basketball tournaments at the Olympic Games. Twelve team performance indicators were collated from all men’s teams and matches during the basketball tournament of the 2004-2016 Olympic Games (n = 156). Linear and non-linear analyses examined the relationship between match outcome and team performance indicator characteristics; namely, binary logistic regression and a conditional interference (CI) classification tree. The most parsimonious logistic regression model retained ‘assists’, ‘defensive rebounds’, ‘field-goal percentage’, ‘fouls’, ‘fouls against’, ‘steals’ and ‘turnovers’ (delta AIC <0.01; Akaike weight = 0.28) with a classification accuracy of 85.5%. Conversely, four performance indicators were retained with the CI classification tree with an average classification accuracy of 81.4%. However, it was the combination of ‘field-goal percentage’ and ‘defensive rebounds’ that provided the greatest probability of winning (93.2%). Match outcome during the men’s basketball tournaments at the Olympic Games was identified by a unique combination of performance indicators. Despite the average model accuracy being marginally higher for the logistic regression analysis, the CI classification tree offered a greater practical utility for coaches through its resolution of non-linear phenomena to guide team success. Key points A unique combination of team performance indicators explained 93.2% of winning observations in men’s basketball at the Olympics. Monitoring of these team performance indicators may provide coaches with the capability to devise multiple game plans or strategies to enhance their likelihood of winning. Incorporation of machine learning techniques with team performance indicators may provide a valuable and strategic approach to explain patterns within multivariate datasets in sport science. PMID:29238245

Beyond Body Mass Index. Is the Body Cell Mass Index (BCMI) a useful prognostic factor to describe nutritional, inflammation and muscle mass status in hospitalized elderly?: Body Cell Mass Index links in elderly.

PubMed

Rondanelli, Mariangela; Talluri, Jacopo; Peroni, Gabriella; Donelli, Chiara; Guerriero, Fabio; Ferrini, Krizia; Riggi, Emilia; Sauta, Elisabetta; Perna, Simone; Guido, Davide

2018-06-01

The aim of this study was to establish the effectiveness of Body Cell Mass Index (BCMI) as a prognostic index of (mal)nutrition, inflammation and muscle mass status in the elderly. A cross-sectional observational study has been conducted on 114 elderly patients (80 women and 34 men), with mean age equal to 81.07 ± 6.18 years. We performed a multivariate regression model by Structural Equation Modelling (SEM) framework. We detected the effects over a Mini Nutritional Assessment (MNA) stratification, by performing a multi-group multivariate regression model (via SEM) in two MNA nutritional strata, less and bigger (or equal) than 17. BCMI had a significant effect on albumin (β = +0.062, P = 0.001), adjusting for the other predictors of the model as Body Mass Index (BMI), age, sex, fat mass and cognitive condition. An analogous result is maintained in MNA<17 stratum. BMI has confirmed to be a solid prognostic factor for both free fat mass (FFM) (β = +0.480, P < 0.001) and Skeletal Muscle Index (SMI) (β = +0.265, P < 0.001), assessed by DXA. BCMI also returned suggestive evidences (0.05 < P < 0.10) for both the effect on FFM and on SMI in overall sample. The main result of this study is that the BCMI, compared to BMI, proved to be significantly related to an important marker as albumin in geriatric population. Then, assessing the BCMI could be a valuable, inexpensive, easy to perform tool to investigate the inflammation status of elderly patients. Copyright © 2017 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.

Relationship between non-standard work arrangements and work-related accident absence in Belgium

PubMed Central

Alali, Hanan; Braeckman, Lutgart; Van Hecke, Tanja; De Clercq, Bart; Janssens, Heidi; Wahab, Magd Abdel

2017-01-01

Objectives: The main objective of this study is to examine the relationship between indicators of non-standard work arrangements, including precarious contract, long working hours, multiple jobs, shift work, and work-related accident absence, using a representative Belgian sample and considering several socio-demographic and work characteristics. Methods: This study was based on the data of the fifth European Working Conditions Survey (EWCS). For the analysis, the sample was restricted to 3343 respondents from Belgium who were all employed workers. The associations between non-standard work arrangements and work-related accident absence were studied with multivariate logistic regression modeling techniques while adjusting for several confounders. Results: During the last 12 months, about 11.7% of workers were absent from work because of work-related accident. A multivariate regression model showed an increased injury risk for those performing shift work (OR 1.546, 95% CI 1.074-2.224). The relationship between contract type and occupational injuries was not significant (OR 1.163, 95% CI 0.739-1.831). Furthermore, no statistically significant differences were observed for those performing long working hours (OR 1.217, 95% CI 0.638-2.321) and those performing multiple jobs (OR 1.361, 95% CI 0.827-2.240) in relation to work-related accident absence. Those who rated their health as bad, low educated workers, workers from the construction sector, and those exposed to biomechanical exposure (BM) were more frequent victims of work-related accident absence. No significant gender difference was observed. Conclusion: Indicators of non-standard work arrangements under this study, except shift work, were not significantly associated with work-related accident absence. To reduce the burden of occupational injuries, not only risk reduction strategies and interventions are needed but also policy efforts are to be undertaken to limit shift work. In general, preventive measures and more training on the job are needed to ensure the safety and well-being of all workers. PMID:28111414

Relationship between non-standard work arrangements and work-related accident absence in Belgium.

PubMed

Alali, Hanan; Braeckman, Lutgart; Van Hecke, Tanja; De Clercq, Bart; Janssens, Heidi; Wahab, Magd Abdel

2017-03-28

The main objective of this study is to examine the relationship between indicators of non-standard work arrangements, including precarious contract, long working hours, multiple jobs, shift work, and work-related accident absence, using a representative Belgian sample and considering several socio-demographic and work characteristics. This study was based on the data of the fifth European Working Conditions Survey (EWCS). For the analysis, the sample was restricted to 3343 respondents from Belgium who were all employed workers. The associations between non-standard work arrangements and work-related accident absence were studied with multivariate logistic regression modeling techniques while adjusting for several confounders. During the last 12 months, about 11.7% of workers were absent from work because of work-related accident. A multivariate regression model showed an increased injury risk for those performing shift work (OR 1.546, 95% CI 1.074-2.224). The relationship between contract type and occupational injuries was not significant (OR 1.163, 95% CI 0.739-1.831). Furthermore, no statistically significant differences were observed for those performing long working hours (OR 1.217, 95% CI 0.638-2.321) and those performing multiple jobs (OR 1.361, 95% CI 0.827-2.240) in relation to work-related accident absence. Those who rated their health as bad, low educated workers, workers from the construction sector, and those exposed to biomechanical exposure (BM) were more frequent victims of work-related accident absence. No significant gender difference was observed. Indicators of non-standard work arrangements under this study, except shift work, were not significantly associated with work-related accident absence. To reduce the burden of occupational injuries, not only risk reduction strategies and interventions are needed but also policy efforts are to be undertaken to limit shift work. In general, preventive measures and more training on the job are needed to ensure the safety and well-being of all workers.

Kinetic therapy in multiple trauma patients with severe blunt chest trauma: an analysis at a level-1 trauma center.

PubMed

Zeckey, C; Wendt, K; Mommsen, P; Winkelmann, M; Frömke, C; Weidemann, J; Stübig, T; Krettek, C; Hildebrand, F

2015-01-01

Chest trauma is a relevant risk factor for mortality after multiple trauma. Kinetic therapy (KT) represents a potential treatment option in order to restore pulmonary function. Decision criteria for performing kinetic therapy are not fully elucidated. The purpose of this study was to investigate the decision making process to initiate kinetic therapy in a well defined multiple trauma cohort. A retrospective analysis (2000-2009) of polytrauma patients (age > 16 years, ISS ⩾ 16) with severe chest trauma (AIS(Chest) ⩾ 3) was performed. Patients with AIS(Head) ⩾ 3 were excluded. Patients receiving either kinetic (KT+) or lung protective ventilation strategy (KT-) were compared. Chest trauma was classified according to the AIS(Chest), Pulmonary Contusion Score (PCS), Wagner Jamieson Score and Thoracic Trauma Severity Score (TTS). There were multiple outcome parameters investigated included mortality, posttraumatic complications and clinical data. A multivariate regression analysis was performed. Two hundred and eighty-three patients were included (KT+: n=160; KT-: n=123). AIS(Chest), age and gender were comparable in both groups. There were significant higher values of the ISS, PCS, Wagner Jamieson Score and TTS in group KT+. The incidence of posttraumatic complications and mortality was increased compared to group KT- (p< 0.05). Despite that, kinetic therapy failed to be an independent risk factor for mortality in multivariate logistic regression analysis. Kinetic therapy is an option in severely injured patients with severe chest trauma. Decision making is not only based on anatomical aspects such as the AIS(Chest), but on overall injury severity, pulmonary contusions and physiological deterioration. It could be assumed that the increased mortality in patients receiving KT is primarily caused by these factors and does not reflect an independent adverse effect of KT. Furthermore, KT was not shown to be an independent risk factor for mortality.

Impact of concomitant mitral valve repair for severe mitral regurgitation at the time of continuous-flow left ventricular assist device insertion.

PubMed

Sandoval, Elena; Singh, Steve K; Carillo, Julius A; Baldwin, Andrew C W; Ono, Masahiro; Anand, Jatin; Frazier, O H; Mallidi, Hari R

2017-10-01

Mitral regurgitation (MR) is common in patients with end-stage heart failure. We assessed the effect of performing concomitant mitral valve repair during continuous-flow left ventricular assist device (CF-LVAD) implantation in patients with severe preoperative MR. We performed a single-centre, retrospective review of all patients who underwent CF-LVAD implantation between December 1999 and December 2013 (n = 469). Patients with severe preoperative MR (n = 78) were identified and then stratified according to whether they underwent concomitant valve repair. Univariate and survival analyses were performed, and multivariable regression was used to determine predictors of survival. Of the 78 patients with severe MR, 21 underwent valve repair at the time of CF-LVAD implantation (repair group) and 57 did not (non-repair group). A comparison of the 2 groups showed significant differences between groups: INTERMACS I 16.985 vs 9.52%, (P = 0.039), cardiopulmonary bypass time 82.09 vs 109.4 min (P = 0.0042) and the use of HeartMate II 63.16 vs 100% (P = 0.001). Survival analysis suggested trends towards improved survival and a lower incidence of heart failure-related readmissions in the repair group. Multivariable regression analysis showed no significant independent predictors of survival (mitral valve repair: odds ratio 0.4, 95% confidence interval 0.8-1.5; P = 0.2). Despite the lack of statistical significance, trends towards improved survival and a lower incidence of heart failure events suggest that mitral valve repair may be beneficial in patients undergoing CF-LVAD implantation. Given the known relationship between severe MR and mortality, further study is encouraged to confirm the value of mitral valve repair in these patients. © The Author 2017. Published by Oxford University Press on behalf of the European Association for Cardio-Thoracic Surgery. All rights reserved.

Routine perioperative ketorolac administration is not associated with hemorrhage in pediatric neurosurgery patients.

PubMed

Richardson, Marlin Dustin; Palmeri, Nicholas O; Williams, Sarah A; Torok, Michelle R; O'Neill, Brent R; Handler, Michael H; Hankinson, Todd C

2016-01-01

OBJECT NSAIDs are effective perioperative analgesics. Many surgeons are reluctant to use NSAIDs perioperatively because of a theoretical increase in the risk for bleeding events. The authors assessed the effect of routine perioperative ketorolac use on intracranial hemorrhage in children undergoing a wide range of neurosurgical procedures. METHODS A retrospective single-institution analysis of 1451 neurosurgical cases was performed. Data included demographics, type of surgery, and perioperative ketorolac use. Outcomes included bleeding events requiring return to the operating room, bleeding seen on postoperative imaging, and the development of renal failure or gastrointestinal tract injury. Variables associated with both the exposure and outcomes (p < 0.20) were evaluated as potential confounders for bleeding on postoperative imaging, and multivariable logistic regression was performed. Bivariable analysis was performed for bleeding events. Odds ratios and 95% CIs were estimated. RESULTS Of the 1451 patients, 955 received ketorolac. Multivariate regression analysis demonstrated no significant association between clinically significant bleeding events (OR 0.69; 95% CI 0.15-3.1) or radiographic hemorrhage (OR 0.81; 95% CI 0.43-1.51) and the perioperative administration of ketorolac. Treatment with a medication that creates a known bleeding risk (OR 3.11; 95% CI 1.01-9.57), surgical procedure (OR 2.35; 95% CI 1.11-4.94), and craniotomy/craniectomy (OR 2.43; 95% CI 1.19-4.94) were associated with a significantly elevated risk for radiographically identified hemorrhage. CONCLUSIONS Short-term ketorolac therapy does not appear to be associated with a statistically significant increase in the risk of bleeding documented on postoperative imaging in pediatric neurosurgical patients and may be considered as part of a perioperative analgesic regimen. Although no association was found between ketorolac and clinically significant bleeding events, a larger study needs to be conducted to control for confounding factors, because of the rarity of these events.

Outcomes after suboccipital decompression without dural opening in children with Chiari malformation Type I

PubMed Central

Kennedy, Benjamin C.; Kelly, Kathleen M.; Phan, Michelle Q.; Bruce, Samuel S.; McDowell, Michael M.; Anderson, Richard C. E.; Feldstein, Neil A.

2015-01-01

Object Symptomatic pediatric Chiari malformation Type I (CM-I) is most often treated with posterior fossa decompression (PFD), but controversy exists over whether the dura needs to be opened during PFD. While dural opening as a part of PFD has been suggested to result in a higher rate of resolution of CM symptoms, it has also been shown to lead to more frequent complications. In this paper, the authors present the largest reported series of outcomes after PFD without dural opening surgery, as well as identify risk factors for recurrence. Methods The authors performed a retrospective review of 156 consecutive pediatric patients in whom the senior authors performed PFD without dural opening from 2003 to 2013. Patient demographics, clinical symptoms and signs, radiographic findings, intraoperative ultrasound results, and neuromonitoring findings were reviewed. Univariate and multivariate regression analyses were performed to determine risk factors for recurrence of symptoms and the need for reoperation. Results Over 90% of patients had a good clinical outcome, with improvement or resolution of their symptoms at last follow-up (mean 32 months). There were no major complications. The mean length of hospital stay was 2.0 days. In a multivariate regression model, partial C-2 laminectomy was an independent risk factor associated with reoperation (p = 0.037). Motor weakness on presentation was also associated with reoperation but only with trend-level significance (p = 0.075). No patient with < 8 mm of tonsillar herniation required reoperation. Conclusions The vast majority (> 90%) of children with symptomatic CM-I will have improvement or resolution of symptoms after a PFD without dural opening. A non–dural opening approach avoids major complications. While no patient with tonsillar herniation < 8 mm required reoperation, children with tonsillar herniation at or below C-2 have a higher risk for failure when this approach is used. PMID:25932779

Disparities in Adherence to Pediatric Sepsis Guidelines across a Spectrum of Emergency Departments: A Multicenter, Cross-Sectional Observational In Situ Simulation Study.

PubMed

Kessler, David O; Walsh, Barbara; Whitfill, Travis; Dudas, Robert A; Gangadharan, Sandeep; Gawel, Marcie; Brown, Linda; Auerbach, Marc

2016-03-01

Each year in the United States, 72,000 pediatric patients develop septic shock, at a cost of $4.8 billion. Adherence to practice guidelines can significantly reduce mortality; however, few methods to compare performance across a spectrum of emergency departments (EDs) have been described. We employed standardized, in situ simulations to measure and compare adherence to pediatric sepsis guidelines across a spectrum of EDs. We hypothesized that pediatric EDs (PEDs) would have greater adherence to the guidelines than general EDs (GEDs). We also explored factors associated with improved performance. This multi-center observational study examined in situ teams caring for a simulated infant in septic shock. The primary outcome was overall adherence to the pediatric sepsis guideline as measured by six subcomponent metrics. Characteristics of teams were compared using multivariable logistic regression to describe factors associated with improved performance. We enrolled 47 interprofessional teams from 24 EDs. Overall, 21/47 teams adhered to all six sepsis metrics (45%). PEDs adhered to all six metrics more than GEDs (93% vs. 22%; difference 71%, 95% confidence interval [CI] 43-84). Adherent teams had significantly higher Emergency Medical Services for Children readiness scores, MD composition of physicians to total team members, teamwork scores, provider perceptions of pediatric preparedness, and provider perceptions of sepsis preparedness. In a multivariable regression model, only greater composite team experience had greater adjusted odds of achieving an adherent sepsis score (adjusted odds ratio 1.38, 95% CI 1.01-1.88). Using standardized in situ scenarios, we revealed high variability in adherence to the pediatric sepsis guideline across a spectrum of EDs. PEDs demonstrated greater adherence to the guideline than GEDs; however, in adjusted analysis, only composite team experience level of the providers was associated with improved guideline adherence. Copyright © 2016 Elsevier Inc. All rights reserved.

Regional regression models of watershed suspended-sediment discharge for the eastern United States

NASA Astrophysics Data System (ADS)

Roman, David C.; Vogel, Richard M.; Schwarz, Gregory E.

2012-11-01

SummaryEstimates of mean annual watershed sediment discharge, derived from long-term measurements of suspended-sediment concentration and streamflow, often are not available at locations of interest. The goal of this study was to develop multivariate regression models to enable prediction of mean annual suspended-sediment discharge from available basin characteristics useful for most ungaged river locations in the eastern United States. The models are based on long-term mean sediment discharge estimates and explanatory variables obtained from a combined dataset of 1201 US Geological Survey (USGS) stations derived from a SPAtially Referenced Regression on Watershed attributes (SPARROW) study and the Geospatial Attributes of Gages for Evaluating Streamflow (GAGES) database. The resulting regional regression models summarized for major US water resources regions 1-8, exhibited prediction R2 values ranging from 76.9% to 92.7% and corresponding average model prediction errors ranging from 56.5% to 124.3%. Results from cross-validation experiments suggest that a majority of the models will perform similarly to calibration runs. The 36-parameter regional regression models also outperformed a 16-parameter national SPARROW model of suspended-sediment discharge and indicate that mean annual sediment loads in the eastern United States generally correlates with a combination of basin area, land use patterns, seasonal precipitation, soil composition, hydrologic modification, and to a lesser extent, topography.

Regional regression models of watershed suspended-sediment discharge for the eastern United States

USGS Publications Warehouse

Roman, David C.; Vogel, Richard M.; Schwarz, Gregory E.

2012-01-01

Estimates of mean annual watershed sediment discharge, derived from long-term measurements of suspended-sediment concentration and streamflow, often are not available at locations of interest. The goal of this study was to develop multivariate regression models to enable prediction of mean annual suspended-sediment discharge from available basin characteristics useful for most ungaged river locations in the eastern United States. The models are based on long-term mean sediment discharge estimates and explanatory variables obtained from a combined dataset of 1201 US Geological Survey (USGS) stations derived from a SPAtially Referenced Regression on Watershed attributes (SPARROW) study and the Geospatial Attributes of Gages for Evaluating Streamflow (GAGES) database. The resulting regional regression models summarized for major US water resources regions 1–8, exhibited prediction R2 values ranging from 76.9% to 92.7% and corresponding average model prediction errors ranging from 56.5% to 124.3%. Results from cross-validation experiments suggest that a majority of the models will perform similarly to calibration runs. The 36-parameter regional regression models also outperformed a 16-parameter national SPARROW model of suspended-sediment discharge and indicate that mean annual sediment loads in the eastern United States generally correlates with a combination of basin area, land use patterns, seasonal precipitation, soil composition, hydrologic modification, and to a lesser extent, topography.

Death Anxiety as a Predictor of Posttraumatic Stress Levels among Individuals with Spinal Cord Injuries

ERIC Educational Resources Information Center

Martz, Erin

2004-01-01

Because the onset of a spinal cord injury may involve a brush with death and because serious injury and disability can act as a reminder of death, death anxiety was examined as a predictor of posttraumatic stress levels among individuals with disabilities. This cross-sectional study used multiple regression and multivariate multiple regression to…

Cellulose I crystallinity determination using FT-Raman spectroscopy : univariate and multivariate methods

Treesearch

Umesh P. Agarwal; Richard S. Reiner; Sally A. Ralph

2010-01-01

Two new methods based on FT–Raman spectroscopy, one simple, based on band intensity ratio, and the other using a partial least squares (PLS) regression model, are proposed to determine cellulose I crystallinity. In the simple method, crystallinity in cellulose I samples was determined based on univariate regression that was first developed using the Raman band...

Predicting Potential Changes in Suitable Habitat and Distribution by 2100 for Tree Species of the Eastern United States

Treesearch

Louis R Iverson; Anantha M. Prasad; Mark W. Schwartz; Mark W. Schwartz

2005-01-01

We predict current distribution and abundance for tree species present in eastern North America, and subsequently estimate potential suitable habitat for those species under a changed climate with 2 x CO2. We used a series of statistical models (i.e., Regression Tree Analysis (RTA), Multivariate Adaptive Regression Splines (MARS), Bagging Trees (...

Per capita community-level effects of an invasive grass, Microstegium vimineum, on vegetation in mesic forests in northern Mississippi (USA)

Treesearch

J. Stephen Brewer

2010-01-01

Quantifying per capita impacts of invasive species on resident communities requires integrating regression analyses with experiments under natural conditions. Using multivariate and univariate approaches, I regressed the abundance of 105 resident species of groundcover plants and tree seedlings against the abundance and height of an invasive grass, Microstegium...

Regression analysis for LED color detection of visual-MIMO system

NASA Astrophysics Data System (ADS)

Banik, Partha Pratim; Saha, Rappy; Kim, Ki-Doo

2018-04-01

Color detection from a light emitting diode (LED) array using a smartphone camera is very difficult in a visual multiple-input multiple-output (visual-MIMO) system. In this paper, we propose a method to determine the LED color using a smartphone camera by applying regression analysis. We employ a multivariate regression model to identify the LED color. After taking a picture of an LED array, we select the LED array region, and detect the LED using an image processing algorithm. We then apply the k-means clustering algorithm to determine the number of potential colors for feature extraction of each LED. Finally, we apply the multivariate regression model to predict the color of the transmitted LEDs. In this paper, we show our results for three types of environmental light condition: room environmental light, low environmental light (560 lux), and strong environmental light (2450 lux). We compare the results of our proposed algorithm from the analysis of training and test R-Square (%) values, percentage of closeness of transmitted and predicted colors, and we also mention about the number of distorted test data points from the analysis of distortion bar graph in CIE1931 color space.

Stock price forecasting for companies listed on Tehran stock exchange using multivariate adaptive regression splines model and semi-parametric splines technique

NASA Astrophysics Data System (ADS)

Rounaghi, Mohammad Mahdi; Abbaszadeh, Mohammad Reza; Arashi, Mohammad

2015-11-01

One of the most important topics of interest to investors is stock price changes. Investors whose goals are long term are sensitive to stock price and its changes and react to them. In this regard, we used multivariate adaptive regression splines (MARS) model and semi-parametric splines technique for predicting stock price in this study. The MARS model as a nonparametric method is an adaptive method for regression and it fits for problems with high dimensions and several variables. semi-parametric splines technique was used in this study. Smoothing splines is a nonparametric regression method. In this study, we used 40 variables (30 accounting variables and 10 economic variables) for predicting stock price using the MARS model and using semi-parametric splines technique. After investigating the models, we select 4 accounting variables (book value per share, predicted earnings per share, P/E ratio and risk) as influencing variables on predicting stock price using the MARS model. After fitting the semi-parametric splines technique, only 4 accounting variables (dividends, net EPS, EPS Forecast and P/E Ratio) were selected as variables effective in forecasting stock prices.

A diagnostic analysis of the VVP single-doppler retrieval technique

NASA Technical Reports Server (NTRS)

Boccippio, Dennis J.

1995-01-01

A diagnostic analysis of the VVP (volume velocity processing) retrieval method is presented, with emphasis on understanding the technique as a linear, multivariate regression. Similarities and differences to the velocity-azimuth display and extended velocity-azimuth display retrieval techniques are discussed, using this framework. Conventional regression diagnostics are then employed to quantitatively determine situations in which the VVP technique is likely to fail. An algorithm for preparation and analysis of a robust VVP retrieval is developed and applied to synthetic and actual datasets with high temporal and spatial resolution. A fundamental (but quantifiable) limitation to some forms of VVP analysis is inadequate sampling dispersion in the n space of the multivariate regression, manifest as a collinearity between the basis functions of some fitted parameters. Such collinearity may be present either in the definition of these basis functions or in their realization in a given sampling configuration. This nonorthogonality may cause numerical instability, variance inflation (decrease in robustness), and increased sensitivity to bias from neglected wind components. It is shown that these effects prevent the application of VVP to small azimuthal sectors of data. The behavior of the VVP regression is further diagnosed over a wide range of sampling constraints, and reasonable sector limits are established.

Influence of pay-for-performance programs on information technology use among child health providers: the devil is in the details.

PubMed

Menachemi, Nir; Struchen-Shellhorn, Wendy; Brooks, Robert G; Simpson, Lisa

2009-01-01

Pay-for-performance programs are used to promote improved health care quality, often through increased use of health information technology. However, little is known about whether pay-for-performance programs influence the adoption of health information technology, especially among child health providers. This study explored how various pay-for-performance compensation methods are related to health information technology use. Survey data from 1014 child health providers practicing in Florida were analyzed by using univariate and multivariate techniques. Questions asked about the adoption of electronic health records and personal digital assistants, as well as types of activities that affected child health provider compensation or income. The most common reported method to affect respondents' compensation was traditional productivity or billing (78%). Of the pay-for-performance-related methods of compensation, child health providers indicated that measures of clinical care (41%), patient surveys and experience (34%), the use of health information technology (29%), and quality bonuses or incentives (27%) were a major or minor factor in their compensation. In multivariate logistic regression analyses, only pay-for-performance programs that compensated directly for health information technology use were associated with an increased likelihood of electronic health record system adoption. Pay-for-performance programs linking measures of clinical quality to compensation were positively associated with personal digital assistant use among child health providers. Pay-for-performance programs that do not directly emphasize health information technology use do not influence the adoption of electronic health records among Florida physicians treating children. Understanding how different pay-for-performance compensation methods incentivize health information technology adoption is important for improving quality.

The prevalence of anxiety and depression in patients with or without hyperhidrosis (HH).

PubMed

Bahar, Rayeheh; Zhou, Pingyu; Liu, Yudan; Huang, Yuanshen; Phillips, Arlie; Lee, Tim K; Su, Mingwan; Yang, Sen; Kalia, Sunil; Zhang, Xuejun; Zhou, Youwen

2016-12-01

There are conflicting data about the correlation between hyperhidrosis (HH) and anxiety and depression. We sought to determine the prevalence of anxiety and depression in patients with or without HH. We examined 2017 consecutive dermatology outpatients from Vancouver, British Columbia, Canada, and Shanghai, China, using Patient Health Questionnaire-9 and Generalized Anxiety Disorder-7 scales for anxiety and depression assessments. Multivariable logistic regression analysis was performed to evaluate if the impact of HH on anxiety and depression is dependent on demographic factors and diagnoses of the patients' presenting skin conditions. The prevalence of anxiety and depression was 21.3% and 27.2% in patients with HH, respectively, and 7.5% and 9.7% in patients without HH, respectively (P value <.001 for both). There were positive correlations between HH severity and the prevalence of anxiety and depression. Multivariable analysis showed that HH-associated increase in anxiety and depression prevalence is independent of demographic factors and presenting skin conditions. The data from the questionnaires relied on the accuracy of patients' self-reports. Both single variant and multivariable analyses showed a significant association between HH and the prevalence of anxiety and depression in a HH severity-dependent manner. Copyright © 2016 American Academy of Dermatology, Inc. Published by Elsevier Inc. All rights reserved.

Natural history definition and a suggested clinical approach to Buerger's disease: a case-control study with survival analysis.

PubMed

Fazeli, Bahare; Ravari, Hassan; Assadi, Reza

2012-08-01

The aim of this study was first to describe the natural history of Buerger's disease (BD) and then to discuss a clinical approach to this disease based on multivariate analysis. One hundred eight patients who corresponded with Shionoya's criteria were selected from 2000 to 2007 for this study. Major amputation was considered the ultimate adverse event. Survival analyses were performed by Kaplan-Meier curves. Independent variables including gender, duration of smoking, number of cigarettes smoked per day, minor amputation events and type of treatments, were determined by multivariate Cox regression analysis. The recorded data demonstrated that BD may present in four forms, including relapsing-remitting (75%), secondary progressive (4.6%), primary progressive (14.2%) and benign BD (6.2%). Most of the amputations occurred due to relapses within the six years after diagnosis of BD. In multivariate analysis, duration of smoking of more than 20 years had a significant relationship with further major amputation among patients with BD. Smoking cessation programs with experienced psychotherapists are strongly recommended for those areas in which Buerger's disease is common. Patients who have smoked for more than 20 years should be encouraged to quit smoking, but should also be recommended for more advanced treatment for limb salvage.

Extraction, isolation, and purification of analytes from samples of marine origin--a multivariate task.

PubMed

Liguori, Lucia; Bjørsvik, Hans-René

2012-12-01

The development of a multivariate study for a quantitative analysis of six different polybrominated diphenyl ethers (PBDEs) in tissue of Atlantic Salmo salar L. is reported. An extraction, isolation, and purification process based on an accelerated solvent extraction system was designed, investigated, and optimized by means of statistical experimental design and multivariate data analysis and regression. An accompanying gas chromatography-mass spectrometry analytical method was developed for the identification and quantification of the analytes, BDE 28, BDE 47, BDE 99, BDE 100, BDE 153, and BDE 154. These PBDEs have been used in commercial blends that were used as flame-retardants for a variety of materials, including electronic devices, synthetic polymers and textiles. The present study revealed that an extracting solvent mixture composed of hexane and CH₂Cl₂ (10:90) provided excellent recoveries of all of the six PBDEs studied herein. A somewhat lower polarity in the extracting solvent, hexane and CH₂Cl₂ (40:60) decreased the analyte %-recoveries, which still remain acceptable and satisfactory. The study demonstrates the necessity to perform an intimately investigation of the extraction and purification process in order to achieve quantitative isolation of the analytes from the specific matrix. Copyright © 2012 Elsevier B.V. All rights reserved.

Evaluation of in-line Raman data for end-point determination of a coating process: Comparison of Science-Based Calibration, PLS-regression and univariate data analysis.

PubMed

Barimani, Shirin; Kleinebudde, Peter

2017-10-01

A multivariate analysis method, Science-Based Calibration (SBC), was used for the first time for endpoint determination of a tablet coating process using Raman data. Two types of tablet cores, placebo and caffeine cores, received a coating suspension comprising a polyvinyl alcohol-polyethylene glycol graft-copolymer and titanium dioxide to a maximum coating thickness of 80µm. Raman spectroscopy was used as in-line PAT tool. The spectra were acquired every minute and correlated to the amount of applied aqueous coating suspension. SBC was compared to another well-known multivariate analysis method, Partial Least Squares-regression (PLS) and a simpler approach, Univariate Data Analysis (UVDA). All developed calibration models had coefficient of determination values (R 2 ) higher than 0.99. The coating endpoints could be predicted with root mean square errors (RMSEP) less than 3.1% of the applied coating suspensions. Compared to PLS and UVDA, SBC proved to be an alternative multivariate calibration method with high predictive power. Copyright © 2017 Elsevier B.V. All rights reserved.

Maternal Complications Associated with Stillbirth Delivery: a Cross-Sectional Analysis

PubMed Central

Gold, Katherine J.; Mozurkewich, Ellen L.; Puder, Karoline S.; Treadwell, Marjorie C.

2016-01-01

This study sought to identify delivery complications associated with stillbirth labor and delivery. We conducted a retrospective chart review evaluating stillbirth demographics, pregnancy and maternal risk factors, and complications of labor and delivery. We performed bivariable analysis and multivariable logistic regression to evaluate factors associated with medical complications and variations by race. Our cohort included 543 mothers with stillbirth, of which two-thirds were African-American. We noted high rates of shoulder dystocia, clinical chorioamnionitis, postpartum hemorrhage, and retained placenta in women with stillbirths. 33 women (6%) experienced at least one serious maternal complication. Complication rates did not vary by maternal race. Providers who perform obstetrical care should be alert to the high rate of maternal medical complications associated with labor and delivery of a stillbirth fetus. PMID:26479679

Maternal complications associated with stillbirth delivery: A cross-sectional analysis.

PubMed

Gold, K J; Mozurkewich, E L; Puder, K S; Treadwell, M C

2016-01-01

This study sought to identify delivery complications associated with stillbirth labour and delivery. We conducted a retrospective chart review evaluating stillbirth demographics, pregnancy and maternal risk factors, and complications of labour and delivery. We performed bivariable analysis and multivariable logistic regression to evaluate factors associated with medical complications and variations by race. Our cohort included 543 mothers with stillbirth, of which two-thirds were African-American. We noted high rates of shoulder dystocia, clinical chorioamnionitis, postpartum haemorrhage and retained placenta in women with stillbirths. Thirty-three women (6%) experienced at least one serious maternal complication. Complication rates did not vary by maternal race. Providers who perform obstetrical care should be alert to the high rate of maternal medical complications associated with labour and delivery of a stillbirth foetus.

Predicting introductory programming performance: A multi-institutional multivariate study

NASA Astrophysics Data System (ADS)

Bergin, Susan; Reilly, Ronan

2006-12-01

A model for predicting student performance on introductory programming modules is presented. The model uses attributes identified in a study carried out at four third-level institutions in the Republic of Ireland. Four instruments were used to collect the data and over 25 attributes were examined. A data reduction technique was applied and a logistic regression model using 10-fold stratified cross validation was developed. The model used three attributes: Leaving Certificate Mathematics result (final mathematics examination at second level), number of hours playing computer games while taking the module and programming self-esteem. Prediction success was significant with 80% of students correctly classified. The model also works well on a per-institution level. A discussion on the implications of the model is provided and future work is outlined.

Development, optimization and validation of gas chromatographic fingerprinting of Brazilian commercial diesel fuel for quality control.

PubMed

dos Santos, Bruno César Diniz Brito; Flumignan, Danilo Luiz; de Oliveira, José Eduardo

2012-10-01

A three-step development, optimization and validation strategy is described for gas chromatography (GC) fingerprints of Brazilian commercial diesel fuel. A suitable GC-flame ionization detection (FID) system was selected to assay a complex matrix such as diesel. The next step was to improve acceptable chromatographic resolution with reduced analysis time, which is recommended for routine applications. Full three-level factorial designs were performed to improve flow rate, oven ramps, injection volume and split ratio in the GC system. Finally, several validation parameters were performed. The GC fingerprinting can be coupled with pattern recognition and multivariate regressions analyses to determine fuel quality and fuel physicochemical parameters. This strategy can also be applied to develop fingerprints for quality control of other fuel types.

Influence factors and forecast of carbon emission in China: structure adjustment for emission peak

NASA Astrophysics Data System (ADS)

Wang, B.; Cui, C. Q.; Li, Z. P.

2018-02-01

This paper introduced Principal Component Analysis and Multivariate Linear Regression Model to verify long-term balance relationships between Carbon Emissions and the impact factors. The integrated model of improved PCA and multivariate regression analysis model is attainable to figure out the pattern of carbon emission sources. Main empirical results indicate that among all selected variables, the role of energy consumption scale was largest. GDP and Population follow and also have significant impacts on carbon emission. Industrialization rate and fossil fuel proportion, which is the indicator of reflecting the economic structure and energy structure, have a higher importance than the factor of urbanization rate and the dweller consumption level of urban areas. In this way, some suggestions are put forward for government to achieve the peak of carbon emissions.

Age-Related Imbalance Is Associated With Slower Walking Speed: An Analysis From the National Health and Nutrition Examination Survey.

PubMed

Xie, Yanjun J; Liu, Elizabeth Y; Anson, Eric R; Agrawal, Yuri

Walking speed is an important dimension of gait function and is known to decline with age. Gait function is a process of dynamic balance and motor control that relies on multiple sensory inputs (eg, visual, proprioceptive, and vestibular) and motor outputs. These sensory and motor physiologic systems also play a role in static postural control, which has been shown to decline with age. In this study, we evaluated whether imbalance that occurs as part of healthy aging is associated with slower walking speed in a nationally representative sample of older adults. We performed a cross-sectional analysis of the previously collected 1999 to 2002 National Health and Nutrition Examination Survey (NHANES) data to evaluate whether age-related imbalance is associated with slower walking speed in older adults aged 50 to 85 years (n = 2116). Balance was assessed on a pass/fail basis during a challenging postural task-condition 4 of the modified Romberg Test-and walking speed was determined using a 20-ft (6.10 m) timed walk. Multivariable linear regression was used to evaluate the association between imbalance and walking speed, adjusting for demographic and health-related covariates. A structural equation model was developed to estimate the extent to which imbalance mediates the association between age and slower walking speed. In the unadjusted regression model, inability to perform the NHANES balance task was significantly associated with 0.10 m/s slower walking speed (95% confidence interval: -0.13 to -0.07; P < .01). In the multivariable regression analysis, inability to perform the balance task was significantly associated with 0.06 m/s slower walking speed (95% confidence interval: -0.09 to -0.03; P < .01), an effect size equivalent to 12 years of age. The structural equation model estimated that age-related imbalance mediates 12.2% of the association between age and slower walking speed in older adults. In a nationally representative sample, age-related balance limitation was associated with slower walking speed. Balance impairment may lead to walking speed declines. In addition, reduced static postural control and dynamic walking speed that occur with aging may share common etiologic origins, including the decline in visual, proprioceptive, and vestibular sensory and motor functions.

Comparison of partial least squares and random forests for evaluating relationship between phenolics and bioactivities of Neptunia oleracea.

PubMed

Lee, Soo Yee; Mediani, Ahmed; Maulidiani, Maulidiani; Khatib, Alfi; Ismail, Intan Safinar; Zawawi, Norhasnida; Abas, Faridah

2018-01-01

Neptunia oleracea is a plant consumed as a vegetable and which has been used as a folk remedy for several diseases. Herein, two regression models (partial least squares, PLS; and random forest, RF) in a metabolomics approach were compared and applied to the evaluation of the relationship between phenolics and bioactivities of N. oleracea. In addition, the effects of different extraction conditions on the phenolic constituents were assessed by pattern recognition analysis. Comparison of the PLS and RF showed that RF exhibited poorer generalization and hence poorer predictive performance. Both the regression coefficient of PLS and the variable importance of RF revealed that quercetin and kaempferol derivatives, caffeic acid and vitexin-2-O-rhamnoside were significant towards the tested bioactivities. Furthermore, principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) results showed that sonication and absolute ethanol are the preferable extraction method and ethanol ratio, respectively, to produce N. oleracea extracts with high phenolic levels and therefore high DPPH scavenging and α-glucosidase inhibitory activities. Both PLS and RF are useful regression models in metabolomics studies. This work provides insight into the performance of different multivariate data analysis tools and the effects of different extraction conditions on the extraction of desired phenolics from plants. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

Performances on the CogState and Standard Neuropsychological Batteries Among HIV Patients Without Dementia

PubMed Central

Overton, Edgar Turner; Kauwe, John S.K.; Paul, Rob; Tashima, Karen; Tate, David F.; Patel, Pragna; Carpenter, Chuck; Patty, David; Brooks, John T.; Clifford, David B

2013-01-01

HIV-associated neurocognitive disorders (HAND) remain prevalent but challenging to diagnose particularly among non-demented individuals. To determine whether a brief computerized battery correlates with formal neurocognitive testing, we identified 46 HIV-infected persons who had undergone both formal neurocognitive testing and a brief computerized battery. Simple detection tests correlated best with formal neuropsychological testing. By multivariable regression model, 53% of the variance in the composite Global Deficit Score was accounted for by elements from the brief computerized tool (p<0.01). These data confirm previous correlation data with the computerized battery, yet illustrate remaining challenges for neurocognitive screening. PMID:21877204

Impact of immunotherapy among patients with melanoma brain metastases managed with radiotherapy.

PubMed

Stokes, William A; Binder, David C; Jones, Bernard L; Oweida, Ayman J; Liu, Arthur K; Rusthoven, Chad G; Karam, Sana D

2017-12-15

Patients with melanoma brain metastases (MBM) have been excluded from trials evaluating immunotherapy in melanoma. As such, immunotherapy's role in MBM is poorly understood, particularly in combination with radiotherapy. The National Cancer Database was queried for patients with MBM receiving brain radiotherapy. They were classified according to immunotherapy receipt. Multivariate Cox regression was performed to identify factors associated with survival. Among 1287 patients, 185 received immunotherapy. Factors associated with improved survival included younger age, academic facility, lower extracranial disease burden, stereotactic radiotherapy, chemotherapy, and immunotherapy. Adding immunotherapy to radiotherapy for MBM is associated with improved survival. Copyright © 2017 Elsevier B.V. All rights reserved.

Analysis of Forest Foliage Using a Multivariate Mixture Model

NASA Technical Reports Server (NTRS)

Hlavka, C. A.; Peterson, David L.; Johnson, L. F.; Ganapol, B.

1997-01-01

Data with wet chemical measurements and near infrared spectra of ground leaf samples were analyzed to test a multivariate regression technique for estimating component spectra which is based on a linear mixture model for absorbance. The resulting unmixed spectra for carbohydrates, lignin, and protein resemble the spectra of extracted plant starches, cellulose, lignin, and protein. The unmixed protein spectrum has prominent absorption spectra at wavelengths which have been associated with nitrogen bonds.