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Sample records for predicting clinical response

  1. Brain Connectivity Predicts Placebo Response across Chronic Pain Clinical Trials

    PubMed Central

    Tétreault, Pascal; Mansour, Ali; Vachon-Presseau, Etienne; Schnitzer, Thomas J.; Apkarian, A. Vania

    2016-01-01

    Placebo response in the clinical trial setting is poorly understood and alleged to be driven by statistical confounds, and its biological underpinnings are questioned. Here we identified and validated that clinical placebo response is predictable from resting-state functional magnetic-resonance-imaging (fMRI) brain connectivity. This also led to discovering a brain region predicting active drug response and demonstrating the adverse effect of active drug interfering with placebo analgesia. Chronic knee osteoarthritis (OA) pain patients (n = 56) underwent pretreatment brain scans in two clinical trials. Study 1 (n = 17) was a 2-wk single-blinded placebo pill trial. Study 2 (n = 39) was a 3-mo double-blinded randomized trial comparing placebo pill to duloxetine. Study 3, which was conducted in additional knee OA pain patients (n = 42), was observational. fMRI-derived brain connectivity maps in study 1 were contrasted between placebo responders and nonresponders and compared to healthy controls (n = 20). Study 2 validated the primary biomarker and identified a brain region predicting drug response. In both studies, approximately half of the participants exhibited analgesia with placebo treatment. In study 1, right midfrontal gyrus connectivity best identified placebo responders. In study 2, the same measure identified placebo responders (95% correct) and predicted the magnitude of placebo’s effectiveness. By subtracting away linearly modeled placebo analgesia from duloxetine response, we uncovered in 6/19 participants a tendency of duloxetine enhancing predicted placebo response, while in another 6/19, we uncovered a tendency for duloxetine to diminish it. Moreover, the approach led to discovering that right parahippocampus gyrus connectivity predicts drug analgesia after correcting for modeled placebo-related analgesia. Our evidence is consistent with clinical placebo response having biological underpinnings and shows that the method can also reveal that active

  2. Distinct Functional Connectivities Predict Clinical Response with Emotion Regulation Therapy

    PubMed Central

    Fresco, David M.; Roy, Amy K.; Adelsberg, Samantha; Seeley, Saren; García-Lesy, Emmanuel; Liston, Conor; Mennin, Douglas S.

    2017-01-01

    Despite the success of available medical and psychosocial treatments, a sizable subgroup of individuals with commonly co-occurring disorders, generalized anxiety disorder (GAD) and major depressive disorder (MDD), fail to make sufficient treatment gains thereby prolonging their deficits in life functioning and satisfaction. Clinically, these patients often display temperamental features reflecting heightened sensitivity to underlying motivational systems related to threat/safety and reward/loss (e.g., somatic anxiety) as well as inordinate negative self-referential processing (e.g., worry, rumination). This profile may reflect disruption in two important neural networks associated with emotional/motivational salience (e.g., salience network) and self-referentiality (e.g., default network, DN). Emotion Regulation Therapy (ERT) was developed to target this hypothesized profile and its neurobehavioral markers. In the present study, 22 GAD patients (with and without MDD) completed resting state MRI scans before receiving 16 sessions of ERT. To test study these hypotheses, we examined the associations between baseline patterns of intrinsic functional connectivity (iFC) of the insula and of hubs within the DN (anterior and dorsal medial prefrontal cortex [MPFC] and posterior cingulate cortex [PCC]) and treatment-related changes in worry, somatic anxiety symptoms and decentering. Results suggest that greater treatment linked reductions in worry were associated with iFC clusters in both the insular and parietal cortices. Greater treatment linked gains in decentering, a metacognitive process that involves the capacity to observe items that arise in the mind with healthy psychological distance that is targeted by ERT, was associated with iFC clusters in the anterior and posterior DN. The current study adds to the growing body of research implicating disruptions in the default and salience networks as promising targets of treatment for GAD with and without co-occurring MDD

  3. Distinct Functional Connectivities Predict Clinical Response with Emotion Regulation Therapy.

    PubMed

    Fresco, David M; Roy, Amy K; Adelsberg, Samantha; Seeley, Saren; García-Lesy, Emmanuel; Liston, Conor; Mennin, Douglas S

    2017-01-01

    Despite the success of available medical and psychosocial treatments, a sizable subgroup of individuals with commonly co-occurring disorders, generalized anxiety disorder (GAD) and major depressive disorder (MDD), fail to make sufficient treatment gains thereby prolonging their deficits in life functioning and satisfaction. Clinically, these patients often display temperamental features reflecting heightened sensitivity to underlying motivational systems related to threat/safety and reward/loss (e.g., somatic anxiety) as well as inordinate negative self-referential processing (e.g., worry, rumination). This profile may reflect disruption in two important neural networks associated with emotional/motivational salience (e.g., salience network) and self-referentiality (e.g., default network, DN). Emotion Regulation Therapy (ERT) was developed to target this hypothesized profile and its neurobehavioral markers. In the present study, 22 GAD patients (with and without MDD) completed resting state MRI scans before receiving 16 sessions of ERT. To test study these hypotheses, we examined the associations between baseline patterns of intrinsic functional connectivity (iFC) of the insula and of hubs within the DN (anterior and dorsal medial prefrontal cortex [MPFC] and posterior cingulate cortex [PCC]) and treatment-related changes in worry, somatic anxiety symptoms and decentering. Results suggest that greater treatment linked reductions in worry were associated with iFC clusters in both the insular and parietal cortices. Greater treatment linked gains in decentering, a metacognitive process that involves the capacity to observe items that arise in the mind with healthy psychological distance that is targeted by ERT, was associated with iFC clusters in the anterior and posterior DN. The current study adds to the growing body of research implicating disruptions in the default and salience networks as promising targets of treatment for GAD with and without co-occurring MDD.

  4. Predictive Factors of Clinical Response of Infliximab Therapy in Active Nonradiographic Axial Spondyloarthritis Patients

    PubMed Central

    Lin, Zhiming; Liao, Zetao; Huang, Jianlin; Ai, Maixing; Pan, Yunfeng; Wu, Henglian; Lu, Jun; Cao, Shuangyan; Li, Li; Wei, Qiujing; Tang, Deshen; Wei, Yanlin; Li, Tianwang; Wu, Yuqiong; Xu, Manlong; Li, Qiuxia; Jin, Ou; Yu, Buyun; Gu, Jieruo

    2015-01-01

    Objectives. To evaluate the efficiency and the predictive factors of clinical response of infliximab in active nonradiographic axial spondyloarthritis patients. Methods. Active nonradiographic patients fulfilling ESSG criteria for SpA but not fulfilling modified New York criteria were included. All patients received infliximab treatment for 24 weeks. The primary endpoint was ASAS20 response at weeks 12 and 24. The abilities of baseline parameters and response at week 2 to predict ASAS20 response at weeks 12 and 24 were assessed using ROC curve and logistic regression analysis, respectively. Results. Of 70 axial SpA patients included, the proportions of patients achieving an ASAS20 response at weeks 2, 6, 12, and 24 were 85.7%, 88.6%, 87.1%, and 84.3%, respectively. Baseline MRI sacroiliitis score (AUC = 0.791; P = 0.005), CRP (AUC = 0.75; P = 0.017), and ASDAS (AUC = 0.778, P = 0.007) significantly predicted ASAS20 response at week 12. However, only ASDAS (AUC = 0.696, P = 0.040) significantly predicted ASAS20 response at week 24. Achievement of ASAS20 response after the first infliximab infusion was a significant predictor of subsequent ASAS20 response at weeks 12 and 24 (wald χ2 = 6.87, P = 0.009, and wald χ2 = 5.171, P = 0.023). Conclusions. Infliximab shows efficiency in active nonradiographic axial spondyloarthritis patients. ASDAS score and first-dose response could help predicting clinical efficacy of infliximab therapy in these patients. PMID:26273654

  5. Pharmacogenomics of Methotrexate Membrane Transport Pathway: Can Clinical Response to Methotrexate in Rheumatoid Arthritis Be Predicted?

    PubMed Central

    Lima, Aurea; Bernardes, Miguel; Azevedo, Rita; Medeiros, Rui; Seabra, Vitor

    2015-01-01

    Background: Methotrexate (MTX) is widely used for rheumatoid arthritis (RA) treatment. Single nucleotide polymorphisms (SNPs) could be used as predictors of patients’ therapeutic outcome variability. Therefore, this study aims to evaluate the influence of SNPs in genes encoding for MTX membrane transport proteins in order to predict clinical response to MTX. Methods: Clinicopathological data from 233 RA patients treated with MTX were collected, clinical response defined, and patients genotyped for 23 SNPs. Genotype and haplotype analyses were performed using multivariate methods and a genetic risk index (GRI) for non-response was created. Results: Increased risk for non-response was associated to SLC22A11 rs11231809 T carriers; ABCC1 rs246240 G carriers; ABCC1 rs3784864 G carriers; CGG haplotype for ABCC1 rs35592, rs2074087 and rs3784864; and CGG haplotype for ABCC1 rs35592, rs246240 and rs3784864. GRI demonstrated that patients with Index 3 were 16-fold more likely to be non-responders than those with Index 1. Conclusions: This study revealed that SLC22A11 and ABCC1 may be important to identify those patients who will not benefit from MTX treatment, highlighting the relevance in translating these results to clinical practice. However, further validation by independent studies is needed to develop the field of personalized medicine to predict clinical response to MTX treatment. PMID:26086825

  6. Clinical utility of C-reactive protein to predict treatment response during cystic fibrosis pulmonary exacerbations

    PubMed Central

    Sharma, Ashutosh; Kirkpatrick, Gordon; Chen, Virginia; Skolnik, Kate; Hollander, Zsuzsanna; Wilcox, Pearce; Quon, Bradley S.

    2017-01-01

    Rationale C-reactive protein (CRP) is a systemic marker of inflammation that correlates with disease status in cystic fibrosis (CF). The clinical utility of CRP measurement to guide pulmonary exacerbation (PEx) treatment decisions remains uncertain. Objectives To determine whether monitoring CRP during PEx treatment can be used to predict treatment response. We hypothesized that early changes in CRP can be used to predict treatment response. Methods We reviewed all PEx events requiring hospitalization for intravenous (IV) antibiotics over 2 years at our institution. 83 PEx events met our eligibility criteria. CRP levels from admission to day 5 were evaluated to predict treatment non-response, using a modified version of a prior published composite definition. CRP was also evaluated to predict time until next exacerbation (TUNE). Measurements and main results 53% of 83 PEx events were classified as treatment non-response. Paradoxically, 24% of PEx events were characterized by a ≥ 50% increase in CRP levels within the first five days of treatment. Absolute change in CRP from admission to day 5 was not associated with treatment non-response (p = 0.58). Adjusted for FEV1% predicted, admission log10 CRP was associated with treatment non-response (OR: 2.39; 95% CI: 1.14 to 5.91; p = 0.03) and shorter TUNE (HR: 1.60; 95% CI: 1.13 to 2.27; p = 0.008). The area under the receiver operating characteristics (ROC) curve of admission CRP to predict treatment non-response was 0.72 (95% CI 0.61–0.83; p<0.001). 23% of PEx events were characterized by an admission CRP of > 75 mg/L with a specificity of 90% for treatment non-response. Conclusions Admission CRP predicts treatment non-response and time until next exacerbation. A very elevated admission CRP (>75mg/L) is highly specific for treatment non-response and might be used to target high-risk patients for future interventional studies aimed at improving exacerbation outcomes. PMID:28178305

  7. Clinical application of fluctuation dissipation theory - Prediction of heart rate response to spontaneous breathing trial

    NASA Astrophysics Data System (ADS)

    Niestemski, Liang R.; Chen, Man; Prevost, Robert; McRae, Michael; Cholleti, Sharath; Najarro, Gabriel; Buchman, Timothy G.; Deem, Michael W.

    2013-03-01

    Contrary to the traditional view of the healthy physiological state as being a single static state, variation in physiologic variables has more recently been suggested to be a key component of the healthy state. Indeed, aging and disease are characterized by a loss of such variability. We apply the conceptual framework of fluctuation-dissipation theory (FDT) to predict the response to a common clinical intervention from historical fluctuations in physiologic time series data. The non-equilibrium FDT relates the response of a system to a perturbation to natural fluctuations in the stationary state of the system. We seek to understand with the FDT a common clinical perturbation, the spontaneous breathing trial (SBT), in which mechanical ventilation is briefly suspended while the patient breathes freely for a period of time. As a stress upon the heart of the patient, the SBT can be characterized as a perturbation of heart rate dynamics. A non-equilibrium, but steady-state FDT allows us to predict the heart rate recovery after the SBT stress. We show that the responses of groups of similar patients to the spontaneous breathing trial can be predicted by this approach. This mathematical framework may serve as part of the basis for personalized critical care.

  8. Clinical Parameters Predicting Pathologic Tumor Response After Preoperative Chemoradiotherapy for Rectal Cancer

    SciTech Connect

    Yoon, Sang Min; Kim, Dae Yong Kim, Tae Hyun; Jung, Kyung Hae; Chang, Hee Jin; Koom, Woong Sub; Lim, Seok-Byung; Choi, Hyo Seong; Jeong, Seung-Yong; Park, Jae-Gahb

    2007-11-15

    Purpose: To identify pretreatment clinical parameters that could predict pathologic tumor response to preoperative chemoradiotherapy (CRT) for rectal cancer. Methods and Materials: The study involved 351 patients who underwent preoperative CRT followed by surgery between October 2001 and July 2006. Tumor responses to preoperative CRT were assessed in terms of tumor downstaging and tumor regression. Statistical analyses were performed to identify clinical factors associated with pathologic tumor response. Results: Tumor downstaging (defined as ypT2 or less) was observed in 167 patients (47.6%), whereas tumor regression (defined as Dworak's Regression Grades 3 or 4) was observed in 103 patients (29.3%) and complete regression in 51 patients (14.5%). Multivariate analysis found that predictors of downstaging were pretreatment hemoglobin level (p = 0.045), cN0 classification (p < 0.001), and serum carcinoembryonic antigen (CEA) level (p < 0.001), that predictors of tumor regression were cN0 classification (p = 0.044) and CEA level (p < 0.001), and that the predictor of complete regression was CEA level (p = 0.004). Conclusions: The data suggest that pretreatment CEA level is the most important clinical predictor of pathologic tumor response. It may be of benefit in the selection of treatment options as well as the assessment of individual prognosis.

  9. Can a Clinical Test of Reaction Time Predict a Functional Head-Protective Response?

    PubMed Central

    ECKNER, JAMES T.; LIPPS, DAVID B.; KIM, HOGENE; RICHARDSON, JAMES K.; ASHTON-MILLER, JAMES A.

    2015-01-01

    Purpose Reaction time is commonly prolonged after a sport-related concussion. Besides being a marker for injury, a rapid reaction time is necessary for protective maneuvers that can reduce the frequency and severity of additional head impacts. The purpose of this study was to determine whether a clinical test of simple visuomotor reaction time predicted the time taken to raise the hands to protect the head from a rapidly approaching ball. Methods Twenty-six healthy adult participants recruited from campus and community recreation and exercise facilities completed two experimental protocols during a single session: a manual visuomotor simple reaction time test (RTclin) and a sport-related head-protective response (RTsprt). RTclin measured the time required to catch a thin vertically oriented device on its release by the tester and was calculated from the distance the device fell before being arrested. RTsprt measured the time required to raise the hands from waist level to block a foam tennis ball fired toward the subject’s face from an air cannon and was determined using an optoelectronic camera system. A correlation coefficient was calculated between RTclin and RTsprt, with linear regression used to assess for effect modification by other covariates. Results A strong positive correlation was found between RTclin and RTsprt (r = 0.725, P < 0.001) independent of age, gender, height, or weight. Conclusions RTclin is predictive of a functional sport-related head-protective response. To our knowledge, this is the first demonstration of a clinical test predicting the ability to protect the head in a simulated sport environment. This correlation with a functional head-protective response is a relevant consideration for the potential use of RTclin as part of a multifaceted concussion assessment program. PMID:20689458

  10. Empirically and Clinically Useful Decision Making in Psychotherapy: Differential Predictions with Treatment Response Models

    ERIC Educational Resources Information Center

    Lutz, Wolfgang; Saunders, Stephen M.; Leon, Scott C.; Martinovich, Zoran; Kosfelder, Joachim; Schulte, Dietmar; Grawe, Klaus; Tholen, Sven

    2006-01-01

    In the delivery of clinical services, outcomes monitoring (i.e., repeated assessments of a patient's response to treatment) can be used to support clinical decision making (i.e., recurrent revisions of outcome expectations on the basis of that response). Outcomes monitoring can be particularly useful in the context of established practice research…

  11. Population pharmacokinetic–pharmacodynamic modelling in oncology: a tool for predicting clinical response

    PubMed Central

    Bender, Brendan C; Schindler, Emilie; Friberg, Lena E

    2015-01-01

    In oncology trials, overall survival (OS) is considered the most reliable and preferred endpoint to evaluate the benefit of drug treatment. Other relevant variables are also collected from patients for a given drug and its indication, and it is important to characterize the dynamic effects and links between these variables in order to improve the speed and efficiency of clinical oncology drug development. However, the drug-induced effects and causal relationships are often difficult to interpret because of temporal differences. To address this, population pharmacokinetic–pharmacodynamic (PKPD) modelling and parametric time-to-event (TTE) models are becoming more frequently applied. Population PKPD and TTE models allow for exploration towards describing the data, understanding the disease and drug action over time, investigating relevance of biomarkers, quantifying patient variability and in designing successful trials. In addition, development of models characterizing both desired and adverse effects in a modelling framework support exploration of risk-benefit of different dosing schedules. In this review, we have summarized population PKPD modelling analyses describing tumour, tumour marker and biomarker responses, as well as adverse effects, from anticancer drug treatment data. Various model-based metrics used to drive PD response and predict OS for oncology drugs and their indications are also discussed. PMID:24134068

  12. A clinical tool to predict failed response to therapy in children with severe pneumonia.

    PubMed

    Mamtani, Manju; Patel, Archana; Hibberd, Patricia L; Tuan, Tran Anh; Jeena, Prakash; Chisaka, Noel; Hassan, Mumtaz; Radovan, Irene Maulen; Thea, Donald M; Qazi, Shamim; Kulkarni, Hemant

    2009-04-01

    Severe pneumonia in children under 5 years of age continues to be an important clinical entity with treatment failure rates as high as 20%. Where severe pneumonias are common, predictive tools for treatment failure like chest radiography and pulse oximetry are not available or affordable. Thus, there is a need for development of simple, accurate and inexpensive clinical tools for prediction of treatment failure. Using clinical, chest radiographic and pulse oximetry data from 1702 children recruited in the Amoxicillin Penicillin Pneumonia International Study (APPIS) trial we developed and validated a simple clinical tool. For development, a randomly derived development sample (n = 889) was used. The tool which was based on the results of multivariate logistic regression models was validated on a separate sample of 813 children. The derived clinical tool in its final form contained three clinical predictors: age of child, excess age-specific respiratory rate at baseline and at 24 hr of hospitalization. This tool had a 70% and 66% predictive accuracy in the development and validation samples, respectively. The tool is presented as an easy-to-use nomogram. It is possible to predict the likelihood of treatment failure in children with severe pneumonia based on clinical features that are simple and inexpensive to measure.

  13. Baseline gut microbiota predicts clinical response and colitis in metastatic melanoma patients treated with ipilimumab.

    PubMed

    Chaput, N; Lepage, P; Coutzac, C; Soularue, E; Le Roux, K; Monot, C; Boselli, L; Routier, E; Cassard, L; Collins, M; Vaysse, T; Marthey, L; Eggermont, A; Asvatourian, V; Lanoy, E; Mateus, C; Robert, C; Carbonnel, F

    2017-03-27

    Ipilimumab, an immune checkpoint inhibitor targeting CTLA-4, prolongs survival in a subset of patients with metastatic melanoma but can induce immune-related adverse events, including enterocolitis. We hypothesized that baseline gut microbiota could predict ipilimumab anti-tumor response and/or intestinal toxicity.

  14. Prediction of Response to Treatment in a Randomized Clinical Trial of Marital Therapy

    ERIC Educational Resources Information Center

    Atkins, David C.; Berns, Sara B.; George, William H.; Doss, Brian D.; Gattis, Krista; Christensen, Andrew

    2005-01-01

    This study investigated demographic, intrapersonal, and interpersonal predictors of treatment response in a randomized clinical trial of 134 distressed married couples, which examined traditional (N. S. Jacobson & G. Margolin, 1979) and integrative (N. S. Jacobson & A. Christensen, 1996) behavioral couple therapy. Results based on hierarchical…

  15. Machine Learning Approaches for Integrating Clinical and Imaging Features in LLD Classification and Response Prediction

    PubMed Central

    Patel, Meenal J.; Andreescu, Carmen; Price, Julie C.; Edelman, Kathryn L.; Reynolds, Charles F.; Aizenstein, Howard J.

    2015-01-01

    Objective Currently, depression diagnosis relies primarily on behavioral symptoms and signs, and treatment is guided by trial and error instead of evaluating associated underlying brain characteristics. Unlike past studies, we attempted to estimate accurate prediction models for late-life depression diagnosis and treatment response using multiple machine learning methods with inputs of multi-modal imaging and non-imaging whole brain and network-based features. Methods Late-life depression patients (medicated post-recruitment) [n=33] and elderly non-depressed individuals [n=35] were recruited. Their demographics and cognitive ability scores were recorded, and brain characteristics were acquired using multi-modal magnetic resonance imaging pre-treatment. Linear and nonlinear learning methods were tested for estimating accurate prediction models. Results A learning method called alternating decision trees estimated the most accurate prediction models for late-life depression diagnosis (87.27% accuracy) and treatment response (89.47% accuracy). The diagnosis model included measures of age, mini-mental state examination score, and structural imaging (e.g. whole brain atrophy and global white mater hyperintensity burden). The treatment response model included measures of structural and functional connectivity. Conclusions Combinations of multi-modal imaging and/or non-imaging measures may help better predict late-life depression diagnosis and treatment response. As a preliminary observation, we speculate the results may also suggest that different underlying brain characteristics defined by multi-modal imaging measures—rather than region-based differences—are associated with depression versus depression recovery since to our knowledge this is the first depression study to accurately predict both using the same approach. These findings may help better understand late-life depression and identify preliminary steps towards personalized late-life depression treatment

  16. Predicting beneficial effects of atomoxetine and citalopram on response inhibition in Parkinson's disease with clinical and neuroimaging measures.

    PubMed

    Ye, Zheng; Rae, Charlotte L; Nombela, Cristina; Ham, Timothy; Rittman, Timothy; Jones, Peter Simon; Rodríguez, Patricia Vázquez; Coyle-Gilchrist, Ian; Regenthal, Ralf; Altena, Ellemarije; Housden, Charlotte R; Maxwell, Helen; Sahakian, Barbara J; Barker, Roger A; Robbins, Trevor W; Rowe, James B

    2016-03-01

    Recent studies indicate that selective noradrenergic (atomoxetine) and serotonergic (citalopram) reuptake inhibitors may improve response inhibition in selected patients with Parkinson's disease, restoring behavioral performance and brain activity. We reassessed the behavioral efficacy of these drugs in a larger cohort and developed predictive models to identify patient responders. We used a double-blind randomized three-way crossover design to investigate stopping efficiency in 34 patients with idiopathic Parkinson's disease after 40 mg atomoxetine, 30 mg citalopram, or placebo. Diffusion-weighted and functional imaging measured microstructural properties and regional brain activations, respectively. We confirmed that Parkinson's disease impairs response inhibition. Overall, drug effects on response inhibition varied substantially across patients at both behavioral and brain activity levels. We therefore built binary classifiers with leave-one-out cross-validation (LOOCV) to predict patients' responses in terms of improved stopping efficiency. We identified two optimal models: (1) a "clinical" model that predicted the response of an individual patient with 77-79% accuracy for atomoxetine and citalopram, using clinically available information including age, cognitive status, and levodopa equivalent dose, and a simple diffusion-weighted imaging scan; and (2) a "mechanistic" model that explained the behavioral response with 85% accuracy for each drug, using drug-induced changes of brain activations in the striatum and presupplementary motor area from functional imaging. These data support growing evidence for the role of noradrenaline and serotonin in inhibitory control. Although noradrenergic and serotonergic drugs have highly variable effects in patients with Parkinson's disease, the individual patient's response to each drug can be predicted using a pattern of clinical and neuroimaging features.

  17. Study of the prediction system for clinical response to M-VAC neoadjuvant chemotherapy for bladder cancer.

    PubMed

    Takata, R; Obara, W; Fujioka, T

    2010-01-01

    Neoadjuvant chemotherapy for invasive bladder cancer, involving a regimen of M-VAC, can manage micrometastasis and improve the prognosis. However, some patients suffer from severe adverse drug reactions without any effect, and no method yet exists for predicting the response of an individual patient to chemotherapy. Our purpose in this study is to establish a method for predicting the response to the M-VAC therapy. We analyzed gene-expression profiles of biopsy materials from 40 invasive bladder cancers using a cDNA microarray consisting of 27 648 genes, after populations of cancer cells had been purified by laser-microbeam microdissection. We identified 14 predictive genes that were expressed differently between nine responder and nine non-responder tumors and devised a prediction-scoring system that clearly separated the responder group from the non-responder group. This system accurately predicted the clinical response for 19 of the 22 additional test cases. The group of patients with positive predictive scores had significantly longer survival times than that with negative scores. As real-time RT-PCR data were highly concordant with the cDNA microarray data for those 14 genes, we developed a quantitative RT-PCR-based prediction system that could be feasible for routine clinical use. Taken together, our results suggest that the sensitivity of an invasive bladder cancer to the M-VAC neoadjuvant chemotherapy can be predicted by expression patterns in this set of genes, a step toward achievement of "personalized therapy" for treatment of this disease.

  18. Predicting beneficial effects of atomoxetine and citalopram on response inhibition in Parkinson's disease with clinical and neuroimaging measures

    PubMed Central

    Ye, Zheng; Rae, Charlotte L.; Nombela, Cristina; Ham, Timothy; Rittman, Timothy; Jones, Peter Simon; Rodríguez, Patricia Vázquez; Coyle‐Gilchrist, Ian; Regenthal, Ralf; Altena, Ellemarije; Housden, Charlotte R.; Maxwell, Helen; Sahakian, Barbara J.; Barker, Roger A.; Robbins, Trevor W.

    2016-01-01

    Abstract Recent studies indicate that selective noradrenergic (atomoxetine) and serotonergic (citalopram) reuptake inhibitors may improve response inhibition in selected patients with Parkinson's disease, restoring behavioral performance and brain activity. We reassessed the behavioral efficacy of these drugs in a larger cohort and developed predictive models to identify patient responders. We used a double‐blind randomized three‐way crossover design to investigate stopping efficiency in 34 patients with idiopathic Parkinson's disease after 40 mg atomoxetine, 30 mg citalopram, or placebo. Diffusion‐weighted and functional imaging measured microstructural properties and regional brain activations, respectively. We confirmed that Parkinson's disease impairs response inhibition. Overall, drug effects on response inhibition varied substantially across patients at both behavioral and brain activity levels. We therefore built binary classifiers with leave‐one‐out cross‐validation (LOOCV) to predict patients’ responses in terms of improved stopping efficiency. We identified two optimal models: (1) a “clinical” model that predicted the response of an individual patient with 77–79% accuracy for atomoxetine and citalopram, using clinically available information including age, cognitive status, and levodopa equivalent dose, and a simple diffusion‐weighted imaging scan; and (2) a “mechanistic” model that explained the behavioral response with 85% accuracy for each drug, using drug‐induced changes of brain activations in the striatum and presupplementary motor area from functional imaging. These data support growing evidence for the role of noradrenaline and serotonin in inhibitory control. Although noradrenergic and serotonergic drugs have highly variable effects in patients with Parkinson's disease, the individual patient's response to each drug can be predicted using a pattern of clinical and neuroimaging features. Hum Brain Mapp 37:1026–1037

  19. A Prediction Algorithm for Drug Response in Patients with Mesial Temporal Lobe Epilepsy Based on Clinical and Genetic Information

    PubMed Central

    Carvalho, Benilton S.; Bilevicius, Elizabeth; Alvim, Marina K. M.; Lopes-Cendes, Iscia

    2017-01-01

    Mesial temporal lobe epilepsy is the most common form of adult epilepsy in surgical series. Currently, the only characteristic used to predict poor response to clinical treatment in this syndrome is the presence of hippocampal sclerosis. Single nucleotide polymorphisms (SNPs) located in genes encoding drug transporter and metabolism proteins could influence response to therapy. Therefore, we aimed to evaluate whether combining information from clinical variables as well as SNPs in candidate genes could improve the accuracy of predicting response to drug therapy in patients with mesial temporal lobe epilepsy. For this, we divided 237 patients into two groups: 75 responsive and 162 refractory to antiepileptic drug therapy. We genotyped 119 SNPs in ABCB1, ABCC2, CYP1A1, CYP1A2, CYP1B1, CYP2C9, CYP2C19, CYP2D6, CYP2E1, CYP3A4, and CYP3A5 genes. We used 98 additional SNPs to evaluate population stratification. We assessed a first scenario using only clinical variables and a second one including SNP information. The random forests algorithm combined with leave-one-out cross-validation was used to identify the best predictive model in each scenario and compared their accuracies using the area under the curve statistic. Additionally, we built a variable importance plot to present the set of most relevant predictors on the best model. The selected best model included the presence of hippocampal sclerosis and 56 SNPs. Furthermore, including SNPs in the model improved accuracy from 0.4568 to 0.8177. Our findings suggest that adding genetic information provided by SNPs, located on drug transport and metabolism genes, can improve the accuracy for predicting which patients with mesial temporal lobe epilepsy are likely to be refractory to drug treatment, making it possible to identify patients who may benefit from epilepsy surgery sooner. PMID:28052106

  20. Early response predicts subsequent response to olanzapine long-acting injection in a randomized, double-blind clinical trial of treatment for schizophrenia

    PubMed Central

    2011-01-01

    Background In patients with schizophrenia, early non-response to oral antipsychotic therapy robustly predicts subsequent non-response to continued treatment with the same medication. This study assessed whether early response predicted later response when using a long-acting injection (LAI) antipsychotic. Methods Data were taken from an 8-week, randomized, double-blind, placebo-controlled study of olanzapine LAI in acutely ill patients with schizophrenia (n = 233). Early response was defined as ≥30% improvement from baseline to Week 4 in Positive and Negative Syndrome Scale (PANSS0-6) Total score. Subsequent response was defined as ≥40% baseline-to-endpoint improvement in PANSS0-6 Total score. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and predictive accuracy were calculated. Clinical and functional outcomes were compared between Early Responders and Early Non-responders. Results Early response/non-response to olanzapine LAI predicted later response/non-response with high sensitivity (85%), specificity (72%), PPV (78%), NPV (80%), and overall accuracy (79%). Compared to Early Non-responders, Early Responders had significantly greater improvement in PANSS0-6 Total scores at all time points and greater baseline-to-endpoint improvement in PANSS subscale scores, Quality of Life Scale scores, and Short Form-36 Health Survey scores (all p ≤ .01). Among Early Non-responders, 20% demonstrated response by Week 8. Patients who lacked early improvement (at Week 4) in Negative Symptoms and Disorganized Thoughts were more likely to continue being non-responders at Week 8. Conclusions Among acutely ill patients with schizophrenia, early response predicted subsequent response to olanzapine LAI. Early Responders experienced significantly better clinical and functional outcomes than Early Non-responders. Findings are consistent with previous research on oral antipsychotics. Clinical Trials Registry F1D-MC-HGJZ: Comparison of

  1. Near-infrared spectroscopy in schizophrenia: a possible biomarker for predicting clinical outcome and treatment response.

    PubMed

    Koike, Shinsuke; Nishimura, Yukika; Takizawa, Ryu; Yahata, Noriaki; Kasai, Kiyoto

    2013-01-01

    Functional near-infrared spectroscopy (fNIRS) is a relatively new technique that can measure hemoglobin changes in brain tissues, and its use in psychiatry has been progressing rapidly. Although it has several disadvantages (e.g., relatively low spatial resolution and the possibility of shallow coverage in the depth of brain regions) compared with other functional neuroimaging techniques (e.g., functional magnetic resonance imaging and positron emission tomography), fNIRS may be a candidate instrument for clinical use in psychiatry, as it can measure brain activity in naturalistic position easily and non-invasively. fNIRS instruments are also small and work silently, and can be moved almost everywhere including schools and care units. Previous fNIRS studies have shown that patients with schizophrenia have impaired activity and characteristic waveform patterns in the prefrontal cortex during the letter version of the verbal fluency task, and part of these results have been approved as one of the Advanced Medical Technologies as an aid for the differential diagnosis of depressive symptoms by the Ministry of Health, Labor and Welfare of Japan in 2009, which was the first such approval in the field of psychiatry. Moreover, previous studies suggest that the activity in the frontopolar prefrontal cortex is associated with their functions in chronic schizophrenia and is its next candidate biomarker. Future studies aimed at exploring fNIRS differences in various clinical stages, longitudinal changes, drug effects, and variations during different task paradigms will be needed to develop more accurate biomarkers that can be used to aid differential diagnosis, the comprehension of the present condition, the prediction of outcome, and the decision regarding treatment options in schizophrenia. Future fNIRS researches will require standardized measurement procedures, probe settings, analytical methods and tools, manuscript description, and database systems in an fNIRS community.

  2. Clinical trials for predictive medicine.

    PubMed

    Simon, Richard

    2012-11-10

    Developments in biotechnology and genomics are providing a biological basis for the heterogeneity of clinical course and response to treatment that have long been apparent to clinicians. The ability to molecularly characterize human diseases presents new opportunities to develop more effective treatments and new challenges for the design and analysis of clinical trials. In oncology, treatment of broad populations with regimens that benefit a minority of patients is less economically sustainable with expensive molecularly targeted therapeutics. The established molecular heterogeneity of human diseases requires the development of new paradigms for the design and analysis of randomized clinical trials as a reliable basis for predictive medicine. We review prospective designs for the development of new therapeutics and predictive biomarkers to inform their use. We cover designs for a wide range of settings. At one extreme is the development of a new drug with a single candidate biomarker and strong biological evidence that marker negative patients are unlikely to benefit from the new drug. At the other extreme are Phase III clinical trials involving both genome-wide discovery of a predictive classifier and internal validation of that classifier. We have outlined a prediction-based approach to the analysis of randomized clinical trials that both preserves the Type I error and provides a reliable internally validated basis for predicting which patients are most likely or unlikely to benefit from the new regimen.

  3. Pre-treatment SNOT-22 Score Predicts Response to Endoscopic Polypectomy in Clinic (EPIC) Our experience in 30 adults.

    PubMed

    Caulley, Lisa; Lasso, Andrea; Rudmik, Luke; Kilty, Shaun J

    2016-01-23

    Endoscopic polypectomy in clinic (EPIC) has demonstrated substantial short-term improvement in patient reported quality of life measures and a pilot economic analysis of EPIC confirmed its cost-effectiveness in comparison to endoscopic sinus surgery. The purpose of this study was to evaluate if pre-treatment Sino Nasal Outcome Tests-22 (SNOT-22) score predicts response to EPIC treatment in parallel to the results found for endoscopic sinus surgery. Depending on the baseline SNOT-22 group the probability of achieving minimal clinically important difference was 75-100% post-EPIC. The relative improvement in quality of life on average was 62% with 90% realising a minimal clinically important difference in their SNOT-22 score. These results are comparable to what has been found for endoscopic sinus surgery. The results of this study reflect the published results of the effect of endoscopic sinus surgery on SNOT-22 scores 6 months post-intervention which themselves have been shown to predict SNOT-22 scores at 18 months post-endoscopic sinus surgery. In select patients with chronic rhinosinusitis with nasal polyps the EPIC procedure may provide SNOT-22 score improvements similar to those seen with patients who undergo endoscopic sinus surgery This article is protected by copyright. All rights reserved.

  4. A Review of Modeling Approaches to Predict Drug Response in Clinical Oncology

    PubMed Central

    2017-01-01

    Model-based approaches have emerged as important tools for quantitatively understanding temporal relationships between drug dose, concentration, and effect over the course of treatment, and have now become central to optimal drug development and tailored drug treatment. In oncology, the therapeutic index of a chemotherapeutic drug is typically narrow and a full dose–response relationship is not available, often because of treatment failure. Noting the benefits of model-based approaches and the low therapeutic index of oncology drugs, in recent years, modeling approaches have been increasingly used to streamline oncologic drug development through early identification and quantification of dose–response relationships. With this background, this report reviews publications that used model-based approaches to evaluate drug treatment outcome variables in oncology therapeutics, ranging from tumor size dynamics to tumor/biomarker time courses and survival response. PMID:27873489

  5. Can clinical response to cyclosporin in chronic severe asthma be predicted by an in vitro T-lymphocyte proliferation assay?

    PubMed

    Alexander, A G; Barnes, N C; Kay, A B; Corrigan, C J

    1996-07-01

    This study tests the hypothesis that the clinical response to cyclosporin therapy of patients with chronic severe asthma is related to the sensitivity of their T-lymphocytes to the antiproliferative effects of cyclosporin in vitro. In a previous study, we observed such a relationship with glucocorticoids and the same lectin-driven proliferation assay was used in the present study. Peripheral blood mononuclear cells were obtained from 33 patients participating in a cross-over trial of oral cyclosporin therapy during both cyclosporin and placebo treatment periods, and cultured in the presence of phytohaemagglutinin and serial dilutions of cyclosporin and dexamethasone. Proliferation was measured by tritiated thymidine uptake. Both cyclosporin and dexamethasone inhibited T-lymphocyte proliferation in a concentration-dependent manner in vitro at concentrations encompassing those achieved in peripheral blood during therapy in vivo. T-lymphocytes from the asthmatic patients showed a range of sensitivity to the antiproliferative effects of cyclosporin, but this could not be correlated with improvements in peak expiratory flow rate (PEFR) or forced expiratory volume in one second (FEV1) during cyclosporin therapy as compared with placebo. In contrast to previous observations with glucocorticoids, this in vitro T-lymphocyte proliferation assay is not predictive of clinical response to cyclosporin therapy in chronic severe asthmatics.

  6. Immune contexture and histological response after neoadjuvant chemotherapy predict clinical outcome of lung cancer patients.

    PubMed

    Remark, Romain; Lupo, Audrey; Alifano, Marco; Biton, Jerome; Ouakrim, Hanane; Stefani, Alessandro; Cremer, Isabelle; Goc, Jeremy; Régnard, Jean-Francois; Dieu-Nosjean, Marie-Caroline; Damotte, Diane

    2016-01-01

    There is now growing evidence that the immune contexture influences cancer progression and clinical outcome of patients with non-small cell lung cancer (NSCLC). If chemotherapy is widely used to treat patients with advanced-stage NSCLC, it remains unclear how it could modify the immune contexture and impact its prognostic value. Here, we analyzed two retrospective cohorts, respectively composed of 122 stage III-N2 NSCLC patients treated with chemotherapy before surgery and 39 stage-matched patients treated by surgery only. In patients treated with neoadjuvant chemotherapy, the histological characteristics, the expression of PD-L1 protein, and the tumor immune microenvironment (CD8(+) T cells, DC-LAMP(+) mature dendritic cells, and CD68(+) macrophages) were evaluated and their prognostic value assessed together with standard clinical parameters. By analyzing pre- and post-treatment specimens, we did not find any changes in the PD-L1 expression. We also found that the tumor immune contexture in patients treated with neoadjuvant chemotherapy exhibited a similar pattern that the one found in chemotherapy-naive patients, with comparable densities of tumor-infiltrating CD8(+) and DC-LAMP(+) cells and a similar spatial organization. The percentage of residual viable tumor cells and the immune pattern (CD8(+) and DC-LAMP(+) cell densities) were significantly associated with the clinical outcome and allowed the identification of short- and long-term survivors, respectively. In multivariate analysis, the immune pattern was found to be the strongest independent prognostic factor. In conclusion, this study decrypts the complex interplay between cancer and immune cells in patients undergoing chemotherapy and supports potential beneficial synergistic effect of immunotherapy and chemotherapy.

  7. Predicting response to epigenetic therapy

    PubMed Central

    Treppendahl, Marianne B.; Kristensen, Lasse S.; Grønbæk, Kirsten

    2014-01-01

    Drugs targeting the epigenome are new promising cancer treatment modalities; however, not all patients receive the same benefit from these drugs. In contrast to conventional chemotherapy, responses may take several months after the initiation of treatment to occur. Accordingly, identification of good pretreatment predictors of response is of great value. Many clinical parameters and molecular targets have been tested in preclinical and clinical studies with varying results, leaving room for optimization. Here we provide an overview of markers that may predict the efficacy of FDA- and EMA-approved epigenetic drugs. PMID:24382389

  8. Prediction of conductive hearing loss based on acoustic ear-canal response using a multivariate clinical decision theory.

    PubMed

    Piskorski, P; Keefe, D H; Simmons, J L; Gorga, M P

    1999-03-01

    This study evaluated the accuracy of acoustic response tests in predicting conductive hearing loss in 161 ears of subjects from the age of 2 to 10 yr, using as a "gold standard" the air-bone gap to classify ears as normal or impaired. The acoustic tests included tympanometric peak-compensated static admittance magnitude (SA) and tympanometric gradient at 226 Hz, and admittance-reflectance (YR) measurements from 0.5 to 8 kHz. The performance of individual, frequency-specific, YR test variables as predictors was assessed. By applying logistic regression (LR) and discriminant analysis (DA) techniques to the multivariate YR response, two univariate functions were calculated as the linear combinations of YR variables across frequency that best separated normal and impaired ears. The tympanometric and YR tests were also combined in a multivariate manner to test whether predictive efficacy improved when 226-Hz tympanometry was added to the predictor set. Conductive hearing loss was predicted based on air-bone gap thresholds at 0.5 and 2 kHz, and on a maximum air-bone gap at any octave frequency from 0.5 to 4 kHz. Each air-bone gap threshold ranged from 5 to 30 dB in 5-dB steps. Areas under the relative operating characteristic curve for DA and LR were larger than for reflectance at 2 kHz, SA and Gr. For constant hit rates of 80% and 90%, both DA and LR scores had lower false-alarm rates than tympanometric tests-LR achieved a false-alarm rate of 6% for a sensitivity of 90%. In general, LR outperformed DA as the multivariate technique of choice. In predicting an impairment at 0.5 kHz, the reflectance scores at 0.5 kHz were less accurate predictors than reflectance at 2 and 4 kHz. This supports the hypothesis that the 2-4-kHz range is a particularly sensitive indicator of middle-ear status, in agreement with the spectral composition of the output predictor from the multivariate analyses. When tympanometric and YR tests were combined, the resulting predictor performed

  9. Activation of c-Jun predicts a poor response to sorafenib in hepatocellular carcinoma: Preliminary Clinical Evidence

    PubMed Central

    Chen, Wei; Xiao, Weikai; Zhang, Kunsong; Yin, Xiaoyu; Lai, Jiaming; Liang, Lijian; Chen, Dong

    2016-01-01

    We determined the mitogen-activated protein kinase (MAPK) gene expression profile of acquired resistance in sorafenib-sensitive hepatocellular carcinoma (HCC) cells and aimed to identify c-Jun as an important molecule mediating the efficacy of sorafenib. Differences in gene expression of the MAPK signaling between untreated and sorafenib-treated HCC cell lines were investigated using real-time polymerase chain reaction array. Western blot and real-time PCR further evaluated the expression of c-Jun. Pathological specimens from 50 patients with advanced HCC were collected to measure p-c-Jun expression. Sorafenib-resistant HCC cells demonstrated greater levels of basal c-Jun mRNA and protein compared with sorafenib-sensitive HCC cells. Sorafenib activated p-c-Jun in a dose- and time-dependent manner in PLC/PRF/5 and MHCC97H cell lines. Decreased expression levels of 6 genes after sorafenib treatment suggested a robust inhibitory impact of sorafenib on MAPK signaling in HCC cells. c-Jun and p-c-Jun expression levels were inversely correlated with the efficacy of sorafenib; a high expression level of p-c-Jun was associated with resistance to sorafenib and poor overall survival in patients with clinical HCC. p-c-Jun may act as a biomarker for predicting responses of sorafenib treatment, thus advocating targeting of JNK/c-Jun signaling as an optimal therapeutic strategy in a subset of HCC. PMID:26964667

  10. Short term response is predictive of long term response to acetylcholinesterase inhibitors in Alzheimer’s disease: A starting point to explore Bayesian approximation in clinical practice

    PubMed Central

    Rota, Eugenia; Ferrero, Patrizia; Ursone, Rita; Migliaretti, Giuseppe

    2007-01-01

    This study was aimed at identifying, in 203 patients with Alzheimer's disease followed during long-term treatment with Acetylcholinesterase inhibitors (ChEIs), the predictive factors of the clinical response among cognition (MMSE), functioning (BADL and IADL) measures and age and gender at the baseline (T0). The ANCOVA test showed a significant association between MMSE scores at time T0 and T3, and the variation T9 to T0, T15 to T0 and T21 to T0 of the MMSE scores, using also gender, age and drug as covariates. The significance was higher for the patients affected by mild dementia. Regarding functional activities, a significant relationship was detected, by the ANCOVA test, only between the scores at T3 and the variation T15 to T0 for BADL, and the variation T9 to T0, T15 to T0 for IADL, respectively. Our results confirm, in a real world setting, that ChEIs provide long-term cognitive benefit, which is correlated to, and predictable by, the short-term response (within the third month) as well as the cognitive status (evaluated by means of the MMSE) at the beginning of the treatment. These factors should be the basis of any cost/effectiveness algorithm in health economic decision models. PMID:18188418

  11. Incremental Value of Cystatin C Over Conventional Renal Metrics for Predicting Clinical Response and Outcomes in Cardiac Resynchronization Therapy: The BIOCRT Study

    PubMed Central

    Chatterjee, Neal A.; Singh, Jagmeet P.; Szymonifka, Jackie; Deaño, Roderick C.; Thai, Wai-ee; Wai, Bryan; Min, James K.; Januzzi, James L.; Truong, Quynh A.

    2015-01-01

    Background Despite the benefit of CRT in select patients with heart failure (HF), there remains significant need for predicting those at risk for adverse outcomes for this effective but costly therapy. CysC, an emerging marker of renal function, is predictive of worsening symptoms and mortality in patients with HF. This study assessed the utility of baseline and serial measures of cystatin C (CysC), compared to conventional creatinine-based measures of renal function (estimated glomerular filtration rate, eGFR), in predicting clinical outcomes following cardiac resynchronization therapy (CRT). Methods In 133 patients, we measured peripheral venous (PV) and coronary sinus (CS) CysC concentrations and peripheral creatinine levels at the time of CRT implant. Study endpoints included clinical response to CRT at 6 months and major adverse cardiac events (MACE) at 2 years. Results While all 3 renal metrics were predictive of MACE (all adjusted p≤0.02), only CysC was associated with CRT non-response at 6 months (adjusted odds ratio 3.6, p=0.02). CysC improved prediction of CRT non-response (p≤0.003) in net reclassification index analysis compared to models utilizing standard renal metrics. Serial CysC >1mg/L was associated with 6-month CRT non-response and reduced 6-minute walk distance as well as 2-year MACE (all p≤0.04). Conclusion In patients undergoing CRT, CysC demonstrated incremental benefit in the prediction of CRT non-response when compared to standard metrics of renal function. Baseline and serial measures of elevated CysC were predictive of CRT non-response and functional status at 6 months as well as long term clinical outcomes. PMID:26710332

  12. Predicting stimulant medication response in ADHD: evidence from an integrated profile of neuropsychological, psychophysiological and clinical factors.

    PubMed

    Hermens, Daniel F; Cooper, Nicholas J; Kohn, Michael; Clarke, Simon; Gordon, Evian

    2005-03-01

    There have been significant advances in understanding the neurobiology of Attention-Deficit/Hyperactivity Disorder (ADHD) and it is timely to examine the ability of biological and psychological markers to predict medication response in this disorder. We evaluated prediction of medication response in adolescent ADHD using neuropsychological testing and psychophysiological measures of central and autonomic function. Fifty ADHD adolescents participated in pre- and post-stimulant medication testing. Separately ranked performance in auditory oddball and visual Working Memory (WM) tasks determined 20 "responders" and 20 "non-responders" with 10 "neutrals" excluded from the discriminant function analyses (DFA). For both oddball and WM performance rankings, the two groups did not differ in age, sex, or handedness. However, responders did have higher levels of symptomatology than non-responders at baseline. Pre-stimulant medication psychophysiology variables were used as predictors in each DFA. Oddball performance correctly classified 85.0% of responders and 95.0% of non-responders. Better response was associated with increased resting beta power (left posteriorly), delayed oddball target N1 (frontally), decreased oddball target P2 (left posteriorly) and decreased WM distractor P3 (right frontally). Working memory performance classified 80.0% of responders and 90.0% of non-responders, with a broadly similar profile of psychophysiological predictors. These finding indicate the value of integrating neuropsychological and psychophysiological data in predicting medication response in ADHD.

  13. Prediction of Treatment Response at 5-year Follow-up in a Randomized Clinical Trial of Behaviorally Based Couple Therapies

    PubMed Central

    Baucom, Brian R.; Atkins, David C.; Rowe, Lorelei Simpson; Doss, Brian D.; Christensen, Andrew

    2014-01-01

    Objective Building on earlier work examining predictors of short- and moderate-term treatment response, demographic, intrapersonal, communication, and interpersonal variables were examined as predictors of clinically significant outcomes five years after couples completed one of two behaviorally based couple therapies. Method One hundred and thirty-four couples were randomly assigned to Integrative Behavioral Couple Therapy (IBCT; Jacobson & Christensen, 1998) or Traditional Behavioral Couple Therapy (TBCT; Jacobson & Margolin, 1979) and followed for 5 years after treatment. Outcomes include clinically significant change categories of relationship satisfaction and marital status at 5-year follow-up. Optimal subsets of predictors were selected using an automated, bootstrapped selection procedure based on Bayesian Information Criterion. Results Higher levels of commitment and being married for a longer period of time were associated with decreased likelihood of divorce/separation (Odds Ratio [OR] = 1.39, p = .004; OR = 0.91, p = .015). Being married for a longer period of time was also associated with increased likelihood of positive, clinically significant change (OR = 1.12, p = .029). Finally, higher levels of wife desired closeness were associated with increased odds of positive, clinically significant change and decreased odds of divorce for moderately distressed, IBCT couples (OR = 1.16, p = 0.002; OR = 0.85, p = 0.007, respectively) whereas the opposite was true for moderately distressed, TBCT couples (OR = 0.77, p < 0.001; OR = 1.17, p = 0.002, respectively). Conclusions Commitment-related variables are associated with clinically significant outcomes at 5-year follow-up as well as at termination and moderate-term follow-up. Public health significance This study indicates that couples who begin marital therapy with higher levels of commitment are least likely to get divorced and most likely to report improvements in relationship satisfaction five years after

  14. Clinical Model for Predicting Hepatocellular Carcinomas in Patients with Post-Sustained Virologic Responses of Chronic Hepatitis C: A Case Control Study

    PubMed Central

    Zeng, Qing-Lei; Li, Bing; Zhang, Xue-Xiu; Chen, Yan; Fu, Yan-Ling; Lv, Jun; Liu, Yan-Min; Yu, Zu-Jiang

    2016-01-01

    Background/Aims No clinical model exists to predict the occurrence of hepatocellular carcinoma in sustained virologic response-achieving (HCC after SVR) patients with chronic hepatitis C (CHC). Methods We performed a case-control study using a clinical database to research the risk factors for HCC after SVR. A predictive model based on risk factors was established, and the area under the receiver operating characteristic curve (AUC) was calculated. Results In the multivariate model, an initial diagnosis of compensated cirrhosis and post-SVR albumin reductions of 1 g/L were associated with 21.7-fold (95% CI, 4.2 to 112.3; p<0.001) and 1.3-fold (95% CI, 1.1 to 1.7; p=0.004) increases in the risk of HCC after SVR, respectively. A predictive model based on an initial diagnosis of compensated cirrhosis (yes, +1; no, 0) and post-SVR albumin ≤36.0 g/L (yes, +1; not, 0) predicted the occurrence of HCC after SVR with a cutoff value of >0, an AUC of 0.880, a sensitivity of 0.833, a specificity of 0.896, and a negative predictive value of 0.956. Conclusions An initial diagnosis of compensated cirrhosis combined with a post-SVR albumin value of ≤36.0 g/L predicts the occurrence of HCC after SVR in patients with CHC. PMID:27257023

  15. Clinical value of serum anti-mullerian hormone and inhibin B in prediction of ovarian response in patients with polycystic ovary syndrome.

    PubMed

    Zhang, Fan; Liu, Xiao-Ling; Rong, Nan; Huang, Xiao-Wen

    2017-02-01

    The present study aimed to investigate the clinical value of serum anti-mullerian hormone (AMH) and inhibin B (INHB) in predicting the ovarian response of patients with polycystic ovary syndrome (PCOS). A total of 120 PCOS patients were enrolled and divided into three groups in terms of the ovarian response: a low-response group (n=36), a normal-response group (n=44), and a high-response group (n=40). The serum AMH and INHB levels were measured by enzyme-linked immunosorbent assay (ELISA). The follicle stimulating hormone (FSH), luteinizing hormone (LH), and estradiol (E2) levels were determined by chemiluminescence microparticle immunoassay. The correlation of the serum AMH and INHB levels with other indicators was analyzed. A receiver operating characteristic (ROC) curve was established to analyze the prediction of ovarian response by AMH and INHB. The results showed that there were significant differences in age, body mass index (BMI), FSH, total gonadotropin-releasing hormone (GnRH), LH, E2, and antral follicle counts (AFCs) between the groups (P<0.05). The serum AMH and INHB levels were increased significantly with the ovarian response of PCOS patients increasing (P<0.05). The serum AMH and INHB levels were negatively correlated with the age, BMI, FSH level, Gn, and E2 levels (P<0.05). They were positively correlated with the LH levels and AFCs (P<0.05). ROC curve analysis of serum AMH and INHB in prediction of a low ovarian response showed that the area under the ROC curve (AUC) value of the serum AMH level was 0.817, with a cut-off value of 1.29 ng/mL. The sensitivity and specificity were 71.2% and 79.6%, respectively. The AUC value of serum INHB was 0.674, with a cut-off value of 38.65 ng/mL, and the sensitivity and specificity were 50.7% and 74.5%, respectively. ROC curve analysis showed when the serum AMH and INHB levels were used to predict a high ovarian response, the AUC value of the serum AMH level was 0.742, with a cut-off value of 2.84 ng/mL, and

  16. The good EULAR response at the first year is strongly predictive of clinical remission in rheumatoid arthritis: results from the TARAC cohort.

    PubMed

    Darawankul, Budsakorn; Chaiamnuay, Sumapa; Pakchotanon, Rattapol; Asavatanabodee, Paijit; Narongroeknawin, Pongthorn

    2015-01-01

    The purpose of this study was to identify the prevalence and prognostic factors of clinical remission in patients with rheumatoid arthritis (RA). The Thai Army Rheumatoid Arthritis Cohort (TARAC) patients were included if baseline data were available. Clinical remission was defined as 28-joint count disease activity scores (DAS28) <2.6 in the last two consecutive visits, at least 3 months apart. Three hundred and thirty-five patients were enrolled, and 89.9 % were female. Mean (SD) age was 61 years (11.4), and mean disease duration was 145.9 months (93.7). Rheumatoid factor (RF) and anti-citrullinated protein antibody (ACPA) were positive in 69.9 and 67.8 %, respectively. Eighty-nine percent of patients were treated with synthetic DMARDs, of which 29 % received monotherapy. The combination of biologic and synthetic DMARDs was used in 10.4 % of the patients. Clinical remission was observed in 49 patients (14.6 %). Early diagnosis and treatment within 12 months of onset (odds ratio (OR) 1.95, 95 % confidence interval (CI) 1.02-3.74, p = 0.04), rheumatoid factor negativity (OR 2.10, 95 % CI 1.04-4.21, p = 0.04) and good EULAR response at the end of the first year of treatment (OR 2.75, 95 % CI 1.08-6.99, p = 0.03) were associated with clinical remission in univariate analysis. In multivariate regression analysis, only a good EULAR response at the first year was significantly correlated with clinical remission in this study (OR 3.1, 95 % CI 1.15-8.36, p = 0.03). Although remission is currently a treatment goal in patients with RA, only one-seventh of patients have achieved sustained clinical remission in clinical practice. The good EULAR response at the end of the first year was an independent predictive factor of clinical remission.

  17. Clinical Utility of Fractional exhaled Nitric Oxide (FeNO) as a Biomarker to Predict Severity of Disease and Response to Inhaled Corticosteroid (ICS) in Asthma Patients

    PubMed Central

    Saka, Vinodkumar; Tamilarasu, Kadhiravan; Rajaram, Manju; Selvarajan, Sandhiya; Chandrasekaran, Adithan

    2016-01-01

    Introduction Bronchial asthma is a common chronic inflammatory airway disease diagnosed and is based on symptomatic history and Pulmonary Function Tests (PFT). Fractional exhaled Nitric Oxide (FeNO) is exclusively a non-invasive biomarker of on-going eosinophilic airway inflammation which remains unpredictable only with PFTs. FeNO measurement is recommended in predicting asthma severity and Inhaled Corticosteroid (ICS) response but further research is required to understand its clinical utility and agreement with current recommendations in a specific population. Aim To estimate FeNO levels in Tamilian patients with mild-to-moderate persistent asthma and to correlate with disease severity and ICS response. Materials and Methods The study was a prospective cohort with a single group of 102 persistent asthma patients under standard ICS regimen for 8 weeks (follow-up period). PFT and FeNO were measured using portable spirometry and chemiluminescence based exhaled breath analyser, at baseline and during follow-up visits. Based on PFT and FeNO parameters, the study population was sub-grouped with respect to asthma severity (as mild, moderate and moderately severe), FeNO cut-off (> or < 50ppb) and ICS response classification (good vs poor ICS responders). Results Significant decrease in mean FeNO levels were found in mild, moderate and moderately severe asthmatic groups following ICS treatment (90.15±27.36, 75.74±31.98 and 77.18±32.79 ppb) compared to similar baseline FeNO levels (103.03±34.08, 91.38±37.60 and 97.90±43.84 ppb) in all the above groups. Similarly, significant decrease in mean FeNO levels was found - FeNO>50ppb, good and poor ICS responders groups, in post- ICS treatment (89.63±24.04, 77.90±31.12 and 86.49±32.57 ppb) compared to baseline levels (110.183±1.23, 97.12±42.04 and 99.68±34.71 ppb). Conclusion The observed baseline FeNO values in all groups as stated above did not show significant difference to differentiate asthma severity or ICS

  18. Predicting and measuring fluid responsiveness with echocardiography.

    PubMed

    Miller, Ashley; Mandeville, Justin

    2016-06-01

    Echocardiography is ideally suited to guide fluid resuscitation in critically ill patients. It can be used to assess fluid responsiveness by looking at the left ventricle, aortic outflow, inferior vena cava and right ventricle. Static measurements and dynamic variables based on heart-lung interactions all combine to predict and measure fluid responsiveness and assess response to intravenous fluid resuscitation. Thorough knowledge of these variables, the physiology behind them and the pitfalls in their use allows the echocardiographer to confidently assess these patients and in combination with clinical judgement manage them appropriately.

  19. Predicting and measuring fluid responsiveness with echocardiography

    PubMed Central

    Mandeville, Justin

    2016-01-01

    Echocardiography is ideally suited to guide fluid resuscitation in critically ill patients. It can be used to assess fluid responsiveness by looking at the left ventricle, aortic outflow, inferior vena cava and right ventricle. Static measurements and dynamic variables based on heart–lung interactions all combine to predict and measure fluid responsiveness and assess response to intravenous fluid resuscitation. Thorough knowledge of these variables, the physiology behind them and the pitfalls in their use allows the echocardiographer to confidently assess these patients and in combination with clinical judgement manage them appropriately. PMID:27249550

  20. Predicting clinically significant response to cognitive behavior therapy for chronic insomnia in general medical practice: analysis of outcome data at 12 months posttreatment.

    PubMed

    Espie, C A; Inglis, S J; Harvey, L

    2001-02-01

    The clinical efficacy of cognitive behavior therapy (CBT) for chronic insomnia has been established, yet clinical effectiveness is less clear. This study presents data on 109 patients from general practice during a formal evaluation of clinical effectiveness. Two thirds achieved normative values of < or =30 min for sleep latency and wakefulness during the night after CBT. Furthermore, almost half of the sample reduced sleeplessness by > or =50%. Logistic regression revealed that initial severity did not contraindicate good outcome. Rather, greater sleep disturbance was positively associated with large symptom reduction, although lower endpoint scores were less likely. Similarly, symptoms of anxiety, depression, and thinking errors positively predicted good outcome. Hypnotic using patients responded equally well to CBT, and demographic factors were of no significant predictive value. It is concluded that CBT is clinically and durably effective for persistent insomnia in routine practice.

  1. Motion Predicts Clinical Callus Formation

    PubMed Central

    Elkins, Jacob; Marsh, J. Lawrence; Lujan, Trevor; Peindl, Richard; Kellam, James; Anderson, Donald D.; Lack, William

    2016-01-01

    Background: Mechanotransduction is theorized to influence fracture-healing, but optimal fracture-site motion is poorly defined. We hypothesized that three-dimensional (3-D) fracture-site motion as estimated by finite element (FE) analysis would influence callus formation for a clinical series of supracondylar femoral fractures treated with locking-plate fixation. Methods: Construct-specific FE modeling simulated 3-D fracture-site motion for sixty-six supracondylar femoral fractures (OTA/AO classification of 33A or 33C) treated at a single institution. Construct stiffness and directional motion through the fracture were investigated to assess the validity of construct stiffness as a surrogate measure of 3-D motion at the fracture site. Callus formation was assessed radiographically for all patients at six, twelve, and twenty-four weeks postoperatively. Univariate and multivariate linear regression analyses examined the effects of longitudinal motion, shear (transverse motion), open fracture, smoking, and diabetes on callus formation. Construct types were compared to determine whether their 3-D motion profile was associated with callus formation. Results: Shear disproportionately increased relative to longitudinal motion with increasing bridge span, which was not predicted by our assessment of construct stiffness alone. Callus formation was not associated with open fracture, smoking, or diabetes at six, twelve, or twenty-four weeks. However, callus formation was associated with 3-D fracture-site motion at twelve and twenty-four weeks. Longitudinal motion promoted callus formation at twelve and twenty-four weeks (p = 0.017 for both). Shear inhibited callus formation at twelve and twenty-four weeks (p = 0.017 and p = 0.022, respectively). Titanium constructs with a short bridge span demonstrated greater longitudinal motion with less shear than did the other constructs, and this was associated with greater callus formation (p < 0.001). Conclusions: In this study of

  2. Clinical ethics revisited: responses

    PubMed Central

    Benatar, Solomon R; Bhutta, Zulfiqar A; Daar, Abdallah S; Hope, Tony; MacRae, Sue; Roberts, Laura W; Sharpe, Virginia A

    2001-01-01

    This series of responses was commissioned to accompany the article by Singer et al, which can be found at . If you would like to comment on the article by Singer et al or any of the responses, please email us on editorial@biomedcentral.com. PMID:11346457

  3. The Clinical Prediction of Dangerousness.

    DTIC Science & Technology

    1985-05-01

    3. D. (1983). The reliability of psychiatric and psycho- logical diagnosis. Clinical Psychology Review , 3, 103-145. McCord, J. (1979). Some child...patients. Clinical Psychology Review , 4, 379-401. New Jersey v. Krol, 344 A .2d 289 (1975). Newton, C., & Zimring, F. (1970). Firearms and violence in

  4. Outcome Prediction in Clinical Treatment Processes.

    PubMed

    Huang, Zhengxing; Dong, Wei; Ji, Lei; Duan, Huilong

    2016-01-01

    Clinical outcome prediction, as strong implications for health service delivery of clinical treatment processes (CTPs), is important for both patients and healthcare providers. Prior studies typically use a priori knowledge, such as demographics or patient physical factors, to estimate clinical outcomes at early stages of CTPs (e.g., admission). They lack the ability to deal with temporal evolution of CTPs. In addition, most of the existing studies employ data mining or machine learning methods to generate a prediction model for a specific type of clinical outcome, however, a mathematical model that predicts multiple clinical outcomes simultaneously, has not yet been established. In this study, a hybrid approach is proposed to provide a continuous predictive monitoring service on multiple clinical outcomes. More specifically, a probabilistic topic model is applied to discover underlying treatment patterns of CTPs from electronic medical records. Then, the learned treatment patterns, as low-dimensional features of CTPs, are exploited for clinical outcome prediction across various stages of CTPs based on multi-label classification. The proposal is evaluated to predict three typical classes of clinical outcomes, i.e., length of stay, readmission time, and the type of discharge, using 3492 pieces of patients' medical records of the unstable angina CTP, extracted from a Chinese hospital. The stable model was characterized by 84.9% accuracy and 6.4% hamming-loss with 3 latent treatment patterns discovered from data, which outperforms the benchmark multi-label classification algorithms for clinical outcome prediction. Our study indicates the proposed approach can potentially improve the quality of clinical outcome prediction, and assist physicians to understand the patient conditions, treatment inventions, and clinical outcomes in an integrated view.

  5. Clinical Usefulness of Urinary Fatty Acid Binding Proteins in Assessing the Severity and Predicting Treatment Response of Pneumonia in Critically Ill Patients: A Cross-Sectional Study.

    PubMed

    Tsao, Tsung-Cheng; Tsai, Han-Chen; Chang, Shi-Chuan

    2016-05-01

    pneumonia severity and in predicting treatment response, respectively. Further studies with larger populations are needed to verify these issues.

  6. Early prediction of histopathological response of rectal tumors after one week of preoperative radiochemotherapy using 18 F-FDG PET-CT imaging. A prospective clinical study

    PubMed Central

    2012-01-01

    Background Preoperative radiochemotherapy (RCT) is standard in locally advanced rectal cancer (LARC). Initial data suggest that the tumor’s metabolic response, i.e. reduction of its 18 F-FDG uptake compared with the baseline, observed after two weeks of RCT, may correlate with histopathological response. This prospective study evaluated the ability of a very early metabolic response, seen after only one week of RCT, to predict the histopathological response to treatment. Methods Twenty patients with LARC who received standard RCT regimen followed by radical surgery participated in this study. Maximum standardized uptake value (SUV-MAX), measured by PET-CT imaging at baseline and on day 8 of RCT, and the changes in FDG uptake (ΔSUV-MAX), were compared with the histopathological response at surgery. Response was classified by tumor regression grade (TRG) and by achievement of pathological complete response (pCR). Results Absolute SUV-MAX values at both time points did not correlate with histopathological response. However, patients with pCR had a larger drop in SUV-MAX after one week of RCT (median: -35.31% vs −18.42%, p = 0.046). In contrast, TRG did not correlate with ΔSUV-MAX. The changes in FGD-uptake predicted accurately the achievement of pCR: only patients with a decrease of more than 32% in SUV-MAX had pCR while none of those whose tumors did not show any decrease in SUV-MAX had pCR. Conclusions A decrease in ΔSUV-MAX after only one week of RCT for LARC may be able to predict the achievement of pCR in the post-RCT surgical specimen. Validation in a larger independent cohort is planned. PMID:22853868

  7. Predicting Diabetes: Clinical, Biological, and Genetic Approaches

    PubMed Central

    Balkau, Beverley; Lange, Céline; Fezeu, Leopold; Tichet, Jean; de Lauzon-Guillain, Blandine; Czernichow, Sebastien; Fumeron, Frederic; Froguel, Philippe; Vaxillaire, Martine; Cauchi, Stephane; Ducimetière, Pierre; Eschwège, Eveline

    2008-01-01

    OBJECTIVE—To provide a simple clinical diabetes risk score and to identify characteristics that predict later diabetes using variables available in the clinic setting as well as biological variables and polymorphisms. RESEARCH DESIGN AND METHODS—Incident diabetes was studied in 1,863 men and 1,954 women, 30–65 years of age at baseline, with diabetes defined by treatment or by fasting plasma glucose ≥7.0 mmol/l at 3-yearly examinations over 9 years. Sex-specific logistic regression equations were used to select variables for prediction. RESULTS—A total of 140 men and 63 women developed diabetes. The predictive clinical variables were waist circumference and hypertension in both sexes, smoking in men, and diabetes in the family in women. Discrimination, as measured by the area under the receiver operating curves (AROCs), were 0.713 for men and 0.827 for women, a little higher than for the Finish Diabetes Risk (FINDRISC) score, with fewer variables in the score. Combining clinical and biological variables, the predictive equation included fasting glucose, waist circumference, smoking, and γ-glutamyltransferase for men and fasting glucose, BMI, triglycerides, and diabetes in family for women. The number of TCF7L2 and IL6 deleterious alleles was predictive in both sexes, but after including the above clinical and biological variables, this variable was only predictive in women (P < 0.03) and the AROC statistics increased only marginally. CONCLUSIONS—The best clinical predictor of diabetes is adiposity, and baseline glucose is the best biological predictor. Clinical and biological predictors differed marginally between men and women. The genetic polymorphisms added little to the prediction of diabetes. PMID:18689695

  8. In vivo mesolimbic D2/3 receptor binding predicts posttherapeutic clinical responses in restless legs syndrome: a positron emission tomography study.

    PubMed

    Oboshi, Yumi; Ouchi, Yasuomi; Yagi, Shunsuke; Kono, Satoshi; Nakai, Noriyoshi; Yoshikawa, Etsuji; Futatsubashi, Masami; Terada, Tatsuhiro; Kim, Kang; Harada, Kiyoshi

    2012-04-01

    Although D2/3 agonists have been used as a first-line medication for idiopathic restless legs syndrome (iRLS), findings on D2/3 receptors have been inconsistent. Here, we aimed to clarify the contribution of D2/3 receptor function to the clinical symptoms of iRLS by comparing the binding potential (BP(ND)) of [(11)C]raclopride with clinical improvements after D2/3 stimulation by pramipexole. Eight drug-naïve, iRLS patients and eight age-matched healthy subjects were scanned with positron emission tomography (PET). After PET scans, all patients received pramipexole (0.125 mg) orally for 2 weeks. Patients were evaluated every day with several standardized clinical tests. The BP(ND) values were compared using regions of interest and voxel-based methods. Results showed that the mean magnitude of [(11)C]raclopride BP(ND) in the mesolimbic dopamine region (nucleus accumbens (NA) and caudate) was significantly lower in the iRLS group. No significant differences between groups were observed in the putamen. The NA [(11)C]raclopride BP(ND) levels correlated negatively with clinical severity scores and positively with the degree of posttreatment improvement in iRLS. The present results suggest that alterations in mesolimbic D2/3 receptor function reflect the pathophysiology of iRLS, and the baseline availability of D2/3 receptors may predict the clinical outcome after D2/3 agonist treatment.

  9. Does psychological testing help to predict the response to acupuncture or massage/relaxation therapy in patients presenting to a general neurology clinic with headache?

    PubMed

    Wylie, K R; Jackson, C; Crawford, P M

    1997-06-01

    Patients with chronic headache were offered treatment by acupuncture or massage with relaxation instead of a change in their prescribed medication. They were randomly allocated to either treatment. There was a significant improvement in pain ratings with both treatment types. Specifically a greater effect was seen in migraine patients treated by massage with relaxation when compared to acupuncture. No psychological factors were found to predict response to either treatment. At the end of the study, 13% of patients were significantly more worried that there may be a more serious cause underlying their headache despite reassurance and an improvement in their headache scores.

  10. Participants' responsibilities in clinical research.

    PubMed

    Resnik, David B; Ness, Elizabeth

    2012-12-01

    Discussions on the ethics and regulation of clinical research have a great deal to say about the responsibilities of investigators, sponsors, research institutions and institutional review boards, but very little about the responsibilities of research participants. In this article, we discuss the responsibilities of participants in clinical research. We argue that competent adult participants are responsible for complying with study requirements and fulfilling other obligations they undertake when they make an informed choice to enroll in a study. These responsibilities are based on duties related to promise-keeping, avoiding harm to one's self or others, beneficence and reciprocity. Investigators and research staff should inform participants about their responsibilities during the consent process, and should stress the importance of fulfilling study requirements. They should address any impediments to compliance, and they may provide participants with financial incentives for meeting study requirements. In very rare cases, coercive measures may be justified to prevent immanent harm to others resulting from non-compliance with study requirements.

  11. [18F]FLT-PET to predict pharmacodynamic and clinical response to cetuximab therapy in Ménétrier’s disease

    PubMed Central

    McKinley, Eliot T.; Smith, R. Adam; Tanksley, Jarred P.; Washington, Mary Kay; Walker, Ronald; Coffey, Robert J.; Manning, H. Charles

    2013-01-01

    Molecular imaging biomarkers of proliferation hold great promise for quantifying response to personalized medicine. One such approach utilizes the positron emission tomography (PET) tracer 3′-deoxy-3′ [18F]-fluorothymidine ([18F]FLT), an investigational agent whose uptake reflects thymidine salvage-dependent DNA synthesis. The goal of this study was to evaluate [18F]FLT-PET in the setting of Ménétrier’s disease (MD), a rare, premalignant hyperproliferative disorder of the stomach treatable with cetuximab therapy. Over 15 months, a patient with confirmed MD underwent cetuximab therapy and was followed with sequential [18F]FLT-PET. For comparison to MD, an [18F]FLT-PET study was conducted in another patient to quantify uptake in a normal stomach.Prior to cetuximab therapy, stomach tissue in MD was easily visualized with [18F]FLT-PET, with pre-treatment uptake levels exceeding normal stomach uptake by approximately 4-fold. Diminished [18F]FLT-PET in MD was observed following the initial and subsequent doses of cetuximab and correlated with clinical resolution of the disease. To our knowledge, this study reports the first clinical use of [18F]FLT-PET to assess proliferation in a premalignant disorder. We illustrate that the extent of MD involvement throughout the stomach could be easily visualized using [18F]FLT-PET, and that response to cetuximab could be followed quantitatively and non-invasively in sequential [18F]FLT-PET studies. Thus, [18F]FLT-PET appears to have potential to monitor response to treatment in this and potentially other hyperproliferative disorders. PMID:22821337

  12. Drug Response Prediction as a Link Prediction Problem

    PubMed Central

    Stanfield, Zachary; Coşkun, Mustafa; Koyutürk, Mehmet

    2017-01-01

    Drug response prediction is a well-studied problem in which the molecular profile of a given sample is used to predict the effect of a given drug on that sample. Effective solutions to this problem hold the key for precision medicine. In cancer research, genomic data from cell lines are often utilized as features to develop machine learning models predictive of drug response. Molecular networks provide a functional context for the integration of genomic features, thereby resulting in robust and reproducible predictive models. However, inclusion of network data increases dimensionality and poses additional challenges for common machine learning tasks. To overcome these challenges, we here formulate drug response prediction as a link prediction problem. For this purpose, we represent drug response data for a large cohort of cell lines as a heterogeneous network. Using this network, we compute “network profiles” for cell lines and drugs. We then use the associations between these profiles to predict links between drugs and cell lines. Through leave-one-out cross validation and cross-classification on independent datasets, we show that this approach leads to accurate and reproducible classification of sensitive and resistant cell line-drug pairs, with 85% accuracy. We also examine the biological relevance of the network profiles. PMID:28067293

  13. Predicting responses from Rasch measures.

    PubMed

    Linacre, John M

    2010-01-01

    There is a growing family of Rasch models for polytomous observations. Selecting a suitable model for an existing dataset, estimating its parameters and evaluating its fit is now routine. Problems arise when the model parameters are to be estimated from the current data, but used to predict future data. In particular, ambiguities in the nature of the current data, or overfit of the model to the current dataset, may mean that better fit to the current data may lead to worse fit to future data. The predictive power of several Rasch and Rasch-related models are discussed in the context of the Netflix Prize. Rasch-related models are proposed based on Singular Value Decomposition (SVD) and Boltzmann Machines.

  14. ¹⁸F-FDG PET/CT in the early prediction of pathological response in aggressive subtypes of breast cancer: review of the literature and recommendations for use in clinical trials.

    PubMed

    Groheux, David; Mankoff, David; Espié, Marc; Hindié, Elif

    2016-05-01

    Early assessment of response to neoadjuvant chemotherapy (NAC) might be helpful in avoiding the toxicity of ineffective chemotherapy and allowing refinement of treatment. We conducted a review of the literature regarding the applicability of (18)F-FDG PET/CT to the prediction of an early pathological response in different subgroups of breast cancer. Clinical research in this field has intensified in the last few years. Early studies by various groups have shown the potential of (18)F-FDG PET/CT in the early assessment of response to NAC. However, interim PET/CT in breast cancer has not yet gained wide acceptance compared to its use in other settings such as lymphomas. This is in part due to a lack of consensus that early evaluation of response can be used to direct change in therapy in the neoadjuvant breast cancer setting, and only limited data showing that response-adaptive therapy leads to improved outcomes. However, one major element that has hampered the use of (18)F-FDG PET/CT in directing neoadjuvant therapy is its evaluation in populations with mixed subtypes of breast cancer. However, major improvements have occurred in recent years. Pilot studies have highlighted the need for considering breast cancer subtype and the type of treatment, and have offered criteria for the use of PET/CT for the early prediction of response in specific settings. (18)F-FDG PET/CT has considerable potential for the early prediction of pathological complete response to NAC in aggressive subtypes such as triple-negative or HER2-positive breast cancers. The results of a multicentre trial that used early metabolic response on (18)F-FDG PET/CT as a means to select poor responders to adapt neoadjuvant treatment have recently been published. Other trials are ongoing or being planned.

  15. SCCA-IC serum levels are predictive of clinical response in HCV chronic hepatitis to antiviral therapy: a multicentric prospective study.

    PubMed

    Fransvea, E; Trerotoli, P; Sacco, R; Bernabucci, V; Milella, M; Napoli, N; Mazzocca, A; Renna, E; Quaranta, M; Angarano, G; Villa, E; Antonaci, S; Giannelli, G

    2012-10-01

    The combination of pegylated interferon (Peg-IFN) and ribavirin is currently the gold standard therapy in patients with HCV chronic infection. The duration of therapy, as well as the therapeutic dosage, depend on the genotype. Identification of the genotype and rapid virological response (RVR) are widely accepted as the most important predictors of clinical outcome during antiviral therapy but to optimize cost-benefits and to reduce possible side effects, further prognostic factors are needed. Squamous cell carcinoma antigens immunocomplex (SCCA-IC) has been reported to be increased in the serum of patients with liver cancer. In this multicentric prospective study, we investigated the serum levels of SCCA-IC in 103 patients with HCV chronic infection. Serum HCV-RNA was detected before the beginning of treatment, after 4, 12, 24 or 48 weeks, and at week 24 during follow-up. RVR, early virological response and sustained virological response (SVR) were assessed following the international guidelines. SCCA-IC levels were higher in responders (238 AU, interquartile difference 130-556 AU) and decreased significantly to 125 AU (70-290 AU). The mean baseline value in nonresponders was 149 AU (86.5-306.5 AU), but after 4 weeks of treatment the serum levels decreased to 115 AU (80-280 AU): the profile of reduction was different between patients with or without a positive SVR. Logistic regression with SVR as dependent variable identified as significant independent variables: the reduction in SCCA-IC after 1 month (OR = 4.82; 95% CI 1.39-16.67; P = 0.131) and a genotype other than 1 (OR = 0.094; 95% CI 0.21-0.42; P = 0.002); sex and age were also significant factors influencing SVR. SCCA-IC seems to be a reliable independent prognostic marker of therapeutic effectiveness in anti-HCV positive patients undergoing antiviral therapy.

  16. Caregiver Responsiveness to the Family Bereavement Program: What predicts responsiveness? What does responsiveness predict?

    PubMed Central

    Schoenfelder, Erin N.; Sandler, Irwin N.; Millsap, Roger E.; Wolchik, Sharlene A.; Berkel, Cady; Ayers, Timothy S.

    2013-01-01

    The study developed a multi-dimensional measure to assess participant responsiveness to a preventive intervention, and applied this measure to study how participant baseline characteristics predict responsiveness and how responsiveness predicts program outcomes. The study was conducted with caregivers who participated in the parenting-focused component of the Family Bereavement Program (FBP), a prevention program for families that have experienced parental death. The sample consisted of 89 caregivers assigned to the intervention condition in the efficacy trial of the FBP. Positive parenting, caregiver depression, and child externalizing problems at baseline were found to predict caregivers’ use of program skills outside the group, and more child internalizing problems predicted more positive perceptions of the group environment. Higher levels of skill use during the program predicted increased positive parenting at the 11-month follow-up, whereas positive perceptions of the group environment predicted decreased caregiver depressive symptoms at follow-up. Caregiver skill use mediated the relation between baseline positive parenting and improvements in positive parenting at 11-month follow-up, and skill use and perceived group environment mediated changes in caregiver depression from baseline to 11-month follow-up. PMID:23404661

  17. No predictive effect of body mass index on clinical response in patients with rheumatoid arthritis after 24 weeks of biological disease-modifying antirheumatic drugs: a single-center study.

    PubMed

    Kim, Seong-Kyu; Choe, Jung-Yoon; Park, Sung-Hoon; Lee, Hwajeong

    2016-05-01

    The aim of this study was to determine whether body mass index (BMI) is associated with clinical response to biologics in patients with rheumatoid arthritis (RA). We enrolled 68 patients with RA who were treated with biological disease-modifying antirheumatic drugs (bDMARDs). Biologics included abatacept, tocilizumab, and tumor necrosis factor-α (TNF-α) blockers (etanercept and adalimumab). Baseline BMI (kg/m(2)) was classified as normal (BMI < 23.0), overweight (23.0 ≤ BMI < 25.0), or obese (BMI ≥ 25.0). Improvement of disease activity score 28 (DAS28) and achievement of the European League Against Rheumatism (EULAR) remission and responses between baseline and 24 weeks were our measures of clinical improvement. Mean baseline BMI before treatment with bDMARDs in patients with RA was 22.2 (SD 3.6). DAS28-ESR and DAS28-CRP were significantly reduced from baseline after 24 weeks of treatment with bDMARDs (p < 0.001 of both). ∆DAS28-ESR and ∆DAS28-CRP were not found among patients with normal, overweight, or obese BMI (p = 0.133 and p = 0.255, respectively) nor were EULAR responses or EULAR remission (p = 0.540 and p = 0.957, respectively). Logistic regression analysis showed no relationship of BMI with EULAR clinical responses (p = 0.093 for good response and p = 0.878 for EULAR remission). This study reveals that BMI is not a predictive factor of clinical response to bDMARDs in patients with RA.

  18. Resting state functional connectivity predicts neurofeedback response

    PubMed Central

    Scheinost, Dustin; Stoica, Teodora; Wasylink, Suzanne; Gruner, Patricia; Saksa, John; Pittenger, Christopher; Hampson, Michelle

    2014-01-01

    Tailoring treatments to the specific needs and biology of individual patients—personalized medicine—requires delineation of reliable predictors of response. Unfortunately, these have been slow to emerge, especially in neuropsychiatric disorders. We have recently described a real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback protocol that can reduce contamination-related anxiety, a prominent symptom of many cases of obsessive-compulsive disorder (OCD). Individual response to this intervention is variable. Here we used patterns of brain functional connectivity, as measured by baseline resting-state fMRI (rs-fMRI), to predict improvements in contamination anxiety after neurofeedback training. Activity of a region of the orbitofrontal cortex (OFC) and anterior prefrontal cortex, Brodmann area (BA) 10, associated with contamination anxiety in each subject was measured in real time and presented as a neurofeedback signal, permitting subjects to learn to modulate this target brain region. We have previously reported both enhanced OFC/BA 10 control and improved anxiety in a group of subclinically anxious subjects after neurofeedback. Five individuals with contamination-related OCD who underwent the same protocol also showed improved clinical symptomatology. In both groups, these behavioral improvements were strongly correlated with baseline whole-brain connectivity in the OFC/BA 10, computed from rs-fMRI collected several days prior to neurofeedback training. These pilot data suggest that rs-fMRI can be used to identify individuals likely to benefit from rt-fMRI neurofeedback training to control contamination anxiety. PMID:25309375

  19. Is the Ultimate Treatment Response Predictable with Early Response in Major Depressive Episode?

    PubMed Central

    ÇİFTÇİ, Aslı; ULAŞ, Halis; TOPUZOĞLU, Ahmet; TUNCA, Zeliha

    2016-01-01

    Introduction New evidence suggests that the efficacy of antidepressants occurs within the first weeks of treatment and this early response predicts the later response. The purpose of the present study was to investigate if the partial response in the first week predicts the response at the end of treatment in patients with major depressive disorder who are treated with either antidepressant medication or electroconvulsive therapy. Methods Inpatients from Dokuz Eylül University Hospital with a major depressive episode, treated with antidepressant medication (n=52) or electroconvulsive therapy (ECT) (n=48), were recruited for the study. The data were retrospectively collected to decide whether a 25% decrease in the Hamilton Depression Rating Scale (HDRS) score at the first week of treatment predicts a 50% decrease at the third week using validity analysis. In addition, the effects of socio-demographic and clinical variables on the treatment response were assessed. Results A 25% decrease in the HDRS score in the first week of treatment predicted a 50% decrease in the HDRS score in the third week with a 78.3% positive predictive value, 62.1% negative predictive value, 62.1% sensitivity, and 78.3% specificity for antidepressant medications and an 88% positive predictive value, 52.2% negative predictive value, 66.7% sensitivity, and 80% specificity for ECT. The number of previous hospitalizations, comorbid medical illnesses, number of depressive episodes, duration of illness, and duration of the current episode were related to the treatment response. Conclusion Treatment response in the first week predicted the response in the third week with a high specificity and a high positive predictive value. Close monitoring of the response from the first week of treatment may thus help the clinician to predict the subsequent response. PMID:28373802

  20. On understanding and predicting groundwater response time.

    PubMed

    Sophocleous, Marios

    2012-01-01

    An aquifer system, when perturbed, has a tendency to evolve to a new equilibrium, a process that can take from just a few seconds to possibly millions of years. The time scale on which a system adjusts to a new equilibrium is often referred to as "response time" or "lag time." Because groundwater response time affects the physical and economic viability of various management options in a basin, natural resource managers are increasingly interested in incorporating it into policy. However, the processes of how groundwater responds to land-use change are not well understood, making it difficult to predict the timing of groundwater response to such change. The difficulty in estimating groundwater response time is further compounded because the data needed to quantify this process are not usually readily available. This article synthesizes disparate pieces of information on aquifer response times into a relatively brief but hopefully comprehensive review that the community of water professionals can use to better assess the impact of aquifer response time in future groundwater management investigations. A brief exposition on dimensional/scaling analysis is presented first, followed by an overview of aquifer response time for simplified aquifer systems. The aquifer response time is considered first from a water-quantity viewpoint and later expanded to incorporate groundwater age and water-quality aspects. Monitoring programs today, as well as water policies and regulations, should address this issue of aquifer response time so that more realistic management expectations can be reached.

  1. Host genetics predict clinical deterioration in HCV-related cirrhosis.

    PubMed

    King, Lindsay Y; Johnson, Kara B; Zheng, Hui; Wei, Lan; Gudewicz, Thomas; Hoshida, Yujin; Corey, Kathleen E; Ajayi, Tokunbo; Ufere, Nneka; Baumert, Thomas F; Chan, Andrew T; Tanabe, Kenneth K; Fuchs, Bryan C; Chung, Raymond T

    2014-01-01

    Single nucleotide polymorphisms (SNPs) in the epidermal growth factor (EGF, rs4444903), patatin-like phospholipase domain-containing protein 3 (PNPLA3, rs738409) genes, and near the interleukin-28B (IL28B, rs12979860) gene are linked to treatment response, fibrosis, and hepatocellular carcinoma (HCC) in chronic hepatitis C. Whether these SNPs independently or in combination predict clinical deterioration in hepatitis C virus (HCV)-related cirrhosis is unknown. We genotyped SNPs in EGF, PNPLA3, and IL28B from liver tissue from 169 patients with biopsy-proven HCV cirrhosis. We estimated risk of clinical deterioration, defined as development of ascites, encephalopathy, variceal hemorrhage, HCC, or liver-related death using Cox proportional hazards modeling. During a median follow-up of 6.6 years, 66 of 169 patients experienced clinical deterioration. EGF non-AA, PNPLA3 non-CC, and IL28B non-CC genotypes were each associated with increased risk of clinical deterioration in age, sex, and race-adjusted analysis. Only EGF non-AA genotype was independently associated with increased risk of clinical deterioration (hazard ratio [HR] 2.87; 95% confidence interval [CI] 1.31-6.25) after additionally adjusting for bilirubin, albumin, and platelets. Compared to subjects who had 0-1 unfavorable genotypes, the HR for clinical deterioration was 1.79 (95%CI 0.96-3.35) for 2 unfavorable genotypes and 4.03 (95%CI 2.13-7.62) for unfavorable genotypes for all three loci (Ptrend<0.0001). In conclusion, among HCV cirrhotics, EGF non-AA genotype is independently associated with increased risk for clinical deterioration. Specific PNPLA3 and IL28B genotypes also appear to be associated with clinical deterioration. These SNPs have potential to identify patients with HCV-related cirrhosis who require more intensive monitoring for decompensation or future therapies preventing disease progression.

  2. Host Genetics Predict Clinical Deterioration in HCV-Related Cirrhosis

    PubMed Central

    King, Lindsay Y.; Johnson, Kara B.; Zheng, Hui; Wei, Lan; Gudewicz, Thomas; Hoshida, Yujin; Corey, Kathleen E.; Ajayi, Tokunbo; Ufere, Nneka; Baumert, Thomas F.; Chan, Andrew T.; Tanabe, Kenneth K.; Fuchs, Bryan C.; Chung, Raymond T.

    2014-01-01

    Single nucleotide polymorphisms (SNPs) in the epidermal growth factor (EGF, rs4444903), patatin-like phospholipase domain-containing protein 3 (PNPLA3, rs738409) genes, and near the interleukin-28B (IL28B, rs12979860) gene are linked to treatment response, fibrosis, and hepatocellular carcinoma (HCC) in chronic hepatitis C. Whether these SNPs independently or in combination predict clinical deterioration in hepatitis C virus (HCV)-related cirrhosis is unknown. We genotyped SNPs in EGF, PNPLA3, and IL28B from liver tissue from 169 patients with biopsy-proven HCV cirrhosis. We estimated risk of clinical deterioration, defined as development of ascites, encephalopathy, variceal hemorrhage, HCC, or liver-related death using Cox proportional hazards modeling. During a median follow-up of 6.6 years, 66 of 169 patients experienced clinical deterioration. EGF non-AA, PNPLA3 non-CC, and IL28B non-CC genotypes were each associated with increased risk of clinical deterioration in age, sex, and race-adjusted analysis. Only EGF non-AA genotype was independently associated with increased risk of clinical deterioration (hazard ratio [HR] 2.87; 95% confidence interval [CI] 1.31–6.25) after additionally adjusting for bilirubin, albumin, and platelets. Compared to subjects who had 0–1 unfavorable genotypes, the HR for clinical deterioration was 1.79 (95%CI 0.96–3.35) for 2 unfavorable genotypes and 4.03 (95%CI 2.13–7.62) for unfavorable genotypes for all three loci (Ptrend<0.0001). In conclusion, among HCV cirrhotics, EGF non-AA genotype is independently associated with increased risk for clinical deterioration. Specific PNPLA3 and IL28B genotypes also appear to be associated with clinical deterioration. These SNPs have potential to identify patients with HCV-related cirrhosis who require more intensive monitoring for decompensation or future therapies preventing disease progression. PMID:25504078

  3. Electronic clinical predictive thermometer using logarithm for temperature prediction

    NASA Technical Reports Server (NTRS)

    Cambridge, Vivien J. (Inventor); Koger, Thomas L. (Inventor); Nail, William L. (Inventor); Diaz, Patrick (Inventor)

    1998-01-01

    A thermometer that rapidly predicts body temperature based on the temperature signals received from a temperature sensing probe when it comes into contact with the body. The logarithms of the differences between the temperature signals in a selected time frame are determined. A line is fit through the logarithms and the slope of the line is used as a system time constant in predicting the final temperature of the body. The time constant in conjunction with predetermined additional constants are used to compute the predicted temperature. Data quality in the time frame is monitored and if unacceptable, a different time frame of temperature signals is selected for use in prediction. The processor switches to a monitor mode if data quality over a limited number of time frames is unacceptable. Determining the start time on which the measurement time frame for prediction is based is performed by summing the second derivatives of temperature signals over time frames. When the sum of second derivatives in a particular time frame exceeds a threshold, the start time is established.

  4. Mouse models of human AML accurately predict chemotherapy response

    PubMed Central

    Zuber, Johannes; Radtke, Ina; Pardee, Timothy S.; Zhao, Zhen; Rappaport, Amy R.; Luo, Weijun; McCurrach, Mila E.; Yang, Miao-Miao; Dolan, M. Eileen; Kogan, Scott C.; Downing, James R.; Lowe, Scott W.

    2009-01-01

    The genetic heterogeneity of cancer influences the trajectory of tumor progression and may underlie clinical variation in therapy response. To model such heterogeneity, we produced genetically and pathologically accurate mouse models of common forms of human acute myeloid leukemia (AML) and developed methods to mimic standard induction chemotherapy and efficiently monitor therapy response. We see that murine AMLs harboring two common human AML genotypes show remarkably diverse responses to conventional therapy that mirror clinical experience. Specifically, murine leukemias expressing the AML1/ETO fusion oncoprotein, associated with a favorable prognosis in patients, show a dramatic response to induction chemotherapy owing to robust activation of the p53 tumor suppressor network. Conversely, murine leukemias expressing MLL fusion proteins, associated with a dismal prognosis in patients, are drug-resistant due to an attenuated p53 response. Our studies highlight the importance of genetic information in guiding the treatment of human AML, functionally establish the p53 network as a central determinant of chemotherapy response in AML, and demonstrate that genetically engineered mouse models of human cancer can accurately predict therapy response in patients. PMID:19339691

  5. Mouse models of human AML accurately predict chemotherapy response.

    PubMed

    Zuber, Johannes; Radtke, Ina; Pardee, Timothy S; Zhao, Zhen; Rappaport, Amy R; Luo, Weijun; McCurrach, Mila E; Yang, Miao-Miao; Dolan, M Eileen; Kogan, Scott C; Downing, James R; Lowe, Scott W

    2009-04-01

    The genetic heterogeneity of cancer influences the trajectory of tumor progression and may underlie clinical variation in therapy response. To model such heterogeneity, we produced genetically and pathologically accurate mouse models of common forms of human acute myeloid leukemia (AML) and developed methods to mimic standard induction chemotherapy and efficiently monitor therapy response. We see that murine AMLs harboring two common human AML genotypes show remarkably diverse responses to conventional therapy that mirror clinical experience. Specifically, murine leukemias expressing the AML1/ETO fusion oncoprotein, associated with a favorable prognosis in patients, show a dramatic response to induction chemotherapy owing to robust activation of the p53 tumor suppressor network. Conversely, murine leukemias expressing MLL fusion proteins, associated with a dismal prognosis in patients, are drug-resistant due to an attenuated p53 response. Our studies highlight the importance of genetic information in guiding the treatment of human AML, functionally establish the p53 network as a central determinant of chemotherapy response in AML, and demonstrate that genetically engineered mouse models of human cancer can accurately predict therapy response in patients.

  6. Prediction of Psilocybin Response in Healthy Volunteers

    PubMed Central

    Studerus, Erich; Gamma, Alex; Kometer, Michael; Vollenweider, Franz X.

    2012-01-01

    Responses to hallucinogenic drugs, such as psilocybin, are believed to be critically dependent on the user's personality, current mood state, drug pre-experiences, expectancies, and social and environmental variables. However, little is known about the order of importance of these variables and their effect sizes in comparison to drug dose. Hence, this study investigated the effects of 24 predictor variables, including age, sex, education, personality traits, drug pre-experience, mental state before drug intake, experimental setting, and drug dose on the acute response to psilocybin. The analysis was based on the pooled data of 23 controlled experimental studies involving 409 psilocybin administrations to 261 healthy volunteers. Multiple linear mixed effects models were fitted for each of 15 response variables. Although drug dose was clearly the most important predictor for all measured response variables, several non-pharmacological variables significantly contributed to the effects of psilocybin. Specifically, having a high score in the personality trait of Absorption, being in an emotionally excitable and active state immediately before drug intake, and having experienced few psychological problems in past weeks were most strongly associated with pleasant and mystical-type experiences, whereas high Emotional Excitability, low age, and an experimental setting involving positron emission tomography most strongly predicted unpleasant and/or anxious reactions to psilocybin. The results confirm that non-pharmacological variables play an important role in the effects of psilocybin. PMID:22363492

  7. Prediction of psilocybin response in healthy volunteers.

    PubMed

    Studerus, Erich; Gamma, Alex; Kometer, Michael; Vollenweider, Franz X

    2012-01-01

    Responses to hallucinogenic drugs, such as psilocybin, are believed to be critically dependent on the user's personality, current mood state, drug pre-experiences, expectancies, and social and environmental variables. However, little is known about the order of importance of these variables and their effect sizes in comparison to drug dose. Hence, this study investigated the effects of 24 predictor variables, including age, sex, education, personality traits, drug pre-experience, mental state before drug intake, experimental setting, and drug dose on the acute response to psilocybin. The analysis was based on the pooled data of 23 controlled experimental studies involving 409 psilocybin administrations to 261 healthy volunteers. Multiple linear mixed effects models were fitted for each of 15 response variables. Although drug dose was clearly the most important predictor for all measured response variables, several non-pharmacological variables significantly contributed to the effects of psilocybin. Specifically, having a high score in the personality trait of Absorption, being in an emotionally excitable and active state immediately before drug intake, and having experienced few psychological problems in past weeks were most strongly associated with pleasant and mystical-type experiences, whereas high Emotional Excitability, low age, and an experimental setting involving positron emission tomography most strongly predicted unpleasant and/or anxious reactions to psilocybin. The results confirm that non-pharmacological variables play an important role in the effects of psilocybin.

  8. Predicting Flutter and Forced Response in Turbomachinery

    NASA Technical Reports Server (NTRS)

    VanZante, Dale E.; Adamczyk, John J.; Srivastava, Rakesh; Bakhle, Milind A.; Shabbir, Aamir; Chen, Jen-Ping; Janus, J. Mark; To, Wai-Ming; Barter, John

    2005-01-01

    TURBO-AE is a computer code that enables detailed, high-fidelity modeling of aeroelastic and unsteady aerodynamic characteristics for prediction of flutter, forced response, and blade-row interaction effects in turbomachinery. Flow regimes that can be modeled include subsonic, transonic, and supersonic, with attached and/or separated flow fields. The three-dimensional Reynolds-averaged Navier-Stokes equations are solved numerically to obtain extremely accurate descriptions of unsteady flow fields in multistage turbomachinery configurations. Blade vibration is simulated by use of a dynamic-grid-deformation technique to calculate the energy exchange for determining the aerodynamic damping of vibrations of blades. The aerodynamic damping can be used to assess the stability of a blade row. TURBO-AE also calculates the unsteady blade loading attributable to such external sources of excitation as incoming gusts and blade-row interactions. These blade loadings, along with aerodynamic damping, are used to calculate the forced responses of blades to predict their fatigue lives. Phase-lagged boundary conditions based on the direct-store method are used to calculate nonzero interblade phase-angle oscillations; this practice eliminates the need to model multiple blade passages, and, hence, enables large savings in computational resources.

  9. Predicting allopurinol response in patients with gout

    PubMed Central

    Duffull, Stephen B.; Merriman, Tony R.; Dalbeth, Nicola; Barclay, Murray L.; Stamp, Lisa K.

    2015-01-01

    Aims The primary aim of this research was to predict the allopurinol maintenance doses required to achieve the target plasma urate of ≤0.36 mmol l−1. Methods A population analysis was conducted in nonmem using oxypurinol and urate plasma concentrations from 133 gout patients. Maintenance dose predictions to achieve the recommended plasma urate target were generated. Results The urate response was best described by a direct effects model. Renal function, diuretic use and body size were found to be significant covariates. Dose requirements increased approximately 2‐fold over a 3‐fold range of total body weight and were 1.25–2 fold higher in those taking diuretics. Renal function had only a modest impact on dose requirements. Conclusions Contrary to current guidelines, the model predicted that allopurinol dose requirements were determined primarily by differences in body size and diuretic use. A revised guide to the likely allopurinol doses to achieve the target plasma urate concentration is proposed. PMID:26451524

  10. Clinical predictors of therapeutic response to antipsychotics in schizophrenia.

    PubMed

    Carbon, Maren; Correll, Christoph U

    2014-12-01

    The search for clinical outcome predictors for schizophrenia is as old as the field of psychiatry. However, despite a wealth of large, longitudinal studies into prognostic factors, only very few clinically useful outcome predictors have been identified. The goal of future treatment is to either affect modifiable risk factors, or use nonmodifiable factors to parse patients into therapeutically meaningful subgroups. Most clinical outcome predictors are nonspecific and/or nonmodifiable. Nonmodifiable predictors for poor odds of remission include male sex, younger age at disease onset, poor premorbid adjustment, and severe baseline psychopathology. Modifiable risk factors for poor therapeutic outcomes that clinicians can act upon include longer duration of untreated illness, nonadherence to antipsychotics, comorbidities (especially substance-use disorders), lack of early antipsychotic response, and lack of improvement with non-clozapine antipsychotics, predicting clozapine response. It is hoped that this limited capacity for prediction will improve as pathophysiological understanding increases and/or new treatments for specific aspects of schizophrenia become available.

  11. Predictability of Biogeochemical Responses in Engineered Watersheds

    NASA Astrophysics Data System (ADS)

    Yaeger, M. A.; Voepel, H. E.; Basu, N. B.; Rao, P. C.; Donner, S. D.; Packman, A. I.

    2009-12-01

    Examining the impacts of large-scale human modifications of watersheds (e.g., land-use intensification for food production; hydrologic modification through extensive tile-drainage, etc.) on the hydrologic and biogeochemical responses, and ecological impacts at various scales has been the focus of monitoring and modeling studies over the past two decades. Complex interactions between hydrology and biogeochemistry and the need to predict responses across scales has led to the development of detailed process-based models that are computationally intensive and calibration-dependent. Our overall hypothesis is that human modifications and intensive management of these watersheds have led to more predictable responses, which are typical of engineered, less-complex systems rather than natural, complex systems. We examined monitoring data for nitrogen, phosphorous, silica and chloride in 25 large watersheds (10,000 km2 to 500,000 km2) in the Mississippi River Basin. This sparse dataset was complemented with nitrogen cycling and hydrology output from a whole-basin terrestrial and aquatic modeling system (IBIS-THMB). These sub-basins have diverse land uses, although agriculture still dominates (from ~30% to ~80%). Despite diversity in soils, geology, rainfall patterns, and land use, a linear relationship was observed between the annual cumulative discharge (Q; m3/yr) and the measured nitrate load (L; kg/yr). The slopes of these linear L-Q plots represent the flow-weighted annual average concentrations (Cf), and a linear L-Q relationship indicates an apparent “chemostatic” response of these large watersheds. Analysis of Mississippi River monitoring data for nitrate and IBIS-THMB simulations revealed that Cf is a strong function of land-use (eg, percent corn) that defines the chemical input function. The scatter around the L-Q plots was small for “endogenous” (generated from internal sources) solutes (eg, silica), intermediate for “hybrid” (contributions from both

  12. Response Predicting LTCC Firing Shrinkage: A Response Surface Analysis Study

    SciTech Connect

    Girardi, Michael; Barner, Gregg; Lopez, Cristie; Duncan, Brent; Zawicki, Larry

    2009-02-25

    The Low Temperature Cofired Ceramic (LTCC) technology is used in a variety of applications including military/space electronics, wireless communication, MEMS, medical and automotive electronics. The use of LTCC is growing due to the low cost of investment, short development time, good electrical and mechanical properties, high reliability, and flexibility in design integration (3 dimensional (3D) microstructures with cavities are possible)). The dimensional accuracy of the resulting x/y shrinkage of LTCC substrates is responsible for component assembly problems with the tolerance effect that increases in relation to the substrate size. Response Surface Analysis was used to predict product shrinkage based on specific process inputs (metal loading, layer count, lamination pressure, and tape thickness) with the ultimate goal to optimize manufacturing outputs (NC files, stencils, and screens) in achieving the final product design the first time. Three (3) regression models were developed for the DuPont 951 tape system with DuPont 5734 gold metallization based on green tape thickness.

  13. Predicting response to antimicrobial therapy in children with acute sinusitis

    PubMed Central

    Shaikh, Nader; Wald, Ellen R.; Jeong, Jong H.; Kurs-Lasky, Marcia; Bowen, A’Delbert; Flom, Lynda L.; Hoberman, Alejandro

    2014-01-01

    Objective To determine prognostic factors that independently predict response to antimicrobial therapy in children with acute sinusitis. Study design 206 children meeting a priori clinical criteria for acute sinusitis who were given antimicrobial therapy by their primary care provider were included. The severity of symptoms in the 8 to 12 days after treatment was initiated was followed using a validated scale. We examined the univariate and multivariate association between factors present at the time of diagnosis (symptoms, signs, nasopharyngeal culture result, radiograph results) and time to resolution of symptoms. This study was conducted 8 to 10 years after 7-valent pneumococcal conjugate vaccination was introduced, but before introduction of the 13-valent pneumococcal conjugate vaccination. Results Children with proven nasopharyngeal colonization with Streptococcus pneumoniae improved more rapidly (6.5 vs. 8.5 median days to symptom resolution) than those who were not colonized with S. pneumoniae. Age and radiograph findings did not predict time to symptom resolution. Conclusions In children with acute sinusitis, proven nasopharyngeal colonization with S. pneumoniae at presentation independently predicted time to symptom resolution. Future randomized, placebo-controlled trials could investigate the usefulness of testing for the presence of nasopharyngeal pathogens as a predictor of response to treatment. PMID:24367985

  14. [Clinical Responses To Infants and Families.

    ERIC Educational Resources Information Center

    Fenichel, Emily, Ed.

    1995-01-01

    This journal issue focuses on family service clinical responses to infants and families. In "The Therapeutic Relationship as Human Connectedness," Jeree H. Pawl stresses the importance of caregivers creating in children the sense and experience of human connectedness that arises from the feeling of existing in the mind of someone…

  15. Human models of pain for the prediction of clinical analgesia.

    PubMed

    Lötsch, Jörn; Oertel, Bruno G; Ultsch, Alfred

    2014-10-01

    Human experimental pain models are widely used to study drug effects under controlled conditions. However, efforts to improve both animal and human experimental model selection, on the basis of increased understanding of the underlying pathophysiological pain mechanisms, have been disappointing, with poor translation of results to clinical analgesia. We have developed an alternative approach to the selection of suitable pain models that can correctly predict drug efficacy in particular clinical settings. This is based on the analysis of successful or unsuccessful empirical prediction of clinical analgesia using experimental pain models. We analyzed statistically the distribution of published mutual agreements or disagreements between drug efficacy in experimental and clinical pain settings. Significance limits were derived by random permutations of agreements. We found that a limited subset of pain models predicts a large number of clinically relevant pain settings, including efficacy against neuropathic pain for which novel analgesics are particularly needed. Thus, based on empirical evidence of agreement between drugs for their efficacy in experimental and clinical pain settings, it is possible to identify pain models that reliably predict clinical analgesic drug efficacy in cost-effective experimental settings.

  16. Target Predictability, Sustained Attention, and Response Inhibition

    ERIC Educational Resources Information Center

    Carter, Leonie; Russell, Paul N.; Helton, William S.

    2013-01-01

    We examined whether the sustained attention to response task is a better measure of response inhibition or sustained attention. Participants performed a number detection task for 37.3 min using either a Sustained Attention to Response Task (SART; high Go low No-Go) or a more traditionally formatted vigilance task (TFT; high No-Go low Go) response…

  17. DYNAMICALLY EVOLVING CLINICAL PRACTICES AND IMPLICATIONS FOR PREDICTING MEDICAL DECISIONS

    PubMed Central

    CHEN, JONATHAN H; GOLDSTEIN, MARY K; ASCH, STEVEN M; ALTMAN, RUSS B

    2015-01-01

    Automatically data-mining clinical practice patterns from electronic health records (EHR) can enable prediction of future practices as a form of clinical decision support (CDS). Our objective is to determine the stability of learned clinical practice patterns over time and what implication this has when using varying longitudinal historical data sources towards predicting future decisions. We trained an association rule engine for clinical orders (e.g., labs, imaging, medications) using structured inpatient data from a tertiary academic hospital. Comparing top order associations per admission diagnosis from training data in 2009 vs. 2012, we find practice variability from unstable diagnoses with rank biased overlap (RBO)<0.35 (e.g., pneumonia) to stable admissions for planned procedures (e.g., chemotherapy, surgery) with comparatively high RBO>0.6. Predicting admission orders for future (2013) patients with associations trained on recent (2012) vs. older (2009) data improved accuracy evaluated by area under the receiver operating characteristic curve (ROC-AUC) 0.89 to 0.92, precision at ten (positive predictive value of the top ten predictions against actual orders) 30% to 37%, and weighted recall (sensitivity) at ten 2.4% to 13%, (P<10−10). Training with more longitudinal data (2009-2012) was no better than only using recent (2012) data. Secular trends in practice patterns likely explain why smaller but more recent training data is more accurate at predicting future practices. PMID:26776186

  18. Clinical time series prediction: towards a hierarchical dynamical system framework

    PubMed Central

    Liu, Zitao; Hauskrecht, Milos

    2014-01-01

    Objective Developing machine learning and data mining algorithms for building temporal models of clinical time series is important for understanding of the patient condition, the dynamics of a disease, effect of various patient management interventions and clinical decision making. In this work, we propose and develop a novel hierarchical framework for modeling clinical time series data of varied length and with irregularly sampled observations. Materials and methods Our hierarchical dynamical system framework for modeling clinical time series combines advantages of the two temporal modeling approaches: the linear dynamical system and the Gaussian process. We model the irregularly sampled clinical time series by using multiple Gaussian process sequences in the lower level of our hierarchical framework and capture the transitions between Gaussian processes by utilizing the linear dynamical system. The experiments are conducted on the complete blood count (CBC) panel data of 1000 post-surgical cardiac patients during their hospitalization. Our framework is evaluated and compared to multiple baseline approaches in terms of the mean absolute prediction error and the absolute percentage error. Results We tested our framework by first learning the time series model from data for the patient in the training set, and then applying the model in order to predict future time series values on the patients in the test set. We show that our model outperforms multiple existing models in terms of its predictive accuracy. Our method achieved a 3.13% average prediction accuracy improvement on ten CBC lab time series when it was compared against the best performing baseline. A 5.25% average accuracy improvement was observed when only short-term predictions were considered. Conclusion A new hierarchical dynamical system framework that lets us model irregularly sampled time series data is a promising new direction for modeling clinical time series and for improving their predictive

  19. Tumor Endothelial Inflammation Predicts Clinical Outcome in Diverse Human Cancers

    PubMed Central

    Filippo, Matthew; Labay, Edwardine; Beckett, Michael A.; Mauceri, Helena J.; Liang, Hua; Darga, Thomas E.; Perakis, Samantha; Khan, Sajid A.; Sutton, Harold G.; Zhang, Wei; Khodarev, Nikolai N.; Garcia, Joe G. N.; Weichselbaum, Ralph R.

    2012-01-01

    Background Vascular endothelial cells contribute to the pathogenesis of numerous human diseases by actively regulating the stromal inflammatory response; however, little is known regarding the role of endothelial inflammation in the growth of human tumors and its influence on the prognosis of human cancers. Methods Using an experimental model of tumor necrosis factor-alpha (TNF-α)-mediated inflammation, we characterized inflammatory gene expression in immunopurified tumor-associated endothelial cells. These genes formed the basis of a multivariate molecular predictor of overall survival that was trained and validated in four types of human cancer. Results We report that expression of experimentally derived tumor endothelial genes distinguished pathologic tissue specimens from normal controls in several human diseases associated with chronic inflammation. We trained these genes in human cancer datasets and defined a six-gene inflammatory signature that predicted significantly reduced overall survival in breast cancer, colon cancer, lung cancer, and glioma. This endothelial-derived signature predicted outcome independently of, but cooperatively with, standard clinical and pathological prognostic factors. Consistent with these findings, conditioned culture media from human endothelial cells stimulated by pro-inflammatory cytokines accelerated the growth of human colon and breast tumors in immunodeficient mice as compared with conditioned media from untreated endothelial cells. Conclusions This study provides the first prognostic cancer gene signature derived from an experimental model of tumor-associated endothelial inflammation. These findings support the notion that activation of inflammatory pathways in non-malignant tumor-infiltrating endothelial cells contributes to tumor growth and progression in multiple human cancers. Importantly, these results identify endothelial-derived factors that could serve as potential targets for therapy in diverse human cancers

  20. Neuropsychological impairments predict the clinical course in schizophrenia.

    PubMed

    Wölwer, Wolfgang; Brinkmeyer, Jürgen; Riesbeck, Mathias; Freimüller, Lena; Klimke, Ansgar; Wagner, Michael; Möller, Hans-Jürgen; Klingberg, Stefan; Gaebel, Wolfgang

    2008-11-01

    To add to the open question whether cognitive impairments predict clinical outcome in schizophrenia, a sample of 125 first episode patients was assessed at the onset and over one year of controlled long-term treatment within a study of the German Research Network on Schizophrenia. No relapse according to predefined criteria occurred within the first year, but a total of 29 patients fulfilled post-hoc criteria of "clinical deterioration". Impairments in cognitive functioning assessed by the Trail-Making Test B at the onset of long-term treatment differentiated between patients with vs. without later clinical deterioration and proved to be a significant predictor of the clinical course in a regression analysis outperforming initial clinical status as predictor. However, low sensitivity (72%) and specificity (51%) limit possibilities of a transfer to individual predictions. As a linear combination of neuropsychological and psychopathological variables obtained highest predictive validity, such a combination may improve the prediction of the course of schizophrenic disorders and may ultimately lead to a more efficient and comprehensive treatment planning.

  1. Statistical energy analysis response prediction methods for structural systems

    NASA Technical Reports Server (NTRS)

    Davis, R. F.

    1979-01-01

    The results of an effort to document methods for accomplishing response predictions for commonly encountered aerospace structural configurations is presented. Application of these methods to specified aerospace structure to provide sample analyses is included. An applications manual, with the structural analyses appended as example problems is given. Comparisons of the response predictions with measured data are provided for three of the example problems.

  2. Shunting normal pressure hydrocephalus: the predictive value of combined clinical and CT data.

    PubMed Central

    Vanneste, J; Augustijn, P; Tan, W F; Dirven, C

    1993-01-01

    The value of an ordinal global scale derived from combined clinical and CT data (clin/CT scale) to predict the clinical outcome in 112 patients shunted for presumed normal pressure hydrocephalus (NPH) was analysed. The clinical data were retrospectively collected, all CT scans were re-evaluated, and the clin/CT scale was determined blind to the results of further ancillary tests and to the post-surgical outcome. The scale ranked three classes of prediction: on the basis of clinical and CT characteristics, improvement after shunting was probable, possible, or improbable. The predictive value of the clin/CT scale for the subgroup of communicating NPH was established for two different strategies, depending on the strictness of selection criteria for shunting. In the subgroup of patients with presumed communicating NPH, the prevalence of shunt responsiveness was 29%; the best strategy was to shunt only patients with probable shunt-responsive NPH: the sensitivity was 0.54, the specificity 0.84, and the predictive accuracy 0.75, with a limited number of ineffective shunts (11%) and missed improvements (13%). The study illustrates its need to assess the pre-test probability of NPH based on combined clinical and CT data, before establishing the clinical usefulness of an ancillary test. PMID:8459240

  3. Atypical clinical response patterns to ipilimumab.

    PubMed

    Ledezma, Blanca; Binder, Sandra; Hamid, Omid

    2011-08-01

    Patients with advanced melanoma have few treatment options, and survival is poor. However, improved understanding of how the immune system interacts with cancer has led to the development of novel therapies. Ipilimumab is a monoclonal antibody that inhibits cytotoxic T-lymphocyte antigen-4 (CTLA-4), a key negative regulator of host T-cell responses. This article presents cases of patients receiving ipilimumab in clinical trials along with a discussion of their significance and relevance to nursing practice. The patients showed different response patterns to ipilimumab and also had various typical immune-related adverse events (irAEs), which were managed successfully. The atypical response patterns produced by ipilimumab likely reflect its mechanism of action, which requires time for the immune system to mount an effective antitumor response. Meanwhile, lesions may appear to enlarge as a consequence of enhanced T-cell infiltration, although this may not necessarily be true disease progression. Patients receiving ipilimumab may respond very differently compared to how they might react to chemotherapy. Responses can take weeks or months to develop; therefore, clinicians should not terminate treatment prematurely, providing the patient's condition allows for continuation. Early recognition of irAEs combined with prompt management will ensure that events are more likely to resolve without serious consequences.

  4. Clinical and Immunological Responses in Ocular Demodecosis

    PubMed Central

    Kim, Jae Hoon; Chun, Yeoun Sook

    2011-01-01

    The purpose of this study was to investigate clinical and immunological responses to Demodex on the ocular surface. Thirteen eyes in 10 patients with Demodex blepharitis and chronic ocular surface disorders were included in this study and treated by lid scrubbing with tea tree oil for the eradication of Demodex. We evaluated ocular surface manifestations and Demodex counts, and analyzed IL-1β, IL-5, IL-7, IL-12, IL-13, IL-17, granulocyte colony-stimulating factor, and macrophage inflammatory protein-1β in tear samples before and after the treatment. All patients exhibited ocular surface manifestations including corneal nodular opacity, peripheral corneal vascularization, refractory corneal erosion and infiltration, or chronic conjunctival inflammatory signs before treatment. After treatment, Demodex was nearly eradicated, tear concentrations of IL-1β and IL-17 were significantly reduced and substantial clinical improvement was observed in all patients. In conclusion, we believe that Demodex plays an aggravating role in inflammatory ocular surface disorders. PMID:21935281

  5. Clinical and immunological responses in ocular demodecosis.

    PubMed

    Kim, Jae Hoon; Chun, Yeoun Sook; Kim, Jae Chan

    2011-09-01

    The purpose of this study was to investigate clinical and immunological responses to Demodex on the ocular surface. Thirteen eyes in 10 patients with Demodex blepharitis and chronic ocular surface disorders were included in this study and treated by lid scrubbing with tea tree oil for the eradication of Demodex. We evaluated ocular surface manifestations and Demodex counts, and analyzed IL-1β, IL-5, IL-7, IL-12, IL-13, IL-17, granulocyte colony-stimulating factor, and macrophage inflammatory protein-1β in tear samples before and after the treatment. All patients exhibited ocular surface manifestations including corneal nodular opacity, peripheral corneal vascularization, refractory corneal erosion and infiltration, or chronic conjunctival inflammatory signs before treatment. After treatment, Demodex was nearly eradicated, tear concentrations of IL-1β and IL-17 were significantly reduced and substantial clinical improvement was observed in all patients. In conclusion, we believe that Demodex plays an aggravating role in inflammatory ocular surface disorders.

  6. Integrative neuroscience approach to predict ADHD stimulant response.

    PubMed

    Hermens, Daniel F; Rowe, Donald L; Gordon, Evian; Williams, Leanne M

    2006-05-01

    Despite high rates of prescription, little is known about the long-term consequences of stimulant medication therapy for attention-deficit hyperactivity disorder (ADHD) sufferers. Historically, the clinical use of stimulants for ADHD has been based on trial and error before optimal therapy is reached. Concurrently, scientific research on the mechanism of action of stimulants has influenced neurobiological models of ADHD, but has not always informed their prescription. Whilst the two main stimulant types (methylphenidate and dexamphetamine) have numerous similarities, they also differ (slightly) in mechanism and possibly individual response. A further issue relates to differences in cost and availability compounded by the expectation for stimulants to be effective in ameliorating a broad spectrum of ADHD-related symptoms. Thus, there is an increasing need for treating clinicians to prescribe not only the most effective drug, but also the most appropriate dose with the associated release mechanism and schedule for each ADHD patient presented. In this regard, the field is witnessing an emergence of the personalized medicine approach to ADHD, in which treatment decisions are tailored to each individual. This shift requires a new approach to research into treatment response prediction. Given the heterogeneity of ADHD, a profile of information may be required to capture the most sensitive predictors of treatment response in individuals. These profiles will also benefit from the integration of data from clinical rating scales with more direct measures of cognition and brain function. In conclusion, there is a need to establish a more robust normative framework as the baseline for treatment, as well as diagnostic decisions, and as discussed, the growth of integrated neuroscience databases will be important in this regard.

  7. [Application of three compartment model and response surface model to clinical anesthesia using Microsoft Excel].

    PubMed

    Abe, Eiji; Abe, Mari

    2011-08-01

    With the spread of total intravenous anesthesia, clinical pharmacology has become more important. We report Microsoft Excel file applying three compartment model and response surface model to clinical anesthesia. On the Microsoft Excel sheet, propofol, remifentanil and fentanyl effect-site concentrations are predicted (three compartment model), and probabilities of no response to prodding, shaking, surrogates of painful stimuli and laryngoscopy are calculated using predicted effect-site drug concentration. Time-dependent changes in these calculated values are shown graphically. Recent development in anesthetic drug interaction studies are remarkable, and its application to clinical anesthesia with this Excel file is simple and helpful for clinical anesthesia.

  8. Connecting clinical and actuarial prediction with rule-based methods.

    PubMed

    Fokkema, Marjolein; Smits, Niels; Kelderman, Henk; Penninx, Brenda W J H

    2015-06-01

    Meta-analyses comparing the accuracy of clinical versus actuarial prediction have shown actuarial methods to outperform clinical methods, on average. However, actuarial methods are still not widely used in clinical practice, and there has been a call for the development of actuarial prediction methods for clinical practice. We argue that rule-based methods may be more useful than the linear main effect models usually employed in prediction studies, from a data and decision analytic as well as a practical perspective. In addition, decision rules derived with rule-based methods can be represented as fast and frugal trees, which, unlike main effects models, can be used in a sequential fashion, reducing the number of cues that have to be evaluated before making a prediction. We illustrate the usability of rule-based methods by applying RuleFit, an algorithm for deriving decision rules for classification and regression problems, to a dataset on prediction of the course of depressive and anxiety disorders from Penninx et al. (2011). The RuleFit algorithm provided a model consisting of 2 simple decision rules, requiring evaluation of only 2 to 4 cues. Predictive accuracy of the 2-rule model was very similar to that of a logistic regression model incorporating 20 predictor variables, originally applied to the dataset. In addition, the 2-rule model required, on average, evaluation of only 3 cues. Therefore, the RuleFit algorithm appears to be a promising method for creating decision tools that are less time consuming and easier to apply in psychological practice, and with accuracy comparable to traditional actuarial methods.

  9. [Clinical probability of PE: should we use a clinical prediction rule?].

    PubMed

    Le Gal, G; Righini, M; Perrier, A

    2008-12-01

    The determination of the clinical pretest probability using clinical prediction models is an important step in the assessment of patients with suspected pulmonary embolism (PE). It helps establish which test or sequence of tests can effectively corroborate or safely rule out PE. For example, it has been demonstrated that it is safe to withhold anticoagulant therapy in patients with negative d-dimer results and low pretest probability at initial presentation. Clinical probability will also increase the diagnostic yield of ventilation perfusion lung scan. Compared with clinical gestalt, clinical prediction rules provide a standardized and more reproducible estimate of a patient's probability of having a PE. Clinical prediction models combine aspects of the history and physical examination to categorize a patient's probability of having a disease. The models classify patients as having a low, moderate, or high likelihood of having PE. Clinical prediction models have been validated and are well established for the diagnosis of PE in symptomatic patients. They allow all physicians, whatever their expertise, to reliably determine the clinical pretest probability of PE, and thus safely manage their patients using diagnostic and therapeutic algorithms.

  10. Mass Law Predicts Hyperbolic Hypoxic Ventilatory Response

    NASA Astrophysics Data System (ADS)

    Severinghaus, John W.

    The hyperbolic hypoxic ventilatory response vs PaO2, HVRp, is interpreted as relecting a mass hyperbolic relationship of cytochrome PcO2 to cytochrome potential Ec, offset 32 torr by the constant diffusion gradient between arterial blood and cytochrome in CB at its constant metabolic rate dot VO_2 . Ec is taken to be a linear function of redox reduction and CB ventilatory drive. As Ec rises in hypoxia, the absolute potentials of each step in the citric acid cycle rises equally while the potential drop across each step remains constant because flux rate remains constant. A hypothetic HVRs ( dot VE vs SaO2) response curve computed from these assumptions is strikingly non linear. A hypothetic HVRp calculated from an assumed linear HVRs cannot be fit to the observed hyperbolic increase of ventilation in response to isocapnic hypoxia at PO2 less than 40 torr. The incompatibility of these results suggest that in future studies HVRs will not be found to be linear, especially below 80% SaO2 and HVRp will fail to be accurately hyperbolic.

  11. Multidimensional prediction of treatment response to antidepressants with cognitive control and functional MRI.

    PubMed

    Crane, Natania A; Jenkins, Lisanne M; Bhaumik, Runa; Dion, Catherine; Gowins, Jennifer R; Mickey, Brian J; Zubieta, Jon-Kar; Langenecker, Scott A

    2017-02-01

    Predicting treatment response for major depressive disorder can provide a tremendous benefit for our overstretched health care system by reducing number of treatments and time to remission, thereby decreasing morbidity. The present study used neural and performance predictors during a cognitive control task to predict treatment response (% change in Hamilton Depression Rating Scale pre- to post-treatment). Forty-nine individuals diagnosed with major depressive disorder were enrolled with intent to treat in the open-label study; 36 completed treatment, had useable data, and were included in most data analyses. Participants included in the data analysis sample received treatment with escitalopram (n = 22) or duloxetine (n = 14) for 10 weeks. Functional MRI and performance during a Parametric Go/No-go test were used to predict per cent reduction in Hamilton Depression Rating Scale scores after treatment. Haemodynamic response function-based contrasts and task-related independent components analysis (subset of sample: n = 29) were predictors. Independent components analysis component beta weights and haemodynamic response function modelling activation during Commission errors in the rostral and dorsal anterior cingulate, mid-cingulate, dorsomedial prefrontal cortex, and lateral orbital frontal cortex predicted treatment response. In addition, more commission errors on the task predicted better treatment response. Together in a regression model, independent component analysis, haemodynamic response function-modelled, and performance measures predicted treatment response with 90% accuracy (compared to 74% accuracy with clinical features alone), with 84% accuracy in 5-fold, leave-one-out cross-validation. Convergence between performance markers and functional magnetic resonance imaging, including novel independent component analysis techniques, achieved high accuracy in prediction of treatment response for major depressive disorder. The strong link to a task paradigm

  12. Prediction of Psychosis in Youth at High Clinical Risk

    PubMed Central

    Cannon, Tyrone D.; Cadenhead, Kristin; Cornblatt, Barbara; Woods, Scott W.; Addington, Jean; Walker, Elaine; Seidman, Larry J.; Perkins, Diana; Tsuang, Ming; McGlashan, Thomas; Heinssen, Robert

    2011-01-01

    Context Early detection and prospective evaluation of individuals who will develop schizophrenia or other psychotic disorders are critical to efforts to isolate mechanisms underlying psychosis onset and to the testing of preventive interventions, but existing risk prediction approaches have achieved only modest predictive accuracy. Objectives To determine the risk of conversion to psychosis and to evaluate a set of prediction algorithms maximizing positive predictive power in a clinical high-risk sample. Design, Setting, and Participants Longitudinal study with a 2½-year follow-up of 291 prospectively identified treatment-seeking patients meeting Structured Interview for Prodromal Syndromes criteria. The patients were recruited and underwent evaluation across 8 clinical research centers as part of the North American Prodrome Longitudinal Study. Main Outcome Measure Time to conversion to a fully psychotic form of mental illness. Results The risk of conversion to psychosis was 35%, with a decelerating rate of transition during the 2½-year follow-up. Five features assessed at baseline contributed uniquely to the prediction of psychosis: a genetic risk for schizophrenia with recent deterioration in functioning, higher levels of unusual thought content, higher levels of suspicion/paranoia, greater social impairment, and a history of substance abuse. Prediction algorithms combining 2 or 3 of these variables resulted in dramatic increases in positive predictive power (ie, 68%–80%) compared with the prodromal criteria alone. Conclusions These findings demonstrate that prospective ascertainment of individuals at risk for psychosis is feasible, with a level of predictive accuracy comparable to that in other areas of preventive medicine. They provide a benchmark for the rate and shape of the psychosis risk function against which standardized preventive intervention programs can be compared. PMID:18180426

  13. Long‐Term Post‐CABG Survival: Performance of Clinical Risk Models Versus Actuarial Predictions

    PubMed Central

    Carr, Brendan M.; Romeiser, Jamie; Ruan, Joyce; Gupta, Sandeep; Seifert, Frank C.; Zhu, Wei

    2015-01-01

    Abstract Background/aim Clinical risk models are commonly used to predict short‐term coronary artery bypass grafting (CABG) mortality but are less commonly used to predict long‐term mortality. The added value of long‐term mortality clinical risk models over traditional actuarial models has not been evaluated. To address this, the predictive performance of a long‐term clinical risk model was compared with that of an actuarial model to identify the clinical variable(s) most responsible for any differences observed. Methods Long‐term mortality for 1028 CABG patients was estimated using the Hannan New York State clinical risk model and an actuarial model (based on age, gender, and race/ethnicity). Vital status was assessed using the Social Security Death Index. Observed/expected (O/E) ratios were calculated, and the models' predictive performances were compared using a nested c‐index approach. Linear regression analyses identified the subgroup of risk factors driving the differences observed. Results Mortality rates were 3%, 9%, and 17% at one‐, three‐, and five years, respectively (median follow‐up: five years). The clinical risk model provided more accurate predictions. Greater divergence between model estimates occurred with increasing long‐term mortality risk, with baseline renal dysfunction identified as a particularly important driver of these differences. Conclusions Long‐term mortality clinical risk models provide enhanced predictive power compared to actuarial models. Using the Hannan risk model, a patient's long‐term mortality risk can be accurately assessed and subgroups of higher‐risk patients can be identified for enhanced follow‐up care. More research appears warranted to refine long‐term CABG clinical risk models. doi: 10.1111/jocs.12665 (J Card Surg 2016;31:23–30) PMID:26543019

  14. Modeling of Cancer Stem Cell State Transitions Predicts Therapeutic Response

    PubMed Central

    Sehl, Mary E.; Shimada, Miki; Landeros, Alfonso; Lange, Kenneth; Wicha, Max S.

    2015-01-01

    Cancer stem cells (CSCs) possess capacity to both self-renew and generate all cells within a tumor, and are thought to drive tumor recurrence. Targeting the stem cell niche to eradicate CSCs represents an important area of therapeutic development. The complex nature of many interacting elements of the stem cell niche, including both intracellular signals and microenvironmental growth factors and cytokines, creates a challenge in choosing which elements to target, alone or in combination. Stochastic stimulation techniques allow for the careful study of complex systems in biology and medicine and are ideal for the investigation of strategies aimed at CSC eradication. We present a mathematical model of the breast cancer stem cell (BCSC) niche to predict population dynamics during carcinogenesis and in response to treatment. Using data from cell line and mouse xenograft experiments, we estimate rates of interconversion between mesenchymal and epithelial states in BCSCs and find that EMT/MET transitions occur frequently. We examine bulk tumor growth dynamics in response to alterations in the rate of symmetric self-renewal of BCSCs and find that small changes in BCSC behavior can give rise to the Gompertzian growth pattern observed in breast tumors. Finally, we examine stochastic reaction kinetic simulations in which elements of the breast cancer stem cell niche are inhibited individually and in combination. We find that slowing self-renewal and disrupting the positive feedback loop between IL-6, Stat3 activation, and NF-κB signaling by simultaneous inhibition of IL-6 and HER2 is the most effective combination to eliminate both mesenchymal and epithelial populations of BCSCs. Predictions from our model and simulations show excellent agreement with experimental data showing the efficacy of combined HER2 and Il-6 blockade in reducing BCSC populations. Our findings will be directly examined in a planned clinical trial of combined HER2 and IL-6 targeted therapy in HER2

  15. A model for predicting lung cancer response to therapy

    SciTech Connect

    Seibert, Rebecca M. . E-mail: rseiber1@utk.edu; Ramsey, Chester R.; Hines, J. Wesley; Kupelian, Patrick A.; Langen, Katja M.; Meeks, Sanford L.; Scaperoth, Daniel D.

    2007-02-01

    Purpose: Volumetric computed tomography (CT) images acquired by image-guided radiation therapy (IGRT) systems can be used to measure tumor response over the course of treatment. Predictive adaptive therapy is a novel treatment technique that uses volumetric IGRT data to actively predict the future tumor response to therapy during the first few weeks of IGRT treatment. The goal of this study was to develop and test a model for predicting lung tumor response during IGRT treatment using serial megavoltage CT (MVCT). Methods and Materials: Tumor responses were measured for 20 lung cancer lesions in 17 patients that were imaged and treated with helical tomotherapy with doses ranging from 2.0 to 2.5 Gy per fraction. Five patients were treated with concurrent chemotherapy, and 1 patient was treated with neoadjuvant chemotherapy. Tumor response to treatment was retrospectively measured by contouring 480 serial MVCT images acquired before treatment. A nonparametric, memory-based locally weight regression (LWR) model was developed for predicting tumor response using the retrospective tumor response data. This model predicts future tumor volumes and the associated confidence intervals based on limited observations during the first 2 weeks of treatment. The predictive accuracy of the model was tested using a leave-one-out cross-validation technique with the measured tumor responses. Results: The predictive algorithm was used to compare predicted verse-measured tumor volume response for all 20 lesions. The average error for the predictions of the final tumor volume was 12%, with the true volumes always bounded by the 95% confidence interval. The greatest model uncertainty occurred near the middle of the course of treatment, in which the tumor response relationships were more complex, the model has less information, and the predictors were more varied. The optimal days for measuring the tumor response on the MVCT images were on elapsed Days 1, 2, 5, 9, 11, 12, 17, and 18 during

  16. Motor cortex activity predicts response alternation during sensorimotor decisions

    PubMed Central

    Pape, Anna-Antonia; Siegel, Markus

    2016-01-01

    Our actions are constantly guided by decisions based on sensory information. The motor cortex is traditionally viewed as the final output stage in this process, merely executing motor responses based on these decisions. However, it is not clear if, beyond this role, the motor cortex itself impacts response selection. Here, we report activity fluctuations over motor cortex measured using MEG, which are unrelated to choice content and predict responses to a visuomotor task seconds before decisions are made. These fluctuations are strongly influenced by the previous trial's response and predict a tendency to switch between response alternatives for consecutive decisions. This alternation behaviour depends on the size of neural signals still present from the previous response. Our results uncover a response-alternation bias in sensorimotor decision making. Furthermore, they suggest that motor cortex is more than an output stage and instead shapes response selection during sensorimotor decision making. PMID:27713396

  17. Clinical Gestalt and the Prediction of Massive Transfusion after Trauma

    PubMed Central

    Pommerening, Matthew J.; Goodman, Michael D.; Holcomb, John B.; Wade, Charles E.; Fox, Erin E.; del Junco, Deborah J.; Brasel, Karen J.; Bulger, Eileen M.; Cohen, Mitch J.; Alarcon, Louis H.; Schreiber, Martin A.; Myers, John G.; Phelan, Herb A.; Muskat, Peter; Rahbar, Mohammad; Cotton, Bryan A.

    2016-01-01

    Introduction Early recognition and treatment of trauma patients requiring massive transfusion (MT) has been shown to reduce mortality. While many risk factors predicting MT have been demonstrated, there is no universally accepted method or algorithm to identify these patients. We hypothesized that even among experienced trauma surgeons, the clinical gestalt of identifying patients who will require MT is unreliable. Methods Transfusion and mortality outcomes after trauma were observed at 10 U.S. Level-1 trauma centers in patients who survived ≥30 minutes after admission and received ≥1 unit of RBC within 6 hours of arrival. Subjects who received ≥ 10 units within 24 hours of admission were classified as MT patients. Trauma surgeons were asked the clinical gestalt question “Is the patient likely to be massively transfused?” ten minutes after the patients arrival. The performance of clinical gestalt to predict MT was assessed using chi-square tests and ROC analysis to compare gestalt to previously described scoring systems. Results Of the 1,245 patients enrolled, 966 met inclusion criteria and 221 (23%) patients received MT. 415 (43%) were predicted to have a MT and 551(57%) were predicted to not have MT. Patients predicted to have MT were younger, more often sustained penetrating trauma, had higher ISS scores, higher heart rates, and lower systolic blood pressures (all p < 0.05). Gestalt sensitivity was 65.6% and specificity was 63.8%. PPV and NPV were 34.9% and 86.2% respectively. Conclusion Data from this large multicenter trial demonstrates that predicting the need for MT continues to be a challenge. Because of the increased mortality associated with delayed therapy, a more reliable algorithm is needed to identify and treat these severely injured patients earlier. Level of Evidence II; Diagnostic study - Development of diagnostic criteria on basis of consecutive patients (with universally applied reference standard) PMID:25682314

  18. Predictive Response Value of Pre- and Postchemoradiotherapy Variables in Rectal Cancer: An Analysis of Histological Data.

    PubMed

    Santos, Marisa D; Silva, Cristina; Rocha, Anabela; Nogueira, Carlos; Matos, Eduarda; Lopes, Carlos

    2016-01-01

    Background. Neoadjuvant chemoradiotherapy (nCRT) followed by curative surgery in locally advanced rectal cancer (LARC) improves pelvic disease control. Survival improvement is achieved only if pathological response occurs. Mandard tumor regression grade (TRG) proved to be a valid system to measure nCRT response. Potential predictive factors for Mandard response are analyzed. Materials and Methods. 167 patients with LARC were treated with nCRT and curative surgery. Tumor biopsies and surgical specimens were reviewed and analyzed regarding mitotic count, necrosis, desmoplastic reaction, and inflammatory infiltration grade. Surgical specimens were classified according to Mandard TRG. The patients were divided as "good responders" (Mandard TRG1-2) and "bad responders" (Mandard TRG3-5). According to results from our previous data, good responders have better prognosis than bad responders. We examined predictive factors for Mandard response and performed statistical analysis. Results. In univariate analysis, distance from anal verge and ten other postoperative variables related with nCRT tumor response had predictive value for Mandard response. In multivariable analysis only mitotic count, necrosis, and differentiation grade in surgical specimen had predictive value. Conclusions. There is a lack of clinical and pathological preoperative variables able to predict Mandard response. Only postoperative pathological parameters related with nCRT response have predictive value.

  19. Dopamine neurons share common response function for reward prediction error

    PubMed Central

    Eshel, Neir; Tian, Ju; Bukwich, Michael; Uchida, Naoshige

    2016-01-01

    Dopamine neurons are thought to signal reward prediction error, or the difference between actual and predicted reward. How dopamine neurons jointly encode this information, however, remains unclear. One possibility is that different neurons specialize in different aspects of prediction error; another is that each neuron calculates prediction error in the same way. We recorded from optogenetically-identified dopamine neurons in the lateral ventral tegmental area (VTA) while mice performed classical conditioning tasks. Our tasks allowed us to determine the full prediction error functions of dopamine neurons and compare them to each other. We found striking homogeneity among individual dopamine neurons: their responses to both unexpected and expected rewards followed the same function, just scaled up or down. As a result, we could describe both individual and population responses using just two parameters. Such uniformity ensures robust information coding, allowing each dopamine neuron to contribute fully to the prediction error signal. PMID:26854803

  20. Variability in pathogenicity prediction programs: impact on clinical diagnostics

    PubMed Central

    Walters-Sen, Lauren C; Hashimoto, Sayaka; Thrush, Devon Lamb; Reshmi, Shalini; Gastier-Foster, Julie M; Astbury, Caroline; Pyatt, Robert E

    2015-01-01

    Current practice by clinical diagnostic laboratories is to utilize online prediction programs to help determine the significance of novel variants in a given gene sequence. However, these programs vary widely in their methods and ability to correctly predict the pathogenicity of a given sequence change. The performance of 17 publicly available pathogenicity prediction programs was assayed using a dataset consisting of 122 credibly pathogenic and benign variants in genes associated with the RASopathy family of disorders and limb-girdle muscular dystrophy. Performance metrics were compared between the programs to determine the most accurate program for loss-of-function and gain-of-function mechanisms. No one program correctly predicted the pathogenicity of all variants analyzed. A major hindrance to the analysis was the lack of output from a significant portion of the programs. The best performer was MutPred, which had a weighted accuracy of 82.6% in the full dataset. Surprisingly, combining the results of the top three programs did not increase the ability to predict pathogenicity over the top performer alone. As the increasing number of sequence changes in larger datasets will require interpretation, the current study demonstrates that extreme caution must be taken when reporting pathogenicity based on statistical online protein prediction programs in the absence of functional studies. PMID:25802880

  1. Predictability of steel containment response near failure

    SciTech Connect

    Costello, J.F.; Ludwigsen, J.S.; Luk, V.K.; Hessheimer, M.F.

    2000-01-06

    The Nuclear Power Engineering Corporation of Japan and the US Nuclear Regulatory Commission Office of Nuclear Regulatory Research, are co-sponsoring and jointly funding a Cooperative Containment Research Program at Sandia National Laboratories, Albuquerque, New Mexico, USA. As a part of this program, a steel containment vessel model and contact structure assembly was tested to failure with over pressurization at Sandia on December 11--12, 1996. The steel containment vessel model was a mixed-scale model (1:10 in geometry and 1:4 in shell thickness) of a steel containment for an improved Mark-II Boiling Water Reactor plant in Japan. The contact structure, which is a thick, bell-shaped steel shell separated at a nominally uniform distance from the model, provides a simplified representation of features of the concrete reactor shield building in the actual plant. The objective of the internal pressurization test was to provide measurement data of the structural response of the model up to its failure in order to validate analytical modeling, to find its pressure capacity, and to observe the failure model and mechanisms.

  2. Clinical prediction and the idea of a population.

    PubMed

    Armstrong, David

    2017-01-01

    Using an analysis of the British Medical Journal over the past 170 years, this article describes how changes in the idea of a population have informed new technologies of medical prediction. These approaches have largely replaced older ideas of clinical prognosis based on understanding the natural histories of the underlying pathologies. The 19(th)-century idea of a population, which provided a denominator for medical events such as births and deaths, was constrained in its predictive power by its method of enumerating individual bodies. During the 20(th) century, populations were increasingly constructed through inferential techniques based on patient groups and samples seen to possess variable characteristics. The emergence of these new virtual populations created the conditions for the emergence of predictive algorithms that are used to foretell our medical futures.

  3. Neoadjuvant treatment for advanced esophageal cancer: response assessment before surgery and how to predict response to chemoradiation before starting treatment

    PubMed Central

    Hölscher, Arnulf H.; Schmidt, Matthias; Warnecke-Eberz, Ute

    2015-01-01

    Patients with advanced esophageal cancer (T3-4, N) have a poor prognosis. Chemoradiation or chemotherapy before esophagectomy with adequate lymphadenectomy is the standard treatment for patients with resectable advanced esophageal carcinoma. However, only patients with major histopathologic response (regression to less than 10% of the primary tumor) after preoperative treatment will have a prognostic benefit of preoperative chemoradiation. Using current therapy regimens about 40% to 50% of the patients show major histopathological response. The remaining cohort does not benefit from this neoadjuvant approach but might benefit from earlier surgical resection. Therefore, it is an aim to develop tools for response prediction before starting the treatment and for early response assessment identifying responders. The current review discusses the different imaging techniques and the most recent studies about molecular markers for early response prediction. The results show that [18F]-fluorodeoxyglucose-positron emission tomography (FDG-PET) has a good sensitivity but the specificity is not robust enough for routine clinical use. Newer positron emission tomography detector technology, the combination of FDG-PET with computed tomography, additional evaluation criteria and standardization of evaluation may improve the predictive value. There exist a great number of retrospective studies using molecular markers for prediction of response. Until now the clinical use is missing. But the results of first prospective studies are promising. A future perspective may be the combination of imaging technics and special molecular markers for individualized therapy. Another aspect is the response assessment after finishing neoadjuvant treatment protocol. The different clinical methods are discussed. The results show that until now no non-invasive method is valid enough to assess complete histopathological response. PMID:26157318

  4. How to predict clinical relapse in inflammatory bowel disease patients

    PubMed Central

    Liverani, Elisa; Scaioli, Eleonora; Digby, Richard John; Bellanova, Matteo; Belluzzi, Andrea

    2016-01-01

    Inflammatory bowel diseases have a natural course characterized by alternating periods of remission and relapse. Disease flares occur in a random way and are currently unpredictable for the most part. Predictors of benign or unfavourable clinical course are required to facilitate treatment decisions and to avoid overtreatment. The present article provides a literature review of the current evidence on the main clinical, genetic, endoscopic, histologic, serologic and fecal markers to predict aggressiveness of inflammatory bowel disease and discuss their prognostic role, both in Crohn’s disease and ulcerative colitis. No single marker seems to be reliable alone as a flare predictor, even in light of promising evidence regarding the role of fecal markers, in particular fecal calprotectin, which has reported good results recently. In order to improve our daily clinical practice, validated prognostic scores should be elaborated, integrating clinical and biological markers of prognosis. Finally, we propose an algorithm considering clinical history and biological markers to intercept patients with high risk of clinical relapse. PMID:26811644

  5. Both Financial and Cognitive Decline Predict Clinical Progression in MCI

    PubMed Central

    Gerstenecker, Adam; Triebel, Kristen L.; Martin, Roy; Snyder, Scott; Marson, Daniel C.

    2015-01-01

    We investigated the roles of financial/functional and cognitive abilities in predicting clinical progression in patients with mild cognitive impairment (MCI). In a longitudinal sample of 51 patients with consensus-conference diagnosed MCI likely due to Alzheimer’s disease (AD). Two-year change scores were calculated for a performance measure of functional ability, cognitive variables, and three outcome measures used to track progression in neurologic disorders. We examined patterns of financial and cognitive decline across the two-year study period, and used this data and the three outcome variables to construct discrete predictor models of clinical progression in MCI. We found that both financial skills and cognitive abilities declined over the two-year study period, were significantly associated with clinical progression, and contributed unique variance all three predictor models. The resulting models accounted for 40–75% of variance in clinical progression across outcome variables. Taken together, our results indicate that changes in both cognitive abilities and higher-order functional skills appear integral to understanding clinical progression in MCI likely due to AD. Specifically, declines in financial skills contribute unique variance to measures commonly used to track progression in neurological disorders associated with aging and thus represent an important functional marker of clinical progression in prodromal AD. PMID:26900988

  6. Probabilistic prediction of barrier-island response to hurricanes

    USGS Publications Warehouse

    Plant, Nathaniel G.; Stockdon, Hilary F.

    2012-01-01

    Prediction of barrier-island response to hurricane attack is important for assessing the vulnerability of communities, infrastructure, habitat, and recreational assets to the impacts of storm surge, waves, and erosion. We have demonstrated that a conceptual model intended to make qualitative predictions of the type of beach response to storms (e.g., beach erosion, dune erosion, dune overwash, inundation) can be reformulated in a Bayesian network to make quantitative predictions of the morphologic response. In an application of this approach at Santa Rosa Island, FL, predicted dune-crest elevation changes in response to Hurricane Ivan explained about 20% to 30% of the observed variance. An extended Bayesian network based on the original conceptual model, which included dune elevations, storm surge, and swash, but with the addition of beach and dune widths as input variables, showed improved skill compared to the original model, explaining 70% of dune elevation change variance and about 60% of dune and shoreline position change variance. This probabilistic approach accurately represented prediction uncertainty (measured with the log likelihood ratio), and it outperformed the baseline prediction (i.e., the prior distribution based on the observations). Finally, sensitivity studies demonstrated that degrading the resolution of the Bayesian network or removing data from the calibration process reduced the skill of the predictions by 30% to 40%. The reduction in skill did not change conclusions regarding the relative importance of the input variables, and the extended model's skill always outperformed the original model.

  7. Predicting response and survival in chemotherapy-treated triple-negative breast cancer

    PubMed Central

    Prat, A; Lluch, A; Albanell, J; Barry, W T; Fan, C; Chacón, J I; Parker, J S; Calvo, L; Plazaola, A; Arcusa, A; Seguí-Palmer, M A; Burgues, O; Ribelles, N; Rodriguez-Lescure, A; Guerrero, A; Ruiz-Borrego, M; Munarriz, B; López, J A; Adamo, B; Cheang, M C U; Li, Y; Hu, Z; Gulley, M L; Vidal, M J; Pitcher, B N; Liu, M C; Citron, M L; Ellis, M J; Mardis, E; Vickery, T; Hudis, C A; Winer, E P; Carey, L A; Caballero, R; Carrasco, E; Martín, M; Perou, C M; Alba, E

    2014-01-01

    Background: In this study, we evaluated the ability of gene expression profiles to predict chemotherapy response and survival in triple-negative breast cancer (TNBC). Methods: Gene expression and clinical–pathological data were evaluated in five independent cohorts, including three randomised clinical trials for a total of 1055 patients with TNBC, basal-like disease (BLBC) or both. Previously defined intrinsic molecular subtype and a proliferation signature were determined and tested. Each signature was tested using multivariable logistic regression models (for pCR (pathological complete response)) and Cox models (for survival). Within TNBC, interactions between each signature and the basal-like subtype (vs other subtypes) for predicting either pCR or survival were investigated. Results: Within TNBC, all intrinsic subtypes were identified but BLBC predominated (55–81%). Significant associations between genomic signatures and response and survival after chemotherapy were only identified within BLBC and not within TNBC as a whole. In particular, high expression of a previously identified proliferation signature, or low expression of the luminal A signature, was found independently associated with pCR and improved survival following chemotherapy across different cohorts. Significant interaction tests were only obtained between each signature and the BLBC subtype for prediction of chemotherapy response or survival. Conclusions: The proliferation signature predicts response and improved survival after chemotherapy, but only within BLBC. This highlights the clinical implications of TNBC heterogeneity, and suggests that future clinical trials focused on this phenotypic subtype should consider stratifying patients as having BLBC or not. PMID:25101563

  8. Drug-induced death signaling strategy rapidly predicts cancer response to chemotherapy

    PubMed Central

    Montero, Joan; Sarosiek, Kristopher A.; DeAngelo, Joseph D.; Maertens, Ophélia; Ryan, Jeremy; Ercan, Dalia; Piao, Huiying; Horowitz, Neil S.; Berkowitz, Ross S.; Matulonis, Ursula; Jänne, Pasi A.; Amrein, Philip C.; Cichowski, Karen; Drapkin, Ronny; Letai, Anthony

    2015-01-01

    SUMMARY There is a lack of effective predictive biomarkers to precisely assign optimal therapy to cancer patients. While most efforts are directed at inferring drug response phenotype based on genotype, there is very focused and useful phenotypic information to be gained from directly perturbing the patient’s living cancer cell with the drug(s) in question. To satisfy this unmet need we developed the Dynamic BH3 Profiling technique to measure early changes in net pro-apoptotic signaling at the mitochondrion (‘priming’) induced by chemotherapeutic agents in cancer cells, not requiring prolonged ex vivo culture. We find in cell line and clinical experiments that early drug-induced death signaling measured by Dynamic BH3 Profiling predicts chemotherapy response across many cancer types and many agents, including combinations of chemotherapies. We propose that Dynamic BH3 Profiling can be used as a broadly applicable predictive biomarker to predict cytotoxic response of cancers to chemotherapeutics in vivo. PMID:25723171

  9. Clinical chemistry in higher dimensions: Machine-learning and enhanced prediction from routine clinical chemistry data.

    PubMed

    Richardson, Alice; Signor, Ben M; Lidbury, Brett A; Badrick, Tony

    2016-11-01

    Big Data is having an impact on many areas of research, not the least of which is biomedical science. In this review paper, big data and machine learning are defined in terms accessible to the clinical chemistry community. Seven myths associated with machine learning and big data are then presented, with the aim of managing expectation of machine learning amongst clinical chemists. The myths are illustrated with four examples investigating the relationship between biomarkers in liver function tests, enhanced laboratory prediction of hepatitis virus infection, the relationship between bilirubin and white cell count, and the relationship between red cell distribution width and laboratory prediction of anaemia.

  10. 42 CFR 493.1457 - Standard; Clinical consultant responsibilities.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ... 42 Public Health 5 2010-10-01 2010-10-01 false Standard; Clinical consultant responsibilities. 493... Testing Laboratories Performing High Complexity Testing § 493.1457 Standard; Clinical consultant responsibilities. The clinical consultant provides consultation regarding the appropriateness of the...

  11. 42 CFR 493.1419 - Standard; Clinical consultant responsibilities.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ... 42 Public Health 5 2010-10-01 2010-10-01 false Standard; Clinical consultant responsibilities. 493... Testing Laboratories Performing Moderate Complexity Testing § 493.1419 Standard; Clinical consultant responsibilities. The clinical consultant provides consultation regarding the appropriateness of the...

  12. Ventricular repolarization markers for predicting malignant arrhythmias in clinical practice

    PubMed Central

    Castro-Torres, Yaniel; Carmona-Puerta, Raimundo; Katholi, Richard E

    2015-01-01

    Malignant cardiac arrhythmias which result in sudden cardiac death may be present in individuals apparently healthy or be associated with other medical conditions. The way to predict their appearance represents a challenge for the medical community due to the tragic outcomes in most cases. In the last two decades some ventricular repolarization (VR) markers have been found to be useful to predict malignant cardiac arrhythmias in several clinical conditions. The corrected QT, QT dispersion, Tpeak-Tend, Tpeak-Tend dispersion and Tp-e/QT have been studied and implemented in clinical practice for this purpose. These markers are obtained from 12 lead surface electrocardiogram. In this review we discuss how these markers have demonstrated to be effective to predict malignant arrhythmias in medical conditions such as long and short QT syndromes, Brugada syndrome, early repolarization syndrome, acute myocardial ischemia, heart failure, hypertension, diabetes mellitus, obesity and highly trained athletes. Also the main pathophysiological mechanisms that explain the arrhythmogenic predisposition in these diseases and the basis for the VR markers are discussed. However, the same results have not been found in all conditions. Further studies are needed to reach a global consensus in order to incorporate these VR parameters in risk stratification of these patients. PMID:26301231

  13. Clinical Experiences Are Not Predictive of Outcomes on the NATABOC Examination

    PubMed Central

    Turocy, Paula Sammarone; Comfort, Ronald E.; Perrin, David H.; Gieck, Joe H.

    2000-01-01

    Objective: To determine the efficacy of the National Athletic Trainers' Association Board of Certification (NATABOC) clinical experience requirements and individual student characteristics to predict candidate outcomes on the NATABOC certification examination. Design and Setting: For all subjects, we gathered survey information and examination scores. The survey information included age, sex, route to certification, previous athletic training and allied health experience, and clinical education experiences. Subjects: A total of 269 subjects, 22.25% of all first-time candidates for the June and November 1993 NATABOC examinations, were included in this study. Measurements: Data were analyzed for standard descriptive statistics and parametric linear regression and correlational relationships. Results: Total clinical hours, high-risk sport experiences, and previous athletic training experience were not predictive of examination outcomes. Although our results indicated a relationship between previous allied health experience and both outcome on the written section of the examination and age and outcome on the oral/practical section, these characteristics also were not predictive of examination outcomes. Conclusions: Gaining clinical experience hours in excess of 400 hours beyond the 800-or 1500-hour requirement may yield no greater benefit for an entry-level professional than less time. The quality, rather than the quantity, of clinical experiences should be evaluated. More emphasis should be placed on the achievement of an entry level of clinical competency, rather than on total hour collection. Also, because high-risk sport experiences did not predict outcomes on the NATABOC examination, the emphasis of clinical education should be on students' receiving a more structured clinical experience, in which they are progressively required to assume greater responsibilities integrating both cognitive and psychomotor skills, while working under the supervision of a certified

  14. Response to lithium in bipolar disorder: clinical and genetic findings.

    PubMed

    Rybakowski, Janusz K

    2014-06-18

    The use of lithium is a cornerstone for preventing recurrences in bipolar disorder (BD). The response of patients with bipolar disorder to lithium has different levels of magnitude. About one-third of lithium-treated patients are excellent lithium responders (ELR), showing total prevention of the episodes. A number of clinical characteristics were delineated in patients with favorable response to lithium as regards to clinical course, family history of mood disorders, and psychiatric comorbidity. We have also demonstrated that temperamental features of hypomania (a hyperthymic temperament) and a lack of cognitive disorganization predict the best results of lithium prophylaxis. A degree of prevention against manic and depressive episodes has been regarded as an endophenotype for pharmacogenetic studies. The majority of data have been gathered from so-called "candidate" gene studies. The candidates were selected on the basis of neurobiology of bipolar disorder and mechanisms of lithium action including, among others, neurotransmission, intracellular signaling, neuroprotection or circadian rhythms. We demonstrated that response to lithium has been connected with the genotype of BDNF gene and serum BDNF levels and have shown that ELR have normal cognitive functions and serum BDNF levels, even after long-term duration of the illness. A number of genome-wide association studies (GWAS) of BD have been also performed in recent years, some of which also focused on lithium response. The Consortium on Lithium Genetics (ConLiGen) has established the large sample for performing the genome-wide association study (GWAS) of lithium response in BD, and the first results have already been published.

  15. Predicting Sweat Loss Response to Exercise, Environment and Clothing

    DTIC Science & Technology

    1981-07-09

    pp 124-126 Bullard RW, Banerjee MR, MacIntyre BA (1967) The role of the skin in negative feedback regulation of eccrine sweating . In J Biometeorol HI...I .,. . I . .. . . . .I .uj . .I j .. .. . .. . .L .... I . .. .. . AD REPORT NO M-2..90 Predicting Sweat Loss...CATALOG NUMBER M24/140 -A i4113 ot 4. TITLE (mnd Subtitle) S. TYPE OF REPORT & PERIOD COVERED Predicting Sweat Rate Response to Exercise, Environment

  16. Tips for Teachers of Evidence-based Medicine: Clinical Prediction Rules (CPRs) and Estimating Pretest Probability

    PubMed Central

    McGinn, Thomas; Jervis, Ramiro; Wisnivesky, Juan; Keitz, Sheri

    2008-01-01

    Background Clinical prediction rules (CPR) are tools that clinicians can use to predict the most likely diagnosis, prognosis, or response to treatment in a patient based on individual characteristics. CPRs attempt to standardize, simplify, and increase the accuracy of clinicians’ diagnostic and prognostic assessments. The teaching tips series is designed to give teachers advice and materials they can use to attain specific educational objectives. Educational Objectives In this article, we present 3 teaching tips aimed at helping clinical learners use clinical prediction rules and to more accurately assess pretest probability in every day practice. The first tip is designed to demonstrate variability in physician estimation of pretest probability. The second tip demonstrates how the estimate of pretest probability influences the interpretation of diagnostic tests and patient management. The third tip exposes learners to various examples and different types of Clinical Prediction Rules (CPR) and how to apply them in practice. Pilot Testing We field tested all 3 tips with 16 learners, a mix of interns and senior residents. Teacher preparatory time was approximately 2 hours. The field test utilized a board and a data projector; 3 handouts were prepared. The tips were felt to be clear and the educational objectives reached. Potential teaching pitfalls were identified. Conclusion Teaching with these tips will help physicians appreciate the importance of applying evidence to their every day decisions. In 2 or 3 short teaching sessions, clinicians can also become familiar with the use of CPRs in applying evidence consistently in everyday practice. PMID:18491194

  17. An application of site response functions to ground motion prediction

    NASA Astrophysics Data System (ADS)

    Tsuda, K.; Archuleta, R.; Steidl, J.; Koketsu, K.

    2006-12-01

    The prediction of ground motion from large future earthquakes is very important for hazard mitigation in urban areas of Japan. Because the observed ground motions are affected by three factors; the seismic source, attenuation (quality factor) of seismic wave propagation inside the earth, and the effects of the local surface geology, understanding each factor is essential for the ground motion prediction. The effect of surface geology (local soil conditions) on ground motions was documented as early as the 1906 San Francisco earthquake. The correlation between soil type and the degree of damage was again recognized in the 1923 Kanto earthquake. Additionally, accelerometer records from almost all recent large events also have reinforced the role of site effects in the level of strong shaking. Because most cities in Japan are located on thick sedimentary basins, accounting for site response is essential for realistic predictions of ground motion. However, predicting ground motion has uncertainties that arise from all three factors: source, path, and site. The analysis of well-recorded data from dense seismograph arrays can reduce these uncertainties for ground motion prediction. The new technique presented here provides a site response correlation function for estimation of the spatial distribution of site response. This function is based on the known site responses at instrumented sites and is used to estimate the site response at a site for which there is no instrumental records. We initially predict the level of ground motion by using this estimate with the assumption of linear wave propagation. This method is applied to the data from a relatively dense seismic array located near the city of Sendai, Japan by using moderate sized earthquakes with small ground motion levels to estimate linear site response. The array consists of 29 stations: 20 managed by Tohoku Institute of Technology, 6 by Building Research Institute, and 3 by NIED within an area of 20 x 30 km

  18. Predicting Response to Histone Deacetylase Inhibitors Using High-Throughput Genomics

    PubMed Central

    Geeleher, Paul; Loboda, Andrey; Lenkala, Divya; Wang, Fan; LaCroix, Bonnie; Karovic, Sanja; Wang, Jacqueline; Nebozhyn, Michael; Chisamore, Michael; Hardwick, James; Maitland, Michael L.

    2015-01-01

    Background: Many disparate biomarkers have been proposed as predictors of response to histone deacetylase inhibitors (HDI); however, all have failed when applied clinically. Rather than this being entirely an issue of reproducibility, response to the HDI vorinostat may be determined by the additive effect of multiple molecular factors, many of which have previously been demonstrated. Methods: We conducted a large-scale gene expression analysis using the Cancer Genome Project for discovery and generated another large independent cancer cell line dataset across different cancers for validation. We compared different approaches in terms of how accurately vorinostat response can be predicted on an independent out-of-batch set of samples and applied the polygenic marker prediction principles in a clinical trial. Results: Using machine learning, the small effects that aggregate, resulting in sensitivity or resistance, can be recovered from gene expression data in a large panel of cancer cell lines. This approach can predict vorinostat response accurately, whereas single gene or pathway markers cannot. Our analyses recapitulated and contextualized many previous findings and suggest an important role for processes such as chromatin remodeling, autophagy, and apoptosis. As a proof of concept, we also discovered a novel causative role for CHD4, a helicase involved in the histone deacetylase complex that is associated with poor clinical outcome. As a clinical validation, we demonstrated that a common dose-limiting toxicity of vorinostat, thrombocytopenia, can be predicted (r = 0.55, P = .004) several days before it is detected clinically. Conclusion: Our work suggests a paradigm shift from single-gene/pathway evaluation to simultaneously evaluating multiple independent high-throughput gene expression datasets, which can be easily extended to other investigational compounds where similar issues are hampering clinical adoption. PMID:26296641

  19. Prediction and control of neural responses to pulsatile electrical stimulation.

    PubMed

    Campbell, Luke J; Sly, David James; O'Leary, Stephen John

    2012-04-01

    This paper aims to predict and control the probability of firing of a neuron in response to pulsatile electrical stimulation of the type delivered by neural prostheses such as the cochlear implant, bionic eye or in deep brain stimulation. Using the cochlear implant as a model, we developed an efficient computational model that predicts the responses of auditory nerve fibers to electrical stimulation and evaluated the model's accuracy by comparing the model output with pooled responses from a group of guinea pig auditory nerve fibers. It was found that the model accurately predicted the changes in neural firing probability over time to constant and variable amplitude electrical pulse trains, including speech-derived signals, delivered at rates up to 889 pulses s(-1). A simplified version of the model that did not incorporate adaptation was used to adaptively predict, within its limitations, the pulsatile electrical stimulus required to cause a desired response from neurons up to 250 pulses s(-1). Future stimulation strategies for cochlear implants and other neural prostheses may be enhanced using similar models that account for the way that neural responses are altered by previous stimulation.

  20. Prediction and control of neural responses to pulsatile electrical stimulation

    NASA Astrophysics Data System (ADS)

    Campbell, Luke J.; Sly, David James; O'Leary, Stephen John

    2012-04-01

    This paper aims to predict and control the probability of firing of a neuron in response to pulsatile electrical stimulation of the type delivered by neural prostheses such as the cochlear implant, bionic eye or in deep brain stimulation. Using the cochlear implant as a model, we developed an efficient computational model that predicts the responses of auditory nerve fibers to electrical stimulation and evaluated the model's accuracy by comparing the model output with pooled responses from a group of guinea pig auditory nerve fibers. It was found that the model accurately predicted the changes in neural firing probability over time to constant and variable amplitude electrical pulse trains, including speech-derived signals, delivered at rates up to 889 pulses s-1. A simplified version of the model that did not incorporate adaptation was used to adaptively predict, within its limitations, the pulsatile electrical stimulus required to cause a desired response from neurons up to 250 pulses s-1. Future stimulation strategies for cochlear implants and other neural prostheses may be enhanced using similar models that account for the way that neural responses are altered by previous stimulation.

  1. Predictive genomics: a cancer hallmark network framework for predicting tumor clinical phenotypes using genome sequencing data.

    PubMed

    Wang, Edwin; Zaman, Naif; Mcgee, Shauna; Milanese, Jean-Sébastien; Masoudi-Nejad, Ali; O'Connor-McCourt, Maureen

    2015-02-01

    Tumor genome sequencing leads to documenting thousands of DNA mutations and other genomic alterations. At present, these data cannot be analyzed adequately to aid in the understanding of tumorigenesis and its evolution. Moreover, we have little insight into how to use these data to predict clinical phenotypes and tumor progression to better design patient treatment. To meet these challenges, we discuss a cancer hallmark network framework for modeling genome sequencing data to predict cancer clonal evolution and associated clinical phenotypes. The framework includes: (1) cancer hallmarks that can be represented by a few molecular/signaling networks. 'Network operational signatures' which represent gene regulatory logics/strengths enable to quantify state transitions and measures of hallmark traits. Thus, sets of genomic alterations which are associated with network operational signatures could be linked to the state/measure of hallmark traits. The network operational signature transforms genotypic data (i.e., genomic alterations) to regulatory phenotypic profiles (i.e., regulatory logics/strengths), to cellular phenotypic profiles (i.e., hallmark traits) which lead to clinical phenotypic profiles (i.e., a collection of hallmark traits). Furthermore, the framework considers regulatory logics of the hallmark networks under tumor evolutionary dynamics and therefore also includes: (2) a self-promoting positive feedback loop that is dominated by a genomic instability network and a cell survival/proliferation network is the main driver of tumor clonal evolution. Surrounding tumor stroma and its host immune systems shape the evolutionary paths; (3) cell motility initiating metastasis is a byproduct of the above self-promoting loop activity during tumorigenesis; (4) an emerging hallmark network which triggers genome duplication dominates a feed-forward loop which in turn could act as a rate-limiting step for tumor formation; (5) mutations and other genomic alterations have

  2. A clinical tool for predicting survival in ALS

    PubMed Central

    Knibb, Jonathan A; Keren, Noa; Kulka, Anna; Leigh, P Nigel; Martin, Sarah; Shaw, Christopher E; Tsuda, Miho; Al-Chalabi, Ammar

    2016-01-01

    Background Amyotrophic lateral sclerosis (ALS) is a progressive and usually fatal neurodegenerative disease. Survival from diagnosis varies considerably. Several prognostic factors are known, including site of onset (bulbar or limb), age at symptom onset, delay from onset to diagnosis and the use of riluzole and non-invasive ventilation (NIV). Clinicians and patients would benefit from a practical way of using these factors to provide an individualised prognosis. Methods 575 consecutive patients with incident ALS from a population-based registry in South-East England register for ALS (SEALS) were studied. Their survival was modelled as a two-step process: the time from diagnosis to respiratory muscle involvement, followed by the time from respiratory involvement to death. The effects of predictor variables were assessed separately for each time interval. Findings Younger age at symptom onset, longer delay from onset to diagnosis and riluzole use were associated with slower progression to respiratory involvement, and NIV use was associated with lower mortality after respiratory involvement, each with a clinically significant effect size. Riluzole may have a greater effect in younger patients and those with longer delay to diagnosis. A patient's survival time has a roughly 50% chance of falling between half and twice the predicted median. Interpretation A simple and clinically applicable graphical method of predicting an individual patient's survival from diagnosis is presented. The model should be validated in an independent cohort, and extended to include other important prognostic factors. PMID:27378085

  3. Clinical parameters predictive of enlargement of melanocytic choroidal lesions.

    PubMed Central

    Augsburger, J J; Schroeder, R P; Territo, C; Gamel, J W; Shields, J A

    1989-01-01

    The authors followed up 197 melanotic choroidal lesions (62 categorised as benign naevi, 76 classified as suspicious naevi, 41 diagnosed as dormant melanomas, and 18 categorised as active melanomas) left untreated after their initial clinical documentation. Thirty-nine of these lesions enlarged during a five-year follow-up interval (cumulative proportion of lesions that enlarged = 26.2% by Kaplan-Meier method). Individual clinical parameters predictive of lesion enlargement (p less than 0.01) included larger size of the lesion, especially lesion thickness, presence of retinal detachment, location of the lesion's posterior margin within 2 disc diameters of the optic disc, presence of symptoms, and presence of orange pigment clumps on the lesion's surface. The best combination of these parameters for prediction of lesion enlargement, as identified by multivariate Cox regression analysis, consisted of thickness of the lesion, retinal detachment, and symptoms. The five-year incidence of lesion enlargement for patients with none of these prognostic parameters was 5.8%, while that for patients with all three unfavourable parameters simultaneously was 90.6%. Images PMID:2605146

  4. Idiopathic normal pressure hydrocephalus: diagnostic and predictive value of clinical testing, lumbar drainage, and CSF dynamics.

    PubMed

    Mahr, Cynthia V; Dengl, Markus; Nestler, Ulf; Reiss-Zimmermann, Martin; Eichner, Gerrit; Preuß, Matthias; Meixensberger, Jürgen

    2016-09-01

    OBJECTIVE The aim of the study was to analyze the diagnostic and predictive values of clinical tests, CSF dynamics, and intracranial pulsatility tests, compared with external lumbar drainage (ELD), for shunt response in patients with idiopathic normal pressure hydrocephalus (iNPH). METHODS Sixty-eight consecutive patients with suspected iNPH were prospectively evaluated. Preoperative assessment included clinical tests, overnight intracranial pressure (ICP) monitoring, lumbar infusion test (LIFT), and ELD for 24-72 hours. Simple and multiple linear regression analyses were conducted to identify predictive parameters concerning the outcome after shunt therapy. RESULTS Positive response to ELD correctly predicted improvement after CSF diversion in 87.9% of the patients. A Mini-Mental State Examination (MMSE) value below 21 was associated with nonresponse after shunt insertion (specificity 93%, sensitivity 67%). Resistance to outflow of CSF (ROut) > 12 mm Hg/ml/min was false negative in 21% of patients. Intracranial pulsatility parameters yielded different results in various parameters (correlation coefficient between pulse amplitude and ICP, slow wave amplitude, and mean ICP) but did not correlate to outcome. In multiple linear regression analysis, a calculation of presurgical MMSE versus the value after ELD, ROut, and ICP amplitude quotient during LIFT was significantly associated with outcome (p = 0.04). CONCLUSIONS Despite a multitude of invasive tests, presurgical clinical testing and response to ELD yielded the best prediction for improvement of symptoms following surgery. The complication rate of invasive testing was 5.4%. Multiple and simple linear regression analyses indicated that outcome can only be predicted by a combination of parameters, in accordance with a multifactorial pathogenesis of iNPH.

  5. Predicting Readmission at Early Hospitalization Using Electronic Clinical Data

    PubMed Central

    Sun, Xiaowu; Nunez, Carlos M.; Gupta, Vikas; Johannes, Richard S.

    2017-01-01

    Background: Identifying patients at high risk for readmission early during hospitalization may aid efforts in reducing readmissions. We sought to develop an early readmission risk predictive model using automated clinical data available at hospital admission. Methods: We developed an early readmission risk model using a derivation cohort and validated the model with a validation cohort. We used a published Acute Laboratory Risk of Mortality Score as an aggregated measure of clinical severity at admission and the number of hospital discharges in the previous 90 days as a measure of disease progression. We then evaluated the administrative data–enhanced model by adding principal and secondary diagnoses and other variables. We examined the c-statistic change when additional variables were added to the model. Results: There were 1,195,640 adult discharges from 70 hospitals with 39.8% male and the median age of 63 years (first and third quartile: 43, 78). The 30-day readmission rate was 11.9% (n=142,211). The early readmission model yielded a graded relationship of readmission and the Acute Laboratory Risk of Mortality Score and the number of previous discharges within 90 days. The model c-statistic was 0.697 with good calibration. When administrative variables were added to the model, the c-statistic increased to 0.722. Conclusions: Automated clinical data can generate a readmission risk score early at hospitalization with fair discrimination. It may have applied value to aid early care transition. Adding administrative data increases predictive accuracy. The administrative data–enhanced model may be used for hospital comparison and outcome research. PMID:27755391

  6. Clinical prediction rules in practice: review of clinical guidelines and survey of GPs

    PubMed Central

    Plüddemann, Annette; Wallace, Emma; Bankhead, Clare; Keogh, Claire; Van der Windt, Danielle; Lasserson, Daniel; Galvin, Rose; Moschetti, Ivan; Kearley, Karen; O’Brien, Kirsty; Sanders, Sharon; Mallett, Susan; Malanda, Uriell; Thompson, Matthew; Fahey, Tom; Stevens, Richard

    2014-01-01

    Background The publication of clinical prediction rules (CPRs) studies has risen significantly. It is unclear if this reflects increasing usage of these tools in clinical practice or how this may vary across clinical areas. Aim To review clinical guidelines in selected areas and survey GPs in order to explore CPR usefulness in the opinion of experts and use at the point of care. Design and setting A review of clinical guidelines and survey of UK GPs. Method Clinical guidelines in eight clinical domains with published CPRs were reviewed for recommendations to use CPRs including primary prevention of cardiovascular disease, transient ischaemic attack (TIA) and stroke, diabetes mellitus, fracture risk assessment in osteoporosis, lower limb fractures, breast cancer, depression, and acute infections in childhood. An online survey of 401 UK GPs was also conducted. Results Guideline review: Of 7637 records screened by title and/or abstract, 243 clinical guidelines met inclusion criteria. CPRs were most commonly recommended in guidelines regarding primary prevention of cardiovascular disease (67%) and depression (67%). There was little consensus across various clinical guidelines as to which CPR to use preferentially. Survey: Of 401 responders to the GP survey, most were aware of and applied named CPRs in the clinical areas of cardiovascular disease and depression. The commonest reasons for using CPRs were to guide management and conform to local policy requirements. Conclusion GPs use CPRs to guide management but also to comply with local policy requirements. Future research could focus on which clinical areas clinicians would most benefit from CPRs and promoting the use of robust, externally validated CPRs. PMID:24686888

  7. Microtubule-Associated Protein Expression and Predicting Taxane Response

    DTIC Science & Technology

    2009-10-01

    taxanes. Our results indicate that MAP- tau functions as a prognostic factor in both the Yale cohort and the TAX 307 cohort with high MAP- tau ...expression associated with longer overall survival and TTP. Tau does NOT behave as a predictor of response to taxane-based chemotherapy since differences...between low and high MAP- tau groups by treatment arm and response rate were not observed in the TAX 307 clinical trial cohort. Our data supports the

  8. Clinical utility and validity of minoxidil response testing in androgenetic alopecia.

    PubMed

    Goren, Andy; Shapiro, Jerry; Roberts, Janet; McCoy, John; Desai, Nisha; Zarrab, Zoulikha; Pietrzak, Aldona; Lotti, Torello

    2015-01-01

    Clinical response to 5% topical minoxidil for the treatment of androgenetic alopecia (AGA) is typically observed after 3-6 months. Approximately 40% of patients will regrow hair. Given the prolonged treatment time required to elicit a response, a diagnostic test for ruling out nonresponders would have significant clinical utility. Two studies have previously reported that sulfotransferase enzyme activity in plucked hair follicles predicts a patient's response to topical minoxidil therapy. The aim of this study was to assess the clinical utility and validity of minoxidil response testing. In this communication, the present authors conducted an analysis of completed and ongoing studies of minoxidil response testing. The analysis confirmed the clinical utility of a sulfotransferase enzyme test in successfully ruling out 95.9% of nonresponders to topical minoxidil for the treatment of AGA.

  9. Posterior Predictive Assessment of Item Response Theory Models

    ERIC Educational Resources Information Center

    Sinharay, Sandip; Johnson, Matthew S.; Stern, Hal S.

    2006-01-01

    Model checking in item response theory (IRT) is an underdeveloped area. There is no universally accepted tool for checking IRT models. The posterior predictive model-checking method is a popular Bayesian model-checking tool because it has intuitive appeal, is simple to apply, has a strong theoretical basis, and can provide graphical or numerical…

  10. Posterior Predictive Model Checking for Multidimensionality in Item Response Theory

    ERIC Educational Resources Information Center

    Levy, Roy; Mislevy, Robert J.; Sinharay, Sandip

    2009-01-01

    If data exhibit multidimensionality, key conditional independence assumptions of unidimensional models do not hold. The current work pursues posterior predictive model checking, a flexible family of model-checking procedures, as a tool for criticizing models due to unaccounted for dimensions in the context of item response theory. Factors…

  11. Predicting the response of populations to environmental change

    SciTech Connect

    Ives, A.R.

    1995-04-01

    When subject to long-term directional environmental perturbations, changes in population densities depend on the positive and negative feedbacks operating through interactions within and among species in a community. This paper develops techniques to predict the long-term responses of population densities to environmental changes using data on short-term population fluctuations driven by short-term environmental variability. In addition to giving quantitative predictions, the techniques also reveal how different qualitative patterns of species interactions either buffer or accentuate population responses to environmental trends. All of the predictions are based on regression coefficients extracted from time series data, and they can therefore be applied with a minimum of mathematical and statistical gymnastics. 48 refs., 10 figs., 4 tabs.

  12. Predicting methylphenidate response in attention deficit hyperactivity disorder: a preliminary study.

    PubMed

    Johnston, Blair A; Coghill, David; Matthews, Keith; Steele, J Douglas

    2015-01-01

    Methylphenidate (MPH) is established as the main pharmacological treatment for patients with attention deficit hyperactivity disorder (ADHD). Whilst MPH is generally a highly effective treatment, not all patients respond, and some experience adverse reactions. Currently, there is no reliable method to predict how patients will respond, other than by exposure to a trial of medication. In this preliminary study, we sought to investigate whether an accurate predictor of clinical response to methylphenidate could be developed for individual patients, using sociodemographic, clinical and neuropsychological measures. Of the 43 boys with ADHD included in this proof-of-concept study, 30 were classed as responders and 13 as non-responders to MPH, with no significant differences in age nor verbal intelligence quotient (IQ) between the groups. Here we report the application of a multivariate analysis approach to the prediction of clinical response to MPH, which achieved an accuracy of 77% (p = 0.005). The most important variables to the classifier were performance on a 'go/no go' task and comorbid conduct disorder. This preliminary study suggested that further investigation is merited. Achieving a highly significant accuracy of 77% for the prediction of MPH response is an encouraging step towards finding a reliable and clinically useful method that could minimise the number of children needlessly being exposed to MPH.

  13. Prediction of the mortality dose-response relationship in man

    SciTech Connect

    Morris, M.D.; Jones, T.D.

    1987-01-01

    Based upon an extensive data base including 100 separate animal studies, an estimate of the mortality dose-response relationship due to continuous photon radiation is predicted for 70 kg man. The model used in this prediction exercise includes fixed terms accounting for effects of body weight and dose rate, and random terms accounting for inter- and intra-species variation and experimental error. Point predictions and 95% prediction intervals are given for the LD/sub 05/, LD/sub 10/, LD/sub 25/, LD/sub 50/, LD/sub 75/, LD/sub 90/, and LD/sub 95/, for dose rates ranging from 1 to 50 R/min. 6 refs., 5 tabs.

  14. Does Level of Response to SI Joint Block Predict Response to SI Joint Fusion?

    PubMed Central

    Cher, Daniel; Whang, Peter G.; Frank, Clay; Sembrano, Jonathan

    2016-01-01

    Background The degree of pain relief required to diagnose sacroiliac joint (SIJ) dysfunction following a diagnostic SIJ block (SIJB) is not known. No gold standard exists. Response to definitive (i.e., accepted as effective) treatment might be a reference standard. Methods Subgroup analysis of 320 subjects enrolled in two prospective multicenter trials evaluating SIJ fusion (SIJF) in patients with SIJ dysfunction diagnosed by history, physical exam and standardized diagnostic SIJB. A 50% reduction in pain at 30 or 60 minutes following SIJB was considered confirmatory. The absolute and percentage improvements in Visual Analog Scale (VAS) SIJ pain and Oswestry Disability Index (ODI) scores at 6 and 12 months after SIJF were correlated with the average acute improvement in SIJ pain with SIJB. Results The average pain reduction during the first hour after SIJB was 79.3%. Six months after SIJF, the overall mean VAS SIJ pain reduction was 50.9 points (0-100 scale) and the mean ODI reduction was 24.6 points. Reductions at 12 months after SIJF were similar. Examined in multiple ways, improvements in SIJ pain and ODI at 6 and 12 months did not correlate with SIJB findings. Conclusions The degree of pain improvement during SIJB did not predict improvements in pain or ODI scores after SIJF. A 50% SIJB threshold resulted in excellent post-SIJF responses. Using overly stringent selection criteria (i.e. 75%) to qualify patients for SIJF has no basis in evidence and would withhold a beneficial procedure from a substantial number of patients with SIJ dysfunction. Level of Evidence Level 1. Clinical Relevance The degree of pain improvement during an SIJ block does not predict the degree of pain improvement after SIJ fusion. PMID:26913224

  15. Simplified Analysis Model for Predicting Pyroshock Responses on Composite Panel

    NASA Astrophysics Data System (ADS)

    Iwasa, Takashi; Shi, Qinzhong

    A simplified analysis model based on the frequency response analysis and the wave propagation analysis was established for predicting Shock Response Spectrum (SRS) on the composite panel subjected to pyroshock loadings. The complex composite panel was modeled as an isotropic single layer panel defined in NASA Lewis Method. Through the conductance of an impact excitation test on a composite panel with no equipment mounted on, it was presented that the simplified analysis model could estimate the SRS as well as the acceleration peak values in both near and far field in an accurate way. In addition, through the simulation for actual pyroshock tests on an actual satellite system, the simplified analysis model was proved to be applicable in predicting the actual pyroshock responses, while bringing forth several technical issues to estimate the pyroshock test specifications in early design stages.

  16. Prenatal cortisol exposure predicts infant cortisol response to acute stress.

    PubMed

    O'Connor, Thomas G; Bergman, Kristin; Sarkar, Pampa; Glover, Vivette

    2013-03-01

    Experimental animal findings suggest that early stress and glucocorticoid exposure may program the function of the hypothalamic-pituitary-adrenal (HPA) axis in the offspring. The extension of these findings to human development is not yet clear. A prospective longitudinal study was conducted on 125 mothers and their normally developing children. Amniotic fluid was obtained at, on average, 17.2 weeks gestation; infant behavior and cortisol response to a separation-reunion stress was assessed at 17 months. Amniotic fluid cortisol predicted infant cortisol response to separation-reunion stress: infants who were exposed to higher levels of cortisol in utero showed higher pre-stress cortisol values and blunted response to stress exposure. The association was independent of prenatal, obstetric, and socioeconomic factors and child-parent attachment. The findings provide some of the strongest data in humans that HPA axis functioning in the child may be predicted from prenatal cortisol exposure.

  17. Histomorphological Factors Predicting the Response to Neoadjuvant Chemotherapy in Triple-Negative Breast Cancer

    PubMed Central

    Jung, Yoon Yang; Hyun, Chang Lim; Jin, Min-Sun; Park, In Ae; Chung, Yul Ri; Shim, Bobae; Lee, Kyu Ho

    2016-01-01

    Purpose There is no standard targeted therapy for the treatment of triple-negative breast cancer (TNBC). Therefore, its management heavily depends on adjuvant chemotherapy. Using core needle biopsy, this study evaluated the histological factors of TNBC predicting the response to chemotherapy. Methods One hundred forty-three TNBC patients who received single-regimen neoadjuvant chemotherapy (NAC) with the combination of doxorubicin, cyclophosphamide, and docetaxel were enrolled. The core needle biopsy specimens acquired before NAC were used to analyze the clinicopathologic variables and overall performance of the predictive model for therapeutic response. Results Independent predictors of pathologic complete response after NAC were found to be higher number of tumor infiltrating lymphocytes (p=0.007), absence of clear cytoplasm (p=0.008), low necrosis (p=0.018), and high histologic grade (p=0.039). In the receiver operating characteristics curve analysis, the area under curve for the combination of these four variables was 0.777. Conclusion The present study demonstrated that a predictive model using the above four variables can predict therapeutic response to single-regimen NAC with the combination of doxorubicin, cyclophosphamide, and docetaxel in TNBC. Therefore, adding these morphologic variables to clinical and genomic signatures might enhance the ability to predict the therapeutic response to NAC in TNBC. PMID:27721875

  18. A model of placebo response in antidepressant clinical trials.

    PubMed

    Rutherford, Bret R; Roose, Steven P

    2013-07-01

    Placebo response in clinical trials of antidepressant medications is substantial and has been increasing. High placebo response rates hamper efforts to detect signals of efficacy for new antidepressant medications, contributing to trial failures and delaying the delivery of new treatments to market. Media reports seize upon increasing placebo response and modest advantages for active drugs as reasons to question the value of antidepressant medication, which may further stigmatize treatments for depression and dissuade patients from accessing mental health care. Conversely, enhancing the factors responsible for placebo response may represent a strategy for improving available treatments for major depressive disorder. A conceptual framework describing the causes of placebo response is needed in order to develop strategies for minimizing placebo response in clinical trials, maximizing placebo response in clinical practice, and talking with depressed patients about the risks and benefits of antidepressant medications. In this review, the authors examine contributors to placebo response in antidepressant clinical trials and propose an explanatory model. Research aimed at reducing placebo response should focus on limiting patient expectancy and the intensity of therapeutic contact in antidepressant clinical trials, while the optimal strategy in clinical practice may be to combine active medication with a presentation and level of therapeutic contact designed to enhance treatment response.

  19. Apoptosis and other immune biomarkers predict influenza vaccine responsiveness

    PubMed Central

    Furman, David; Jojic, Vladimir; Kidd, Brian; Shen-Orr, Shai; Price, Jordan; Jarrell, Justin; Tse, Tiffany; Huang, Huang; Lund, Peder; Maecker, Holden T; Utz, Paul J; Dekker, Cornelia L; Koller, Daphne; Davis, Mark M

    2013-01-01

    Despite the importance of the immune system in many diseases, there are currently no objective benchmarks of immunological health. In an effort to identifying such markers, we used influenza vaccination in 30 young (20–30 years) and 59 older subjects (60 to >89 years) as models for strong and weak immune responses, respectively, and assayed their serological responses to influenza strains as well as a wide variety of other parameters, including gene expression, antibodies to hemagglutinin peptides, serum cytokines, cell subset phenotypes and in vitro cytokine stimulation. Using machine learning, we identified nine variables that predict the antibody response with 84% accuracy. Two of these variables are involved in apoptosis, which positively associated with the response to vaccination and was confirmed to be a contributor to vaccine responsiveness in mice. The identification of these biomarkers provides new insights into what immune features may be most important for immune health. PMID:23591775

  20. Graphics and statistics for cardiology: clinical prediction rules.

    PubMed

    Woodward, Mark; Tunstall-Pedoe, Hugh; Peters, Sanne Ae

    2017-04-01

    Graphs and tables are indispensable aids to quantitative research. When developing a clinical prediction rule that is based on a cardiovascular risk score, there are many visual displays that can assist in developing the underlying statistical model, testing the assumptions made in this model, evaluating and presenting the resultant score. All too often, researchers in this field follow formulaic recipes without exploring the issues of model selection and data presentation in a meaningful and thoughtful way. Some ideas on how to use visual displays to make wise decisions and present results that will both inform and attract the reader are given. Ideas are developed, and results tested, using subsets of the data that were used to develop the ASSIGN cardiovascular risk score, as used in Scotland.

  1. Physicochemical vs. Vibrational Descriptors for Prediction of Odor Receptor Responses.

    PubMed

    Gabler, Stephan; Soelter, Jan; Hussain, Taufia; Sachse, Silke; Schmuker, Michael

    2013-10-01

    Responses of olfactory receptors (ORs) can be predicted by applying machine learning methods on a multivariate encoding of an odorant's chemical structure. Physicochemical descriptors that encode features of the molecular graph are a popular choice for such an encoding. Here, we explore the EVA descriptor set, which encodes features derived from the vibrational spectrum of a molecule. We assessed the performance of Support Vector Regression (SVR) and Random Forest Regression (RFR) to predict the gradual response of Drosophila ORs. We compared a 27-dimensional variant of the EVA descriptor against a set of 1467 descriptors provided by the eDragon software package, and against a 32-dimensional subset thereof that has been proposed as the basis for an odor metric consisting of 32 descriptors (HADDAD). The best prediction performance was reproducibly achieved using SVR on the highest-dimensional feature set. The low-dimensional EVA and HADDAD feature sets predicted odor-OR interactions with similar accuracy. Adding charge and polarizability information to the EVA descriptor did not improve the results but rather decreased predictive power. Post-hoc in vivo measurements confirmed these results. Our findings indicate that EVA provides a meaningful low-dimensional representation of odor space, although EVA hardly outperformed "classical" descriptor sets.

  2. Lipidomic analysis enables prediction of clinical outcomes in burn patients

    PubMed Central

    Qi, Peter; Abdullahi, Abdikarim; Stanojcic, Mile; Patsouris, David; Jeschke, Marc G.

    2016-01-01

    Recent discoveries have highlighted the novel metabolic functions of adipose tissue in enhancing hypermetabolism after trauma. As the exact function and expression profiles of serum lipids and free fatty acids (FFA) are essentially unknown, we determined the lipidomic expression profile after burn in correlation to clinical outcomes to identify important lipid mediators affecting post-burn outcomes. We conducted a prospective cohort study with 46 adult burn patients and 5 healthy controls at the Ross Tilley Burn Center in Toronto, Canada. Patients were stratified based on major demographic and clinical variables, including age, burn severity, mortality, and sepsis. Serum FFAs and inflammatory markers were measured during acute hospital stay. We found that FFAs were acutely elevated post-burn and returned to baseline over time. Greater burn severity and age were associated with an impaired acute response in unsaturated FFAs and pro-inflammatory cytokines. Elevations in saturated and mono-unsaturated FFAs correlated significantly to increased mortality. In summary, persistent elevation of unsaturated lipids was associated with a functionally altered inflammatory-immunological milieu and worse clinical outcomes. The present lipidomic analysis indicates profound alterations in the lipid profile after burn by characterizing key lipids as potential diagnostic and outcome indicators in critically injured patients. PMID:27982130

  3. Infants, mothers, and dyadic contributions to stability and prediction of social stress response at 6 months.

    PubMed

    Provenzi, Livio; Olson, Karen L; Montirosso, Rosario; Tronick, Ed

    2016-01-01

    The study of infants' interactive style and social stress response to repeated stress exposures is of great interest for developmental and clinical psychologists. Stable maternal and dyadic behavior is critical to sustain infants' development of an adaptive social stress response, but the association between infants' interactive style and social stress response has received scant attention in previous literature. In the present article, overtime stability of infant, maternal, and dyadic behaviors was measured across 2 social stress (i.e., Face-to-Face Still-Face, FFSF) exposures, separated by 15 days. Moreover, infant, maternal, and dyadic behaviors were simultaneously assessed as predictors of infants' social stress to both FFSF exposures. Eighty-one mother-infant dyads underwent the FFSF twice, at 6 months (Exposure 1: the first social stress) and at 6 months and 15 days (Exposure 2: repeated social stress). Infant and mother behavior and dyadic synchrony were microanalytically coded. Overall, individual behavioral stability emerged between FFSF exposures. Infants' response to the first stress was predicted by infant behavior during Exposure 1 Play. Infants' response to the repeated social stress was predicted by infants' response to the first exposure to the Still-Face and by infants' behavior and dyadic synchrony during Exposure 2 Play. Findings reveal stability for individual, but not for dyadic, behavior between 2 social stress exposures at 6 months. Infants' response to repeated social stress was predicted by infants' earlier stress response, infants' own behavior in play, and dyadic synchrony. No predictive effects of maternal behavior were found. Insights for research and clinical work are discussed.

  4. Baseline Brain Activity Predicts Response to Neuromodulatory Pain Treatment

    PubMed Central

    Jensen, Mark P.; Sherlin, Leslie H.; Fregni, Felipe; Gianas, Ann; Howe, Jon D.; Hakimian, Shahin

    2015-01-01

    Objectives The objective of this study was to examine the associations between baseline electroencephalogram (EEG)-assessed brain oscillations and subsequent response to four neuromodulatory treatments. Based on available research, we hypothesized that baseline theta oscillations would prospectively predict response to hypnotic analgesia. Analyses involving other oscillations and the other treatments (meditation, neurofeedback, and both active and sham transcranial direct current stimulation) were viewed as exploratory, given the lack of previous research examining brain oscillations as predictors of response to these other treatments. Design Randomized controlled study of single sessions of four neuromodulatory pain treatments and a control procedure. Methods Thirty individuals with spinal cord injury and chronic pain had their EEG recorded before each session of four active treatments (hypnosis, meditation, EEG biofeedback, transcranial direct current stimulation) and a control procedure (sham transcranial direct stimulation). Results As hypothesized, more presession theta power was associated with greater response to hypnotic analgesia. In exploratory analyses, we found that less baseline alpha power predicted pain reduction with meditation. Conclusions The findings support the idea that different patients respond to different pain treatments and that between-person treatment response differences are related to brain states as measured by EEG. The results have implications for the possibility of enhancing pain treatment response by either 1) better patient/treatment matching or 2) influencing brain activity before treatment is initiated in order to prepare patients to respond. Research is needed to replicate and confirm the findings in additional samples of individuals with chronic pain. PMID:25287554

  5. Prediction of Response to Preoperative Chemoradiotherapy in Rectal Cancer by Multiplex Kinase Activity Profiling

    SciTech Connect

    Folkvord, Sigurd; Flatmark, Kjersti; Dueland, Svein

    2010-10-01

    Purpose: Tumor response of rectal cancer to preoperative chemoradiotherapy (CRT) varies considerably. In experimental tumor models and clinical radiotherapy, activity of particular subsets of kinase signaling pathways seems to predict radiation response. This study aimed to determine whether tumor kinase activity profiles might predict tumor response to preoperative CRT in locally advanced rectal cancer (LARC). Methods and Materials: Sixty-seven LARC patients were treated with a CRT regimen consisting of radiotherapy, fluorouracil, and, where possible, oxaliplatin. Pretreatment tumor biopsy specimens were analyzed using microarrays with kinase substrates, and the resulting substrate phosphorylation patterns were correlated with tumor response to preoperative treatment as assessed by histomorphologic tumor regression grade (TRG). A predictive model for TRG scores from phosphosubstrate signatures was obtained by partial-least-squares discriminant analysis. Prediction performance was evaluated by leave-one-out cross-validation and use of an independent test set. Results: In the patient population, 73% and 15% were scored as good responders (TRG 1-2) or intermediate responders (TRG 3), whereas 12% were assessed as poor responders (TRG 4-5). In a subset of 7 poor responders and 12 good responders, treatment outcome was correctly predicted for 95%. Application of the prediction model on the remaining patient samples resulted in correct prediction for 85%. Phosphosubstrate signatures generated by poor-responding tumors indicated high kinase activity, which was inhibited by the kinase inhibitor sunitinib, and several discriminating phosphosubstrates represented proteins derived from signaling pathways implicated in radioresistance. Conclusions: Multiplex kinase activity profiling may identify functional biomarkers predictive of tumor response to preoperative CRT in LARC.

  6. Specific molecular signatures predict decitabine response in chronic myelomonocytic leukemia.

    PubMed

    Meldi, Kristen; Qin, Tingting; Buchi, Francesca; Droin, Nathalie; Sotzen, Jason; Micol, Jean-Baptiste; Selimoglu-Buet, Dorothée; Masala, Erico; Allione, Bernardino; Gioia, Daniela; Poloni, Antonella; Lunghi, Monia; Solary, Eric; Abdel-Wahab, Omar; Santini, Valeria; Figueroa, Maria E

    2015-05-01

    Myelodysplastic syndromes and chronic myelomonocytic leukemia (CMML) are characterized by mutations in genes encoding epigenetic modifiers and aberrant DNA methylation. DNA methyltransferase inhibitors (DMTis) are used to treat these disorders, but response is highly variable, with few means to predict which patients will benefit. Here, we examined baseline differences in mutations, DNA methylation, and gene expression in 40 CMML patients who were responsive or resistant to decitabine (DAC) in order to develop a molecular means of predicting response at diagnosis. While somatic mutations did not differentiate responders from nonresponders, we identified 167 differentially methylated regions (DMRs) of DNA at baseline that distinguished responders from nonresponders using next-generation sequencing. These DMRs were primarily localized to nonpromoter regions and overlapped with distal regulatory enhancers. Using the methylation profiles, we developed an epigenetic classifier that accurately predicted DAC response at the time of diagnosis. Transcriptional analysis revealed differences in gene expression at diagnosis between responders and nonresponders. In responders, the upregulated genes included those that are associated with the cell cycle, potentially contributing to effective DAC incorporation. Treatment with CXCL4 and CXCL7, which were overexpressed in nonresponders, blocked DAC effects in isolated normal CD34+ and primary CMML cells, suggesting that their upregulation contributes to primary DAC resistance.

  7. Rapid and Highly Accurate Prediction of Poor Loop Diuretic Natriuretic Response in Patients With Heart Failure

    PubMed Central

    Testani, Jeffrey M.; Hanberg, Jennifer S.; Cheng, Susan; Rao, Veena; Onyebeke, Chukwuma; Laur, Olga; Kula, Alexander; Chen, Michael; Wilson, F. Perry; Darlington, Andrew; Bellumkonda, Lavanya; Jacoby, Daniel; Tang, W. H. Wilson; Parikh, Chirag R.

    2015-01-01

    Background Removal of excess sodium and fluid is a primary therapeutic objective in acute decompensated heart failure (ADHF) and commonly monitored with fluid balance and weight loss. However, these parameters are frequently inaccurate or not collected and require a delay of several hours after diuretic administration before they are available. Accessible tools for rapid and accurate prediction of diuretic response are needed. Methods and Results Based on well-established renal physiologic principles an equation was derived to predict net sodium output using a spot urine sample obtained one or two hours following loop diuretic administration. This equation was then prospectively validated in 50 ADHF patients using meticulously obtained timed 6-hour urine collections to quantitate loop diuretic induced cumulative sodium output. Poor natriuretic response was defined as a cumulative sodium output of <50 mmol, a threshold that would result in a positive sodium balance with twice-daily diuretic dosing. Following a median dose of 3 mg (2–4 mg) of intravenous bumetanide, 40% of the population had a poor natriuretic response. The correlation between measured and predicted sodium output was excellent (r=0.91, p<0.0001). Poor natriuretic response could be accurately predicted with the sodium prediction equation (AUC=0.95, 95% CI 0.89–1.0, p<0.0001). Clinically recorded net fluid output had a weaker correlation (r=0.66, p<0.001) and lesser ability to predict poor natriuretic response (AUC=0.76, 95% CI 0.63–0.89, p=0.002). Conclusions In patients being treated for ADHF, poor natriuretic response can be predicted soon after diuretic administration with excellent accuracy using a spot urine sample. PMID:26721915

  8. Impact of triplicate testing on HIV genotypic tropism prediction in routine clinical practice.

    PubMed

    Symons, J; Vandekerckhove, L; Paredes, R; Verhofstede, C; Bellido, R; Demecheleer, E; van Ham, P M; van Lelyveld, S F L; Stam, A J; van Versendaal, D; Nijhuis, M; Wensing, A M J

    2012-06-01

    Guidelines state that the CCR5-inhibitor Maraviroc should be prescribed to patients infected with R5-tropic HIV-1 only. Therefore, viral tropism needs to be assessed phenotypically or genotypically. Preliminary clinical trial data suggest that genotypic analysis in triplicate is associated with improved prediction of virological response by increasing the detection of X4-tropic variants. Our objective was to evaluate the impact of triplicate genotypic analysis on prediction of co-receptor usage in routine clinical practice. Samples from therapy-naive and therapy-experienced patients were collected for routine tropism testing at three European clinical centres. Viral RNA was isolated from plasma and proviral DNA from peripheral blood mononuclear cells. Gp120-V3 was amplified in a triplicate nested RT-PCR procedure and sequenced. Co-receptor usage was predicted using the Geno2Pheno([coreceptor]) algorithm and analysed with a false-positive rate (FPR) of 5.75%, 10%, or an FPR of 20% and according to the current European guidelines on the clinical management of HIV-1 tropism testing. A total of 266 sequences were obtained from 101 patient samples. Discordance in tropism prediction for the triplicates was observed in ten samples using an FPR of 10%. Triplicate testing resulted in a 16.7% increase in X4-predicted samples and to reclassification from R5 to X4 tropism for four cases rendering these patients ineligible for Maraviroc treatment. In conclusion, triplicate genotypic tropism testing increases X4 tropism detection in individual cases, which may prove to be pivotal when CCR5-inhibitor therapy is applied.

  9. Clinical prediction of weaning and extubation in Australian and New Zealand intensive care units.

    PubMed

    Rose, L; Presneill, J J

    2011-07-01

    Our objective was to describe, in Australian and New Zealand adult intensive care units, the relative frequency in which various clinical criteria were used to predict weaning and extubation, and the weaning methods employed. Participant intensivists at 55 intensive care units completed a self-administered questionnaire, using visual analogue scales (0 = not at all predictive, 10 = perfectly predictive, not used = null score) to record the perceived utility of 30 potential predictors. Survey response rate was 71% (164/230). Those variables thought most predictive of weaning readiness were respiratory rate (median score 8.0, interquartile range 7.0 to 8.6) effective cough (7.3, 5.9 to 8.2) and pressure support setting (7.2, 6.0 to 8.0). The most highly rated predictors of extubation success were effective cough (8.0, 7.0 to 9.0), respiratory rate (8.0, 7.0 to 8.5) and Glasgow Coma Score (7.9, 6.1 to 8.3). Variables perceived least predictive of weaning and extubation success were P0.1, Acute Physiological and Chronic Health Evaluation score II, mean arterial pressure, electrolytes and maximum inspiratory pressure (individual median scores < 5). Most popular clinical criteria were those perceived to have high predictive accuracy, both for weaning (respiratory rate 96%, pressure support setting 94% and Glasgow coma score 91%) and extubation readiness (respiratory rate 98%, effective cough 94% and Glasgow Coma Score 92%). Weaning mostly employed pressure support ventilation (55%), with less use of synchronised intermittent mandatory ventilation (32%) and spontaneous breathing trials (13%). Classic ventilatory performance predictors including respiratory rate and effective cough were reported to be of greater clinical utility than other more recently proposed measures.

  10. Perturbation Predictability Can Influence the Long-Latency Stretch Response

    PubMed Central

    Forgaard, Christopher J.; Franks, Ian M.; Maslovat, Dana; Chua, Romeo

    2016-01-01

    Perturbations applied to the upper limbs elicit short (M1: 25–50 ms) and long-latency (M2: 50–100 ms) responses in the stretched muscle. M1 is produced by a spinal reflex loop, and M2 receives contribution from multiple spinal and supra-spinal pathways. While M1 is relatively immutable to voluntary intention, the remarkable feature of M2 is that its size can change based on intention or goal of the participant (e.g., increasing when resisting the perturbation and decreasing when asked to let-go or relax following the perturbation). While many studies have examined modulation of M2 between passive and various active conditions, through the use of constant foreperiods (interval between warning signal and a perturbation), it has also been shown that the magnitude of the M2 response in a passive condition can change based on factors such as habituation and anticipation of perturbation delivery. To prevent anticipation of a perturbation, most studies have used variable foreperiods; however, the range of possible foreperiod duration differs between experiments. The present study examined the influence of different variable foreperiods on modulation of the M2 response. Fifteen participants performed active and passive responses to a perturbation that stretched wrist flexors. Each block of trials had either a short (2.5–3.5 seconds; high predictability) or long (2.5–10.5 seconds; low predictability) variable foreperiod. As expected, no differences were found between any conditions for M1, while M2 was larger in the active rather than passive conditions. Interestingly, within the two passive conditions, the long variable foreperiods resulted in greater activity at the end of the M2 response than the trials with short foreperiods. These results suggest that perturbation predictability, even when using a variable foreperiod, can influence circuitry contributing to the long-latency stretch response. PMID:27727293

  11. Pragmatic hydraulic theory predicts stomatal responses to climatic water deficits.

    PubMed

    Sperry, John S; Wang, Yujie; Wolfe, Brett T; Mackay, D Scott; Anderegg, William R L; McDowell, Nate G; Pockman, William T

    2016-11-01

    Ecosystem models have difficulty predicting plant drought responses, partially from uncertainty in the stomatal response to water deficits in soil and atmosphere. We evaluate a 'supply-demand' theory for water-limited stomatal behavior that avoids the typical scaffold of empirical response functions. The premise is that canopy water demand is regulated in proportion to threat to supply posed by xylem cavitation and soil drying. The theory was implemented in a trait-based soil-plant-atmosphere model. The model predicted canopy transpiration (E), canopy diffusive conductance (G), and canopy xylem pressure (Pcanopy ) from soil water potential (Psoil ) and vapor pressure deficit (D). Modeled responses to D and Psoil were consistent with empirical response functions, but controlling parameters were hydraulic traits rather than coefficients. Maximum hydraulic and diffusive conductances and vulnerability to loss in hydraulic conductance dictated stomatal sensitivity and hence the iso- to anisohydric spectrum of regulation. The model matched wide fluctuations in G and Pcanopy across nine data sets from seasonally dry tropical forest and piñon-juniper woodland with < 26% mean error. Promising initial performance suggests the theory could be useful in improving ecosystem models. Better understanding of the variation in hydraulic properties along the root-stem-leaf continuum will simplify parameterization.

  12. Proteomic profiling predicts drug response to novel targeted anticancer therapeutics.

    PubMed

    Lin, Fan; Li, Zilin; Hua, Yunfen; Lim, Yoon Pin

    2016-01-01

    Most recently approved anti-cancer drugs by the US FDA are targeted therapeutic agents and this represents an important trend for future anticancer therapy. Unlike conventional chemotherapy that rarely considers individual differences, it is crucial for targeted therapies to identify the beneficial subgroup of patients for the treatment. Currently, genomics and transcriptomics are the major 'omic' analytics used in studies of drug response prediction. However, proteomic profiling excels both in its advantages of directly detecting an instantaneous dynamic of the whole proteome, which contains most current diagnostic markers and therapeutic targets. Moreover, proteomic profiling improves understanding of the mechanism for drug resistance and helps finding optimal combination therapy. This article reviews the recent success of applications of proteomic analytics in predicting the response to targeted anticancer therapeutics, and discusses the potential avenues and pitfalls of proteomic platforms and techniques used most in the field.

  13. Prediction of Selection Response for Threshold Dichotomous Traits

    PubMed Central

    Foulley, J. L.

    1992-01-01

    This paper presents a formula to predict expected response to one generation of truncation selection for a dichotomous trait under polygenic additive inheritance. The derivation relies on the threshold liability concept and on the normality assumption of the joint distribution of additive genetic values and their predictors used as selection criteria. This formula accounts for asymmetry of response when both the prevalence of the trait and the selection rate differ from 1/2 via a bivariate normal integral term. The relationship with the classical formula R = ipσ(G) is explained with a Taylor expansion about a zero value of the correlation factor. Properties are illustrated with an example of sire selection based on progeny test performance which shows a departure from usual predictions up to 15-20% at low (0.05) or high (0.95) selection rates. Univariate approximations and extensions to several paths of genetic change are also discussed. PMID:1459435

  14. Predictive coding of music--brain responses to rhythmic incongruity.

    PubMed

    Vuust, Peter; Ostergaard, Leif; Pallesen, Karen Johanne; Bailey, Christopher; Roepstorff, Andreas

    2009-01-01

    During the last decades, models of music processing in the brain have mainly discussed the specificity of brain modules involved in processing different musical components. We argue that predictive coding offers an explanatory framework for functional integration in musical processing. Further, we provide empirical evidence for such a network in the analysis of event-related MEG-components to rhythmic incongruence in the context of strong metric anticipation. This is seen in a mismatch negativity (MMNm) and a subsequent P3am component, which have the properties of an error term and a subsequent evaluation in a predictive coding framework. There were both quantitative and qualitative differences in the evoked responses in expert jazz musicians compared with rhythmically unskilled non-musicians. We propose that these differences trace a functional adaptation and/or a genetic pre-disposition in experts which allows for a more precise rhythmic prediction.

  15. Predictions of F-111 TACT aircraft buffet response

    NASA Technical Reports Server (NTRS)

    Cunningham, Atlee M., Jr.; Coe, Charles F.

    1990-01-01

    A summary is presented for the prediction method development and correlations of predicted response with flight test measurements. The prediction method was based on refinements to the method described by Cunningham. One improvement made use of direct time integration of the correlated fluctuating pressure data to obtain buffet excitation for the various modes of interest. Another improvement incorporated a hybrid technique for scaling measured wind tunnel damping data to full-scale for the modes of interest. A third improvement made use of the diagonalized form of the fully coupled equations of motion. Finally, a mechanism was described for explaining an apparent coupling between the aircraft wing torsion modes and shock induced trailing edge separation that led to very high wing motion on the aircraft that was not observed on the wind tunnel model.

  16. A novel neural-inspired learning algorithm with application to clinical risk prediction.

    PubMed

    Tay, Darwin; Poh, Chueh Loo; Kitney, Richard I

    2015-04-01

    Clinical risk prediction - the estimation of the likelihood an individual is at risk of a disease - is a coveted and exigent clinical task, and a cornerstone to the recommendation of life saving management strategies. This is especially important for individuals at risk of cardiovascular disease (CVD) given the fact that it is the leading causes of death in many developed counties. To this end, we introduce a novel learning algorithm - a key factor that influences the performance of machine learning-based prediction models - and utilities it to develop CVD risk prediction tool. This novel neural-inspired algorithm, called the Artificial Neural Cell System for classification (ANCSc), is inspired by mechanisms that develop the brain and empowering it with capabilities such as information processing/storage and recall, decision making and initiating actions on external environment. Specifically, we exploit on 3 natural neural mechanisms responsible for developing and enriching the brain - namely neurogenesis, neuroplasticity via nurturing and apoptosis - when implementing ANCSc algorithm. Benchmark testing was conducted using the Honolulu Heart Program (HHP) dataset and results are juxtaposed with 2 other algorithms - i.e. Support Vector Machine (SVM) and Evolutionary Data-Conscious Artificial Immune Recognition System (EDC-AIRS). Empirical experiments indicate that ANCSc algorithm (statistically) outperforms both SVM and EDC-AIRS algorithms. Key clinical markers identified by ANCSc algorithm include risk factors related to diet/lifestyle, pulmonary function, personal/family/medical history, blood data, blood pressure, and electrocardiography. These clinical markers, in general, are also found to be clinically significant - providing a promising avenue for identifying potential cardiovascular risk factors to be evaluated in clinical trials.

  17. Prediction of heart rate response to conclusion of the spontaneous breathing trial by fluctuation dissipation theory

    NASA Astrophysics Data System (ADS)

    Chen, Man; Niestemski, Liang Ren; Prevost, Robert; McRae, Michael; Cholleti, Sharath; Najarro, Gabriel; Buchman, Timothy G.; Deem, Michael W.

    2013-02-01

    The non-equilibrium fluctuation dissipation theorem is applied to predict how critically ill patients respond to treatment, based upon data currently collected by standard hospital monitoring devices. This framework is demonstrated on a common procedure in critical care: the spontaneous breathing trial. It is shown that the responses of groups of similar patients to the spontaneous breathing trial can be predicted by the non-equilibrium fluctuation dissipation approach. This mathematical framework, when fully formed and applied to other clinical interventions, may serve as part of the basis for personalized critical care.

  18. Predicting Individual Differences in Response to Sleep Loss

    DTIC Science & Technology

    2011-09-15

    2011 4. TITLE Predicting Individual Differences in Response to Sleep Loss 5a. Contract Number: 5b. Grant Number: 5c. Program Element Number: 5d...ADDRESS(ES) Naval Medical Reserach Unit – Dayton 2624 Q St., Bldg. 851, Area B Wright-Patterson AFB, OH 45433 8. PERFORMING ORGANIZATION... Program Department of the Navy 2300 E Street, NW Washington, DC 20372-5300 10. SPONSOR/MONITOR’S ACRONYM(S) BUMED 11. SPONSOR/MONITOR’S REPORT

  19. Use of reflectance spectrophotometry to predict the response of port wine stains to pulsed dye laser.

    PubMed

    Halachmi, Shlomit; Azaria, Ron; Inbar, Roy; Ad-El, Dean; Lapidoth, Moshe

    2014-01-01

    Reflectance spectroscopy can be used to quantitate subtle differences in color. We applied a portable reflectance spectrometer to determine its utility in the evaluation of pulsed dye laser treatment of port wine stains (PWS) and in prediction of clinical outcome, in a prospective study. Forty-eight patients with PWS underwent one to nine pulsed dye laser treatments. Patient age and skin color as well as PWS surface area, anatomic location, and color were recorded. Pretreatment spectrophotometric measurements were performed. The subjective clinical results of treatment and the quantitative spectrophotometry results were evaluated by two independent teams, and the findings were correlated. The impact of the clinical characteristics on the response to treatment was assessed as well. Patients with excellent to good clinical results of laser treatments had pretreatment spectrophotometric measurements which differed by more than 10%, whereas patients with fair to poor results had spectrophotometric measurements with a difference of of less than 10%. The correlation between the spectrophotometric results and the clinical outcome was 73% (p < 0.01). The impact of the other clinical variables on outcome agreed with the findings in the literature. Spectrophotometry has a higher correlation with clinical outcome and a better predictive value than other nonmeasurable, nonquantitative, dependent variables.

  20. Prediction of individual response to antidepressants and antipsychotics: an integrated concept.

    PubMed

    Preskorn, Sheldon H

    2014-12-01

    In both clinical trials and daily practice, there can be substantial inter- and even intraindividual variability in response--whether beneficial or adverse--to antidepressants and antipsychotic medications. So far, no tools have become available to predict the outcome of these treatments in specific patients. This is because the causes of such variability are often not known, and when they are, there is no way of predicting the effects of their various potential combinations in an individual. Given this background, this paper presents a conceptual framework for understanding known factors and their combinations so that eventually clinicians can better predict what medication(s) to select and at what dose they can optimize the outcome for a given individual. This framework is flexible enough to be readily adaptable as new information becomes available. The causes of variation in patient response are grouped into four categories: (i) genetics; (ii) age; (iii) disease; and (iv) environment (internal). Four cases of increasing complexity are used to illustrate the applicability of this framework in a clinically relevant way In addition, this paper reviews tools that the clinician can use to assess for and quantify such inter- and intraindividual variability. With the information gained, treatment can be adjusted to compensate for such variability, in order to optimize outcome. Finally, the limitations of existing antidepressant and antipsychotic therapy and the way they reduce current ability to predict response is discussed.

  1. Early improvement with pregabalin predicts endpoint response in patients with generalized anxiety disorder: an integrated and predictive data analysis.

    PubMed

    Montgomery, Stuart A; Lyndon, Gavin; Almas, Mary; Whalen, Ed; Prieto, Rita

    2017-01-01

    Generalized anxiety disorder (GAD), a common mental disorder, has several treatment options including pregabalin. Not all patients respond to treatment; quickly determining which patients will respond is an important treatment goal. Patient-level data were pooled from nine phase II and III randomized, double-blind, short-term, placebo-controlled trials of pregabalin for the treatment of GAD. Efficacy outcomes included the change from baseline in the Hamilton Anxiety Scale (HAM-A) total score and psychic and somatic subscales. Predictive modelling assessed baseline characteristics and early clinical responses to determine those predictive of clinical improvement at endpoint. A total of 2155 patients were included in the analysis (1447 pregabalin, 708 placebo). Pregabalin significantly improved the HAM-A total score compared with the placebo at endpoint, treatment difference (95% confidence interval), -2.61 (-3.21 to -2.01), P<0.0001. Pregabalin significantly improved HAM-A psychic and somatic scores compared with placebo, -1.52 (-1.85 to -1.18), P<0.0001, and -1.10 (-1.41 to -0.80), P<0.0001, respectively. Response to pregabalin in the first 1-2 weeks (≥20 or ≥30% improvement in HAM-A total, psychic or somatic score) was predictive of an endpoint greater than or equal to 50% improvement in the HAM-A total score. Pregabalin is an effective treatment option for patients with GAD. Patients with early response to pregabalin are more likely to respond significantly at endpoint.

  2. Prediction of Pharmacological and Xenobiotic Responses to Drugs Based on Time Course Gene Expression Profiles

    PubMed Central

    Hu, LeLe; Feng, KaiYan; Li, Yi-Xue; Cai, Yu-Dong

    2009-01-01

    More and more people are concerned by the risk of unexpected side effects observed in the later steps of the development of new drugs, either in late clinical development or after marketing approval. In order to reduce the risk of the side effects, it is important to look out for the possible xenobiotic responses at an early stage. We attempt such an effort through a prediction by assuming that similarities in microarray profiles indicate shared mechanisms of action and/or toxicological responses among the chemicals being compared. A large time course microarray database derived from livers of compound-treated rats with thirty-four distinct pharmacological and toxicological responses were studied. The mRMR (Minimum-Redundancy-Maximum-Relevance) method and IFS (Incremental Feature Selection) were used to select a compact feature set (141 features) for the reduction of feature dimension and improvement of prediction performance. With these 141 features, the Leave-one-out cross-validation prediction accuracy of first order response using NNA (Nearest Neighbor Algorithm) was 63.9%. Our method can be used for pharmacological and xenobiotic responses prediction of new compounds and accelerate drug development. PMID:19956587

  3. Predicting the response of olfactory sensory neurons to odor mixtures from single odor response

    PubMed Central

    Marasco, Addolorata; De Paris, Alessandro; Migliore, Michele

    2016-01-01

    The response of olfactory receptor neurons to odor mixtures is not well understood. Here, using experimental constraints, we investigate the mathematical structure of the odor response space and its consequences. The analysis suggests that the odor response space is 3-dimensional, and predicts that the dose-response curve of an odor receptor can be obtained, in most cases, from three primary components with specific properties. This opens the way to an objective procedure to obtain specific olfactory receptor responses by manipulating mixtures in a mathematically predictable manner. This result is general and applies, independently of the number of odor components, to any olfactory sensory neuron type with a response curve that can be represented as a sigmoidal function of the odor concentration. PMID:27053070

  4. Predicting the response of olfactory sensory neurons to odor mixtures from single odor response

    NASA Astrophysics Data System (ADS)

    Marasco, Addolorata; de Paris, Alessandro; Migliore, Michele

    2016-04-01

    The response of olfactory receptor neurons to odor mixtures is not well understood. Here, using experimental constraints, we investigate the mathematical structure of the odor response space and its consequences. The analysis suggests that the odor response space is 3-dimensional, and predicts that the dose-response curve of an odor receptor can be obtained, in most cases, from three primary components with specific properties. This opens the way to an objective procedure to obtain specific olfactory receptor responses by manipulating mixtures in a mathematically predictable manner. This result is general and applies, independently of the number of odor components, to any olfactory sensory neuron type with a response curve that can be represented as a sigmoidal function of the odor concentration.

  5. Predictive factors associated with hepatitis C antiviral therapy response

    PubMed Central

    Cavalcante, Lourianne Nascimento; Lyra, André Castro

    2015-01-01

    Hepatitis C virus (HCV) infection may lead to significant liver injury, and viral, environmental, host, immunologic and genetic factors may contribute to the differences in the disease expression and treatment response. In the early 2000s, dual therapy using a combination of pegylated interferon plus ribavirin (PR) became the standard of care for HCV treatment. In this PR era, predictive factors of therapy response related to virus and host have been identified. In 2010/2011, therapeutic regimens for HCV genotype 1 patients were modified, and the addition of NS3/4a protease inhibitors (boceprevir or telaprevir) to dual therapy increased the effectiveness and chances of sustained virologic response (SVR). Nevertheless, the first-generation triple therapy is associated with many adverse events, some of which are serious and associated with death, particularly in cirrhotic patients. This led to the need to identify viral and host predictive factors that might influence the SVR rate to triple therapy and avoid unnecessary exposure to these drugs. Over the past four years, hepatitis C treatment has been rapidly changing with the development of new therapies and other developments. Currently, with the more recent generations of pangenotipic antiviral therapies, there have been higher sustained virologic rates, and prognostic factors may not have the same importance and strength as before. Nonetheless, some variables may still be consistent with the low rates of non-response with regimens that include sofosbuvir, daclatasvir and ledipasvir. In this manuscript, we review the predictive factors of therapy response across the different treatment regimens over the last decade including the new antiviral drugs. PMID:26140082

  6. An IL28B Genotype-Based Clinical Prediction Model for Treatment of Chronic Hepatitis C

    PubMed Central

    O'Brien, Thomas R.; Everhart, James E.; Morgan, Timothy R.; Lok, Anna S.; Chung, Raymond T.; Shao, Yongwu; Shiffman, Mitchell L.; Dotrang, Myhanh; Sninsky, John J.; Bonkovsky, Herbert L.; Pfeiffer, Ruth M.

    2011-01-01

    Background Genetic variation in IL28B and other factors are associated with sustained virological response (SVR) after pegylated-interferon/ribavirin treatment for chronic hepatitis C (CHC). Using data from the HALT-C Trial, we developed a model to predict a patient's probability of SVR based on IL28B genotype and clinical variables. Methods HALT-C enrolled patients with advanced CHC who had failed previous interferon-based treatment. Subjects were re-treated with pegylated-interferon/ribavirin during trial lead-in. We used step-wise logistic regression to calculate adjusted odds ratios (aOR) and create the predictive model. Leave-one-out cross-validation was used to predict a priori probabilities of SVR and determine area under the receiver operator characteristics curve (AUC). Results Among 646 HCV genotype 1-infected European American patients, 14.2% achieved SVR. IL28B rs12979860-CC genotype was the strongest predictor of SVR (aOR, 7.56; p<.0001); the model also included HCV RNA (log10 IU/ml), AST∶ALT ratio, Ishak fibrosis score and prior ribavirin treatment. For this model AUC was 78.5%, compared to 73.0% for a model restricted to the four clinical predictors and 60.0% for a model restricted to IL28B genotype (p<0.001). Subjects with a predicted probability of SVR <10% had an observed SVR rate of 3.8%; subjects with a predicted probability >10% (43.3% of subjects) had an SVR rate of 27.9% and accounted for 84.8% of subjects actually achieving SVR. To verify that consideration of both IL28B genotype and clinical variables is required for treatment decisions, we calculated AUC values from published data for the IDEAL Study. Conclusion A clinical prediction model based on IL28B genotype and clinical variables can yield useful individualized predictions of the probability of treatment success that could increase SVR rates and decrease the frequency of futile treatment among patients with CHC. PMID:21760886

  7. Impairing loyalty: corporate responsibility for clinical misadventure.

    PubMed

    Kipnis, Kenneth

    2011-09-01

    A medical device manufacturer pays a surgeon to demonstrate a novel medical instrument in a live broadcast to an audience of specialists in another city. The surgical patient is unaware of the broadcast and unaware of the doctor's relationship with the manufacturer. It turns out that the patient required a different surgical approach to her condition-one that would not have allowed a demonstration of the instrument--and she later dies. The paper is an exploration of whether the manufacturer shares, along with the doctor, responsibility for the death of the patient. Three arguments for corporate responsibility are considered; two are criticized and the third is offered as sound.

  8. Neutrophil biomarkers predict response to therapy with tumor necrosis factor inhibitors in rheumatoid arthritis.

    PubMed

    Wright, Helen L; Cox, Trevor; Moots, Robert J; Edwards, Steven W

    2017-03-01

    Neutrophils are implicated in the pathology of rheumatoid arthritis (RA), but the mechanisms regulating their activation are largely unknown. RA is a heterogeneous disease, and whereas many patients show clinical improvement during TNF inhibitor (TNFi) therapy, a significant proportion fails to respond. In vitro activation of neutrophils with agents, including TNF, results in rapid and selective changes in gene expression, but how neutrophils contribute to TNF signaling in RA and whether TNFi sensitivity involves differential neutrophil responses are unknown. With the use of RNA sequencing (RNA-Seq), we analyzed blood neutrophils from 20 RA patients, pre-TNFi therapy, to identify biomarkers of response, measured by a decrease in disease activity score based on 28 joint count (DAS28), 12 wk post-therapy. Biomarkers were validated by quantitative PCR (qPCR) of blood neutrophils from 2 further independent cohorts of RA patients: 16 pre-TNFi and 16 predisease-modifying anti-rheumatic drugs (DMARDs). Twenty-three neutrophil transcripts predicted a 12-wk response to TNFi: 10 (IFN-regulated) genes predicting a European League against Rheumatism (EULAR) good response and 13 different genes [neutrophil granule protein (NGP) genes] predicting a nonresponse. Statistical analysis indicated a predictive sensitivity and specificity of each gene in the panel of >80%, with some 100% specific. A combination of 3 genes [cytidine monophosphate kinase 2 (CMPK2), IFN-induced protein with tetratricopeptide repeats 1B (IFIT1B), and RNASE3] had the greatest predictive power [area under the curve (AUC) 0.94]. No correlation was found for a response to DMARDs. We conclude that this panel of genes is selective for predicting a response to TNFi and is not a surrogate marker for disease improvement. We also show that in RA, there is great plasticity in neutrophil phenotype, with circulating cells expressing genes normally only expressed in more immature cells.

  9. Ensemble ecosystem modeling for predicting ecosystem response to predator reintroduction.

    PubMed

    Baker, Christopher M; Gordon, Ascelin; Bode, Michael

    2017-04-01

    Introducing a new or extirpated species to an ecosystem is risky, and managers need quantitative methods that can predict the consequences for the recipient ecosystem. Proponents of keystone predator reintroductions commonly argue that the presence of the predator will restore ecosystem function, but this has not always been the case, and mathematical modeling has an important role to play in predicting how reintroductions will likely play out. We devised an ensemble modeling method that integrates species interaction networks and dynamic community simulations and used it to describe the range of plausible consequences of 2 keystone-predator reintroductions: wolves (Canis lupus) to Yellowstone National Park and dingoes (Canis dingo) to a national park in Australia. Although previous methods for predicting ecosystem responses to such interventions focused on predicting changes around a given equilibrium, we used Lotka-Volterra equations to predict changing abundances through time. We applied our method to interaction networks for wolves in Yellowstone National Park and for dingoes in Australia. Our model replicated the observed dynamics in Yellowstone National Park and produced a larger range of potential outcomes for the dingo network. However, we also found that changes in small vertebrates or invertebrates gave a good indication about the potential future state of the system. Our method allowed us to predict when the systems were far from equilibrium. Our results showed that the method can also be used to predict which species may increase or decrease following a reintroduction and can identify species that are important to monitor (i.e., species whose changes in abundance give extra insight into broad changes in the system). Ensemble ecosystem modeling can also be applied to assess the ecosystem-wide implications of other types of interventions including assisted migration, biocontrol, and invasive species eradication.

  10. Microsatellite Instability Predicts Clinical Outcome in Radiation-Treated Endometrioid Endometrial Cancer

    SciTech Connect

    Bilbao, Cristina; Lara, Pedro Carlos; Ramirez, Raquel; Henriquez-Hernandez, Luis Alberto; Rodriguez, German; Falcon, Orlando; Leon, Laureano; Perucho, Manuel

    2010-01-15

    Purpose: To elucidate whether microsatellite instability (MSI) predicts clinical outcome in radiation-treated endometrioid endometrial cancer (EEC). Methods and Materials: A consecutive series of 93 patients with EEC treated with extrafascial hysterectomy and postoperative radiotherapy was studied. The median clinical follow-up of patients was 138 months, with a maximum of 232 months. Five quasimonomorphic mononucleotide markers (BAT-25, BAT-26, NR21, NR24, and NR27) were used for MSI classification. Results: Twenty-five patients (22%) were classified as MSI. Both in the whole series and in early stages (I and II), univariate analysis showed a significant association between MSI and poorer 10-year local disease-free survival, disease-free survival, and cancer-specific survival. In multivariate analysis, MSI was excluded from the final regression model in the whole series, but in early stages MSI provided additional significant predictive information independent of traditional prognostic and predictive factors (age, stage, grade, and vascular invasion) for disease-free survival (hazard ratio [HR] 3.25, 95% confidence interval [CI] 1.01-10.49; p = 0.048) and cancer-specific survival (HR 4.20, 95% CI 1.23-14.35; p = 0.022) and was marginally significant for local disease-free survival (HR 3.54, 95% CI 0.93-13.46; p = 0.064). Conclusions: These results suggest that MSI may predict radiotherapy response in early-stage EEC.

  11. Predicting MCI outcome with clinically available MRI and CSF biomarkers

    PubMed Central

    Heister, D.; Brewer, J.B.; Magda, S.; Blennow, K.

    2011-01-01

    Objective: To determine the ability of clinically available volumetric MRI (vMRI) and CSF biomarkers, alone or in combination with a quantitative learning measure, to predict conversion to Alzheimer disease (AD) in patients with mild cognitive impairment (MCI). Methods: We stratified 192 MCI participants into positive and negative risk groups on the basis of 1) degree of learning impairment on the Rey Auditory Verbal Learning Test; 2) medial temporal atrophy, quantified from Food and Drug Administration–approved software for automated vMRI analysis; and 3) CSF biomarker levels. We also stratified participants based on combinations of risk factors. We computed Cox proportional hazards models, controlling for age, to assess 3-year risk of converting to AD as a function of risk group and used Kaplan-Meier analyses to determine median survival times. Results: When risk factors were examined separately, individuals testing positive showed significantly higher risk of converting to AD than individuals testing negative (hazard ratios [HR] 1.8–4.1). The joint presence of any 2 risk factors substantially increased risk, with the combination of greater learning impairment and increased atrophy associated with highest risk (HR 29.0): 85% of patients with both risk factors converted to AD within 3 years, vs 5% of those with neither. The presence of medial temporal atrophy was associated with shortest median dementia-free survival (15 months). Conclusions: Incorporating quantitative assessment of learning ability along with vMRI or CSF biomarkers in the clinical workup of MCI can provide critical information on risk of imminent conversion to AD. PMID:21998317

  12. Predictive models of spatial transcriptional response to high salinity.

    PubMed

    Uygun, Sahra; Seddon, Alexander E; Azodi, Christina B; Shiu, Shin-Han

    2017-04-03

    Plants are exposed to a variety of environmental conditions, and their ability to respond to environment variation depends on the proper regulation of gene expression in an organ, tissue, and cell type specific manner. Although our knowledge is accumulating on how stress responses are regulated, a genome-wide model of how plant transcription factors (TFs) and cis-regulatory elements (CREs) control spatially specific stress response has yet to emerge. Using Arabidopsis thaliana as a model, we identified a set of 1,894 putative CREs (pCREs) that are associated with high salinity (salt) up-regulated genes in the root or the shoot. These pCREs led to computational models that can better predict salt up-regulated genes in root and shoot compared to models based on known TF binding motifs. In addition, we incorporated TF binding sites identified via large-scale in vitro assays, chromatin accessibility, evolutionary conservation and pCRE combinatorial relations in machine learning models, and found that only consideration of pCRE combinations led to better performance in salt up-regulation prediction in root and shoot. Our results suggest that the plant organ transcriptional response to high salinity is regulated by a core set of pCREs and provide a genome-wide view on the cis-regulatory code of plant spatial transcriptional responses to environmental stress.

  13. Validation of a Deterministic Vibroacoustic Response Prediction Model

    NASA Technical Reports Server (NTRS)

    Caimi, Raoul E.; Margasahayam, Ravi

    1997-01-01

    This report documents the recently completed effort involving validation of a deterministic theory for the random vibration problem of predicting the response of launch pad structures in the low-frequency range (0 to 50 hertz). Use of the Statistical Energy Analysis (SEA) methods is not suitable in this range. Measurements of launch-induced acoustic loads and subsequent structural response were made on a cantilever beam structure placed in close proximity (200 feet) to the launch pad. Innovative ways of characterizing random, nonstationary, non-Gaussian acoustics are used for the development of a structure's excitation model. Extremely good correlation was obtained between analytically computed responses and those measured on the cantilever beam. Additional tests are recommended to bound the problem to account for variations in launch trajectory and inclination.

  14. Nonlinear random response prediction using MSC/NASTRAN

    NASA Technical Reports Server (NTRS)

    Robinson, J. H.; Chiang, C. K.; Rizzi, S. A.

    1993-01-01

    An equivalent linearization technique was incorporated into MSC/NASTRAN to predict the nonlinear random response of structures by means of Direct Matrix Abstract Programming (DMAP) modifications and inclusion of the nonlinear differential stiffness module inside the iteration loop. An iterative process was used to determine the rms displacements. Numerical results obtained for validation on simple plates and beams are in good agreement with existing solutions in both the linear and linearized regions. The versatility of the implementation will enable the analyst to determine the nonlinear random responses for complex structures under combined loads. The thermo-acoustic response of a hexagonal thermal protection system panel is used to highlight some of the features of the program.

  15. Myopodin methylation is a prognostic biomarker and predicts antiangiogenic response in advanced kidney cancer.

    PubMed

    Pompas-Veganzones, N; Sandonis, V; Perez-Lanzac, Alberto; Beltran, M; Beardo, P; Juárez, A; Vazquez, F; Cozar, J M; Alvarez-Ossorio, J L; Sanchez-Carbayo, Marta

    2016-10-01

    Myopodin is a cytoskeleton protein that shuttles to the nucleus depending on the cellular differentiation and stress. It has shown tumor suppressor functions. Myopodin methylation status was useful for staging bladder and colon tumors and predicting clinical outcome. To our knowledge, myopodin has not been tested in kidney cancer to date. The purpose of this study was to evaluate whether myopodin methylation status could be clinically useful in renal cancer (1) as a prognostic biomarker and 2) as a predictive factor of response to antiangiogenic therapy in patients with metastatic disease. Methylation-specific polymerase chain reactions (MS-PCR) were used to evaluate myopodin methylation in 88 kidney tumors. These belonged to patients with localized disease and no evidence of disease during follow-up (n = 25) (group 1), and 63 patients under antiangiogenic therapy (sunitinib, sorafenib, pazopanib, and temsirolimus), from which group 2 had non-metastatic disease at diagnosis (n = 32), and group 3 showed metastatic disease at diagnosis (n = 31). Univariate and multivariate Cox analyses were utilized to assess outcome and response to antiangiogenic agents taking progression, disease-specific survival, and overall survival as clinical endpoints. Myopodin was methylated in 50 out of the 88 kidney tumors (56.8 %). Among the 88 cases analyzed, 10 of them recurred (11.4 %), 51 progressed (57.9 %), and 40 died of disease (45.4 %). Myopodin methylation status correlated to MSKCC Risk score (p = 0.050) and the presence of distant metastasis (p = 0.039). Taking all patients, an unmethylated myopodin identified patients with shorter progression-free survival, disease-specific survival, and overall survival. Using also in univariate and multivariate models, an unmethylated myopodin predicted response to antiangiogenic therapy (groups 2 and 3) using progression-free survival, disease-specific, and overall survival as clinical endpoints. Myopodin was revealed

  16. Germline genetic testing to predict drug response and toxicity in oncology--reality or fiction?

    PubMed

    Soh, Thomas I P; Yong, Wei Peng

    2011-08-01

    In addition to 6-mercaptopurine, 5-fluorouracil and irinotecan, the United States Food and Drug Administration (US FDA) has recently recommended label change for tamoxifen, to include pharmacogenetic information on treatment outcome. With the increasing availability of pharmacogenetic testing, on germline as well as somatic mutations, oncologists are now able to identify individuals at risk of severe treatment toxicity or poor treatment response. However, there are still knowledge gaps to fill before rationalised therapy based on pharmacogenetics can be fully integrated into clinical practice. This review provides an overview on the application of pharmacogenetic testing for germ line mutations in oncology to predict response and toxicity.

  17. Response expectancy versus response hope in predicting birth-related emotional distress and pain.

    PubMed

    Anton, Raluca; David, Daniel

    2013-01-01

    Response expectancies and response hopes have been shown to be two distinct constructs with important implications for nonvolitional outcomes. More specifically, studies show that response expectancies: (1) are sufficient to cause nonvolitional outcomes, (2) are not mediated by other psychological variables, and (3) are self-confirming while seemingly automatic. A new programmatic research line has differentiated between people's response expectancies and their response hopes regarding nonvolitional outcomes and showed that even if response hope and response expectancy are separate constructs, they are not unrelated. These concepts have not yet been studied in pregnant women. Moreover, determining the causal factors that best explain the variance of emotional distress and pain in pregnancy is of great importance. Thus, the aim of this study was to investigate the interrelations between response expectancy and response hope in pregnant women with respect to (1) emotional distress prior to giving birth and (2) pain during giving birth. Additionally, self-reported labor hours were analyzed as a secondary outcome. Results show that response expectancy for pain directly predicts pain, and that the discrepancy between response hopes and response expectancies is a strong predictor of investigated outcomes. Thus, our results support the idea that preventive psychological interventions for pregnant women should emphasize adjusting response expectancies and response hopes regarding the pain and emotional distress associated with giving birth. We believe that the results have both theoretical and practical implications and the topic deserves further investigation.

  18. Prediction of HR/BP response to the spontaneous breathing trial by fluctuation dissipation theory

    NASA Astrophysics Data System (ADS)

    Chen, Man

    2014-03-01

    We applied the non-equilibrium fluctuation dissipation theorem to predict how critically-ill patients respond to treatment, based on both heart rate data and blood pressure data collected by standard hospital monitoring devices. The non-equilibrium fluctuation dissipation theorem relates the response of a system to a perturbation to the fluctuations in the stationary state of the system. It is shown that the response of patients to a standard procedure performed on patients, the spontaneous breathing trial (SBT), can be predicted by the non-equilibrium fluctuation dissipation approach. We classify patients into different groups according to the patients' characteristics. For each patient group, we extend the fluctuation dissipation theorem to predict interactions between blood pressure and beat-to-beat dynamics of heart rate in response to a perturbation (SBT), We also extract the form of the perturbation function directly from the physiological data, which may help to reduce the prediction error. We note this method is not limited to the analysis of the heart rate dynamics, but also can be applied to analyze the response of other physiological signals to other clinical interventions.

  19. Clinical applications of the human brainstem responses to auditory stimuli

    NASA Technical Reports Server (NTRS)

    Galambos, R.; Hecox, K.

    1975-01-01

    A technique utilizing the frequency following response (FFR) (obtained by auditory stimulation, whereby the stimulus frequency and duration are mirror-imaged in the resulting brainwaves) as a clinical tool for hearing disorders in humans of all ages is presented. Various medical studies are discussed to support the clinical value of the technique. The discovery and origin of the FFR and another significant brainstem auditory response involved in studying the eighth nerve is also discussed.

  20. Predicting the Response to Intravenous Immunoglobulins in an Animal Model of Chronic Neuritis

    PubMed Central

    Pfaff, Johannes; Mathys, Christian; Mausberg, Anne K.; Bendszus, Martin; Pham, Mirko; Hartung, Hans-Peter; Kieseier, Bernd C.

    2016-01-01

    Chronic inflammatory demyelinating polyradiculoneuropathy (CIDP) is a disabling autoimmune disorder of the peripheral nervous system (PNS). Intravenous immunoglobulins (IVIg) are effective in CIDP, but the treatment response varies greatly between individual patients. Understanding this interindividual variability and predicting the response to IVIg constitute major clinical challenges in CIDP. We previously established intercellular adhesion molecule (ICAM)-1 deficient non-obese diabetic (NOD) mice as a novel animal model of CIDP. Here, we demonstrate that similar to human CIDP patients, ICAM-1 deficient NOD mice respond to IVIg treatment by clinical and histological measures. Nerve magnetic resonance imaging and histology demonstrated that IVIg ameliorates abnormalities preferentially in distal parts of the sciatic nerve branches. The IVIg treatment response also featured great heterogeneity allowing us to identify IVIg responders and non-responders. An increased production of interleukin (IL)-17 positively predicted IVIg treatment responses. In human sural nerve biopsy sections, high numbers of IL-17 producing cells were associated with younger age and shorter disease duration. Thus, our novel animal model can be utilized to identify prognostic markers of treatment responses in chronic inflammatory neuropathies and we identify IL-17 production as one potential such prognostic marker. PMID:27711247

  1. Does stroke location predict walk speed response to gait rehabilitation?

    PubMed Central

    Jones, P. Simon; Pomeroy, Valerie M.; Wang, Jasmine; Schlaug, Gottfried; Tulasi Marrapu, S.; Geva, Sharon; Rowe, Philip J.; Chandler, Elizabeth; Kerr, Andrew

    2015-01-01

    Abstract Objectives Recovery of independent ambulation after stroke is a major goal. However, which rehabilitation regimen best benefits each individual is unknown and decisions are currently made on a subjective basis. Predictors of response to specific therapies would guide the type of therapy most appropriate for each patient. Although lesion topography is a strong predictor of upper limb response, walking involves more distributed functions. Earlier studies that assessed the cortico‐spinal tract (CST) were negative, suggesting other structures may be important. Experimental Design: The relationship between lesion topography and response of walking speed to standard rehabilitation was assessed in 50 adult‐onset patients using both volumetric measurement of CST lesion load and voxel‐based lesion–symptom mapping (VLSM) to assess non‐CST structures. Two functional mobility scales, the functional ambulation category (FAC) and the modified rivermead mobility index (MRMI) were also administered. Performance measures were obtained both at entry into the study (3–42 days post‐stroke) and at the end of a 6‐week course of therapy. Baseline score, age, time since stroke onset and white matter hyperintensities score were included as nuisance covariates in regression models. Principal Observations: CST damage independently predicted response to therapy for FAC and MRMI, but not for walk speed. However, using VLSM the latter was predicted by damage to the putamen, insula, external capsule and neighbouring white matter. Conclusions Walk speed response to rehabilitation was affected by damage involving the putamen and neighbouring structures but not the CST, while the latter had modest but significant impact on everyday functions of general mobility and gait. Hum Brain Mapp 37:689–703, 2016. © 2015 Wiley Periodicals, Inc. PMID:26621010

  2. MGMT expression predicts response to temozolomide in pancreatic neuroendocrine tumors.

    PubMed

    Cros, J; Hentic, O; Rebours, V; Zappa, M; Gille, N; Theou-Anton, N; Vernerey, D; Maire, F; Lévy, P; Bedossa, P; Paradis, V; Hammel, P; Ruszniewski, P; Couvelard, A

    2016-08-01

    Temozolomide (TEM) showed encouraging results in well-differentiated pancreatic neuroendocrine tumors (WDPNETs). Low O(6)-methylguanine-DNA methyltransferase (MGMT) expression and MGMT promoter methylation within tumors correlate with a better outcome under TEM-based chemotherapy in glioblastoma. We aimed to assess whether MGMT expression and MGMT promoter methylation could help predict the efficacy of TEM-based chemotherapy in patients with WDPNET. Consecutive patients with progressive WDPNET and/or liver involvement over 50% who received TEM between 2006 and 2012 were retrospectively studied. Tumor response was assessed according to Response Evaluation Criteria in Solid Tumors (RECIST) 1.1 guidelines. Nuclear expression of MGMT was assessed by immunochemistry (H-score, 0-300) and MGMT promoter methylation by pyrosequencing. Forty-three patients (21 men, 58years (27-84)) with grade 1 WDPNET (n=6) or 2 (n=36) were analyzed. Objective response, stable disease, and progression rates were seen in 17 patients (39.5%), 18 patients (41.9%), and 8 patients (18.6%), respectively. Low MGMT expression (≤50) was associated with radiological objective response (P=0.04) and better progression-free survival (PFS) (HR=0.35 (0.15-0.81), P=0.01). Disease control rate at 18months of treatment remained satisfying with an MGMT score up to 100 (74%) but dropped with a higher expression. High MGMT promoter methylation was associated with a low MGMT expression and longer PFS (HR=0.37 (0.29-1.08), P=0.05). Low MGMT score (≤50) appears to predict an objective tumor response, whereas an intermediate MGMT score (50-100) seems to be associated with prolonged stable disease.

  3. Brachial artery peak velocity variation to predict fluid responsiveness in mechanically ventilated patients

    PubMed Central

    2009-01-01

    Introduction Although several parameters have been proposed to predict the hemodynamic response to fluid expansion in critically ill patients, most of them are invasive or require the use of special monitoring devices. The aim of this study is to determine whether noninvasive evaluation of respiratory variation of brachial artery peak velocity flow measured using Doppler ultrasound could predict fluid responsiveness in mechanically ventilated patients. Methods We conducted a prospective clinical research in a 17-bed multidisciplinary ICU and included 38 mechanically ventilated patients for whom fluid administration was planned due to the presence of acute circulatory failure. Volume expansion (VE) was performed with 500 mL of a synthetic colloid. Patients were classified as responders if stroke volume index (SVi) increased ≥ 15% after VE. The respiratory variation in Vpeakbrach (ΔVpeakbrach) was calculated as the difference between maximum and minimum values of Vpeakbrach over a single respiratory cycle, divided by the mean of the two values and expressed as a percentage. Radial arterial pressure variation (ΔPPrad) and stroke volume variation measured using the FloTrac/Vigileo system (ΔSVVigileo), were also calculated. Results VE increased SVi by ≥ 15% in 19 patients (responders). At baseline, ΔVpeakbrach, ΔPPrad and ΔSVVigileo were significantly higher in responder than nonresponder patients [14 vs 8%; 18 vs. 5%; 13 vs 8%; P < 0.0001, respectively). A ΔVpeakbrach value >10% predicted fluid responsiveness with a sensitivity of 74% and a specificity of 95%. A ΔPPrad value >10% and a ΔSVVigileo >11% predicted volume responsiveness with a sensitivity of 95% and 79%, and a specificity of 95% and 89%, respectively. Conclusions Respiratory variations in brachial artery peak velocity could be a feasible tool for the noninvasive assessment of fluid responsiveness in patients with mechanical ventilatory support and acute circulatory failure. Trial Registration

  4. Prediction of individual response to antidepressants and antipsychotics: an integrated concept

    PubMed Central

    Preskorn, Sheldon H.

    2014-01-01

    In both clinical trials and daily practice, there can be substantial inter- and even intraindividual variability in response—whether beneficial or adverse—to antidepressants and antipsychotic medications. So far, no tools have become available to predict the outcome of these treatments in specific patients. This is because the causes of such variability are often not known, and when they are, there is no way of predicting the effects of their various potential combinations in an individual. Given this background, this paper presents a conceptual framework for understanding known factors and their combinations so that eventually clinicians can better predict what medication(s) to select and at what dose they can optimize the outcome for a given individual. This framework is flexible enough to be readily adaptable as new information becomes available. The causes of variation in patient response are grouped into four categories: (i) genetics; (ii) age; (iii) disease; and (iv) environment (internal). Four cases of increasing complexity are used to illustrate the applicability of this framework in a clinically relevant way In addition, this paper reviews tools that the clinician can use to assess for and quantify such inter- and intraindividual variability. With the information gained, treatment can be adjusted to compensate for such variability, in order to optimize outcome. Finally, the limitations of existing antidepressant and antipsychotic therapy and the way they reduce current ability to predict response is discussed. PMID:25733958

  5. Using biomarkers to predict treatment response in major depressive disorder: evidence from past and present studies.

    PubMed

    Thase, Michael E

    2014-12-01

    Major depressive disorder (MDD) is a heterogeneous condition with a variable response to a wide range of treatments. Despite intensive efforts, no biomarker has been identified to date that can reliably predict response or non-response to any form of treatment, nor has one been identified that can be used to identify those at high risk of developing treatment-resistant depression (ie, non-response to a sequence of treatments delivered for adequate duration and intensity). This manuscript reviews some past areas of research that have proved informative, such as studies using indexes of hypercortisolism or sleep disturbance, and more recent research findings using measures of inflammation and different indicators of regional cortical activation to predict treatment response. It is concluded that, although no method has yet been demonstrated to be sufficiently accurate to be applied in clinical practice, progress has been made. It thus seems likely that--at some point in the not-too-distant future--it will be possible to prospectively identify, at least for some MDD patients, the likelihood of response or non-response to cognitive therapy or various antidepressant medications.

  6. Dissociation predicts treatment response in eye-movement desensitization and reprocessing for posttraumatic stress disorder.

    PubMed

    Bae, Hwallip; Kim, Daeho; Park, Yong Chon

    2016-01-01

    Using clinical data from a specialized trauma clinic, this study investigated pretreatment clinical factors predicting response to eye-movement desensitization and reprocessing (EMDR) among adult patients diagnosed with posttraumatic stress disorder (PTSD). Participants were evaluated using the Clinician-Administered PTSD Scale (CAPS), the Symptom Checklist-90-Revised, the Beck Depression Inventory, and the Dissociative Experiences Scale before treatment and were reassessed using the CAPS after treatment and at 6-month follow-up. A total of 69 patients underwent an average of 4 sessions of EMDR, and 60 (87%) completed the posttreatment evaluation, including 8 participants who terminated treatment prematurely. Intent-to-treat analysis revealed that 39 (65%) of the 60 patients were classified as responders and 21 (35%) as nonresponders when response was defined as more than a 30% decrease in total CAPS score. The nonresponders had higher levels of dissociation (depersonalization and derealization) and numbing symptoms, but other PTSD symptoms, such as avoidance, hyperarousal, and intrusion, were not significantly different. The number of psychiatric comorbidities was also associated with treatment nonresponse. The final logistic regression model yielded 2 significant variables: dissociation (p < .001) and more than 2 comorbidities compared to none (p < .05). These results indicate that complex symptom patterns in PTSD may predict treatment response and support the inclusion of the dissociative subtype of PTSD in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition.

  7. Cancer Pharmacogenomics: Integrating Discoveries in Basic, Clinical and Population Sciences to Advance Predictive Cancer Care

    Cancer.gov

    Cancer Pharmacogenomics: Integrating Discoveries in Basic, Clinical and Population Sciences to Advance Predictive Cancer Care, a 2010 workshop sponsored by the Epidemiology and Genomics Research Program.

  8. Prediction of peak pressure from clinical and radiological measurements in patients with diabetes

    PubMed Central

    Guldemond, Nick A; Leffers, Pieter; Walenkamp, Geert HIM; Schaper, Nicolaas C; Sanders, Antal P; Nieman, Fred HM; van Rhijn, Lodewijk W

    2008-01-01

    Background Various structural and functional factors of foot function have been associated with high local plantar pressures. The therapist focuses on these features which are thought to be responsible for plantar ulceration in patients with diabetes. Risk assessment of the diabetic foot would be made easier if locally elevated plantar pressure could be indicated with a minimum set of clinical measures. Methods Ninety three patients were evaluated through vascular, orthopaedic, neurological and radiological assessment. A pressure platform was used to quantify the barefoot peak pressure for six forefoot regions: big toe (BT) and metatarsals one (MT-1) to five (MT-5). Stepwise regression modelling was performed to determine which set of the clinical and radiological measures explained most variability in local barefoot plantar peak pressure in each of the six forefoot regions. Comprehensive models were computed with independent variables from the clinical and radiological measurements. The difference between the actual plantar pressure and the predicted value was examined through Bland-Altman analysis. Results Forefoot pressures were significant higher in patients with neuropathy, compared to patients without neuropathy for the whole forefoot, the MT-1 region and the MT-5 region (respectively 138 kPa, 173 kPa and 88 kPa higher: mean difference). The clinical models explained up to 39 percent of the variance in local peak pressures. Callus formation and toe deformity were identified as relevant clinical predictors for all forefoot regions. Regression models with radiological variables explained about 26 percent of the variance in local peak pressures. For most regions the combination of clinical and radiological variables resulted in a higher explained variance. The Bland and Altman analysis showed a major discrepancy between the predicted and the actual peak pressure values. Conclusion At best, clinical and radiological measurements could only explain about 34

  9. Genetic studies of DRD4 and clinical response to neuroleptic medications

    SciTech Connect

    Kennedy, J.L.; Petronis, A.; Gao, J.

    1994-09-01

    Clozapine is an atypical antipsychotic drug that, like most other medications, is effective for some people and not for others. This variable response across individuals is likely significantly determined by genetic factors. An important candidate gene to investigate in clozapine response is the dopamine D4 receptor gene (DRD4). The D4 receptor has a higher affinity for clozapine than any of the other dopamine receptors. Furthermore, recent work by our consortium has shown a remarkable level of variability in the part of the gene coding for the third cytoplasmic loop. We have also identified polymorphisms in the upstream 5{prime} putative regulatory region and at two other sites. These polymorphisms were typed in a group of treatment-resistant schizophrenia subjects who were subsequently placed on clozapine (n = 60). In a logistic regression analysis, we compared genotype at the DRD4 polymorphism to response versus non-response to clozapine. Neither the exon-III nor any of the 5{prime} polymorphisms alone significantly predicted response; however, when the information from these polymorphisms was combined, more predictive power was obtained. In a correspondence analysis of the four DRD4 polymorphisms vs. response, we were able to predict 76% of the variance in response. Refinement of the analyses will include assessment of subfactors involved in clinical response phenotype and incorporation of the debrisoquine metabolizing locus (CYP2D6) into the prediction algorithm.

  10. Shrinking the Psoriasis Assessment Gap: Early Gene-Expression Profiling Accurately Predicts Response to Long-Term Treatment.

    PubMed

    Correa da Rosa, Joel; Kim, Jaehwan; Tian, Suyan; Tomalin, Lewis E; Krueger, James G; Suárez-Fariñas, Mayte

    2017-02-01

    There is an "assessment gap" between the moment a patient's response to treatment is biologically determined and when a response can actually be determined clinically. Patients' biochemical profiles are a major determinant of clinical outcome for a given treatment. It is therefore feasible that molecular-level patient information could be used to decrease the assessment gap. Thanks to clinically accessible biopsy samples, high-quality molecular data for psoriasis patients are widely available. Psoriasis is therefore an excellent disease for testing the prospect of predicting treatment outcome from molecular data. Our study shows that gene-expression profiles of psoriasis skin lesions, taken in the first 4 weeks of treatment, can be used to accurately predict (>80% area under the receiver operating characteristic curve) the clinical endpoint at 12 weeks. This could decrease the psoriasis assessment gap by 2 months. We present two distinct prediction modes: a universal predictor, aimed at forecasting the efficacy of untested drugs, and specific predictors aimed at forecasting clinical response to treatment with four specific drugs: etanercept, ustekinumab, adalimumab, and methotrexate. We also develop two forms of prediction: one from detailed, platform-specific data and one from platform-independent, pathway-based data. We show that key biomarkers are associated with responses to drugs and doses and thus provide insight into the biology of pathogenesis reversion.

  11. Predicting Non-Response to Juvenile Drug Court Interventions

    PubMed Central

    Halliday-Boykins, Colleen A.; Schaeffer, Cindy M.; Henggeler, Scott W.; Chapman, Jason E.; Cunningham, Phillippe B.; Randall, Jeff; Shapiro, Steven B.

    2010-01-01

    Using data from a recent randomized clinical trial involving juvenile drug court (JDC), youth marijuana use trajectories and the predictors of treatment non-response were examined. Participants were 118 juvenile offenders meeting diagnostic criteria for substance use disorders assigned to JDC and their families. Urine drug screen results were gathered from weekly court visits for 6 months, and youth reported their marijuana use over 12 months. Semiparametric mixture modeling jointly estimated and classified trajectories of both marijuana use indices. Youth were classified into responder versus non-responder trajectory groups based on both outcomes. Regression analyses examined pretreatment individual, family, and extrafamilial predictors of non-response. Results indicated that youth whose caregivers reported illegal drug use pretreatment were almost 10 times as likely to be classified into the non-responder trajectory group. No other variable significantly distinguished drug use trajectory groups. Findings have implications for the design of interventions to improve JDC outcomes. PMID:20826076

  12. Predicting Drug Response in Human Prostate Cancer from Preclinical Analysis of In Vivo Mouse Models.

    PubMed

    Mitrofanova, Antonina; Aytes, Alvaro; Zou, Min; Shen, Michael M; Abate-Shen, Cory; Califano, Andrea

    2015-09-29

    Although genetically engineered mouse (GEM) models are often used to evaluate cancer therapies, extrapolation of such preclinical data to human cancer can be challenging. Here, we introduce an approach that uses drug perturbation data from GEM models to predict drug efficacy in human cancer. Network-based analysis of expression profiles from in vivo treatment of GEM models identified drugs and drug combinations that inhibit the activity of FOXM1 and CENPF, which are master regulators of prostate cancer malignancy. Validation of mouse and human prostate cancer models confirmed the specificity and synergy of a predicted drug combination to abrogate FOXM1/CENPF activity and inhibit tumorigenicity. Network-based analysis of treatment signatures from GEM models identified treatment-responsive genes in human prostate cancer that are potential biomarkers of patient response. More generally, this approach allows systematic identification of drugs that inhibit tumor dependencies, thereby improving the utility of GEM models for prioritizing drugs for clinical evaluation.

  13. Predictive value of XPG rs2296147T>C polymorphism on clinical outcomes of cancer patients

    PubMed Central

    Wang, Yuhan; Han, Yingying; Weng, Qiang; Yuan, Zhengrong

    2016-01-01

    The Xeroderma pigmentosum complementation group G (XPG) rs2296147T>C polymorphism is suspected to associate with the clinical outcomes of cancer patients. However, the results are inconsistent. This meta-analysis aimed to evaluate the reliable predictive value of XPG rs2296147T>C polymorphism on clinical outcomes of cancer patients. A total of 11 eligible studies were enrolled in this meta-analysis. Our results indicated that the cancer patients with TT and CT genotypes were significantly associated with better respond rates when compared with the CC genotype (TT versus (vs.) CC: odds ratio (OR) = 2.05, 95% confidence intervals (CIs), 1.32-3.20, P = 0.002; TT+CT vs. CC: OR= 1.57, 95% CI, 1.14-2.17, P = 0.005). The TT genotype and/or T allele might be associated with higher survival time for cancer patients than the CC genotype and/or C allele. The cumulative meta-analyses showed an apparent beneficial objective response of TT genotype on cancer patients. In conclusion, this meta-analysis suggests that the XPG rs2296147T>C polymorphism is associated with the clinical outcomes of cancer patients. The XPG rs2296147T>C polymorphism might be a predictive factor of prognosis in cancers patients and contribute to individual treatment in the future. PMID:27588464

  14. Global genetic variations predict brain response to faces.

    PubMed

    Dickie, Erin W; Tahmasebi, Amir; French, Leon; Kovacevic, Natasa; Banaschewski, Tobias; Barker, Gareth J; Bokde, Arun; Büchel, Christian; Conrod, Patricia; Flor, Herta; Garavan, Hugh; Gallinat, Juergen; Gowland, Penny; Heinz, Andreas; Ittermann, Bernd; Lawrence, Claire; Mann, Karl; Martinot, Jean-Luc; Nees, Frauke; Nichols, Thomas; Lathrop, Mark; Loth, Eva; Pausova, Zdenka; Rietschel, Marcela; Smolka, Michal N; Ströhle, Andreas; Toro, Roberto; Schumann, Gunter; Paus, Tomáš

    2014-08-01

    Face expressions are a rich source of social signals. Here we estimated the proportion of phenotypic variance in the brain response to facial expressions explained by common genetic variance captured by ∼ 500,000 single nucleotide polymorphisms. Using genomic-relationship-matrix restricted maximum likelihood (GREML), we related this global genetic variance to that in the brain response to facial expressions, as assessed with functional magnetic resonance imaging (fMRI) in a community-based sample of adolescents (n = 1,620). Brain response to facial expressions was measured in 25 regions constituting a face network, as defined previously. In 9 out of these 25 regions, common genetic variance explained a significant proportion of phenotypic variance (40-50%) in their response to ambiguous facial expressions; this was not the case for angry facial expressions. Across the network, the strength of the genotype-phenotype relationship varied as a function of the inter-individual variability in the number of functional connections possessed by a given region (R(2) = 0.38, p<0.001). Furthermore, this variability showed an inverted U relationship with both the number of observed connections (R2 = 0.48, p<0.001) and the magnitude of brain response (R(2) = 0.32, p<0.001). Thus, a significant proportion of the brain response to facial expressions is predicted by common genetic variance in a subset of regions constituting the face network. These regions show the highest inter-individual variability in the number of connections with other network nodes, suggesting that the genetic model captures variations across the adolescent brains in co-opting these regions into the face network.

  15. Global Genetic Variations Predict Brain Response to Faces

    PubMed Central

    Dickie, Erin W.; Tahmasebi, Amir; French, Leon; Kovacevic, Natasa; Banaschewski, Tobias; Barker, Gareth J.; Bokde, Arun; Büchel, Christian; Conrod, Patricia; Flor, Herta; Garavan, Hugh; Gallinat, Juergen; Gowland, Penny; Heinz, Andreas; Ittermann, Bernd; Lawrence, Claire; Mann, Karl; Martinot, Jean-Luc; Nees, Frauke; Nichols, Thomas; Lathrop, Mark; Loth, Eva; Pausova, Zdenka; Rietschel, Marcela; Smolka, Michal N.; Ströhle, Andreas; Toro, Roberto; Schumann, Gunter; Paus, Tomáš

    2014-01-01

    Face expressions are a rich source of social signals. Here we estimated the proportion of phenotypic variance in the brain response to facial expressions explained by common genetic variance captured by ∼500,000 single nucleotide polymorphisms. Using genomic-relationship-matrix restricted maximum likelihood (GREML), we related this global genetic variance to that in the brain response to facial expressions, as assessed with functional magnetic resonance imaging (fMRI) in a community-based sample of adolescents (n = 1,620). Brain response to facial expressions was measured in 25 regions constituting a face network, as defined previously. In 9 out of these 25 regions, common genetic variance explained a significant proportion of phenotypic variance (40–50%) in their response to ambiguous facial expressions; this was not the case for angry facial expressions. Across the network, the strength of the genotype-phenotype relationship varied as a function of the inter-individual variability in the number of functional connections possessed by a given region (R2 = 0.38, p<0.001). Furthermore, this variability showed an inverted U relationship with both the number of observed connections (R2 = 0.48, p<0.001) and the magnitude of brain response (R2 = 0.32, p<0.001). Thus, a significant proportion of the brain response to facial expressions is predicted by common genetic variance in a subset of regions constituting the face network. These regions show the highest inter-individual variability in the number of connections with other network nodes, suggesting that the genetic model captures variations across the adolescent brains in co-opting these regions into the face network. PMID:25122193

  16. A germline predictive signature of response to platinum chemotherapy in esophageal cancer.

    PubMed

    Rumiato, Enrica; Boldrin, Elisa; Malacrida, Sandro; Battaglia, Giorgio; Bocus, Paolo; Castoro, Carlo; Cagol, Matteo; Chiarion-Sileni, Vanna; Ruol, Alberto; Amadori, Alberto; Saggioro, Daniela

    2016-05-01

    Platinum-based neoadjuvant therapy is the standard treatment for esophageal cancer (EC). At present, no reliable response markers exist, and patient therapeutic outcome is variable and very often unpredictable. The aim of this study was to understand the contribution of host constitutive DNA polymorphisms in discriminating between responder and nonresponder patients. DNA collected from 120 EC patients treated with platinum-based neoadjuvant chemotherapy was analyzed using drug metabolism enzymes and transporters (DMET) array platform that interrogates polymorphisms in 225 genes of drug metabolism and disposition. Four gene variants of DNA repair machinery, 2 in ERCC1 (rs11615; rs3212986), and 2 in XPD (rs1799793; rs13181) were also studied. Association analysis was performed with pTest software and corrected by permutation test. Predictive models of response were created using the receiver-operating characteristics curve approach and adjusted by the bootstrap procedure. Sixteen single nucleotide polymorphisms (SNPs) of the DMET array resulted significantly associated with either good or poor response; no association was found for the 4 variants mapping in DNA repair genes. The predictive power of 5 DMET SNPs mapping in ABCC2, ABCC3, CYP2A6, PPARG, and SLC7A8 genes was greater than that of clinical factors alone (area under the curve [AUC] = 0.74 vs 0.62). Interestingly, their combination with the clinical variables significantly increased the predictivity of the model (AUC = 0.78 vs 0.62, P = 0.0016). In conclusion, we identified a genetic signature of response to platinum-based neoadjuvant chemotherapy in EC patients. Our results also disclose the potential benefit of combining genetic and clinical variables for personalized EC management.

  17. Aggression Questionnaire hostility scale predicts anger in response to mistreatment.

    PubMed

    Felsten, G; Hill, V

    1999-01-01

    We tested the hypotheses that the hostility and anger scales of the Buss and Perry (1992) [Buss, A. H. & Perry, M. (1992). The Aggression Questionnaire. Journal of Personality and Social Psychology, 63, 452-459.] Aggression Questionnaire would predict anger in college students in response to mistreatment. We found low and high hostility groups did not differ in anger at baseline or after completing a task without provocation, but the high hostility group reported greater anger than the low group after the onset of provocation, which required all students to redo completed tasks because some students (confederates) were observed cheating. Hostility also influenced anxiety and depression, but only anger was greater as a result of the provocation in the high than in the low hostility group. The anger scale did not predict anger in response to provocation, but anger was higher in the high than the low anger group before the provocation. These findings support the construct validity of the Aggression Questionnaire hostility scale as a measure of suspicion, resentment and sensitivity to mistreatment.

  18. Photopheresis: a clinically relevant immunobiologic response modifier.

    PubMed

    Edelson, R L

    1991-12-30

    pathogenic clone(s), the return of these cells to an immunocompetent individual, the removal of the photo-damaged cells from the blood by the reticuloendothelial system and the preferential induction of an immune response against cells of the pathologically expanded clone(s).

  19. Stress responsiveness predicts individual variation in mate selectivity.

    PubMed

    Vitousek, Maren N; Romero, L Michael

    2013-06-15

    Steroid hormones, including glucocorticoids, mediate a variety of behavioral and physiological processes. Circulating hormone concentrations vary substantially within populations, and although hormone titers predict reproductive success in several species, little is known about how individual variation in circulating hormone concentrations is linked with most reproductive behaviors in free-living organisms. Mate choice is an important and often costly component of reproduction that also varies substantially within populations. We examined whether energetically costly mate selection behavior in female Galápagos marine iguanas (Amblyrhynchus cristatus) was associated with individual variation in the concentrations of hormones previously shown to differ between reproductive and non-reproductive females during the breeding season (corticosterone and testosterone). Stress-induced corticosterone levels - which are suppressed in female marine iguanas during reproduction - were individually repeatable throughout the seven-week breeding period. Mate selectivity was strongly predicted by individual variation in stress-induced corticosterone: reproductive females that secreted less corticosterone in response to a standardized stressor assessed more displaying males. Neither baseline corticosterone nor testosterone predicted variation in mate selectivity. Scaled body mass was not significantly associated with mate selectivity, but females that began the breeding period in lower body condition showed a trend towards being less selective about potential mates. These results provide the first evidence that individual variation in the corticosterone stress response is associated with how selective females are in their choice of a mate, an important contributor to fitness in many species. Future research is needed to determine the functional basis of this association, and whether transient acute increases in circulating corticosterone directly mediate mate choice behaviors.

  20. Prediction and Early Evaluation of Anticancer Therapy Response: From Imaging of Drug Efflux Pumps to Targeted Therapy Response.

    PubMed

    Meng, Qingqing; Li, Zheng; Li, Shaoshun

    2016-01-01

    Multidrug resistance (MDR) describes the resistance of tumor cells to chemotherapy and has been ascribed to the overexpression of drug efflux pumps. Molecular imaging of drug efflux pumps is helpful to identify the patients who may be resistant to the chemotherapy and thus will avoid the unnecessary treatment and increase the therapeutic effectiveness. Imaging probes targeting drug efflux pumps can non-invasively evaluate the Pgp function and play an important role in identification of MDR, prediction of response, and monitoring MDR modulation. On the other hand, new anticancer agents based on molecular targets such as epidermal growth factor receptor (EGFR) and angiogenic factor receptor may potentially be combined with chemotherapeutic drugs to overcome the MDR. Imaging of molecular targets visualize treatment response of patients at molecular level vividly and help to select right patients for certain targeted anticancer therapy. Among all the imaging modalities, nuclear imaging including positron emission tomography (PET) and single photon emission computed tomography (SPECT) imaging has the greatest promise for rapid translation to the clinic and can realize quantitative visualization of biochemical processes in vivo. In this review, we will summarize the nuclear imaging probes utilized for predicting and evaluating the early anticancer therapy response.99mTc labeled agents and PET based radiopharmaceuticals like 18F-Paclitaxel, 11C-Verapamil for drug efflux pumps imaging will be discussed here. Moreover, molecular imaging probes used for targeted therapy response evaluation like 18F-Tamoxifen,89Zr-Trastuzumab will also be introduced in this review.

  1. COX-2 verexpression in pretreatment biopsies predicts response of rectal cancers to neoadjuvant radiochemotherapy

    SciTech Connect

    Smith, Fraser M.; Reynolds, John V. . E-mail: reynoldsjv@stjames.ie; Kay, Elaine W.; Crotty, Paul; Murphy, James O.; Hollywood, Donal; Gaffney, Eoin F.; Stephens, Richard B.; Kennedy, M. John

    2006-02-01

    Purpose: To determine the utility of COX-2 expression as a response predictor for patients with rectal cancer who are undergoing neoadjuvant radiochemotherapy (RCT). Methods and Materials: Pretreatment biopsies (PTB) from 49 patients who underwent RCT were included. COX-2 and proliferation in PTB were assessed by immunohistochemistry (IHC) and apoptosis was detected by TUNEL stain. Response to treatment was assessed by a 5-point tumor-regression grade (TRG) based on the ratio of residual tumor to fibrosis. Results: Good response (TRG 1 + 2), moderate response (TRG 3), and poor response (TRG 4 + 5) were seen in 21 patients (42%), 11 patients (22%), and 17 patients (34%), respectively. Patients with COX-2 overexpression in PTB were more likely to demonstrate moderate or poor response (TRG 3 + 4) to treatment than were those with normal COX-2 expression (p = 0.026, chi-square test). Similarly, poor response was more likely if patients had low levels of spontaneous apoptosis in PTBs (p = 0.0007, chi-square test). Conclusions: COX-2 overexpression and reduced apoptosis in PTB can predict poor response of rectal cancer to RCT. As COX-2 inhibitors are commercially available, their administration to patients who overexpress COX-2 warrants assessment in clinical trials in an attempt to increase overall response rates.

  2. Prediction of cortical responses to simultaneous electrical stimulation of the retina

    NASA Astrophysics Data System (ADS)

    Halupka, Kerry J.; Shivdasani, Mohit N.; Cloherty, Shaun L.; Grayden, David B.; Wong, Yan T.; Burkitt, Anthony N.; Meffin, Hamish

    2017-02-01

    Objective. Simultaneous electrical stimulation of multiple electrodes has shown promise in diversifying the responses that can be elicited by retinal prostheses compared to interleaved single electrode stimulation. However, the effects of interactions between electrodes are not well understood and clinical trials with simultaneous stimulation have produced inconsistent results. We investigated the effects of multiple electrode stimulation of the retina by developing a model of cortical responses to retinal stimulation. Approach. Electrical stimuli consisting of temporally sparse, biphasic current pulses, with amplitudes sampled from a bi-dimensional Gaussian distribution, were simultaneously delivered to the retina across a 42-channel electrode array implanted in the suprachoroidal space of anesthetized cats. Visual cortex activity was recorded using penetrating microelectrode arrays. These data were used to identify a linear-nonlinear model of cortical responses to retinal stimulation. The ability of the model to generalize was tested by predicting responses to non-white patterned stimuli. Main results. The model accurately predicted two cortical activity measures: multi-unit neural responses and evoked potential responses to white noise stimuli. The model also provides information about electrical receptive fields, including the relative effects of each stimulating electrode on every recording site. Significance. We have demonstrated a simple model that accurately describes cortical responses to simultaneous stimulation of a suprachoroidal retinal prosthesis. Overall, our results demonstrate that cortical responses to simultaneous multi-electrode stimulation of the retina are repeatable and predictable, and that interactions between electrodes during simultaneous stimulation are predominantly linear. The model shows promise for determining optimal stimulation paradigms for exploiting interactions between electrodes to shape neural activity, thereby improving

  3. Predicting drug response and toxicity based on gene polymorphisms.

    PubMed

    Robert, Jacques; Morvan, Valérie Le; Smith, Denis; Pourquier, Philippe; Bonnet, Jacques

    2005-06-01

    The sequencing of the human genome has allowed the identification of thousands of gene polymorphisms, most often single nucleotide polymorphims (SNP), which may play an important role in the expression level and activity of the corresponding proteins. When these polymorphisms occur at the level of drug metabolising enzymes or transporters, the disposition of the drug may be altered and, consequently, its efficacy may be compromised or its toxicity enhanced. Polymorphisms can also occur at the level of proteins directly involved in drug action, either when the protein is the target of the drug or when the protein is involved in the repair of drug-induced lesions. There again, these polymorphisms may lead to alterations in drug efficacy and/or toxicity. The identification of functional polymorphisms in patients undergoing chemotherapy may help the clinician prescribe the optimal drug combination or schedule and predict with more accuracy the response to these prescriptions. We have recorded in this review the polymorphisms that have been identified up till now in genes involved in anticancer drug activity. Some of them appear especially important in predicting drug toxicity and should be determined in routine before drug administration; this is the case of the most common variations of thiopurine methyltransferase for 6-mercaptopurine and of dihydropyrimidine dehydrogenase for fluorouracil. Other appear determinant for drug response, such as the common SNPs found in glutathione S-transferase P1 or xereoderma pigmentosum group D enzyme for the activity of oxaliplatin. However, confusion factors may exist between the role of gene polymorphisms in cancer risk or overall prognosis and their role in drug response.

  4. A novel neural response algorithm for protein function prediction

    PubMed Central

    2012-01-01

    Background Large amounts of data are being generated by high-throughput genome sequencing methods. But the rate of the experimental functional characterization falls far behind. To fill the gap between the number of sequences and their annotations, fast and accurate automated annotation methods are required. Many methods, such as GOblet, GOFigure, and Gotcha, are designed based on the BLAST search. Unfortunately, the sequence coverage of these methods is low as they cannot detect the remote homologues. Adding to this, the lack of annotation specificity advocates the need to improve automated protein function prediction. Results We designed a novel automated protein functional assignment method based on the neural response algorithm, which simulates the neuronal behavior of the visual cortex in the human brain. Firstly, we predict the most similar target protein for a given query protein and thereby assign its GO term to the query sequence. When assessed on test set, our method ranked the actual leaf GO term among the top 5 probable GO terms with accuracy of 86.93%. Conclusions The proposed algorithm is the first instance of neural response algorithm being used in the biological domain. The use of HMM profiles along with the secondary structure information to define the neural response gives our method an edge over other available methods on annotation accuracy. Results of the 5-fold cross validation and the comparison with PFP and FFPred servers indicate the prominent performance by our method. The program, the dataset, and help files are available at http://www.jjwanglab.org/NRProF/. PMID:23046521

  5. Predicting the Response of the Mars Ionosphere to Solar Flares

    NASA Astrophysics Data System (ADS)

    Fallows, K.; Withers, P.; Gonzalez, G.

    2015-12-01

    The increased soft X-ray irradiance during solar flares generates increased electron densities in the lower ionosphere of Mars. The relative changes in electron density during a flare are greater for larger flares and also at lower altitudes and larger flares, due to the wavelength dependence of both the flux increase during the flare and the absorption of flux by the neutral atmosphere. These relationships have been explored [Bougher et al. 2001, Fox et al. 2004, Mendillo et al. 2006, Mahajan et al. 2011, Lollo et al. 2012] but not quantified, which has impeded the validation of simulations of the ionospheric effects of solar flares. Such simulations are necessary for developing accurate descriptions of the physical processes governing ionospheric behavior under extreme conditions. We present a response function, a mathematical expression for the change in electron density during a solar flare as a function of the change in solar flux and an optical depth proxy. This response function is based on analysis of 20 Mars Global Surveyor (MGS) radio occultation electron density profiles measured during solar flares. Characterizing the response as a function of optical depth, rather than altitude, provides the best description of ionospheric variability during a flare; otherwise non-negligible solar zenith angle effects are present. We demonstrate that the response function can be used to predict ionospheric electron densities during a specified solar flare by reproducing profiles known to be disturbed by a solar flare. We also demonstrate that the response function can be used to infer the strength of solar flares not visible at Earth by finding the flux enhancement required to reproduce an apparently flare affected profile given an undisturbed profile on the same date.

  6. Use of generalised additive models to categorise continuous variables in clinical prediction

    PubMed Central

    2013-01-01

    Background In medical practice many, essentially continuous, clinical parameters tend to be categorised by physicians for ease of decision-making. Indeed, categorisation is a common practice both in medical research and in the development of clinical prediction rules, particularly where the ensuing models are to be applied in daily clinical practice to support clinicians in the decision-making process. Since the number of categories into which a continuous predictor must be categorised depends partly on the relationship between the predictor and the outcome, the need for more than two categories must be borne in mind. Methods We propose a categorisation methodology for clinical-prediction models, using Generalised Additive Models (GAMs) with P-spline smoothers to determine the relationship between the continuous predictor and the outcome. The proposed method consists of creating at least one average-risk category along with high- and low-risk categories based on the GAM smooth function. We applied this methodology to a prospective cohort of patients with exacerbated chronic obstructive pulmonary disease. The predictors selected were respiratory rate and partial pressure of carbon dioxide in the blood (PCO2), and the response variable was poor evolution. An additive logistic regression model was used to show the relationship between the covariates and the dichotomous response variable. The proposed categorisation was compared to the continuous predictor as the best option, using the AIC and AUC evaluation parameters. The sample was divided into a derivation (60%) and validation (40%) samples. The first was used to obtain the cut points while the second was used to validate the proposed methodology. Results The three-category proposal for the respiratory rate was ≤ 20;(20,24];> 24, for which the following values were obtained: AIC=314.5 and AUC=0.638. The respective values for the continuous predictor were AIC=317.1 and AUC=0.634, with no statistically

  7. Latent variable indirect response joint modeling of a continuous and a categorical clinical endpoint.

    PubMed

    Hu, Chuanpu; Szapary, Philippe O; Mendelsohn, Alan M; Zhou, Honghui

    2014-08-01

    Informative exposure-response modeling of clinical endpoints is important in drug development. There has been much recent progress in latent variable modeling of ordered categorical endpoints, including the application of indirect response (IDR) models and accounting for residual correlations between multiple categorical endpoints. This manuscript describes a framework of latent-variable-based IDR models that facilitate easy simultaneous modeling of a continuous and a categorical clinical endpoint. The model was applied to data from two phase III clinical trials of subcutaneously administered ustekinumab for the treatment of psoriatic arthritis, where Psoriasis Area and Severity Index scores and 20, 50, and 70 % improvement in the American College of Rheumatology response criteria were used as efficacy endpoints. Visual predictive check and external validation showed reasonable parameter estimation precision and model performance.

  8. Predicting Future Clinical Adjustment from Treatment Outcome and Process Variables.

    ERIC Educational Resources Information Center

    Patterson, G. R.; Forgatch, Marion S.

    1995-01-01

    Issues related to the use of outcome and process data from the treatment of antisocial children to predict future childhood adjustment were examined through a study of 69 children. Data supported the hypothesis that measures of processes thought to produce changes in child behavior would serve to predict future adjustment. (SLD)

  9. Tumor immune profiling predicts response to anti–PD-1 therapy in human melanoma

    PubMed Central

    Daud, Adil I.; Loo, Kimberly; Pauli, Mariela L.; Sanchez-Rodriguez, Robert; Sandoval, Priscila Munoz; Taravati, Keyon; Tsai, Katy; Nosrati, Adi; Nardo, Lorenzo; Alvarado, Michael D.; Algazi, Alain P.; Pampaloni, Miguel H.; Lobach, Iryna V.; Hwang, Jimmy; Pierce, Robert H.; Gratz, Iris K.; Krummel, Matthew F.

    2016-01-01

    BACKGROUND. Immune checkpoint blockade is revolutionizing therapy for advanced cancer, but many patients do not respond to treatment. The identification of robust biomarkers that predict clinical response to specific checkpoint inhibitors is critical in order to stratify patients and to rationally select combinations in the context of an expanding array of therapeutic options. METHODS. We performed multiparameter flow cytometry on freshly isolated metastatic melanoma samples from 2 cohorts of 20 patients each prior to treatment and correlated the subsequent clinical response with the tumor immune phenotype. RESULTS. Increasing fractions of programmed cell death 1 high/cytotoxic T lymphocyte–associated protein 4 high (PD-1hiCTLA-4hi) cells within the tumor-infiltrating CD8+ T cell subset strongly correlated with response to therapy (RR) and progression-free survival (PFS). Functional analysis of these cells revealed a partially exhausted T cell phenotype. Assessment of metastatic lesions during anti–PD-1 therapy demonstrated a release of T cell exhaustion, as measured by an accumulation of highly activated CD8+ T cells within tumors, with no effect on Tregs. CONCLUSIONS. Our data suggest that the relative abundance of partially exhausted tumor-infiltrating CD8+ T cells predicts response to anti–PD-1 therapy. This information can be used to appropriately select patients with a high likelihood of achieving a clinical response to PD-1 pathway inhibition. FUNDING. This work was funded by a generous gift provided by Inga-Lill and David Amoroso as well as a generous gift provided by Stephen Juelsgaard and Lori Cook. PMID:27525433

  10. Hepatic IFIT3 predicts interferon-α therapeutic response in patients of hepatocellular carcinoma.

    PubMed

    Yang, Yingyun; Zhou, Ye; Hou, Jin; Bai, Chunmei; Li, Zhenyang; Fan, Jia; Ng, Irene O L; Zhou, Weiping; Sun, Huichuan; Dong, Qiongzhu; Lee, Joyce M F; Lo, Chung-Mau; Man, Kwan; Yang, Yun; Li, Nan; Ding, Guoshan; Yu, Yizhi; Cao, Xuetao

    2017-03-13

    Adjuvant interferon-α (IFN-α) therapy is used to control certain types of cancer in clinics. For hepatocellular carcinoma (HCC), IFN-α therapy is effective in only a subgroup of HCC patients, therefore identifying biomarkers to predict the response to IFN-α therapy is of high significance and clinical utility. As the induced IFN-stimulated genes (ISGs) expression following IFN-α treatment plays pivotal roles in IFN-α effects, we screened ISGs expression in HCC tissues and found several ISGs were significantly decreased in HCC. Interestingly, expressions of IFN-induced proteins with tetratricopeptide repeats (IFIT) family members, including IFIT1, IFIT2, IFIT3, and IFIT5, were all decreased in HCC tissues. We further analyzed the expressions of IFIT family members in HCC and their roles in patients' responses to IFN-α therapy in two independent randomized controlled IFN-α therapy clinical trials of HCC patients. We found that higher expression of IFIT3, but not other IFITs, in HCC tissues predicts better response to IFN-α therapy, suggesting that IFIT3 may be a useful predictor of the response to IFN-α therapy in HCC patients. Mechanistically, IFIT3 enhanced the antitumor effects of IFN-α by promoting IFN-α effector responses both in vitro and in vivo. IFIT3 could bind signal transducer and activator of transcription 1 (STAT1) and STAT2 to enhance STAT1/STAT2 hetero-dimerization and nuclear translocation upon IFN-α treatment, thus promoting IFN-α effector signaling.

  11. CT contrast predicts pancreatic cancer treatment response to verteporfin-based photodynamic therapy

    NASA Astrophysics Data System (ADS)

    Jermyn, Michael; Davis, Scott C.; Dehghani, Hamid; Huggett, Matthew T.; Hasan, Tayyaba; Pereira, Stephen P.; Bown, Stephen G.; Pogue, Brian W.

    2014-04-01

    The goal of this study was to determine dominant factors affecting treatment response in pancreatic cancer photodynamic therapy (PDT), based on clinically available information in the VERTPAC-01 trial. This trial investigated the safety and efficacy of verteporfin PDT in 15 patients with locally advanced pancreatic adenocarcinoma. CT scans before and after contrast enhancement from the 15 patients in the VERTPAC-01 trial were used to determine venous-phase blood contrast enhancement and this was correlated with necrotic volume determined from post-treatment CT scans, along with estimation of optical absorption in the pancreas for use in light modeling of the PDT treatment. Energy threshold contours yielded estimates for necrotic volume based on this light modeling. Both contrast-derived venous blood content and necrotic volume from light modeling yielded strong correlations with observed necrotic volume (R2 = 0.85 and 0.91, respectively). These correlations were much stronger than those obtained by correlating energy delivered versus necrotic volume in the VERTPAC-01 study and in retrospective analysis from a prior clinical study. This demonstrates that contrast CT can provide key surrogate dosimetry information to assess treatment response. It also implies that light attenuation is likely the dominant factor in the VERTPAC treatment response, as opposed to other factors such as drug distribution. This study is the first to show that contrast CT provides needed surrogate dosimetry information to predict treatment response in a manner which uses standard-of-care clinical images, rather than invasive dosimetry methods.

  12. Response rate of catatonia to electroconvulsive therapy and its clinical correlates.

    PubMed

    Raveendranathan, Dhanya; Narayanaswamy, Janardhanan C; Reddi, Senthil V

    2012-08-01

    Electroconvulsive therapy (ECT) is an important treatment for catatonia. We aimed to study the response rate of catatonia treated with ECT and its clinical correlates in a large sample of inpatients. The ECT parameters of all patients (n = 63) admitted with catatonia between the months of January and December 2007 were examined. The number of ECTs administered, seizure threshold, failure to achieve adequate seizures and clinical signs pertaining to catatonia were analyzed. Response was considered as complete resolution of catatonic symptoms with Bush Francis Catatonia Rating Scale (BFCRS) score becoming zero. ECT was mostly started after failed lorazepam treatment except in 6 patients where ECT was the first choice. Patients who responded in 4 ECT sessions were considered fast responders (mean session number for response is 4 sessions) and response with 5 or more ECTs was considered slow response. Fast responders had significantly lower duration of catatonia (19.67 ± 21.66 days, P = 0.02) and higher BFCRS score at presentation (17.25 ± 6.21, P = 0.03). Presence of waxy flexibility and gegenhalten (22.60% vs. 0%, P = 0.01) predicted faster response, whereas presence of echophenomena (3.2% vs. 24.0%) predicted slow response. The response rate to catatonia appears to be associated with the severity and duration of catatonia, and the presence of certain catatonic signs.

  13. Prediction of the Chemoreflex Gain by Common Clinical Variables in Heart Failure

    PubMed Central

    Mirizzi, Gianluca; Giannoni, Alberto; Ripoli, Andrea; Iudice, Giovanni; Bramanti, Francesca; Emdin, Michele; Passino, Claudio

    2016-01-01

    Background Peripheral and central chemoreflex sensitivity, assessed by the hypoxic or hypercapnic ventilatory response (HVR and HCVR, respectively), is enhanced in heart failure (HF) patients, is involved in the pathophysiology of the disease, and is under investigation as a potential therapeutic target. Chemoreflex sensitivity assessment is however demanding and, therefore, not easily applicable in the clinical setting. We aimed at evaluating whether common clinical variables, broadly obtained by routine clinical and instrumental evaluation, could predict increased HVR and HCVR. Methods and results 191 patients with systolic HF (left ventricular ejection fraction—LVEF—<50%) underwent chemoreflex assessment by rebreathing technique to assess HVR and HCVR. All patients underwent clinical and neurohormonal evaluation, comprising: echocardiogram, cardiopulmonary exercise test (CPET), daytime cardiorespiratory monitoring for breathing pattern evaluation. Regarding HVR, multivariate penalized logistic regression, Bayesian Model Averaging (BMA) logistic regression and random forest analysis identified, as predictors, the presence of periodic breathing and increased slope of the relation between ventilation and carbon dioxide production (VE/VCO2) during exercise. Again, the above-mentioned statistical tools identified as HCVR predictors plasma levels of N-terminal fragment of proBNP and VE/VCO2 slope. Conclusions In HF patients, the simple assessment of breathing pattern, alongside with ventilatory efficiency during exercise and natriuretic peptides levels identifies a subset of patients presenting with increased chemoreflex sensitivity to either hypoxia or hypercapnia. PMID:27099934

  14. The development of pharmacogenomic models to predict drug response.

    PubMed

    Adam, G I

    2001-05-01

    The potential of pharmacogenomic research to improve general healthcare, reduce morbidity and mortality resulting from treatment side effects, and to accelerate therapeutic compound discovery, design and development in the pharmaceutical industry, remains largely unfulfilled. Major contributory factors affecting progress in the field include: (i) the need for large clinical populations and control/placebo-treated cohorts to be studied; (ii) the difficulty in evaluating drug response in many instances with empirical or qualitative treatment response values often not available; (iii) interactions of underlying biochemical pathways are often not fully understood, both in terms of mode-of-action of a therapeutic compound itself and in the occurrence of side effects; (iv) population-specific frequency data for reported genetic variants and physically mapped genomic markers for evaluation in drug response studies are often not readily and/or publicly available; (v) accurate, high-throughput, cost-effective polymorphism detection and screening methods are required; and (vi) sophisticated biostatistical data analysis programs to perform the multi-parametric tests and permutation analyses necessary are still under development. With the recent human genome sequence draft release by both the public and commercial initiatives, in addition to the publication of locus and chromosome-specific polymorphism maps and TSC (The SNP Consortium) SNP map information expected in late-2001, it is hoped that at least some of the inherent difficulties in pharmacogenomics research will soon be alleviated.

  15. Prospective assessment of a gene signature potentially predictive of clinical benefit in metastatic melanoma patients following MAGE-A3 immunotherapeutic (PREDICT)

    PubMed Central

    Saiag, P.; Gutzmer, R.; Ascierto, P. A.; Maio, M.; Grob, J.-J.; Murawa, P.; Dreno, B.; Ross, M.; Weber, J.; Hauschild, A.; Rutkowski, P.; Testori, A.; Levchenko, E.; Enk, A.; Misery, L.; Vanden Abeele, C.; Vojtek, I.; Peeters, O.; Brichard, V. G.; Therasse, P.

    2016-01-01

    Background Genomic profiling of tumor tissue may aid in identifying predictive or prognostic gene signatures (GS) in some cancers. Retrospective gene expression profiling of melanoma and non-small-cell lung cancer led to the characterization of a GS associated with clinical benefit, including improved overall survival (OS), following immunization with the MAGE-A3 immunotherapeutic. The goal of the present study was to prospectively evaluate the predictive value of the previously characterized GS. Patients and methods An open-label prospective phase II trial (‘PREDICT’) in patients with MAGE-A3-positive unresectable stage IIIB-C/IV-M1a melanoma. Results Of 123 subjects who received the MAGE-A3 immunotherapeutic, 71 (58.7%) displayed the predictive GS (GS+). The 1-year OS rate was 83.1%/83.3% in the GS+/GS− populations. The rate of progression-free survival at 12 months was 5.8%/4.1% in GS+/GS− patients. The median time-to-treatment failure was 2.7/2.4 months (GS+/GS−). There was one complete response (GS−) and two partial responses (GS+). The MAGE-A3 immunotherapeutic was similarly immunogenic in both populations and had a clinically acceptable safety profile. Conclusion Treatment of patients with MAGE-A3-positive unresectable stage IIIB-C/IV-M1a melanoma with the MAGE-A3 immunotherapeutic demonstrated an overall 1-year OS rate of 83.5%. GS− and GS+ patients had similar 1-year OS rates, indicating that in this study, GS was not predictive of outcome. Unexpectedly, the objective response rate was lower in this study than in other studies carried out in the same setting with the MAGE-A3 immunotherapeutic. Investigation of a GS to predict clinical benefit to adjuvant MAGE-A3 immunotherapeutic treatment is ongoing in another melanoma study. This study is registered at www.clinicatrials.gov NCT00942162. PMID:27502712

  16. PET-Based Treatment Response Evaluation in Rectal Cancer: Prediction and Validation

    SciTech Connect

    Janssen, Marco H.M.; Oellers, Michel C.; Stiphout, Ruud G.P.M. van; Riedl, Robert G.; Bogaard, Jorgen van den; Buijsen, Jeroen; Lambin, Philippe; Lammering, Guido

    2012-02-01

    select patients to be considered for less invasive surgical interventions or even a 'wait and see' policy. Also, based on the predicted response, early modifications of the treatment protocol are possible, which might result in an improved clinical outcome.

  17. Gene expression profile predictive of response to chemotherapy in metastatic colorectal cancer.

    PubMed

    Estevez-Garcia, Purificacion; Rivera, Fernando; Molina-Pinelo, Sonia; Benavent, Marta; Gómez, Javier; Limón, Maria Luisa; Pastor, Maria Dolores; Martinez-Perez, Julia; Paz-Ares, Luis; Carnero, Amancio; Garcia-Carbonero, Rocio

    2015-03-20

    Fluoropyrimidine-based chemotherapy (CT) has been the mainstay of care of metastatic colorectal cancer (mCRC) for years. Response rates are only observed, however, in about half of treated patients, and there are no reliable tools to prospectively identify patients more likely to benefit from therapy. The purpose of our study was to identify a gene expression profile predictive of CT response in mCRC. Whole genome expression analyses (Affymetrix GeneChip HG-U133 Plus 2.0) were performed in fresh frozen tumor samples of 37 mCRC patients (training cohort). Differential gene expression profiles among the two study conditions (responders versus non-responders) were assessed using supervised class prediction algorithms. A set of 161 differentially expressed genes in responders (23 patients; 62%) versus non-responders (14 patients; 38%) was selected for further assessment and validation by RT-qPCR (TaqMan Low Density Arrays (TLDA) 7900 HT Micro Fluidic Cards) in an independent multi-institutional cohort (53 mCRC patients). Seven of these genes were confirmed as significant predictors of response. Patients with a favorable predictive signature had significantly greater response rate (58% vs. 13%, p = 0.024), progression-free survival (61% vs. 13% at 1 year, HR = 0.32, p = 0.009) and overall survival (32 vs. 16 months, HR = 0.21, p = 0.003) than patients with an unfavorable gene signature. This is the first study to validate a gene-expression profile predictive of response to CT in mCRC patients. Larger and prospective confirmatory studies are required, however, in order to successfully provide oncologists with adequate tools to optimize treatment selection in routine clinical practice.

  18. Quantitative optical imaging of primary tumor organoid metabolism predicts drug response in breast cancer

    PubMed Central

    Walsh, Alex J.; Cook, Rebecca S.; Sanders, Melinda E.; Aurisicchio, Luigi; Ciliberto, Gennaro; Arteaga, Carlos L.; Skala, Melissa C.

    2014-01-01

    There is a need for technologies to predict the efficacy of cancer treatment in individual patients. Here we show that optical metabolic imaging of organoids derived from primary tumors can predict therapeutic response of xenografts and measure anti-tumor drug responses in human-tumor derived organoids. Optical metabolic imaging quantifies the fluorescence intensity and lifetime of NADH and FAD, co-enzymes of metabolism. As early as 24 hours after treatment with clinically relevant anti-cancer drugs, the optical metabolic imaging index of responsive organoids decreased (p<0.001) and was further reduced when effective therapies were combined (p<5×10–6), with no change in drug-resistant organoids. Drug response in xenograft-derived organoids was validated with tumor growth measurements in vivo and stains for proliferation and apoptosis. Heterogeneous cellular responses to drug treatment were also resolved in organoids. Optical metabolic imaging shows potential as a high-throughput screen to test the efficacy of a panel of drugs to select optimal drug combinations. PMID:25100563

  19. Quantitative optical imaging of primary tumor organoid metabolism predicts drug response in breast cancer.

    PubMed

    Walsh, Alex J; Cook, Rebecca S; Sanders, Melinda E; Aurisicchio, Luigi; Ciliberto, Gennaro; Arteaga, Carlos L; Skala, Melissa C

    2014-09-15

    There is a need for technologies to predict the efficacy of cancer treatment in individual patients. Here, we show that optical metabolic imaging of organoids derived from primary tumors can predict the therapeutic response of xenografts and measure antitumor drug responses in human tumor-derived organoids. Optical metabolic imaging quantifies the fluorescence intensity and lifetime of NADH and FAD, coenzymes of metabolism. As early as 24 hours after treatment with clinically relevant anticancer drugs, the optical metabolic imaging index of responsive organoids decreased (P < 0.001) and was further reduced when effective therapies were combined (P < 5 × 10(-6)), with no change in drug-resistant organoids. Drug response in xenograft-derived organoids was validated with tumor growth measurements in vivo and staining for proliferation and apoptosis. Heterogeneous cellular responses to drug treatment were also resolved in organoids. Optical metabolic imaging shows potential as a high-throughput screen to test the efficacy of a panel of drugs to select optimal drug combinations. Cancer Res; 74(18); 5184-94. ©2014 AACR.

  20. Exploring the Inflammatory Metabolomic Profile to Predict Response to TNF-α Inhibitors in Rheumatoid Arthritis.

    PubMed

    Cuppen, Bart V J; Fu, Junzeng; van Wietmarschen, Herman A; Harms, Amy C; Koval, Slavik; Marijnissen, Anne C A; Peeters, Judith J W; Bijlsma, Johannes W J; Tekstra, Janneke; van Laar, Jacob M; Hankemeier, Thomas; Lafeber, Floris P J G; van der Greef, Jan

    2016-01-01

    In clinical practice, approximately one-third of patients with rheumatoid arthritis (RA) respond insufficiently to TNF-α inhibitors (TNFis). The aim of the study was to explore the use of a metabolomics to identify predictors for the outcome of TNFi therapy, and study the metabolomic fingerprint in active RA irrespective of patients' response. In the metabolomic profiling, lipids, oxylipins, and amines were measured in serum samples of RA patients from the observational BiOCURA cohort, before start of biological treatment. Multivariable logistic regression models were established to identify predictors for good- and non-response in patients receiving TNFi (n = 124). The added value of metabolites over prediction using clinical parameters only was determined by comparing the area under receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, positive- and negative predictive value and by the net reclassification index (NRI). The models were further validated by 10-fold cross validation and tested on the complete TNFi treatment cohort including moderate responders. Additionally, metabolites were identified that cross-sectionally associated with the RA disease activity score based on a 28-joint count (DAS28), erythrocyte sedimentation rate (ESR) or C-reactive protein (CRP). Out of 139 metabolites, the best-performing predictors were sn1-LPC(18:3-ω3/ω6), sn1-LPC(15:0), ethanolamine, and lysine. The model that combined the selected metabolites with clinical parameters showed a significant larger AUC-ROC than that of the model containing only clinical parameters (p = 0.01). The combined model was able to discriminate good- and non-responders with good accuracy and to reclassify non-responders with an improvement of 30% (total NRI = 0.23) and showed a prediction error of 0.27. For the complete TNFi cohort, the NRI was 0.22. In addition, 88 metabolites were associated with DAS28, ESR or CRP (p<0.05). Our study established an accurate prediction model

  1. Exploring the Inflammatory Metabolomic Profile to Predict Response to TNF-α Inhibitors in Rheumatoid Arthritis

    PubMed Central

    van Wietmarschen, Herman A.; Harms, Amy C.; Koval, Slavik; Marijnissen, Anne C. A.; Peeters, Judith J. W.; Bijlsma, Johannes W. J.; Tekstra, Janneke; van Laar, Jacob M.; Hankemeier, Thomas; Lafeber, Floris P. J. G.; van der Greef, Jan

    2016-01-01

    In clinical practice, approximately one-third of patients with rheumatoid arthritis (RA) respond insufficiently to TNF-α inhibitors (TNFis). The aim of the study was to explore the use of a metabolomics to identify predictors for the outcome of TNFi therapy, and study the metabolomic fingerprint in active RA irrespective of patients’ response. In the metabolomic profiling, lipids, oxylipins, and amines were measured in serum samples of RA patients from the observational BiOCURA cohort, before start of biological treatment. Multivariable logistic regression models were established to identify predictors for good- and non-response in patients receiving TNFi (n = 124). The added value of metabolites over prediction using clinical parameters only was determined by comparing the area under receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, positive- and negative predictive value and by the net reclassification index (NRI). The models were further validated by 10-fold cross validation and tested on the complete TNFi treatment cohort including moderate responders. Additionally, metabolites were identified that cross-sectionally associated with the RA disease activity score based on a 28-joint count (DAS28), erythrocyte sedimentation rate (ESR) or C-reactive protein (CRP). Out of 139 metabolites, the best-performing predictors were sn1-LPC(18:3-ω3/ω6), sn1-LPC(15:0), ethanolamine, and lysine. The model that combined the selected metabolites with clinical parameters showed a significant larger AUC-ROC than that of the model containing only clinical parameters (p = 0.01). The combined model was able to discriminate good- and non-responders with good accuracy and to reclassify non-responders with an improvement of 30% (total NRI = 0.23) and showed a prediction error of 0.27. For the complete TNFi cohort, the NRI was 0.22. In addition, 88 metabolites were associated with DAS28, ESR or CRP (p<0.05). Our study established an accurate prediction

  2. Prestimulation phase predicts the TMS-evoked response

    PubMed Central

    Johnson, Jeffrey S.; Postle, Bradley R.

    2014-01-01

    Prestimulation oscillatory phase and power in particular frequency bands predict perception of at-threshold visual stimuli and of transcranial magnetic stimulation (TMS)-induced phosphenes. These effects may be due to changes in cortical excitability, such that certain ranges of power and/or phase values result in a state in which a particular brain area is more receptive to input, thereby biasing behavior. However, the effects of trial-by-trial fluctuations in phase and power of ongoing oscillations on the brain's electrical response to TMS itself have thus far not been addressed. The present study adopts a combined TMS and electroencepalography (EEG) approach to determine whether the TMS-evoked response is sensitive to momentary fluctuations in prestimulation phase and/or power in different frequency bands. Specifically, TMS was applied to superior parietal lobule while subjects performed a short-term memory task. Results showed that the prestimulation phase, particularly within the beta (15–25 Hz) band, predicted pulse-by-pulse variations in the global mean field amplitude. No such relationship was observed between prestimulation power and the global mean field amplitude. Furthermore, TMS-evoked power in the beta band fluctuated with prestimulation phase in the beta band in a manner that differed from spontaneous brain activity. These effects were observed in areas at and distal to the stimulation site. Together, these results confirm the idea that fluctuating phase of ongoing neuronal oscillations create “windows of excitability” in the brain, and they give insight into how TMS interacts with ongoing brain activity on a pulse-by-pulse basis. PMID:25008413

  3. The multiform motor cortical output: Kinematic, predictive and response coding.

    PubMed

    Sartori, Luisa; Betti, Sonia; Chinellato, Eris; Castiello, Umberto

    2015-09-01

    Observing actions performed by others entails a subliminal activation of primary motor cortex reflecting the components encoded in the observed action. One of the most debated issues concerns the role of this output: Is it a mere replica of the incoming flow of information (kinematic coding), is it oriented to anticipate the forthcoming events (predictive coding) or is it aimed at responding in a suitable fashion to the actions of others (response coding)? The aim of the present study was to disentangle the relative contribution of these three levels and unify them into an integrated view of cortical motor coding. We combined transcranial magnetic stimulation (TMS) and electromyography recordings at different timings to probe the excitability of corticospinal projections to upper and lower limb muscles of participants observing a soccer player performing: (i) a penalty kick straight in their direction and then coming to a full stop, (ii) a penalty kick straight in their direction and then continuing to run, (iii) a penalty kick to the side and then continuing to run. The results show a modulation of the observer's corticospinal excitability in different effectors at different times reflecting a multiplicity of motor coding. The internal replica of the observed action, the predictive activation, and the adaptive integration of congruent and non-congruent responses to the actions of others can coexist in a not mutually exclusive way. Such a view offers reconciliation among different (and apparently divergent) frameworks in action observation literature, and will promote a more complete and integrated understanding of recent findings on motor simulation, motor resonance and automatic imitation.

  4. Specific regulatory motifs predict glucocorticoid responsiveness of hippocampal gene expression.

    PubMed

    Datson, N A; Polman, J A E; de Jonge, R T; van Boheemen, P T M; van Maanen, E M T; Welten, J; McEwen, B S; Meiland, H C; Meijer, O C

    2011-10-01

    The glucocorticoid receptor (GR) is an ubiquitously expressed ligand-activated transcription factor that mediates effects of cortisol in relation to adaptation to stress. In the brain, GR affects the hippocampus to modulate memory processes through direct binding to glucocorticoid response elements (GREs) in the DNA. However, its effects are to a high degree cell specific, and its target genes in different cell types as well as the mechanisms conferring this specificity are largely unknown. To gain insight in hippocampal GR signaling, we characterized to which GRE GR binds in the rat hippocampus. Using a position-specific scoring matrix, we identified evolutionary-conserved putative GREs from a microarray based set of hippocampal target genes. Using chromatin immunoprecipitation, we were able to confirm GR binding to 15 out of a selection of 32 predicted sites (47%). The majority of these 15 GREs are previously undescribed and thus represent novel GREs that bind GR and therefore may be functional in the rat hippocampus. GRE nucleotide composition was not predictive for binding of GR to a GRE. A search for conserved flanking sequences that may predict GR-GRE interaction resulted in the identification of GC-box associated motifs, such as Myc-associated zinc finger protein 1, within 2 kb of GREs with GR binding in the hippocampus. This enrichment was not present around nonbinding GRE sequences nor around proven GR-binding sites from a mesenchymal stem-like cell dataset that we analyzed. GC-binding transcription factors therefore may be unique partners for DNA-bound GR and may in part explain cell-specific transcriptional regulation by glucocorticoids in the context of the hippocampus.

  5. Prediction of spectral acceleration response ordinates based on PGA attenuation

    USGS Publications Warehouse

    Graizer, V.; Kalkan, E.

    2009-01-01

    Developed herein is a new peak ground acceleration (PGA)-based predictive model for 5% damped pseudospectral acceleration (SA) ordinates of free-field horizontal component of ground motion from shallow-crustal earthquakes. The predictive model of ground motion spectral shape (i.e., normalized spectrum) is generated as a continuous function of few parameters. The proposed model eliminates the classical exhausted matrix of estimator coefficients, and provides significant ease in its implementation. It is structured on the Next Generation Attenuation (NGA) database with a number of additions from recent Californian events including 2003 San Simeon and 2004 Parkfield earthquakes. A unique feature of the model is its new functional form explicitly integrating PGA as a scaling factor. The spectral shape model is parameterized within an approximation function using moment magnitude, closest distance to the fault (fault distance) and VS30 (average shear-wave velocity in the upper 30 m) as independent variables. Mean values of its estimator coefficients were computed by fitting an approximation function to spectral shape of each record using robust nonlinear optimization. Proposed spectral shape model is independent of the PGA attenuation, allowing utilization of various PGA attenuation relations to estimate the response spectrum of earthquake recordings.

  6. Turbomachinery Forced Response Prediction System (FREPS): User's Manual

    NASA Technical Reports Server (NTRS)

    Morel, M. R.; Murthy, D. V.

    1994-01-01

    The turbomachinery forced response prediction system (FREPS), version 1.2, is capable of predicting the aeroelastic behavior of axial-flow turbomachinery blades. This document is meant to serve as a guide in the use of the FREPS code with specific emphasis on its use at NASA Lewis Research Center (LeRC). A detailed explanation of the aeroelastic analysis and its development is beyond the scope of this document, and may be found in the references. FREPS has been developed by the NASA LeRC Structural Dynamics Branch. The manual is divided into three major parts: an introduction, the preparation of input, and the procedure to execute FREPS. Part 1 includes a brief background on the necessity of FREPS, a description of the FREPS system, the steps needed to be taken before FREPS is executed, an example input file with instructions, presentation of the geometric conventions used, and the input/output files employed and produced by FREPS. Part 2 contains a detailed description of the command names needed to create the primary input file that is required to execute the FREPS code. Also, Part 2 has an example data file to aid the user in creating their own input files. Part 3 explains the procedures required to execute the FREPS code on the Cray Y-MP, a computer system available at the NASA LeRC.

  7. DNA Repair Biomarkers Predict Response to Neoadjuvant Chemoradiotherapy in Esophageal Cancer

    SciTech Connect

    Alexander, Brian M.; Niemierko, Andrzej; Weaver, David T.; Mak, Raymond H.; Fidias, Panagiotis; Wain, John; Choi, Noah C.

    2012-05-01

    Purpose: The addition of neoadjuvant chemoradiotherapy prior to surgical resection for esophageal cancer has improved clinical outcomes in some trials. Pathologic complete response (pCR) following neoadjuvant therapy is associated with better clinical outcome in these patients, but only 22% to 40% of patients achieve pCR. Because both chemotherapy and radiotherapy act by inducing DNA damage, we analyzed proteins selected from multiple DNA repair pathways, using quantitative immunohistochemistry coupled with a digital pathology platform, as possible biomarkers of treatment response and clinical outcome. Methods and Materials: We identified 79 patients diagnosed with esophageal cancer between October 1994 and September 2002, with biopsy tissue available, who underwent neoadjuvant chemoradiotherapy prior to surgery at the Massachusetts General Hospital and used their archived, formalin-fixed, paraffin-embedded biopsy samples to create tissue microarrays (TMA). TMA sections were stained using antibodies against proteins in various DNA repair pathways including XPF, FANCD2, PAR, MLH1, PARP1, and phosphorylated MAPKAP kinase 2 (pMK2). Stained TMA slides were evaluated using machine-based image analysis, and scoring incorporated both the intensity and the quantity of positive tumor nuclei. Biomarker scores and clinical data were assessed for correlations with clinical outcome. Results: Higher scores for MLH1 (p = 0.018) and lower scores for FANCD2 (p = 0.037) were associated with pathologic response to neoadjuvant chemoradiation on multivariable analysis. Staining of MLH1, PARP1, XPF, and PAR was associated with recurrence-free survival, and staining of PARP1 and FANCD2 was associated with overall survival on multivariable analysis. Conclusions: DNA repair proteins analyzed by immunohistochemistry may be useful as predictive markers for response to neoadjuvant chemoradiotherapy in patients with esophageal cancer. These results are hypothesis generating and need

  8. Predicting success or failure of immunotherapy for cancer: insights from a clinically applicable mathematical model.

    PubMed

    Babbs, Charles F

    2012-01-01

    The objective of this study was to create a clinically applicable mathematical model of immunotherapy for cancer and use it to explore differences between successful and unsuccessful treatment scenarios. The simplified predator-prey model includes four lumped parameters: tumor growth rate, g; immune cell killing efficiency, k; immune cell signaling factor, λ; and immune cell half-life decay, μ. The predator-prey equations as functions of time, t, for normalized tumor cell numbers, y, (the prey) and immunocyte numbers, ×, (the predators) are: dy/dt = gy - kx and dx/dt = λxy - μx. A parameter estimation procedure that capitalizes on available clinical data and the timing of clinically observable phenomena gives mid-range benchmarks for parameters representing the unstable equilibrium case in which the tumor neither grows nor shrinks. Departure from this equilibrium results in oscillations in tumor cell numbers and in many cases complete elimination of the tumor. Several paradoxical phenomena are predicted, including increasing tumor cell numbers prior to a population crash, apparent cure with late recurrence, one or more cycles of tumor growth prior to eventual tumor elimination, and improved tumor killing with initially weaker immune parameters or smaller initial populations of immune cells. The model and the parameter estimation techniques are easily adapted to various human cancers that evoke an immune response. They may help clinicians understand and predict certain strange and unexpected effects in the world of tumor immunity and lead to the design of clinical trials to test improved treatment protocols for patients.

  9. Comparison of Statistical and Clinical Predictions of Functional Outcome after Ischemic Stroke

    PubMed Central

    Thompson, Douglas D.; Murray, Gordon D.; Sudlow, Cathie L. M.; Dennis, Martin; Whiteley, William N.

    2014-01-01

    Background To determine whether the predictions of functional outcome after ischemic stroke made at the bedside using a doctor’s clinical experience were more or less accurate than the predictions made by clinical prediction models (CPMs). Methods and Findings A prospective cohort study of nine hundred and thirty one ischemic stroke patients recruited consecutively at the outpatient, inpatient and emergency departments of the Western General Hospital, Edinburgh between 2002 and 2005. Doctors made informal predictions of six month functional outcome on the Oxford Handicap Scale (OHS). Patients were followed up at six months with a validated postal questionnaire. For each patient we calculated the absolute predicted risk of death or dependence (OHS≥3) using five previously described CPMs. The specificity of a doctor’s informal predictions of OHS≥3 at six months was good 0.96 (95% CI: 0.94 to 0.97) and similar to CPMs (range 0.94 to 0.96); however the sensitivity of both informal clinical predictions 0.44 (95% CI: 0.39 to 0.49) and clinical prediction models (range 0.38 to 0.45) was poor. The prediction of the level of disability after stroke was similar for informal clinical predictions (ordinal c-statistic 0.74 with 95% CI 0.72 to 0.76) and CPMs (range 0.69 to 0.75). No patient or clinician characteristic affected the accuracy of informal predictions, though predictions were more accurate in outpatients. Conclusions CPMs are at least as good as informal clinical predictions in discriminating between good and bad functional outcome after ischemic stroke. The place of these models in clinical practice has yet to be determined. PMID:25299053

  10. Physicians' Professionally Responsible Power: A Core Concept of Clinical Ethics.

    PubMed

    McCullough, Laurence B

    2016-02-01

    The gathering of power unto themselves by physicians, a process supported by evidence-based practice, clinical guidelines, licensure, organizational culture, and other social factors, makes the ethics of power--the legitimation of physicians' power--a core concept of clinical ethics. In the absence of legitimation, the physician's power over patients becomes problematic, even predatory. As has occurred in previous issues of the Journal, the papers in the 2016 clinical ethics issue bear on the professionally responsible deployment of power by physicians. This introduction explores themes of physicians' power in papers from an international group of authors who address autonomy and trust, the virtues of perinatal hospice, conjoined twins in ethics and law, addiction and autonomy in clinical research on addicting substances, euthanasia of patients with dementia in Belgium, and a pragmatic approach to clinical futility.

  11. Physicians’ Professionally Responsible Power: A Core Concept of Clinical Ethics

    PubMed Central

    McCullough, Laurence B.

    2016-01-01

    The gathering of power unto themselves by physicians, a process supported by evidence-based practice, clinical guidelines, licensure, organizational culture, and other social factors, makes the ethics of power—the legitimation of physicians’ power—a core concept of clinical ethics. In the absence of legitimation, the physician’s power over patients becomes problematic, even predatory. As has occurred in previous issues of the Journal, the papers in the 2016 clinical ethics issue bear on the professionally responsible deployment of power by physicians. This introduction explores themes of physicians’ power in papers from an international group of authors who address autonomy and trust, the virtues of perinatal hospice, conjoined twins in ethics and law, addiction and autonomy in clinical research on addicting substances, euthanasia of patients with dementia in Belgium, and a pragmatic approach to clinical futility. PMID:26671961

  12. Personalized medicine in psoriasis: developing a genomic classifier to predict histological response to Alefacept

    PubMed Central

    2010-01-01

    Background Alefacept treatment is highly effective in a select group patients with moderate-to-severe psoriasis, and is an ideal candidate to develop systems to predict who will respond to therapy. A clinical trial of 22 patients with moderate to severe psoriasis treated with alefacept was conducted in 2002-2003, as a mechanism of action study. Patients were classified as responders or non-responders to alefacept based on histological criteria. Results of the original mechanism of action study have been published. Peripheral blood was collected at the start of this clinical trial, and a prior analysis demonstrated that gene expression in PBMCs differed between responders and non-responders, however, the analysis performed could not be used to predict response. Methods Microarray data from PBMCs of 16 of these patients was analyzed to generate a treatment response classifier. We used a discriminant analysis method that performs sample classification from gene expression data, via "nearest shrunken centroid method". Centroids are the average gene expression for each gene in each class divided by the within-class standard deviation for that gene. Results A disease response classifier using 23 genes was created to accurately predict response to alefacept (12.3% error rate). While the genes in this classifier should be considered as a group, some of the individual genes are of great interest, for example, cAMP response element modulator (CREM), v-MAF avian musculoaponeurotic fibrosarcoma oncogene family (MAFF), chloride intracellular channel protein 1 (CLIC1, also called NCC27), NLR family, pyrin domain-containing 1 (NLRP1), and CCL5 (chemokine, cc motif, ligand 5, also called regulated upon activation, normally T expressed, and presumably secreted/RANTES). Conclusions Although this study is small, and based on analysis of existing microarray data, we demonstrate that a treatment response classifier for alefacept can be created using gene expression of PBMCs in

  13. Analysis of clinical trials with biologics using dose-time-response models.

    PubMed

    Lange, Markus R; Schmidli, Heinz

    2015-09-30

    Biologics such as monoclonal antibodies are increasingly and successfully used for the treatment of many chronic diseases. Unlike conventional small drug molecules, which are commonly given as tablets once daily, biologics are typically injected at much longer time intervals, that is, weeks or months. Hence, both the dose and the time interval have to be optimized during the drug development process for biologics. To identify an adequate regimen for the investigated biologic, the dose-time-response relationship must be well characterized, based on clinical trial data. The proposed approach uses semi-mechanistic nonlinear regression models to describe and predict the time-changing response for complex dosing regimens. Both likelihood-based and Bayesian methods for inference and prediction are discussed. The methodology is illustrated with data from a clinical study in an auto-immune disease.

  14. Early clinical prediction of neurological outcome following out of hospital cardiac arrest managed with therapeutic hypothermia

    PubMed Central

    Ruknuddeen, Mohammed Ishaq; Ramadoss, Rajaram; Rajajee, V.; Grzeskowiak, Luke E.; Rajagopalan, Ram E.

    2015-01-01

    Background: Therapeutic hypothermia (TH) may improve neurological outcome in comatose patients following out of hospital cardiac arrest (OHCA). The reliability of clinical prediction of neurological outcome following TH remains unclear. In particular, there is very limited data on survival and predictors of neurological outcome following TH for OHCA from resource-constrained settings in general and South Asia in specific. Objective: The objective was to identify factors predicting unfavorable neurological outcome at hospital discharge in comatose survivors of OHCA treated with hypothermia. Design: Retrospective chart review. Setting: Urban 200-bed hospital in Chennai, India. Methods: Predictors of unfavorable neurological outcome (cerebral performance category score [3–5]) at hospital discharge were evaluated among patients admitted between January 2006 and December 2012 following OHCA treated with TH. Hypothermia was induced with cold intravenous saline bolus, ice packs and cold-water spray with bedside fan. Predictors of unfavorable neurological outcome were examined through multivariate exact logistic regression analysis. Results: A total of 121 patients were included with 106/121 (87%) experiencing the unfavorable neurological outcome. Independent predictors of unfavorable neurological outcome included: Status myoclonus <24 h (odds ratio [OR] 21.79, 95% confidence interval [CI] 2.89-Infinite), absent brainstem reflexes (OR 50.09, 6.55-Infinite), and motor response worse than flexion on day 3 (OR 99.41, 12.21-Infinite). All 3 variables had 100% specificity and positive predictive value. Conclusion: Status myoclonus within 24 h, absence of brainstem reflexes and motor response worse than flexion on day 3 reliably predict unfavorable neurological outcome in comatose patients with OHCA treated with TH. PMID:26195855

  15. [Predict response to decitabine in patients with myelodysplastic syndrome and related neoplasms].

    PubMed

    Zhao, Y S; Guo, J; Xu, F; Wu, D; Wu, L Y; Song, L L; Xiao, C; Li, X; Chang, C K

    2017-02-14

    Objective: To identify clinical and molecular signatures for predicting response to decitabine (DAC) in patients with myelodysplastic syndrome (MDS) and related neoplasms. Methods: The clinical characteristics of 109 patients with MDS and related neoplasms who were treated with DAC were analyzed retrospectively and the next target sequencing was performed to define recurrently mutated genes in these disease samples, to examine the association of the clinical and molecular signatures with response to DAC treatment. Results: Of 109 MDS and related neoplasms patients, there were 70 males and 39 females, the median age was 61 years old (ranges: 17-85 years old) . According to the international prognostic scoring system (IPSS) , 46 cases were included in the relatively low risk group (low risk and intermediate-1 risk) , 63 in the relative high risk group (intermediate-2 and high risk) . There were 21 cases with complex karyotype, 17 chromosome 7 abnormality and 17 monosomal karyotype. The median courses of DAC treatment was 4 (2-11) . A total of 74 patients achieved response (67.9%) and 30 (27.5%) achieved complete response (CR) . Univariate analysis found that CR was higher in patients with high risk of IPSS, complex karyotypes, monosomal karyotypes, chromosome 7 abnormality, and platelet doubling after one cycle of DAC treatment. Patients with TP53 gene mutation were more likely to receive CR, 10 of 15 patients with TP53 mutations achieved CR. (66.7%) , which was significantly higher than that of the patients without TP53 gene mutation (21.3%) (P=0.001) . Multivariate analysis showed that TP53 gene mutation, platelet doubling after one cycle of DAC treatment and the complex karyotype were independent prognostic factors for CR. Of them, TP53 gene mutation is the strongest predictor (OR=4.39, 95%CI, 1.20-16.06, P=0.026) . Conclusion: TP53 mutation, platelet doubling after one cycle of DAC treatment and complex karyotypes could predict CR to DAC.

  16. Graph-guided joint prediction of class label and clinical scores for the Alzheimer's disease.

    PubMed

    Yu, Guan; Liu, Yufeng; Shen, Dinggang

    2016-09-01

    Accurate diagnosis of Alzheimer's disease and its prodromal stage, i.e., mild cognitive impairment, is very important for early treatment. Over the last decade, various machine learning methods have been proposed to predict disease status and clinical scores from brain images. It is worth noting that many features extracted from brain images are correlated significantly. In this case, feature selection combined with the additional correlation information among features can effectively improve classification/regression performance. Typically, the correlation information among features can be modeled by the connectivity of an undirected graph, where each node represents one feature and each edge indicates that the two involved features are correlated significantly. In this paper, we propose a new graph-guided multi-task learning method incorporating this undirected graph information to predict multiple response variables (i.e., class label and clinical scores) jointly. Specifically, based on the sparse undirected feature graph, we utilize a new latent group Lasso penalty to encourage the correlated features to be selected together. Furthermore, this new penalty also encourages the intrinsic correlated tasks to share a common feature subset. To validate our method, we have performed many numerical studies using simulated datasets and the Alzheimer's Disease Neuroimaging Initiative dataset. Compared with the other methods, our proposed method has very promising performance.

  17. Dielectric response based characterization and strength prediction of cementitious materials

    NASA Astrophysics Data System (ADS)

    Manchiryal, Ram Kishore

    Electrical property based methods are powerful tools to sense the properties of cement based materials. Among the several non-invasive investigative techniques, those based on monitoring the electrical properties during the initial setting and in the subsequent hardening period have immense potential in performance prediction of concrete. Electrical impedance spectroscopy (EIS) has emerged as one of the promising techniques to non-invasively probe the microstructure and property development in cement based materials. This thesis reports the results of a systematic investigation carried out to understand the influence of material parameters on the dielectric response of cement pastes and concretes, and also a methodology to property prediction in cementitious system using electrical properties. The influence of cement type, water-to-cementing materials ratio (w/cm), and the presence of fly ash as a cement replacement material on the conductivity of cement pastes is studied. The electrical conductivity---time relationships of cement pastes and concretes are expressed using a model that facilitates the extraction of initial and final conductivities, and a characteristic time parameter. These terms are used to derive information about the microstructural changes occurring with time in cement pastes. The experimental results are subjected to a range analysis to isolate the significant factors and factor interactions that influence the initial and final conductivities as well as the time parameter from the conductivity-time model for concrete mixtures. The material parameters that influence the measured conductivity are identified and their influence quantified. The changes in dielectric constant and conductivity spectra of cement paste and concretes are attributed to the polarization phenomena. There is an observed dielectric enhancement for fly ash modified pastes. The dielectric response of concrete is very similar to that of pastes, and the effect of dilution by the

  18. Pressure Ulcers in Adults: Prediction and Prevention. Clinical Practice Guideline Number 3.

    ERIC Educational Resources Information Center

    Agency for Health Care Policy and Research (DHHS/PHS), Rockville, MD.

    This package includes a clinical practice guideline, quick reference guide for clinicians, and patient's guide to predicting and preventing pressure ulcers in adults. The clinical practice guideline includes the following: overview of the incidence and prevalence of pressure ulcers; clinical practice guideline (introduction, risk assessment tools…

  19. Correlation of clinical predictions and surgical results in maxillary superior repositioning.

    PubMed

    Tabrizi, Reza; Zamiri, Barbad; Kazemi, Hamidreza

    2014-05-01

    This is a prospective study to evaluate the accuracy of clinical predictions related to surgical results in subjects who underwent maxillary superior repositioning without anterior-posterior movement. Surgeons' predictions according to clinical (tooth show at rest and at the maximum smile) and cephalometric evaluation were documented for the amount of maxillary superior repositioning. Overcorrection or undercorrection was documented for every subject 1 year after the operations. Receiver operating characteristic curve test was used to find a cutoff point in prediction errors and to determine positive predictive value (PPV) and negative predictive value. Forty subjects (14 males and 26 females) were studied. Results showed a significant difference between changes in the tooth show at rest and at the maximum smile line before and after surgery. Analysis of the data demonstrated no correlation between the predictive data and the surgical results. The incidence of undercorrection (25%) was more common than overcorrection (7.5%). The cutoff point for errors in predictions was 5 mm for tooth show at rest and 15 mm at the maximum smile. When the amount of the presurgical tooth show at rest was more than 5 mm, 50.5% of clinical predictions did not match the clinical results (PPV), and 75% of clinical predictions showed the same results when the tooth show was less than 5 mm (negative predictive value). When the amount of presurgical tooth shown in the maximum smile line was more than 15 mm, 75% of clinical predictions did not match with clinical results (PPV), and 25% of the predictions had the same results because the tooth show at the maximum smile was lower than 15 mm. Clinical predictions according to the tooth show at rest and at the maximum smile have a poor correlation with clinical results in maxillary superior repositioning for vertical maxillary excess. The risk of errors in predictions increased when the amount of superior repositioning of the maxilla increased

  20. Exposure-response modeling of clinical end points using latent variable indirect response models.

    PubMed

    Hu, C

    2014-06-04

    Exposure-response modeling facilitates effective dosing regimen selection in clinical drug development, where the end points are often disease scores and not physiological variables. Appropriate models need to be consistent with pharmacology and identifiable from the time courses of available data. This article describes a general framework of applying mechanism-based models to various types of clinical end points. Placebo and drug model parameterization, interpretation, and assessment are discussed with a focus on the indirect response models.

  1. Exposure–Response Modeling of Clinical End Points Using Latent Variable Indirect Response Models

    PubMed Central

    Hu, C

    2014-01-01

    Exposure–response modeling facilitates effective dosing regimen selection in clinical drug development, where the end points are often disease scores and not physiological variables. Appropriate models need to be consistent with pharmacology and identifiable from the time courses of available data. This article describes a general framework of applying mechanism-based models to various types of clinical end points. Placebo and drug model parameterization, interpretation, and assessment are discussed with a focus on the indirect response models. PMID:24897307

  2. Doctor AI: Predicting Clinical Events via Recurrent Neural Networks

    PubMed Central

    Choi, Edward; Bahadori, Mohammad Taha; Schuetz, Andy; Stewart, Walter F.; Sun, Jimeng

    2017-01-01

    Leveraging large historical data in electronic health record (EHR), we developed Doctor AI, a generic predictive model that covers observed medical conditions and medication uses. Doctor AI is a temporal model using recurrent neural networks (RNN) and was developed and applied to longitudinal time stamped EHR data from 260K patients over 8 years. Encounter records (e.g. diagnosis codes, medication codes or procedure codes) were input to RNN to predict (all) the diagnosis and medication categories for a subsequent visit. Doctor AI assesses the history of patients to make multilabel predictions (one label for each diagnosis or medication category). Based on separate blind test set evaluation, Doctor AI can perform differential diagnosis with up to 79% recall@30, significantly higher than several baselines. Moreover, we demonstrate great generalizability of Doctor AI by adapting the resulting models from one institution to another without losing substantial accuracy. PMID:28286600

  3. Neutrophil-to-Lymphocyte Ratio for Predicting Loss of Response to Infliximab in Ulcerative Colitis

    PubMed Central

    Nishida, Yu; Yamagami, Hirokazu; Yukawa, Tomomi; Otani, Koji; Nagami, Yasuaki; Tanaka, Fumio; Taira, Koichi; Kamata, Noriko; Tanigawa, Tetsuya; Shiba, Masatsugu; Watanabe, Kenji; Watanabe, Toshio; Tominaga, Kazunari; Fujiwara, Yasuhiro

    2017-01-01

    Objectives Neutrophil-to-lymphocyte ratio (NLR) has been used to determine the outcome in malignancies and coronary heart disease. Some reports considered the value of NLR as a predictor of response to infliximab in patients with Crohn’s disease or rheumatoid arthritis; however, no similar studies have been reported for ulcerative colitis (UC). This study aimed to evaluate the clinical significance of the baseline NLR in patients with UC treated by infliximab. Materials and Methods Patients with moderate-to-severe active UC who received the first infliximab infusion in our hospital between 2010 and 2015, who showed clinical response during the induction period, were retrospectively evaluated for long-term outcomes and risk factors for loss of response (LOR) during infliximab maintenance therapy. Baseline inflammatory markers including NLR were measured within one week before the initiation of infliximab. Results Fifty-nine patients with moderate-to-severe active UC started treatment with infliximab and 37 patients (62.7%) experienced clinical response after induction therapy. Fourteen of 37 patients on maintenance therapy lost the response during follow-up. Baseline NLR of patients with LOR was significantly higher than in patients with sustained response. The NLR cut-off value of 4.488 was predictive of LOR, using receiver operating characteristic analysis (sensitivity: 78.6%, specificity: 78.3%). A univariate analysis revealed a significant relationship between relapse-free survival and the NLR (P = 0.018). Multivariate analysis indicated the NLR as an independent prognostic factor for LOR (hazard ratio = 3.86, 95% confidence interval: 1.20–12.4, P = 0.023). Conclusions Baseline NLR is a useful prognostic marker in patients with moderate-to-severe active UC treated with infliximab, and may contribute to appropriate use of infliximab. PMID:28076386

  4. Comparing Predictions Made by a Prediction Model, Clinical Score, and Physicians

    PubMed Central

    Farion, K.J.; Wilk, S.; Michalowski, W.; O’Sullivan, D.; Sayyad-Shirabad, J.

    2013-01-01

    Summary Background Asthma exacerbations are one of the most common medical reasons for children to be brought to the hospital emergency department (ED). Various prediction models have been proposed to support diagnosis of exacerbations and evaluation of their severity. Objectives First, to evaluate prediction models constructed from data using machine learning techniques and to select the best performing model. Second, to compare predictions from the selected model with predictions from the Pediatric Respiratory Assessment Measure (PRAM) score, and predictions made by ED physicians. Design A two-phase study conducted in the ED of an academic pediatric hospital. In phase 1 data collected prospectively using paper forms was used to construct and evaluate five prediction models, and the best performing model was selected. In phase 2 data collected prospectively using a mobile system was used to compare the predictions of the selected prediction model with those from PRAM and ED physicians. Measurements Area under the receiver operating characteristic curve and accuracy in phase 1; accuracy, sensitivity, specificity, positive and negative predictive values in phase 2. Results In phase 1 prediction models were derived from a data set of 240 patients and evaluated using 10-fold cross validation. A naive Bayes (NB) model demonstrated the best performance and it was selected for phase 2. Evaluation in phase 2 was conducted on data from 82 patients. Predictions made by the NB model were less accurate than the PRAM score and physicians (accuracy of 70.7%, 73.2% and 78.0% respectively), however, according to McNemar’s test it is not possible to conclude that the differences between predictions are statistically significant. Conclusion Both the PRAM score and the NB model were less accurate than physicians. The NB model can handle incomplete patient data and as such may complement the PRAM score. However, it requires further research to improve its accuracy. PMID:24155790

  5. Depression Risk Predicts Blunted Neural Responses to Gains and Enhanced Responses to Losses in Healthy Children

    PubMed Central

    Luking, Katherine R.; Pagliaccio, David; Luby, Joan L.; Barch, Deanna M.

    2016-01-01

    Objective Maternal major depressive disorder (MDD) increases risk for MDD and predicts reduced reward responding in adolescent offspring. However, it is unclear whether alterations in neural response to reward can be detected in school-aged children at high risk prior to the typical increase in reward response observed in adolescence. Method To assess relationships between neural response to gain/loss feedback, MDD risk, and child depressive symptoms, forty-seven psychiatrically healthy 7–10-year-old children (16 at high-risk given maternal MDD) completed questionnaires and a functional magnetic resonance imaging (fMRI) card-guessing game where candy was gained and lost. Results High-risk children showed both blunted response to gain and greater deactivation/reduced activation to loss within the ventral striatum and anterior insula. Within the striatum, risk-group differences in response to loss feedback were significantly larger than for gain, with greater deactivation to loss predicting risk-group status above and beyond blunted gain activation. Anhedonia was related to reduced deactivation to loss (i.e. reduced sensitivity to loss), while negative mood was related to enhanced deactivation to loss (i.e. enhanced sensitivity to loss) in the ventral striatum. Conclusion High-risk children showed blunted ventral striatal activation to gain feedback, but ventral striatal deactivation to loss was a stronger predictor of MDD risk. Further, relationships between response to loss and elevated depressive symptoms within the ventral striatum and cingulate differed depending on the type of depressive symptom. Together these results highlight the potentially important role of response to loss of reward in childhood risk for depression. PMID:27015724

  6. [Dose-response relation: relevance for clinical practice].

    PubMed

    Klinkhardt, U; Harder, S

    1998-12-15

    Dose-finding studies are performed routinely in patients and--if appropriate surrogate models exist--also in healthy volunteers. Such studies aim at establishing the optimal dose range for further clinical studies on the efficacy and the risk-benefit ratio of a new drug. The dose-response relationship of a drug is most often described by a sigmoidal curve. Its parameters include the mean effective dose, the maximal effect and the steepness. Interpretation of such curves should be done in the context of the intended clinical indications of the drug. The risk-benefit ratio of a drug can be assessed by overlapping the dose-response curve of wanted and unwanted clinical effects, again, any overlapping (which can be described e.g. by the therapeutic index) should be seen in the context of the indication and available therapeutic alternatives.

  7. Analysis of Factors that Predict Clinical Performance in Medical School

    ERIC Educational Resources Information Center

    White, Casey B.; Dey, Eric L.; Fantone, Joseph C.

    2009-01-01

    Academic achievement indices including GPAs and MCAT scores are used to predict the spectrum of medical student academic performance types. However, use of these measures ignores two changes influencing medical school admissions: student diversity and affirmative action, and an increased focus on communication skills. To determine if GPA and MCAT…

  8. Predictive and Prognostic Molecular Biomarkers for Response to Neoadjuvant Chemoradiation in Rectal Cancer

    PubMed Central

    Dayde, Delphine; Tanaka, Ichidai; Jain, Rekha; Tai, Mei Chee; Taguchi, Ayumu

    2017-01-01

    The standard of care in locally advanced rectal cancer is neoadjuvant chemoradiation (nCRT) followed by radical surgery. Response to nCRT varies among patients and pathological complete response is associated with better outcome. However, there is a lack of effective methods to select rectal cancer patients who would or would not have a benefit from nCRT. The utility of clinicopathological and radiological features are limited due to lack of adequate sensitivity and specificity. Molecular biomarkers have the potential to predict response to nCRT at an early time point, but none have currently reached the clinic. Integration of diverse types of biomarkers including clinicopathological and imaging features, identification of mechanistic link to tumor biology, and rigorous validation using samples which represent disease heterogeneity, will allow to develop a sensitive and cost-effective molecular biomarker panel for precision medicine in rectal cancer. Here, we aim to review the recent advance in tissue- and blood-based molecular biomarker research and illustrate their potential in predicting nCRT response in rectal cancer. PMID:28272347

  9. Predictive and Prognostic Molecular Biomarkers for Response to Neoadjuvant Chemoradiation in Rectal Cancer.

    PubMed

    Dayde, Delphine; Tanaka, Ichidai; Jain, Rekha; Tai, Mei Chee; Taguchi, Ayumu

    2017-03-07

    The standard of care in locally advanced rectal cancer is neoadjuvant chemoradiation (nCRT) followed by radical surgery. Response to nCRT varies among patients and pathological complete response is associated with better outcome. However, there is a lack of effective methods to select rectal cancer patients who would or would not have a benefit from nCRT. The utility of clinicopathological and radiological features are limited due to lack of adequate sensitivity and specificity. Molecular biomarkers have the potential to predict response to nCRT at an early time point, but none have currently reached the clinic. Integration of diverse types of biomarkers including clinicopathological and imaging features, identification of mechanistic link to tumor biology, and rigorous validation using samples which represent disease heterogeneity, will allow to develop a sensitive and cost-effective molecular biomarker panel for precision medicine in rectal cancer. Here, we aim to review the recent advance in tissue- and blood-based molecular biomarker research and illustrate their potential in predicting nCRT response in rectal cancer.

  10. Transient brain responses predict the temporal dynamics of sound detection in humans.

    PubMed

    Mäkinen, Ville; May, Patrick; Tiitinen, Hannu

    2004-02-01

    The neural events leading up to the conscious experience of stimulus events have remained elusive. Here we describe stimulation conditions under which activation in human auditory cortex can be used to predict the temporal dynamics of behavioral sound detection. Subjects were presented with auditory stimuli whose energy smoothly increased from a silent to a clearly audible level over either 1, 1.5, or 2 s. Magnetoencephalographic (MEG) recordings were carried out in the passive and active recording conditions. In the active condition, the subjects were instructed to attend to the auditory stimuli and to press a response key when these became audible. In both conditions, the stimuli elicited a prominent transient response whose emergence is unexplainable by changes in stimulus intensity alone. This transient response was larger in amplitude over the right hemisphere and in the active condition. Importantly, behavioral sound detection followed this brain activation with a constant delay of 180 ms, and further the latency variations of the brain response were directly carried over to behavioral reaction times. Thus, noninvasively measured transient events in the human auditory cortex can be used to predict accurately the temporal course of sound detection and may therefore turn out to be useful in clinical settings.

  11. Prediction of Mass Spectral Response Factors from Predicted Chemometric Data for Druglike Molecules

    NASA Astrophysics Data System (ADS)

    Cramer, Christopher J.; Johnson, Joshua L.; Kamel, Amin M.

    2017-02-01

    A method is developed for the prediction of mass spectral ion counts of drug-like molecules using in silico calculated chemometric data. Various chemometric data, including polar and molecular surface areas, aqueous solvation free energies, and gas-phase and aqueous proton affinities were computed, and a statistically significant relationship between measured mass spectral ion counts and the combination of aqueous proton affinity and total molecular surface area was identified. In particular, through multilinear regression of ion counts on predicted chemometric data, we find that log10(MS ion counts) = -4.824 + c 1•PA + c 2•SA, where PA is the aqueous proton affinity of the molecule computed at the SMD(aq)/M06-L/MIDI!//M06-L/MIDI! level of electronic structure theory, SA is the total surface area of the molecule in its conjugate base form, and c 1 and c 2 have values of -3.912 × 10-2 mol kcal-1 and 3.682 × 10-3 Å-2. On a 66-molecule training set, this regression exhibits a multiple R value of 0.791 with p values for the intercept, c 1, and c 2 of 1.4 × 10-3, 4.3 × 10-10, and 2.5 × 10-6, respectively. Application of this regression to an 11-molecule test set provides a good correlation of prediction with experiment ( R = 0.905) albeit with a systematic underestimation of about 0.2 log units. This method may prove useful for semiquantitative analysis of drug metabolites for which MS response factors or authentic standards are not readily available.

  12. Clinical spectrum of dopa-responsive dystonia and related disorders.

    PubMed

    Lee, Woong-Woo; Jeon, Beom Seok

    2014-07-01

    Dopa-responsive dystonia (DRD) has a classic presentation of childhood or adolescent-onset dystonia, mild parkinsonism, marked diurnal fluctuations, improvement with sleep or rest, and a dramatic and sustained response to low doses of L-dopa without motor fluctuations or dyskinesias. However, there have been many papers on patients with a wide range of features, which report them as DRD mainly because they had dystonic syndromes with L-dopa responsiveness. Many mutations in the dopaminergic system have been found as molecular genetic defects. Therefore, the clinical and genetic spectra of DRD are unclear, which lead to difficulties in diagnostic work-ups and planning treatments. We propose the concept of DRD and DRD-plus to clarify the confusion in this area and to help understand the pathophysiology and clinical features, which will help in guiding diagnostic investigations and planning treatments. We critically reviewed the literature on atypical cases and discussed the limitations of the gene study.

  13. Monitoring Regulatory Immune Responses in Tumor Immunotherapy Clinical Trials

    PubMed Central

    Olson, Brian M.; McNeel, Douglas G.

    2013-01-01

    While immune monitoring of tumor immunotherapy often focuses on the generation of productive Th1-type inflammatory immune responses, the importance of regulatory immune responses is often overlooked, despite the well-documented effects of regulatory immune responses in suppressing anti-tumor immunity. In a variety of malignancies, the frequency of regulatory cell populations has been shown to correlate with disease progression and a poor prognosis, further emphasizing the importance of characterizing the effects of immunotherapy on these populations. This review focuses on the role of suppressive immune populations (regulatory T cells, myeloid-derived suppressor cells, and tumor-associated macrophages) in inhibiting anti-tumor immunity, how these populations have been used in the immune monitoring of clinical trials, the prognostic value of these responses, and how the monitoring of these regulatory responses can be improved in the future. PMID:23653893

  14. Admission Privileges and Clinical Responsibilities for Interventional Radiologists

    SciTech Connect

    Al-Kutoubi, Aghiad

    2015-04-15

    Although clinical involvement by interventional radiologists in the care of their patients was advocated at the inception of the specialty, the change into the clinical paradigm has been slow and patchy for reasons related to pattern of practice, financial remuneration or absence of training. The case for the value of clinical responsibilities has been made in a number of publications and the consequences of not doing so have been manifest in the erosion of the role of the interventional radiologists particularly in the fields of peripheral vascular and neuro intervention. With the recent recognition of interventional radiology (IR) as a primary specialty in the USA and the formation of IR division in the Union of European Medical Specialists and subsequent recognition of the subspecialty in many European countries, it is appropriate to relook at the issue and emphasize the need for measures to promote the clinical role of the interventional radiologist.

  15. Prognostic and Predictive Biomarkers of Endocrine Responsiveness for Estrogen Receptor Positive Breast Cancer.

    PubMed

    Ma, Cynthia X; Bose, Ron; Ellis, Matthew J

    2016-01-01

    The estrogen-dependent nature of breast cancer is the fundamental basis for endocrine therapy. The presence of estrogen receptor (ER), the therapeutic target of endocrine therapy, is a prerequisite for this therapeutic approach. However, estrogen-independent growth often exists de novo at diagnosis or develops during the course of endocrine therapy. Therefore ER alone is insufficient in predicting endocrine therapy efficacy. Several RNA-based multigene assays are now available in clinical practice to assess distant recurrence risk, with majority of these assays evaluated in patients treated with 5 years of adjuvant endocrine therapy. While MammaPrint and Oncotype Dx are most predictive of recurrence risk within the first 5 years of diagnosis, Prosigna, Breast Cancer Index (BCI), and EndoPredict Clin have also demonstrated utility in predicting late recurrence. In addition, PAM50, or Prosigna, provides further biological insights by classifying breast cancers into intrinsic molecular subtypes. Additional strategies are under investigation in prospective clinical trials to differentiate endocrine sensitive and resistant tumors and include on-treatment Ki-67 and Preoperative Endocrine Prognostic Index (PEPI) score in the setting of neoadjuvant endocrine therapy. These biomarkers have become important tools in clinical practice for the identification of low risk patients for whom chemotherapy could be avoided. However, there is much work ahead toward the development of a molecular classification that informs the biology and novel therapeutic targets in high-risk disease as chemotherapy has only modest benefit in this population. The recognition of somatic mutations and their relationship to endocrine therapy responsiveness opens important opportunities toward this goal.

  16. Mechanistic patient-specific predictive correlation of tumor drug response with microenvironment and perfusion measurements

    PubMed Central

    Pascal, Jennifer; Bearer, Elaine L.; Wang, Zhihui; Koay, Eugene J.; Curley, Steven A.; Cristini, Vittorio

    2013-01-01

    Physical properties of the microenvironment influence penetration of drugs into tumors. Here, we develop a mathematical model to predict the outcome of chemotherapy based on the physical laws of diffusion. The most important parameters in the model are the volume fraction occupied by tumor blood vessels and their average diameter. Drug delivery to cells, and kill thereof, are mediated by these microenvironmental properties and affected by the diffusion penetration distance after extravasation. To calculate parameter values we fit the model to histopathology measurements of the fraction of tumor killed after chemotherapy in human patients with colorectal cancer metastatic to liver (coefficient of determination R2 = 0.94). To validate the model in a different tumor type, we input patient-specific model parameter values from glioblastoma; the model successfully predicts extent of tumor kill after chemotherapy (R2 = 0.7–0.91). Toward prospective clinical translation, we calculate blood volume fraction parameter values from in vivo contrast-enhanced computed tomography imaging from a separate cohort of patients with colorectal cancer metastatic to liver, and demonstrate accurate model predictions of individual patient responses (average relative error = 15%). Here, patient-specific data from either in vivo imaging or histopathology drives output of the model’s formulas. Values obtained from standard clinical diagnostic measurements for each individual are entered into the model, producing accurate predictions of tumor kill after chemotherapy. Clinical translation will enable the rational design of individualized treatment strategies such as amount, frequency, and delivery platform of drug and the need for ancillary non–drug-based treatment. PMID:23940372

  17. MicroRNA-31 Emerges as a Predictive Biomarker of Pathological Response and Outcome in Locally Advanced Rectal Cancer.

    PubMed

    Caramés, Cristina; Cristobal, Ion; Moreno, Víctor; Marín, Juan P; González-Alonso, Paula; Torrejón, Blanca; Minguez, Pablo; Leon, Ana; Martín, José I; Hernández, Roberto; Pedregal, Manuel; Martín, María J; Cortés, Delia; García-Olmo, Damian; Fernández, María J; Rojo, Federico; García-Foncillas, Jesús

    2016-06-03

    Neoadjuvant chemoradiotherapy (CRT) followed by total mesorectal excision has emerged as the standard treatment for locally advanced rectal cancer (LARC) patients. However, many cases do not respond to neoadjuvant CRT, suffering unnecessary toxicities and surgery delays. Thus, identification of predictive biomarkers for neoadjuvant CRT is a current clinical need. In the present study, microRNA-31 expression was measured in formalin-fixed paraffin-embedded (FFPE) biopsies from 78 patients diagnosed with LARC who were treated with neoadjuvant CRT. Then, the obtained results were correlated with clinical and pathological characteristics and outcome. High microRNA-31 (miR-31) levels were found overexpressed in 34.2% of cases. Its overexpression significantly predicted poor pathological response (p = 0.018) and worse overall survival (OS) (p = 0.008). The odds ratio for no pathological response among patients with miR-31 overexpression was 0.18 (Confidence Interval = 0.06 to 0.57; p = 0.003). Multivariate analysis corroborated the clinical impact of miR-31 in determining pathological response to neoadjuvant CRT as well as OS. Altogether, miR-31 quantification emerges as a novel valuable clinical tool to predict both pathological response and outcome in LARC patients.

  18. MicroRNA-31 Emerges as a Predictive Biomarker of Pathological Response and Outcome in Locally Advanced Rectal Cancer

    PubMed Central

    Caramés, Cristina; Cristobal, Ion; Moreno, Víctor; Marín, Juan P.; González-Alonso, Paula; Torrejón, Blanca; Minguez, Pablo; Leon, Ana; Martín, José I.; Hernández, Roberto; Pedregal, Manuel; Martín, María J.; Cortés, Delia; García-Olmo, Damian; Fernández, María J.; Rojo, Federico; García-Foncillas, Jesús

    2016-01-01

    Neoadjuvant chemoradiotherapy (CRT) followed by total mesorectal excision has emerged as the standard treatment for locally advanced rectal cancer (LARC) patients. However, many cases do not respond to neoadjuvant CRT, suffering unnecessary toxicities and surgery delays. Thus, identification of predictive biomarkers for neoadjuvant CRT is a current clinical need. In the present study, microRNA-31 expression was measured in formalin-fixed paraffin-embedded (FFPE) biopsies from 78 patients diagnosed with LARC who were treated with neoadjuvant CRT. Then, the obtained results were correlated with clinical and pathological characteristics and outcome. High microRNA-31 (miR-31) levels were found overexpressed in 34.2% of cases. Its overexpression significantly predicted poor pathological response (p = 0.018) and worse overall survival (OS) (p = 0.008). The odds ratio for no pathological response among patients with miR-31 overexpression was 0.18 (Confidence Interval = 0.06 to 0.57; p = 0.003). Multivariate analysis corroborated the clinical impact of miR-31 in determining pathological response to neoadjuvant CRT as well as OS. Altogether, miR-31 quantification emerges as a novel valuable clinical tool to predict both pathological response and outcome in LARC patients. PMID:27271609

  19. Measures of endothelial dysfunction predict response to cardiac resynchronisation therapy

    PubMed Central

    Warriner, David R; Lawford, Patricia; Sheridan, Paul J

    2016-01-01

    Objectives Cardiac resynchronisation therapy (CRT) improves morbidity and mortality in heart failure (HF). Impaired endothelial function, as measured by flow-mediated dilation (FMD) is associated with increased morbidity and mortality in HF and may help to differentiate responders from non-responders. Methods 19 patients were recruited, comprising 94% men, mean age 69±8 years, New York Heart Association functional classes II–IV, QRSd 161±21 ms and mean left ventricular ejection fraction 26±8%. Markers of response and FMD were measured at baseline, 6 and 12 months following CRT. Results 14 patients were responders to CRT. Responders had significant improvements in VO2 (12.6±1.7 to 14.7±1.5 mL/kg/min, p<0.05), quality of life score (44.4±22.9–24.1±21.3, p<0.01), left ventricular end diastolic volume (201.5±72.5 mL–121.3±72.0 mL, p<0.01) and 6-min walk distance (374.0±112.8 m at baseline to 418.1±105.3 m, p<0.05). Baseline FMD in responders was 2.9±1.9% and 7.4±3.73% in non-responders (p<0.05). Conclusions Response to CRT at 6 and 12 months is predicted by baseline FMD. This study confirms that FMD identifies responders to CRT, due to endothelium-dependent mechanisms alone. PMID:27335654

  20. Molecular Biomarkers for Prediction of Targeted Therapy Response in Metastatic Breast Cancer: Trick or Treat?

    PubMed Central

    Toss, Angela; Venturelli, Marta; Peterle, Chiara; Piacentini, Federico; Cascinu, Stefano; Cortesi, Laura

    2017-01-01

    In recent years, the study of genomic alterations and protein expression involved in the pathways of breast cancer carcinogenesis has provided an increasing number of targets for drugs development in the setting of metastatic breast cancer (i.e., trastuzumab, everolimus, palbociclib, etc.) significantly improving the prognosis of this disease. These drugs target specific molecular abnormalities that confer a survival advantage to cancer cells. On these bases, emerging evidence from clinical trials provided increasing proof that the genetic landscape of any tumor may dictate its sensitivity or resistance profile to specific agents and some studies have already showed that tumors treated with therapies matched with their molecular alterations obtain higher objective response rates and longer survival. Predictive molecular biomarkers may optimize the selection of effective therapies, thus reducing treatment costs and side effects. This review offers an overview of the main molecular pathways involved in breast carcinogenesis, the targeted therapies developed to inhibit these pathways, the principal mechanisms of resistance and, finally, the molecular biomarkers that, to date, are demonstrated in clinical trials to predict response/resistance to targeted treatments in metastatic breast cancer. PMID:28054957

  1. Predicting the Response of Breast Cancer to Neoadjuvant Therapy Using a Mechanically Coupled Reaction-Diffusion Model

    PubMed Central

    Weis, Jared A.; Miga, Michael I.; Arlinghaus, Lori R.; Li, Xia; Abramson, Vandana; Chakravarthy, A. Bapsi; Pendyala, Praveen; Yankeelov, Thomas E.

    2015-01-01

    While there is considerable data on the use of mathematical modeling to describe tumor growth and response to therapy, previous approaches are often not of the form that can be easily applied to clinical data to generate testable predictions in individual patients. Thus, there is a clear need to develop and apply clinically-relevant oncological models that are amenable to available patient data and yet retain the most salient features of response prediction. In this study we show how a biomechanical model of tumor growth can be initialized and constrained by serial patient-specific magnetic resonance imaging data, obtained at two time points early in the course of therapy (before initiation and following one cycle of therapy), to predict the response for individual patients with breast cancer undergoing neoadjuvant therapy. Using our mechanics coupled modeling approach, we are able to predict, after the first cycle of therapy, breast cancer patients that would eventually achieve a complete pathological response and those who would not, with receiver operating characteristic area under the curve (AUC) of 0.87, sensitivity of 92%, and specificity of 84%. Our approach significantly outperformed the AUCs achieved by standard (i.e., not mechanically coupled)reaction-diffusion predictive modeling (0.75), simple analysis of the tumor cellularity estimated from imaging data (0.73), and the Response Evaluation Criteria in Solid Tumors (RECIST; 0.71). Thus, we show the potential for mathematical model prediction for use as a prognostic indicator of response to therapy. The work indicates the considerable promise of image-driven biophysical modeling for predictive frameworks within therapeutic applications. PMID:26333809

  2. Poor Response to Periodontal Treatment May Predict Future Cardiovascular Disease.

    PubMed

    Holmlund, A; Lampa, E; Lind, L

    2017-03-01

    Periodontal disease has been associated with cardiovascular disease (CVD), but whether the response to the treatment of periodontal disease affects this association has not been investigated in any large prospective study. Periodontal data obtained at baseline and 1 y after treatment were available in 5,297 individuals with remaining teeth who were treated at a specialized clinic for periodontal disease. Poor response to treatment was defined as having >10% sites with probing pocket depth >4 mm deep and bleeding on probing at ≥20% of the sites 1 y after active treatment. Fatal/nonfatal incidence rate of CVD (composite end point of myocardial infarction, stroke, and heart failure) was obtained from the Swedish cause-of-death and hospital discharge registers. Poisson regression analysis was performed to analyze future risk of CVD. During a median follow-up of 16.8 y (89,719 person-years at risk), those individuals who did not respond well to treatment (13.8% of the sample) had an increased incidence of CVD ( n = 870) when compared with responders (23.6 vs. 15.3%, P < 0.001). When adjusting for calendar time, age, sex, educational level, smoking, and baseline values for bleeding on probing, probing pocket depth >4 mm, and number of teeth, the incidence rate ratio for CVD among poor responders was 1.28 (95% CI, 1.07 to 1.53; P = 0.007) as opposed to good responders. The incidence rate ratio among poor responders increased to 1.39 (95% CI, 1.13 to 1.73; P = 0.002) for those with the most remaining teeth. Individuals who did not respond well to periodontal treatment had an increased risk for future CVD, indicating that successful periodontal treatment might influence progression of subclinical CVD.

  3. Lateral hip pain: does imaging predict response to localized injection?

    PubMed

    Walker, Peter; Kannangara, Siri; Bruce, Warwick J M; Michael, Dean; Van der Wall, H

    2007-04-01

    Lateral hip pain is a common complaint in patients with a history of lower back pain from spinal disease. These patients often are diagnosed and treated for trochanteric bursitis because of localized pain and tenderness in the lateral hip. We presumed numerous scintigraphic features could provide diagnostic criteria for diagnosing gluteus medius tendinitis and trochanteric bursitis. A study was designed to assess the scintigraphic criteria for diagnosis of trochanteric bursitis and to evaluate the relationship of trochanteric bursitis to gluteus medius tendinitis and lumbar degenerative disease in predicting relapse after injection. We evaluated 97 patients with greater trochanteric pain syndrome to find a correlation between trochanteric bursitis, gluteus medius tendinitis, and spinal degenerative disease using scintigraphy and magnetic resonance imaging. We also evaluated predictors for responding to trochanteric injection of local anesthetic/glucocorticoid injection. We found a correlation between lumbar degenerative disease, gluteus medius tendinopathy, and trochanteric bursitis. Of these, 30 of 48 patients (63%) responded to injection of local anesthetic and glucocorticoids. The major predictor of relapse of pain after injection in 18 patients was the presence of moderate to severe lumbar degenerative disease seen on scintigraphic imaging. We propose a mechanistic model of the greater trochanteric pain syndrome to explain the interrelationship and response to therapy. Scintigraphy can provide sensitive and specific diagnoses of gluteus medius tendinitis and trochanteric bursitis.

  4. Semiautomated Workflow for Clinically Streamlined Glioma Parametric Response Mapping

    PubMed Central

    Keith, Lauren; Ross, Brian D.; Galbán, Craig J.; Luker, Gary D.; Galbán, Stefanie; Zhao, Binsheng; Guo, Xiaotao; Chenevert, Thomas L.; Hoff, Benjamin A.

    2017-01-01

    Management of glioblastoma multiforme remains a challenging problem despite recent advances in targeted therapies. Timely assessment of therapeutic agents is hindered by the lack of standard quantitative imaging protocols for determining targeted response. Clinical response assessment for brain tumors is determined by volumetric changes assessed at 10 weeks post-treatment initiation. Further, current clinical criteria fail to use advanced quantitative imaging approaches, such as diffusion and perfusion magnetic resonance imaging. Development of the parametric response mapping (PRM) applied to diffusion-weighted magnetic resonance imaging has provided a sensitive and early biomarker of successful cytotoxic therapy in brain tumors while maintaining a spatial context within the tumor. Although PRM provides an earlier readout than volumetry and sometimes greater sensitivity compared with traditional whole-tumor diffusion statistics, it is not routinely used for patient management; an automated and standardized software for performing the analysis and for the generation of a clinical report document is required for this. We present a semiautomated and seamless workflow for image coregistration, segmentation, and PRM classification of glioblastoma multiforme diffusion-weighted magnetic resonance imaging scans. The software solution can be integrated using local hardware or performed remotely in the cloud while providing connectivity to existing picture archive and communication systems. This is an important step toward implementing PRM analysis of solid tumors in routine clinical practice. PMID:28286871

  5. Design Characteristics Influence Performance of Clinical Prediction Rules in Validation: A Meta-Epidemiological Study

    PubMed Central

    Ban, Jong-Wook; Emparanza, José Ignacio; Urreta, Iratxe; Burls, Amanda

    2016-01-01

    Background Many new clinical prediction rules are derived and validated. But the design and reporting quality of clinical prediction research has been less than optimal. We aimed to assess whether design characteristics of validation studies were associated with the overestimation of clinical prediction rules’ performance. We also aimed to evaluate whether validation studies clearly reported important methodological characteristics. Methods Electronic databases were searched for systematic reviews of clinical prediction rule studies published between 2006 and 2010. Data were extracted from the eligible validation studies included in the systematic reviews. A meta-analytic meta-epidemiological approach was used to assess the influence of design characteristics on predictive performance. From each validation study, it was assessed whether 7 design and 7 reporting characteristics were properly described. Results A total of 287 validation studies of clinical prediction rule were collected from 15 systematic reviews (31 meta-analyses). Validation studies using case-control design produced a summary diagnostic odds ratio (DOR) 2.2 times (95% CI: 1.2–4.3) larger than validation studies using cohort design and unclear design. When differential verification was used, the summary DOR was overestimated by twofold (95% CI: 1.2 -3.1) compared to complete, partial and unclear verification. The summary RDOR of validation studies with inadequate sample size was 1.9 (95% CI: 1.2 -3.1) compared to studies with adequate sample size. Study site, reliability, and clinical prediction rule was adequately described in 10.1%, 9.4%, and 7.0% of validation studies respectively. Conclusion Validation studies with design shortcomings may overestimate the performance of clinical prediction rules. The quality of reporting among studies validating clinical prediction rules needs to be improved. PMID:26730980

  6. Seasonal Climate Extremes : Mechanism, Predictability and Responses to Global Warming

    NASA Astrophysics Data System (ADS)

    Shongwe, M. E.

    2010-01-01

    Climate extremes are rarely occurring natural phenomena in the climate system. They often pose one of the greatest environmental threats to human and natural systems. Statistical methods are commonly used to investigate characteristics of climate extremes. The fitted statistical properties are often interpolated or extrapolated to give an indication of the likelihood of a certain event within a given period or interval. Under changing climatic conditions, the statistical properties of climate extremes are also changing. It is an important scientific goal to predict how the properties of extreme events change. To achieve this goal, observational and model studies aimed at revealing important features are a necessary prerequisite. Notable progress has been made in understanding mechanisms that influence climate variability and extremes in many parts of the globe including Europe. However, some of the recently observed unprecedented extremes cannot be fully explained from the already identified forcing factors. A better understanding of why these extreme events occur and their sensitivity to certain reinforcing and/or competing factors is useful. Understanding their basic form as well as their temporal variability is also vital and can contribute to global scientific efforts directed at advancing climate prediction capabilities, particularly making skilful forecasts and realistic projections of extremes. In this thesis temperature and precipitation extremes in Europe and Africa, respectively, are investigated. Emphasis is placed on the mechanisms underlying the occurrence of the extremes, their predictability and their likely response to global warming. The focus is on some selected seasons when extremes typically occur. An atmospheric energy budget analysis for the record-breaking European Autumn 2006 event has been carried out with the goal to identify the sources of energy for the extreme event. Net radiational heating is compared to surface turbulent fluxes of

  7. Impact of hierarchies of clinical codes on predicting future days in hospital.

    PubMed

    Yang Xie; Neubauer, Sandra; Schreier, Gunter; Redmond, Stephen J; Lovell, Nigel H

    2015-01-01

    Health insurance claims contain valuable information for predicting the future health of a population. Nowadays, with many mature machine learning algorithms, models can be implemented to predict future medical costs and hospitalizations. However, it is well-known that the way in which the data are represented significantly affects the performance of machine learning algorithms. In health insurance claims, key clinical information mainly comes from the associated clinical codes, such as diagnosis codes and procedure codes, which are hierarchically structured. In this study, it is investigated whether the hierarchies of such clinical codes can be utilized to improve predictive performance in the context of predicting future days in hospital. Empirical investigations were done on data sets of different sizes, considering that the frequency of the appearance of lower-level (more specific) clinical codes could vary significantly in populations of different sizes. The use of bagged trees with feature sets that include only basic demographic features, low-level, medium-level, high-level clinical codes, and a full feature set were compared. The main finding from this study is that different hierarchies of clinical codes do not have a significant impact on the predictive power. Some other findings include: 1) Sample size greatly affects the predictive outcome (more observations result in more stable and more accurate outcomes); 2) Combined use of enriched demographic features and clinical features give better performance as compared to using them separately.

  8. Network-based approaches for drug response prediction and targeted therapy development in cancer.

    PubMed

    Dorel, Mathurin; Barillot, Emmanuel; Zinovyev, Andrei; Kuperstein, Inna

    2015-08-21

    Signaling pathways implicated in cancer create a complex network with numerous regulatory loops and redundant pathways. This complexity explains frequent failure of one-drug-one-target paradigm of treatment, resulting in drug resistance in patients. To overcome the robustness of cell signaling network, cancer treatment should be extended to a combination therapy approach. Integrating and analyzing patient high-throughput data together with the information about biological signaling machinery may help deciphering molecular patterns specific to each patient and finding the best combinations of candidates for therapeutic targeting. We review state of the art in the field of targeted cancer medicine from the computational systems biology perspective. We summarize major signaling network resources and describe their characteristics with respect to applicability for drug response prediction and intervention targets suggestion. Thus discuss methods for prediction of drug sensitivity and intervention combinations using signaling networks together with high-throughput data. Gradual integration of these approaches into clinical routine will improve prediction of response to standard treatments and adjustment of intervention schemes.

  9. Can Nutritional Assessment Tools Predict Response to Nutritional Therapy?

    PubMed

    Patel, Chirag; Omer, Endashaw; Diamond, Sarah J; McClave, Stephen A

    2016-04-01

    Traditional tools and scoring systems for nutritional assessment have focused solely on parameters of poor nutritional status in the past, in an effort to define the elusive concept of malnutrition. Such tools fail to account for the contribution of disease severity to overall nutritional risk. High nutritional risk, caused by either deterioration of nutritional status or greater disease severity (or a combination of both factors), puts the patient in a metabolic stress state characterized by adverse outcome and increased complications. Newer scoring systems for determining nutritional risk, such as the Nutric Score and the Nutritional Risk Score-2002 have created a paradigm shift connecting assessment and treatment with quality outcome measures of success. Clinicians now have the opportunity to identify high risk patients through their initial assessment, provide adequate or sufficient nutrition therapy, and expect improved patient outcomes as a result. These concepts are supported by observational and prospective interventional trials. Greater clinical experience and refinement in these scoring systems are needed in the future to optimize patient response to nutrition therapy.

  10. Computer-aided breast MR image feature analysis for prediction of tumor response to chemotherapy

    SciTech Connect

    Aghaei, Faranak; Tan, Maxine; Liu, Hong; Zheng, Bin; Hollingsworth, Alan B.; Qian, Wei

    2015-11-15

    Purpose: To identify a new clinical marker based on quantitative kinetic image features analysis and assess its feasibility to predict tumor response to neoadjuvant chemotherapy. Methods: The authors assembled a dataset involving breast MR images acquired from 68 cancer patients before undergoing neoadjuvant chemotherapy. Among them, 25 patients had complete response (CR) and 43 had partial and nonresponse (NR) to chemotherapy based on the response evaluation criteria in solid tumors. The authors developed a computer-aided detection scheme to segment breast areas and tumors depicted on the breast MR images and computed a total of 39 kinetic image features from both tumor and background parenchymal enhancement regions. The authors then applied and tested two approaches to classify between CR and NR cases. The first one analyzed each individual feature and applied a simple feature fusion method that combines classification results from multiple features. The second approach tested an attribute selected classifier that integrates an artificial neural network (ANN) with a wrapper subset evaluator, which was optimized using a leave-one-case-out validation method. Results: In the pool of 39 features, 10 yielded relatively higher classification performance with the areas under receiver operating characteristic curves (AUCs) ranging from 0.61 to 0.78 to classify between CR and NR cases. Using a feature fusion method, the maximum AUC = 0.85 ± 0.05. Using the ANN-based classifier, AUC value significantly increased to 0.96 ± 0.03 (p < 0.01). Conclusions: This study demonstrated that quantitative analysis of kinetic image features computed from breast MR images acquired prechemotherapy has potential to generate a useful clinical marker in predicting tumor response to chemotherapy.

  11. Development and validation of a gene expression score that predicts response to fulvestrant in breast cancer patients.

    PubMed

    Knudsen, Steen; Jensen, Thomas; Hansen, Anker; Mazin, Wiktor; Lindemann, Justin; Kuter, Irene; Laing, Naomi; Anderson, Elizabeth

    2014-01-01

    Fulvestrant is a selective estrogen receptor antagonist. Based on the measured growth inhibition of 60 human cancer cell lines (NCI60) in the presence of fulvestrant, as well as the baseline gene expression of the 60 cell lines, a gene expression score that predicts response to fulvestrant was developed. The score is based on 414 genes, 103 of which show increased expression in sensitive cell lines, while 311 show increased expression in the non-responding cell lines. The sensitivity genes primarily sense signaling through estrogen receptor alpha, whereas the resistance genes modulate the PI3K signaling pathway. The latter genes suggest that resistance to fulvestrant can be overcome by drugs targeting the PI3K pathway. The level of this gene expression score and its correlation with fulvestrant response was measured in a panel of 20 breast cancer cell lines. The predicted sensitivity matched the measured sensitivity well (CC = -0.63, P = 0.003). The predictor was applied to tumor biopsies obtained from a Phase II clinical trial. The sensitivity of each patient to treatment with fulvestrant was predicted based on the RNA profile of the biopsy taken before neoadjuvant treatment and without knowledge of the subsequent response. The prediction was then compared to clinical response to show that the responders had a significantly higher sensitivity prediction than the non-responders (P = 0.01). When clinical covariates, tumor grade and estrogen receptor H-score, were included in the prediction, the difference in predicted senstivity between responders and non-responders improved (P = 0.003). Using a pre-defined cutoff to separate patients into predicted sensitive and predicted resistant yielded a positive predictive value of 88% and a negative predictive value of 100% when compared to clinical data. We conclude that pre-screening patients with the new gene expression predictor has the potential to identify those postmenopausal women with locally advanced

  12. FTO predicts weight regain in the Look AHEAD Clinical Trial

    PubMed Central

    McCaffery, Jeanne; Papandonatos, George D.; Huggins, Gordon S.; Peter, Inga; Kahn, Steven E.; Knowler, William C.; Hudnall, Gina Evans; Lipkin, Edward; Kitabchi, Abbas E.; Wagenknecht, Lynne E.; Wing, Rena R.

    2013-01-01

    Background Genome-wide association studies have provided new insights into the genetic factors that contribute to the development of obesity. We hypothesized that these genetic markers would also predict magnitude of weight loss and weight regain after initial weight loss. Methods Established obesity risk alleles available on the Illumina CARe iSelect (IBC) chip were characterized in 3,899 overweight or obese participants with type 2 diabetes from the Look AHEAD (Action for Health in Diabetes), a randomized trial to determine the effects of intensive lifestyle intervention (ILI) and Diabetes Support and Education (DSE) on cardiovascular morbidity and mortality. Primary analyses examined the interaction between 13 obesity-risk polymorphisms in 8 genes and randomized treatment arm in predicting weight change at year 1, and weight regain at year 4 among individuals who lost 3% or more of their baseline weight by year 1. Results No SNPs were significantly associated with magnitude of weight loss or interacted with treatment arm at year 1. However, FTO rs3751812 predicted weight regain within DSE (1.56 kg per risk allele, p = 0.005), but not ILI (p = 0.761), resulting in SNP×treatment arm interaction (p = 0.009). In a partial replication of prior research, the obesity risk (G) allele at BDNF rs6265 was associated with greater weight regain across treatment arms (0.773 kg per risk allele), although results were of borderline statistical significance (p=0.051). Conclusions Variations in the FTO and BDNF loci may contribute risk of weight regain after weight loss. PMID:23628854

  13. Outsmarting cancer: the power of hybrid genomic/proteomic biomarkers to predict drug response.

    PubMed

    Rexer, Brent N; Arteaga, Carlos L

    2014-01-01

    A recent study by Niepel and colleagues describes a novel approach to predicting response to targeted anti-cancer therapies. The authors used biochemical profiling of signaling activity in basal and ligand-stimulated states for a panel of receptor and intracellular kinases to develop predictive models of drug sensitivity. In some cases, the response to ligand stimulation predicted drug response better than did target abundance or genomic alterations in the targeted pathway. Furthermore, combining biochemical profiles with genomic information was better at predicting drug response. This work suggests that incorporating biochemical signaling profiles with genomic alterations should provide powerful predictors of response to molecularly targeted therapies.

  14. The Pain Course: exploring predictors of clinical response to an Internet-delivered pain management program.

    PubMed

    Dear, B F; Gandy, M; Karin, E; Ricciardi, T; Langman, N; Staples, L G; Fogliati, V J; Sharpe, L; McLellan, L F; Titov, N

    2016-10-01

    There is significant interest in the potential of Internet-delivered pain management programs for adults with chronic pain. Understanding the characteristics of people who do and do not benefit from Internet-delivered programs will help to guide their safe and effective use. Using a large sample from a previous randomised controlled trial of an established Internet-delivered pain management program, the Pain Course, this study (n = 463) examined whether several demographic, clinical, psychological, and treatment-related variables could be used to predict clinical response in levels of disability, depression, anxiety, or average pain. Multiple univariate and multivariate stepwise logistic regressions were used to identify unique predictors of clinical improvement, which, consistent with recommendations, was defined as a ≥30% reduction in symptoms or difficulties from baseline. Several unique predictors of clinical improvement were found. However, no particularly decisive or dominant predictors emerged that were common across time points or across the outcome domains. Reflecting this, the identified predictors explained only 18.1%, 13.7%, 7.6%, and 9.5% of the variance in the likelihood of making a clinical improvement in disability, depression, anxiety, and average pain levels, respectively. The current findings suggest that a broad range of patients may benefit from emerging Internet-delivered pain management programs and that it may not be possible to predict who will or will not benefit on the basis of patients' demographic, clinical, and psychological characteristics.

  15. OVARIAN RESERVE TESTS AND THEIR UTILITY IN PREDICTING RESPONSE TO CONTROLLED OVARIAN STIMULATION IN RHESUS MONKEYS

    PubMed Central

    Wu, Julie M.; Takahashi, Diana L; Ingram, Donald K.; Mattison, Julie A.; Roth, George; Ottinger, Mary Ann; Zelinski, Mary B.

    2010-01-01

    Controlled ovarian stimulation (COS) is an alternative to natural breeding in nonhuman primates; however, these protocols are costly with no guarantee of success. Toward the objective of predicting COS outcome in rhesus monkeys, the current study evaluated three clinically used ovarian reserve tests (ORTs): day 3 (d3) follicle-stimulating hormone (FSH) with d3 inhibin B (INHB), the clomiphene citrate challenge test (CCCT), and the exogenous FSH Ovarian Reserve Test (EFORT). A COS was also performed and response was classified as either successful (COS+) or unsuccessful (COS−) and retrospectively compared to ORT predictions. FSH and INHB were assessed for best hormonal index in conjunction with the aforementioned tests. INHB was consistently more accurate than FSH in all ORTs used. Overall, a modified version of the CCCT using INHB values yielded the best percentage of correct predictions. This is the first report of ORT evaluation in rhesus monkeys and may provide a useful diagnostic test prior to costly follicle stimulations, as well as predicting the onset of menopause. PMID:20336797

  16. Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis

    PubMed Central

    Sieberts, Solveig K.; Zhu, Fan; García-García, Javier; Stahl, Eli; Pratap, Abhishek; Pandey, Gaurav; Pappas, Dimitrios; Aguilar, Daniel; Anton, Bernat; Bonet, Jaume; Eksi, Ridvan; Fornés, Oriol; Guney, Emre; Li, Hongdong; Marín, Manuel Alejandro; Panwar, Bharat; Planas-Iglesias, Joan; Poglayen, Daniel; Cui, Jing; Falcao, Andre O.; Suver, Christine; Hoff, Bruce; Balagurusamy, Venkat S. K.; Dillenberger, Donna; Neto, Elias Chaibub; Norman, Thea; Aittokallio, Tero; Ammad-ud-din, Muhammad; Azencott, Chloe-Agathe; Bellón, Víctor; Boeva, Valentina; Bunte, Kerstin; Chheda, Himanshu; Cheng, Lu; Corander, Jukka; Dumontier, Michel; Goldenberg, Anna; Gopalacharyulu, Peddinti; Hajiloo, Mohsen; Hidru, Daniel; Jaiswal, Alok; Kaski, Samuel; Khalfaoui, Beyrem; Khan, Suleiman Ali; Kramer, Eric R.; Marttinen, Pekka; Mezlini, Aziz M.; Molparia, Bhuvan; Pirinen, Matti; Saarela, Janna; Samwald, Matthias; Stoven, Véronique; Tang, Hao; Tang, Jing; Torkamani, Ali; Vert, Jean-Phillipe; Wang, Bo; Wang, Tao; Wennerberg, Krister; Wineinger, Nathan E.; Xiao, Guanghua; Xie, Yang; Yeung, Rae; Zhan, Xiaowei; Zhao, Cheng; Calaza, Manuel; Elmarakeby, Haitham; Heath, Lenwood S.; Long, Quan; Moore, Jonathan D.; Opiyo, Stephen Obol; Savage, Richard S.; Zhu, Jun; Greenberg, Jeff; Kremer, Joel; Michaud, Kaleb; Barton, Anne; Coenen, Marieke; Mariette, Xavier; Miceli, Corinne; Shadick, Nancy; Weinblatt, Michael; de Vries, Niek; Tak, Paul P.; Gerlag, Danielle; Huizinga, Tom W. J.; Kurreeman, Fina; Allaart, Cornelia F.; Louis Bridges Jr., S.; Criswell, Lindsey; Moreland, Larry; Klareskog, Lars; Saevarsdottir, Saedis; Padyukov, Leonid; Gregersen, Peter K.; Friend, Stephen; Plenge, Robert; Stolovitzky, Gustavo; Oliva, Baldo; Guan, Yuanfang; Mangravite, Lara M.

    2016-01-01

    Rheumatoid arthritis (RA) affects millions world-wide. While anti-TNF treatment is widely used to reduce disease progression, treatment fails in ∼one-third of patients. No biomarker currently exists that identifies non-responders before treatment. A rigorous community-based assessment of the utility of SNP data for predicting anti-TNF treatment efficacy in RA patients was performed in the context of a DREAM Challenge (http://www.synapse.org/RA_Challenge). An open challenge framework enabled the comparative evaluation of predictions developed by 73 research groups using the most comprehensive available data and covering a wide range of state-of-the-art modelling methodologies. Despite a significant genetic heritability estimate of treatment non-response trait (h2=0.18, P value=0.02), no significant genetic contribution to prediction accuracy is observed. Results formally confirm the expectations of the rheumatology community that SNP information does not significantly improve predictive performance relative to standard clinical traits, thereby justifying a refocusing of future efforts on collection of other data. PMID:27549343

  17. Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis.

    PubMed

    Sieberts, Solveig K; Zhu, Fan; García-García, Javier; Stahl, Eli; Pratap, Abhishek; Pandey, Gaurav; Pappas, Dimitrios; Aguilar, Daniel; Anton, Bernat; Bonet, Jaume; Eksi, Ridvan; Fornés, Oriol; Guney, Emre; Li, Hongdong; Marín, Manuel Alejandro; Panwar, Bharat; Planas-Iglesias, Joan; Poglayen, Daniel; Cui, Jing; Falcao, Andre O; Suver, Christine; Hoff, Bruce; Balagurusamy, Venkat S K; Dillenberger, Donna; Neto, Elias Chaibub; Norman, Thea; Aittokallio, Tero; Ammad-Ud-Din, Muhammad; Azencott, Chloe-Agathe; Bellón, Víctor; Boeva, Valentina; Bunte, Kerstin; Chheda, Himanshu; Cheng, Lu; Corander, Jukka; Dumontier, Michel; Goldenberg, Anna; Gopalacharyulu, Peddinti; Hajiloo, Mohsen; Hidru, Daniel; Jaiswal, Alok; Kaski, Samuel; Khalfaoui, Beyrem; Khan, Suleiman Ali; Kramer, Eric R; Marttinen, Pekka; Mezlini, Aziz M; Molparia, Bhuvan; Pirinen, Matti; Saarela, Janna; Samwald, Matthias; Stoven, Véronique; Tang, Hao; Tang, Jing; Torkamani, Ali; Vert, Jean-Phillipe; Wang, Bo; Wang, Tao; Wennerberg, Krister; Wineinger, Nathan E; Xiao, Guanghua; Xie, Yang; Yeung, Rae; Zhan, Xiaowei; Zhao, Cheng; Greenberg, Jeff; Kremer, Joel; Michaud, Kaleb; Barton, Anne; Coenen, Marieke; Mariette, Xavier; Miceli, Corinne; Shadick, Nancy; Weinblatt, Michael; de Vries, Niek; Tak, Paul P; Gerlag, Danielle; Huizinga, Tom W J; Kurreeman, Fina; Allaart, Cornelia F; Louis Bridges, S; Criswell, Lindsey; Moreland, Larry; Klareskog, Lars; Saevarsdottir, Saedis; Padyukov, Leonid; Gregersen, Peter K; Friend, Stephen; Plenge, Robert; Stolovitzky, Gustavo; Oliva, Baldo; Guan, Yuanfang; Mangravite, Lara M; Bridges, S Louis; Criswell, Lindsey; Moreland, Larry; Klareskog, Lars; Saevarsdottir, Saedis; Padyukov, Leonid; Gregersen, Peter K; Friend, Stephen; Plenge, Robert; Stolovitzky, Gustavo; Oliva, Baldo; Guan, Yuanfang; Mangravite, Lara M

    2016-08-23

    Rheumatoid arthritis (RA) affects millions world-wide. While anti-TNF treatment is widely used to reduce disease progression, treatment fails in ∼one-third of patients. No biomarker currently exists that identifies non-responders before treatment. A rigorous community-based assessment of the utility of SNP data for predicting anti-TNF treatment efficacy in RA patients was performed in the context of a DREAM Challenge (http://www.synapse.org/RA_Challenge). An open challenge framework enabled the comparative evaluation of predictions developed by 73 research groups using the most comprehensive available data and covering a wide range of state-of-the-art modelling methodologies. Despite a significant genetic heritability estimate of treatment non-response trait (h(2)=0.18, P value=0.02), no significant genetic contribution to prediction accuracy is observed. Results formally confirm the expectations of the rheumatology community that SNP information does not significantly improve predictive performance relative to standard clinical traits, thereby justifying a refocusing of future efforts on collection of other data.

  18. Ethics and epistemology of accurate prediction in clinical research.

    PubMed

    Hey, Spencer Phillips

    2015-07-01

    All major research ethics policies assert that the ethical review of clinical trial protocols should include a systematic assessment of risks and benefits. But despite this policy, protocols do not typically contain explicit probability statements about the likely risks or benefits involved in the proposed research. In this essay, I articulate a range of ethical and epistemic advantages that explicit forecasting would offer to the health research enterprise. I then consider how some particular confidence levels may come into conflict with the principles of ethical research.

  19. High Proliferation Predicts Pathological Complete Response to Neoadjuvant Chemotherapy in Early Breast Cancer

    PubMed Central

    Lluch, Ana; Ribelles, Nuria; Anton-Torres, Antonio; Sanchez-Rovira, Pedro; Albanell, Joan; Calvo, Lourdes; García-Asenjo, Jose Antonio Lopez; Palacios, Jose; Chacon, Jose Ignacio; Ruiz, Amparo; De la Haba-Rodriguez, Juan; Segui-Palmer, Miguel A.; Cirauqui, Beatriz; Margeli, Mireia; Plazaola, Arrate; Barnadas, Agusti; Casas, Maribel; Caballero, Rosalia; Carrasco, Eva; Rojo, Federico

    2016-01-01

    Background. In the neoadjuvant setting, changes in the proliferation marker Ki67 are associated with primary endocrine treatment efficacy, but its value as a predictor of response to chemotherapy is still controversial. Patients and Methods. We analyzed 262 patients with centralized basal Ki67 immunohistochemical evaluation derived from 4 GEICAM (Spanish Breast Cancer Group) clinical trials of neoadjuvant chemotherapy for breast cancer. The objective was to identify the optimal threshold for Ki67 using the receiver-operating characteristic curve method to maximize its predictive value for chemotherapy benefit. We also evaluated the predictive role of the defined Ki67 cutoffs for molecular subtypes defined by estrogen receptor (ER) and human epidermal growth factor receptor 2 (HER2). Results. A basal Ki67 cutpoint of 50% predicted pathological complete response (pCR). Patients with Ki67 >50% achieved a pCR rate of 40% (36 of 91) versus a pCR rate of 19% in patients with Ki67 ≤50% (33 of 171) (p = .0004). Ki67 predictive value was especially relevant in ER-HER2− and ER-HER2+ patients (pCR rates of 42% and 64%, respectively, in patients with Ki67 >50% versus 15% and 45%, respectively, in patients with Ki67 ≤50%; p = .0337 and .3238, respectively). Both multivariate analyses confirmed the independent predictive value of the Ki67 cutpoint of 50%. Conclusion. Basal Ki67 proliferation index >50% should be considered an independent predictive factor for pCR reached after neoadjuvant chemotherapy, suggesting that cell proliferation is a phenomenon closely related to chemosensitivity. These findings could help to identify a group of patients with a potentially favorable long-term prognosis. Implications for Practice: The use of basal Ki67 status as a predictive factor of chemotherapy benefit could facilitate the identification of a patient subpopulation with high probability of achieving pathological complete response when treated with primary chemotherapy, and thus

  20. Nurse-Administered, Gut-Directed Hypnotherapy in IBS: Efficacy and Factors Predicting a Positive Response.

    PubMed

    Lövdahl, Jenny; Ringström, Gisela; Agerforz, Pia; Törnblom, Hans; Simrén, Magnus

    2015-07-01

    Hypnotherapy is an effective treatment in irritable bowel syndrome (IBS). It is often delivered by a psychotherapist and is costly and time consuming. Nurse-administered hypnotherapy could increase availability and reduce costs. In this study the authors evaluate the effectiveness of nurse-administered, gut-directed hypnotherapy and identify factors predicting treatment outcome. Eighty-five patients were included in the study. Participants received hypnotherapy by a nurse once/week for 12 weeks. Patients reported marked improvement in gastrointestinal (GI) and extra-colonic symptoms after treatment, as well as a reduction in GI-specific anxiety, general anxiety, and depression. Fifty-eight percent were responders after the 12 weeks treatment period, and of these 82% had a favorable clinical response already at week 6. Women were more likely than men to respond favorably to the treatment. Nurse-administered hypnotherapy is an effective treatment for IBS. Being female and reporting a favorable response to treatment by week 6 predicted a positive treatment response at the end of the 12 weeks treatment period.

  1. A clinical measure of DNA methylation predicts outcome in de novo acute myeloid leukemia

    PubMed Central

    Luskin, Marlise R.; Gimotty, Phyllis A.; Smith, Catherine; Loren, Alison W.; Figueroa, Maria E.; Harrison, Jenna; Sun, Zhuoxin; Tallman, Martin S.; Paietta, Elisabeth M.; Litzow, Mark R.; Melnick, Ari M.; Levine, Ross L.; Fernandez, Hugo F.; Luger, Selina M.; Master, Stephen R.; Wertheim, Gerald B.W.

    2016-01-01

    BACKGROUND. Variable response to chemotherapy in acute myeloid leukemia (AML) represents a major treatment challenge. Clinical and genetic features incompletely predict outcome. The value of clinical epigenetic assays for risk classification has not been extensively explored. We assess the prognostic implications of a clinical assay for multilocus DNA methylation on adult patients with de novo AML. METHODS. We performed multilocus DNA methylation assessment using xMELP on samples and calculated a methylation statistic (M-score) for 166 patients from UPENN with de novo AML who received induction chemotherapy. The association of M-score with complete remission (CR) and overall survival (OS) was evaluated. The optimal M-score cut-point for identifying groups with differing survival was used to define a binary M-score classifier. This classifier was validated in an independent cohort of 383 patients from the Eastern Cooperative Oncology Group Trial 1900 (E1900; NCT00049517). RESULTS. A higher mean M-score was associated with death and failure to achieve CR. Multivariable analysis confirmed that a higher M-score was associated with death (P = 0.011) and failure to achieve CR (P = 0.034). Median survival was 26.6 months versus 10.6 months for low and high M-score groups. The ability of the M-score to perform as a classifier was confirmed in patients ≤ 60 years with intermediate cytogenetics and patients who achieved CR, as well as in the E1900 validation cohort. CONCLUSION. The M-score represents a valid binary prognostic classifier for patients with de novo AML. The xMELP assay and associated M-score can be used for prognosis and should be further investigated for clinical decision making in AML patients. PMID:27446991

  2. Locus heterogeneity for Waardenburg syndrome is predictive of clinical subtypes

    SciTech Connect

    Farrer, L.A.; Hoth, C.; Arnos, K.S.; Asher, J.H. Jr.; Friedman, T.B.; Grundfast, K.M.; Lalwani, A.K.; Greenberg, J.; Diehl, S.R.

    1994-10-01

    Waardenburg syndrome (WS) is a dominantly inherited and clinically variable syndrome of deafness, pigmentary changes, and distinctive facial features. Clinically, WS type I (WS1) is differentiated from WS type II (WS2) by the high frequency of dystopia canthorum in the family. In some families, WS is caused by mutations in the PAX3 gene on chromosome 2q. We have typed microsatellite markers within and flanking PAX3 in 41 WS1 kindreds and 26 WS2 kindreds in order to estimate the proportion of families with probable mutations in PAX3 and to study the relationship between phenotypic and genotypic heterogeneity. Evaluation of heterogeneity in location scores obtained by multilocus analysis indicated that WS is linked to PAX3 in 60% of all WS families and in 100% of WS1 families. None of the WS2 families were linked. In those families in which equivocal lod scores (between -2 and +1) were found, PAX3 mutations have been identified in 5 of the 15 WS1 families but in none of the 4 WS2 families. Although preliminary studies do not suggest any association between the phenotype and the molecular pathology in 20 families with known PAX3 mutations and in four patients with chromosomal abnormalities in the vicinity of PAX3, the presence of dystopia in multiple family members is a reliable indicator for identifying families likely to have a defect in PAX3. 59 refs., 3 figs., 5 tabs.

  3. Item response theory and the measurement of clinical change.

    PubMed

    Reise, Steven P; Haviland, Mark G

    2005-06-01

    An instrument's sensitivity to detect individual-level change is an important consideration for both psychometric and clinical researchers. In this article, we develop a cognitive problems measure and evaluate its sensitivity to detect change from an item response theory (IRT) perspective. After illustrating assumption checking and model fit assessment, we detail 4 features of IRT modeling: (a) the scale information curve and its relation to the bandwidth of measurement precision, (b) the scale response curve and how it is used to link the latent trait metric with the raw score metric, (c) content-based versus norm-based score referencing, and (d) the level of measurement of the latent trait scale. We conclude that IRT offers an informative, alternative framework for understanding an instrument's psychometric properties and recommend that IRT analyses be considered prior to investigations of change, growth, or the effectiveness of clinical interventions.

  4. Predicting suicidal behaviours using clinical instruments: systematic review and meta-analysis of positive predictive values for risk scales.

    PubMed

    Carter, Gregory; Milner, Allison; McGill, Katie; Pirkis, Jane; Kapur, Navneet; Spittal, Matthew J

    2017-03-16

    BackgroundPrediction of suicidal behaviour is an aspirational goal for clinicians and policy makers; with patients classified as 'high risk' to be preferentially allocated treatment. Clinical usefulness requires an adequate positive predictive value (PPV).AimsTo identify studies of predictive instruments and to calculate PPV estimates for suicidal behaviours.MethodA systematic review identified studies of predictive instruments. A series of meta-analyses produced pooled estimates of PPV for suicidal behaviours.ResultsFor all scales combined, the pooled PPVs were: suicide 5.5% (95% CI 3.9-7.9%), self-harm 26.3% (95% CI 21.8-31.3%) and self-harm plus suicide 35.9% (95% CI 25.8-47.4%). Subanalyses on self-harm found pooled PPVs of 16.1% (95% CI 11.3-22.3%) for high-quality studies, 32.5% (95% CI 26.1-39.6%) for hospital-treated self-harm and 26.8% (95% CI 19.5-35.6%) for psychiatric in-patients.ConclusionsNo 'high-risk' classification was clinically useful. Prevalence imposes a ceiling on PPV. Treatment should reduce exposure to modifiable risk factors and offer effective interventions for selected subpopulations and unselected clinical populations.

  5. Predicting therapeutic response to secondary treatment with bupropion: dichotic listening tests of functional brain asymmetry.

    PubMed

    Bruder, Gerard E; Stewart, Jonathan W; Schaller, Jennifer D; McGrath, Patrick J

    2007-10-31

    Studies using neuroimaging, electrophysiologic and cognitive measures have raised hopes for developing predictors of therapeutic response to antidepressants. Pretreatment measures of functional brain asymmetry have been found to be related to response to the selective serotonin reuptake inhibitor fluoxetine. This report examines the extent to which dichotic listening tests also predict clinical response to an antidepressant with a different mechanism of action, i.e., bupropion. Dichotic listening data were obtained for 17 unmedicated depressed patients who were subsequently treated with bupropion. Right-handed outpatients were tested on dichotic fused-words and complex-tones tests. Seven patients who responded to bupropion and 10 nonresponders did not differ in gender, age or education. Bupropion responders had significantly larger left-hemisphere advantage for perceiving words when compared to nonresponders, but there was no difference in their right-hemisphere advantage for tones. All patients having a left-hemisphere advantage above the normal mean responded to bupropion, whereas only 9% of patients below the normal mean responded to treatment. These findings should encourage further study of the clinical value of dichotic listening and other measures of functional brain asymmetry for identifying depressed patients who most benefit from treatment with different classes of antidepressants.

  6. Clinical exuberance of classic Kaposi's sarcoma and response to radiotherapy.

    PubMed

    Trujillo, Jeniffer Muñoz; Alves, Natália Ribeiro de Magalhães; Medeiros, Paula Mota; Azulay-Abulafia, Luna; Alves, Maria de Fátima Guimarães Scotelaro; Gripp, Alexandre Carlos

    2015-01-01

    Kaposi's sarcoma (KS) is a multicentric vascular neoplasm, with cutaneous and extracutaneous involvement. Different clinical and epidemiological variants have been identified. The classic form is manifested mainly in elderly men with indolent and long-term evolution, with lesions localized primarily in the lower extremities. We present two cases of classic Kaposi's sarcoma (CKS) in two female patients with extensive, exuberant skin involvement and rapid evolution, with good response to radiotherapy.

  7. Should I Pack My Umbrella? Clinical versus Statistical Prediction of Mental Health Decisions

    ERIC Educational Resources Information Center

    Aegisdottir, Stefania; Spengler, Paul M.; White, Michael J.

    2006-01-01

    In this rejoinder, the authors respond to the insightful commentary of Strohmer and Arm, Chwalisz, and Hilton, Harris, and Rice about the meta-analysis on statistical versus clinical prediction techniques for mental health judgments. The authors address issues including the availability of statistical prediction techniques for real-life psychology…

  8. Efficacy of Cognitive and Noncognitive Measures in Predicting Clinical Performance of Residents.

    ERIC Educational Resources Information Center

    Keck, Jonathan W.; And Others

    An attempt was made to determine the increase in predictive efficiency attained by adding noncognitive to cognitive variables to predict clinical performance of medical school graduates in residency training. Fifty-six graduates, 45 males, and 11 females, in 33 reading programs representing various specialties, were evaluated by supervisory…

  9. Sixty-Six Years of Research on the Clinical Versus Actuarial Prediction of Violence

    ERIC Educational Resources Information Center

    Hilton, N. Zoe; Harris, Grant T.; Rice, Marnie E.

    2006-01-01

    In their meta-analysis of clinical versus statistical prediction models, Aegisdottir et al. (this issue) extended previous findings of statistical-method superiority across such variables as clinicians' experience and familiarity with data. In this reaction, the authors are particularly interested in violence prediction, which yields the greatest…

  10. Responsiveness of endpoints in osteoporosis clinical trials--an update.

    PubMed

    Cranney, A; Welch, V; Tugwell, P; Wells, G; Adachi, J D; McGowan, J; Shea, B

    1999-01-01

    As an update of our earlier paper, published as part of the Outcome Measures in Rheumatology Clinical Trials (OMERACT 3) proceedings in 1996, we surveyed the types of outcomes incorporated in recent clinical trials. A literature search was conducted on MEDLINE and Current Contents, from January 1996 to March 1998, using the search strategy recommended by the Cochrane Collaboration for the identification of randomized controlled trials (RCT). Two independent reviewers selected trials according to inclusion criteria. The same reviewers extracted data on clinical and radiographic fractures, pain, quality of life, and bone mineral density (BMD). Seventy-four RCT conducted on bone loss in postmenopausal women were identified. Most trials incorporated biochemical markers and BMD as outcome measures. Fewer trials included vertebral fractures, pain, height, and quality of life. The responsiveness is presented in terms of the sample size needed per group to show a statistically significant difference. The most responsive outcomes were pain, BMD, and biochemical markers. The number needed to treat to prevent one vertebral fracture ranged from 13 to 54, depending on the intervention and population. Investigators should examine the characteristics of the patient population and the nature of the intervention in determining the sample size required to demonstrate a significant effect. The selection of endpoints should be based on their responsiveness, feasibility, and the importance of using standardized outcomes. Standardized outcomes greatly facilitate the synthesis of available information into systematic reviews by groups such as the Cochrane Collaboration.

  11. Predictive values of anti-müllerian hormone, antral follicle count and ovarian response prediction index (ORPI) for assisted reproductive technology outcomes.

    PubMed

    Ashrafi, Mahnaz; Hemat, Mandana; Arabipoor, Arezoo; Salman Yazdi, Reza; Bahman-Abadi, Akram; Cheraghi, Rezvaneh

    2017-01-01

    This prospective study was performed from June 2012 to June 2014 at Royan Institute to compare the predictive values of serum anti-müllerian hormone (AMH), antral follicle count (AFC) and ovarian response prediction index (ORPI) ([AFC × AMH]/age) for in vitro fertilisation/intracytoplasmic sperm injection (IVF/ICSI) cycle outcomes. Five hundred and fifty women included in the study with male factor and unexplained infertility were the first candidates for IVF/ICSI cycles. Serum AMH level was measured by a commercial ELISA kit and AFC was calculated by a transvaginal ultrasonography on day 2-3 of the menstrual cycle before starting ovarian stimulation. All women underwent IVF/ICSI cycles using a long standard protocol with gonadotropin-releasing hormone agonist. The receiver operating characteristic curves (ROC) analysis showed that both AMH and AFC were good predictors of ovarian response with an area under the curves (AUC) > 0.75; even it seems that AFC was being a better predictor. Combining these variables is necessary as ORPI will not improve the prediction value. All the variables had poor predictive ability (AUC <0.60) for clinical pregnancy and live birth rates. Logistic regression analysis showed the AMH less than 0.4 ng/ml and quality of transferred embryos were significant predictors for clinical pregnancy rate.

  12. Compartmentalized immune response reflects clinical severity of beryllium disease.

    PubMed

    Newman, L S; Bobka, C; Schumacher, B; Daniloff, E; Zhen, B; Mroz, M M; King, T E

    1994-07-01

    Although beryllium disease has been associated with a bronchoalveolar lavage (BAL) lymphocytosis and T cell-mediated immune response, we do not know if either the BAL cellular profile or the compartmentalized pulmonary response to the antigen reflect the severity of the disease. We studied 110 subjects divided into three groups of subjects: beryllium disease patients (n = 55), beryllium-sensitized patients without disease (n = 8), and control subjects (n = 47). Evaluation included completion of a respiratory symptom questionnaire, clinical examination, chest radiograph, spirometry, body plethysmographic lung volumes, and diffusing capacity (DLCO). In the patient groups, we performed maximal exercise testing with an indwelling arterial line. In addition, we examined BAL and performed blood and BAL beryllium lymphocyte transformation tests (BeLT) as measures of the beryllium-specific T cell-mediated response in these two compartments. In beryllium disease patients we correlated the BAL cellular constituents with clinical parameters indicative of disease severity. Beryllium disease patients exhibited elevated numbers of white cells and lymphocytes in BAL compared with both other groups; however, this lymphocytic alveolitis was significantly obscured in smokers. The BAL cellular constituents correlated with BAL BeLT but not with the blood BeLT. BAL cellular constituents also correlated with the radiographic profusion of small opacities, FEV1/FVC, DLCO, maximal achievable work load, VO2max, and measures of gas exchange at rest and at maximum exercise. We conclude that the lymphocyte-predominant pulmonary inflammatory response in beryllium disease is related to the magnitude of the localized response to antigen and that BAL cellularity, differential cell count, and BeLT reflect beryllium disease clinical severity.(ABSTRACT TRUNCATED AT 250 WORDS)

  13. Variants in KCNJ11 and BAD do not predict response to ketogenic dietary therapies for epilepsy

    PubMed Central

    Schoeler, Natasha E.; Leu, Costin; White, Jon; Plagnol, Vincent; Ellard, Sian; Matarin, Mar; Yellen, Gary; Thiele, Elizabeth A.; Mackay, Mark; McMahon, Jacinta M.; Scheffer, Ingrid E.; Sander, Josemir W.; Cross, J. Helen; Sisodiya, Sanjay M.

    2015-01-01

    In the absence of specific metabolic disorders, predictors of response to ketogenic dietary therapies (KDT) are unknown. We aimed to determine whether variants in established candidate genes KCNJ11 and BAD influence response to KDT. We sequenced KCNJ11 and BAD in individuals without previously-known glucose transporter type 1 deficiency syndrome or other metabolic disorders, who received KDT for epilepsy. Hospital records were used to obtain demographic and clinical data. Two response phenotypes were used: ≥50% seizure reduction and seizure-freedom at 3-month follow-up. Case/control association tests were conducted with KCNJ11 and BAD variants with minor allele frequency (MAF) > 0.01, using PLINK. Response to KDT in individuals with variants with MAF < 0.01 was evaluated. 303 Individuals had KCNJ11 and 246 individuals had BAD sequencing data and diet response data. Six SNPs in KCNJ11 and two in BAD had MAF > 0.01. Eight variants in KCNJ11 and seven in BAD (of which three were previously-unreported) had MAF < 0.01. No significant results were obtained from association analyses, with either KDT response phenotype. P-values were similar when accounting for ethnicity using a stratified Cochran–Mantel–Haenszel test. There did not seem to be a consistent effect of rare variants on response to KDT, although the cohort size was too small to assess significance. Common variants in KCNJ11 and BAD do not predict response to KDT for epilepsy. We can exclude, with 80% power, association from variants with a MAF of >0.05 and effect size >3. A larger sample size is needed to detect associations from rare variants or those with smaller effect sizes. PMID:26590798

  14. Variants in KCNJ11 and BAD do not predict response to ketogenic dietary therapies for epilepsy.

    PubMed

    Schoeler, Natasha E; Leu, Costin; White, Jon; Plagnol, Vincent; Ellard, Sian; Matarin, Mar; Yellen, Gary; Thiele, Elizabeth A; Mackay, Mark; McMahon, Jacinta M; Scheffer, Ingrid E; Sander, Josemir W; Cross, J Helen; Sisodiya, Sanjay M

    2015-12-01

    In the absence of specific metabolic disorders, predictors of response to ketogenic dietary therapies (KDT) are unknown. We aimed to determine whether variants in established candidate genes KCNJ11 and BAD influence response to KDT. We sequenced KCNJ11 and BAD in individuals without previously-known glucose transporter type 1 deficiency syndrome or other metabolic disorders, who received KDT for epilepsy. Hospital records were used to obtain demographic and clinical data. Two response phenotypes were used: ≥ 50% seizure reduction and seizure-freedom at 3-month follow-up. Case/control association tests were conducted with KCNJ11 and BAD variants with minor allele frequency (MAF)>0.01, using PLINK. Response to KDT in individuals with variants with MAF<0.01 was evaluated. 303 Individuals had KCNJ11 and 246 individuals had BAD sequencing data and diet response data. Six SNPs in KCNJ11 and two in BAD had MAF>0.01. Eight variants in KCNJ11 and seven in BAD (of which three were previously-unreported) had MAF<0.01. No significant results were obtained from association analyses, with either KDT response phenotype. P-values were similar when accounting for ethnicity using a stratified Cochran-Mantel-Haenszel test. There did not seem to be a consistent effect of rare variants on response to KDT, although the cohort size was too small to assess significance. Common variants in KCNJ11 and BAD do not predict response to KDT for epilepsy. We can exclude, with 80% power, association from variants with a MAF of >0.05 and effect size >3. A larger sample size is needed to detect associations from rare variants or those with smaller effect sizes.

  15. Clinical Prediction Making: Examining Influential Factors Related to Clinician Predictions of Recidivism among Juvenile Offenders

    ERIC Educational Resources Information Center

    Calley, Nancy G.; Richardson, Emily M.

    2011-01-01

    This study examined factors influencing clinician predictions of recidivism for juvenile offenders, including youth age at initial juvenile justice system involvement, youth age at discharge, program completion status, clinician perception of strength of the therapeutic relationship, and clinician perception of youth commitment to treatment.…

  16. Bioengineered human myobundles mimic clinical responses of skeletal muscle to drugs.

    PubMed

    Madden, Lauran; Juhas, Mark; Kraus, William E; Truskey, George A; Bursac, Nenad

    2015-01-09

    Existing in vitro models of human skeletal muscle cannot recapitulate the organization and function of native muscle, limiting their use in physiological and pharmacological studies. Here, we demonstrate engineering of electrically and chemically responsive, contractile human muscle tissues ('myobundles') using primary myogenic cells. These biomimetic constructs exhibit aligned architecture, multinucleated and striated myofibers, and a Pax7(+) cell pool. They contract spontaneously and respond to electrical stimuli with twitch and tetanic contractions. Positive correlation between contractile force and GCaMP6-reported calcium responses enables non-invasive tracking of myobundle function and drug response. During culture, myobundles maintain functional acetylcholine receptors and structurally and functionally mature, evidenced by increased myofiber diameter and improved calcium handling and contractile strength. In response to diversely acting drugs, myobundles undergo dose-dependent hypertrophy or toxic myopathy similar to clinical outcomes. Human myobundles provide an enabling platform for predictive drug and toxicology screening and development of novel therapeutics for muscle-related disorders.

  17. Upper esophageal sphincter abnormalities are strongly predictive of treatment response in patients with achalasia

    PubMed Central

    Mathews, Simon C; Ciarleglio, Maria; Chavez, Yamile Haito; Clarke, John O; Stein, Ellen; Chander Roland, Bani

    2014-01-01

    AIM: To investigate the relationship between upper esophageal sphincter abnormalities achalasia treatment METHODS: We performed a retrospective study of 41 consecutive patients referred for high resolution esophageal manometry with a final manometric diagnosis of achalasia. Patients were sub-divided by presence or absence of Upper esophageal sphincter (UES) abnormality, and clinical and manometric profiles were compared. Correlation between UES abnormality and sub-type (i.e., hypertensive, hypotensive or impaired relaxation) and a number of variables, including qualitative treatment response, achalasia sub-type, co-morbid medical illness, psychiatric illness, surgical history, dominant presenting symptom, treatment type, age and gender were also evaluated. RESULTS: Among all 41 patients, 24 (58.54%) had a UES abnormality present. There were no significant differences between the groups in terms of age, gender or any other clinical or demographic profiles. Among those with UES abnormalities, the majority were either hypertensive (41.67%) or had impaired relaxation (37.5%) as compared to hypotensive (20.83%), although this did not reach statistical significance (P = 0.42). There was no specific association between treatment response and treatment type received; however, there was a significant association between UES abnormalities and treatment response. In patients with achalasia and concomitant UES abnormalities, 87.5% had poor treatment response, while only 12.5% had favorable response. In contrast, in patients with achalasia and no UES abnormalities, the majority (78.57%) had good treatment response, as compared to 21.43% with poor treatment response (P = 0.0001). After controlling for achalasia sub-type, those with UES abnormality had 26 times greater odds of poor treatment response than those with no UES abnormality (P = 0.009). Similarly, after controlling for treatment type, those with UES abnormality had 13.9 times greater odds of poor treatment response

  18. Artificial neural network model for the prediction of obsessive-compulsive disorder treatment response.

    PubMed

    Salomoni, Giuliana; Grassi, Massimiliano; Mosini, Paola; Riva, Patrizia; Cavedini, Paolo; Bellodi, Laura

    2009-08-01

    Several patients with obsessive-compulsive disorder (OCD) who are refractory to adequate treatment with first-line treatments are considered treatment-resistant. Further surveys were to be implemented to explore the outcome predictors of the antiobsessional response. Such study was aimed at building a model suitable to predict the final outcome of a mixed OCD pharmacologic and nonpharmacologic treatment approaches. We studied 130 subjects with OCD who underwent pharmacologic (with selective serotonin reuptake inhibitors alone or with selective serotonin reuptake inhibitors and risperidone at low dosage) and/or behavioral therapy (using exposure and response prevention techniques). The following variables were used as predictors: symptoms dimension, as resulting from the Yale-Brown Obsessive-Compulsive Scale items factor analysis; neuropsychologic performances; and epidemiologic variables. The treatment response arising from 3 to 6 months of therapy was used as dependent variable. A conventional logistic regression was used to define a previsional model of treatment response and multilayer perceptrons and to supervise an artificial neural network technique. The 46.9% of the sample resulted to be refractory to treatment. Results obtained with the logistic regression model showed that the only predictors of treatment outcome are hoarding symptoms, repeating rituals, and counting compulsions. Furthermore, using all the variables considered in the models, multilayer perceptrons showed highly better predictive performance as compared with the logistic regression models (93.3% vs 61.5%, respectively, of correct classification of cases). Complex interactions between different clinical and neuropsychologic variables are involved in defining OCD treatment response profile, and nonlinear and interactive modeling strategies, that is, supervised artificial neural networks, seem to be more suitable to investigate this complexity than linear techniques.

  19. In vitro simulation of pathological bone conditions to predict clinical outcome of bone tissue engineered materials

    NASA Astrophysics Data System (ADS)

    Nguyen, Duong Thuy Thi

    treatment strategy should focus on simulating, in vitro, a physiological bone environment to predict clinical effectiveness of engineered bone and understand cellular responses due to the proposed agents and bioactive scaffolds. An in vitro test system can be the necessary catalyst to reduce implant failures and non-unions in fragility fractures.

  20. Modeling Clinical Radiation Responses in the IMRT Era

    NASA Astrophysics Data System (ADS)

    Schwartz, J. L.; Murray, D.; Stewart, R. D.; Phillips, M. H.

    2014-03-01

    The purpose of this review is to highlight the critical issues of radiobiological models, particularly as they apply to clinical radiation therapy. Developing models of radiation responses has a long history that continues to the present time. Many different models have been proposed, but in the field of radiation oncology, the linear-quadratic (LQ) model has had the most impact on the design of treatment protocols. Questions have been raised as to the value of the LQ model given that the biological assumption underlying it has been challenged by molecular analyses of cell and tissue responses to radiation. There are also questions as to use of the LQ model for hypofractionation, especially for high dose treatments using a single fraction. While the LQ model might over-estimate the effects of large radiation dose fractions, there is insufficient information to fully justify the adoption of alternative models. However, there is increasing evidence in the literature that non-targeted and other indirect effects of radiation sometimes produce substantial deviations from LQ-like dose-response curves. As preclinical and clinical hypofractionation studies accumulate, new or refined dose-response models that incorporate high-dose/fraction non-targeted and indirect effects may be required, but for now the LQ model remains a simple, useful tool to guide the design of treatment protocols.

  1. The Use of Factorial Forecasting to Predict Public Response

    ERIC Educational Resources Information Center

    Weiss, David J.

    2012-01-01

    Policies that call for members of the public to change their behavior fail if people don't change; predictions of whether the requisite changes will take place are needed prior to implementation. I propose to solve the prediction problem with Factorial Forecasting, a version of functional measurement methodology that employs group designs. Aspects…

  2. Preterm birth in twin pregnancies: Clinical outcomes and predictive parameters

    PubMed Central

    Dolgun, Zehra Nihal; Inan, Cihan; Altintas, Ahmet Salih; Okten, Sabri Berkem; Sayin, Niyazi Cenk

    2016-01-01

    Objective: To document the neonatal outcomes of preterm birth in twin pregnancies and to investigate whether perinatal and obstetric parameters are associated with clinical outcomes. Methods: This retrospective trial was conducted on data gathered from 176 preterm twins delivered in the obstetrics and gynecology department of our tertiary care center. Data extracted from medical files of 88 pregnant women who gave preterm birth (at 260/7 to 366/7 gestational weeks) to twins were analyzed. Maternal/fetal descriptive and obstetric parameters, sonographic data, route of delivery, indication for cesarean section, birth weight, Apgar scores, head circumference, umbilical cord length and placental weight were noted. Results: The average age of the pregnant women was 28.8±6.4 years and ultrasonographic gestational age was 31.9±2.6 weeks. Apgar scores at 1st minute were affected significantly by fetal body weight (p=0.001), gestational age (p=0.001), height (p=0.004) and head circumference (p=0.011). None of these variables exhibited a noteworthy effect on Apgar scores at 5th minute. Conclusion: Efforts must be made to achieve advancement of gestational age until delivery in the follow-up preterm of twins. A well-established algorithm with special emphasis to risk factors is necessary to standardize and popularize the appropriate management strategy. PMID:27648040

  3. Clinical parameters predictive of malignancy of thyroid follicular neoplasms

    SciTech Connect

    Davis, N.L.; Gordon, M.; Germann, E.; Robins, R.E.; McGregor, G.I. )

    1991-05-01

    Needle aspiration biopsy is commonly employed in the evaluation of thyroid nodules. Unfortunately, the cytologic finding of a 'follicular neoplasm' does not distinguish between a thyroid adenoma and a follicular cancer. The purpose of this study was to identify clinical parameters that characterize patients with an increased risk of having a thyroid follicular cancer who preoperatively have a 'follicular neoplasm' identified by needle aspiration biopsy. A total of 395 patients initially treated at Vancouver General Hospital and the British Columbia Cancer Agency between the years of 1965 and 1985 were identified and their data were entered into a computer database. Patients with thyroid adenomas were compared to patients with follicular cancer using the chi-square test and Student's t-test. Statistically significant parameters that distinguished patients at risk of having a thyroid cancer (p less than 0.05) included age greater than 50 years, nodule size greater than 3 cm, and a history of neck irradiation. Sex, family history of goiter or neoplasm, alcohol and tobacco use, and use of exogenous estrogen were not significant parameters. Patients can be identified preoperatively to be at an increased risk of having a follicular cancer and accordingly appropriate surgical resection can be planned.

  4. Understanding and predicting suicidality using a combined genomic and clinical risk assessment approach

    PubMed Central

    Niculescu, A B; Levey, D F; Phalen, P L; Le-Niculescu, H; Dainton, H D; Jain, N; Belanger, E; James, A; George, S; Weber, H; Graham, D L; Schweitzer, R; Ladd, T B; Learman, R; Niculescu, E M; Vanipenta, N P; Khan, F N; Mullen, J; Shankar, G; Cook, S; Humbert, C; Ballew, A; Yard, M; Gelbart, T; Shekhar, A; Schork, N J; Kurian, S M; Sandusky, G E; Salomon, D R

    2015-01-01

    biomarkers for suicidality. We also identified other potential therapeutic targets or biomarkers for drugs known to mitigate suicidality, such as omega-3 fatty acids, lithium and clozapine. Overall, 14% of the top candidate biomarkers also had evidence for involvement in psychological stress response, and 19% for involvement in programmed cell death/cellular suicide (apoptosis). It may be that in the face of adversity (stress), death mechanisms are turned on at a cellular (apoptosis) and organismal level. Finally, we tested the top increased and decreased biomarkers from the discovery for suicidal ideation (CADM1, CLIP4, DTNA, KIF2C), prioritization with CFG for prior evidence (SAT1, SKA2, SLC4A4), and validation for behavior in suicide completers (IL6, MBP, JUN, KLHDC3) steps in a completely independent test cohort of psychiatric participants for prediction of suicidal ideation (n=108), and in a future follow-up cohort of psychiatric participants (n=157) for prediction of psychiatric hospitalizations due to suicidality. The best individual biomarker across psychiatric diagnoses for predicting suicidal ideation was SLC4A4, with a receiver operating characteristic (ROC) area under the curve (AUC) of 72%. For bipolar disorder in particular, SLC4A4 predicted suicidal ideation with an AUC of 93%, and future hospitalizations with an AUC of 70%. SLC4A4 is involved in brain extracellular space pH regulation. Brain pH has been implicated in the pathophysiology of acute panic attacks. We also describe two new clinical information apps, one for affective state (simplified affective state scale, SASS) and one for suicide risk factors (Convergent Functional Information for Suicide, CFI-S), and how well they predict suicidal ideation across psychiatric diagnoses (AUC of 85% for SASS, AUC of 89% for CFI-S). We hypothesized a priori, based on our previous work, that the integration of the top biomarkers and the clinical information into a universal predictive measure (UP-Suicide) would

  5. Clinical and MRI models predicting amyloid deposition in progressive aphasia and apraxia of speech.

    PubMed

    Whitwell, Jennifer L; Weigand, Stephen D; Duffy, Joseph R; Strand, Edythe A; Machulda, Mary M; Senjem, Matthew L; Gunter, Jeffrey L; Lowe, Val J; Jack, Clifford R; Josephs, Keith A

    2016-01-01

    Beta-amyloid (Aβ) deposition can be observed in primary progressive aphasia (PPA) and progressive apraxia of speech (PAOS). While it is typically associated with logopenic PPA, there are exceptions that make predicting Aβ status challenging based on clinical diagnosis alone. We aimed to determine whether MRI regional volumes or clinical data could help predict Aβ deposition. One hundred and thirty-nine PPA (n = 97; 15 agrammatic, 53 logopenic, 13 semantic and 16 unclassified) and PAOS (n = 42) subjects were prospectively recruited into a cross-sectional study and underwent speech/language assessments, 3.0 T MRI and C11-Pittsburgh Compound B PET. The presence of Aβ was determined using a 1.5 SUVR cut-point. Atlas-based parcellation was used to calculate gray matter volumes of 42 regions-of-interest across the brain. Penalized binary logistic regression was utilized to determine what combination of MRI regions, and what combination of speech and language tests, best predicts Aβ (+) status. The optimal MRI model and optimal clinical model both performed comparably in their ability to accurately classify subjects according to Aβ status. MRI accurately classified 81% of subjects using 14 regions. Small left superior temporal and inferior parietal volumes and large left Broca's area volumes were particularly predictive of Aβ (+) status. Clinical scores accurately classified 83% of subjects using 12 tests. Phonological errors and repetition deficits, and absence of agrammatism and motor speech deficits were particularly predictive of Aβ (+) status. In comparison, clinical diagnosis was able to accurately classify 89% of subjects. However, the MRI model performed well in predicting Aβ deposition in unclassified PPA. Clinical diagnosis provides optimum prediction of Aβ status at the group level, although regional MRI measurements and speech and language testing also performed well and could have advantages in predicting Aβ status in unclassified PPA subjects.

  6. In vivo imaging using fluorescent antibodies to tumor necrosis factor predicts therapeutic response in Crohn's disease.

    PubMed

    Atreya, Raja; Neumann, Helmut; Neufert, Clemens; Waldner, Maximilian J; Billmeier, Ulrike; Zopf, Yurdagül; Willma, Marcus; App, Christine; Münster, Tino; Kessler, Hermann; Maas, Stefanie; Gebhardt, Bernd; Heimke-Brinck, Ralph; Reuter, Eva; Dörje, Frank; Rau, Tilman T; Uter, Wolfgang; Wang, Thomas D; Kiesslich, Ralf; Vieth, Michael; Hannappel, Ewald; Neurath, Markus F

    2014-03-01

    As antibodies to tumor necrosis factor (TNF) suppress immune responses in Crohn's disease by binding to membrane-bound TNF (mTNF), we created a fluorescent antibody for molecular mTNF imaging in this disease. Topical antibody administration in 25 patients with Crohn's disease led to detection of intestinal mTNF(+) immune cells during confocal laser endomicroscopy. Patients with high numbers of mTNF(+) cells showed significantly higher short-term response rates (92%) at week 12 upon subsequent anti-TNF therapy as compared to patients with low amounts of mTNF(+) cells (15%). This clinical response in the former patients was sustained over a follow-up period of 1 year and was associated with mucosal healing observed in follow-up endoscopy. These data indicate that molecular imaging with fluorescent antibodies has the potential to predict therapeutic responses to biological treatment and can be used for personalized medicine in Crohn's disease and autoimmune or inflammatory disorders.

  7. Anti-citrullinated peptide antibodies and their value for predicting responses to biologic agents: a review.

    PubMed

    Martin-Mola, Emilio; Balsa, Alejandro; García-Vicuna, Rosario; Gómez-Reino, Juan; González-Gay, Miguel Angel; Sanmartí, Raimon; Loza, Estíbaliz

    2016-08-01

    Anti-citrullinated peptide antibodies (ACPAs) play an important pathogenic role both at the onset and during the disease course. These antibodies precede the clinical appearance of rheumatoid arthritis (RA) and are associated with a less favorable prognosis, both clinically and radiologically. The objective of this work was to conduct a comprehensive review of studies published through September 2015 of ACPAs' role as a predictor of the therapeutic response to the biological agents in RA patients. The review also includes summary of the biology and detection of ACPAs as well as ACPAs in relation to joint disease and CV disease and the possible role of seroconversion. The reviews of studies examining TNF inhibitors and tocilizumab yielded negative results. In the case of rituximab, the data indicated a greater probability of clinical benefit in ACPA(+) patients versus ACPA(-) patients, as has been previously described for rheumatoid factor. Nonetheless, the effect is discreet and heterogeneous. Another drug that may have greater effectiveness in ACPA(+) patients is abatacept. Some studies have suggested that the drug is more efficient in ACPA(+) patients and that those patients show greater drug retention. In a subanalysis of the AMPLE trial, patients with very high ACPA titers who were treated with abatacept had a statistically significant response compared to patients with lower titers. In summary, the available studies suggest that the presence of or high titers of ACPA may predict a better response to rituximab and/or abatacept. Evidence regarding TNFi and tocilizumab is lacking. However, there is a lack of studies with appropriate designs to demonstrate that some drugs are superior to others for ACPA(+) patients.

  8. Dopa-responsive dystonia--clinical and genetic heterogeneity.

    PubMed

    Wijemanne, Subhashie; Jankovic, Joseph

    2015-07-01

    Dopa-responsive dystonia (DRD) encompasses a group of clinically and genetically heterogeneous disorders that typically manifest as limb-onset, diurnally fluctuating dystonia and exhibit a robust and sustained response to levodopa treatment. Autosomal dominant GTP cyclohydrolase 1 deficiency, also known as Segawa disease, is the most common and best-characterized condition that manifests as DRD, but a similar presentation can be seen with genetic abnormalities that lead to deficiencies in tyrosine hydroxylase, sepiapterin reductase or other enzymes that are involved in the biosynthesis of dopamine. In rare cases, DRD can result from conditions that do not affect the biosynthesis of dopamine; single case reports have shown that DRD can be a manifestation of hereditary spastic paraplegia type 11, spinocerebellar ataxia type 3 and ataxia telangiectasia. This heterogeneity of conditions that underlie DRD frequently leads to misdiagnosis, which delays the appropriate treatment with levodopa. Correct diagnosis at an early stage requires use of the appropriate diagnostic tests, which include a levodopa trial, genetic testing (including whole-exome sequencing), cerebrospinal fluid neurotransmitter analysis, the phenylalanine loading test, and enzyme activity measurements. The selection of tests for use depends on the clinical presentation and level of complexity. This Review presents the common and rarer causes of DRD and their clinical features, and considers the most appropriate approaches to ensure early diagnosis and treatment.

  9. Functional brain network modularity predicts response to cognitive training after brain injury

    PubMed Central

    Chen, Anthony J.-W.; Novakovic-Agopian, Tatjana; Gratton, Caterina; Nomura, Emi M.; D'Esposito, Mark

    2015-01-01

    Objective: We tested the value of measuring modularity, a graph theory metric indexing the relative extent of integration and segregation of distributed functional brain networks, for predicting individual differences in response to cognitive training in patients with brain injury. Methods: Patients with acquired brain injury (n = 11) participated in 5 weeks of cognitive training and a comparison condition (brief education) in a crossover intervention study design. We quantified the measure of functional brain network organization, modularity, from functional connectivity networks during a state of tonic attention regulation measured during fMRI scanning before the intervention conditions. We examined the relationship of baseline modularity with pre- to posttraining changes in neuropsychological measures of attention and executive control. Results: The modularity of brain network organization at baseline predicted improvement in attention and executive function after cognitive training, but not after the comparison intervention. Individuals with higher baseline modularity exhibited greater improvements with cognitive training, suggesting that a more modular baseline network state may contribute to greater adaptation in response to cognitive training. Conclusions: Brain network properties such as modularity provide valuable information for understanding mechanisms that influence rehabilitation of cognitive function after brain injury, and may contribute to the discovery of clinically relevant biomarkers that could guide rehabilitation efforts. PMID:25788557

  10. EEG biomarkers in major depressive disorder: discriminative power and prediction of treatment response.

    PubMed

    Olbrich, Sebastian; Arns, Martijn

    2013-10-01

    Major depressive disorder (MDD) has high population prevalence and is associated with substantial impact on quality of life, not least due to an unsatisfactory time span of sometimes several weeks from initiation of treatment to clinical response. Therefore extensive research focused on the identification of cost-effective and widely available electroencephalogram (EEG)-based biomarkers that not only allow distinguishing between patients and healthy controls but also have predictive value for treatment response for a variety of treatments. In this comprehensive overview on EEG research on MDD, biomarkers that are either assessed at baseline or during the early course of treatment and are helpful in discriminating patients from healthy controls and assist in predicting treatment outcome are reviewed, covering recent decades up to now. Reviewed markers include quantitative EEG (QEEG) measures, connectivity measures, EEG vigilance-based measures, sleep-EEG-related measures and event-related potentials (ERPs). Further, the value and limitations of these different markers are discussed. Finally, the need for integrated models of brain function and the necessity for standardized procedures in EEG biomarker research are highlighted to enhance future research in this field.

  11. Maternal Responsiveness Predicts Child Language at Ages 3 and 4 in a Community-Based Sample of Slow-to-Talk Toddlers

    ERIC Educational Resources Information Center

    Hudson, Sophie; Levickis, Penny; Down, Kate; Nicholls, Ruth; Wake, Melissa

    2015-01-01

    Background: Maternal responsiveness has been shown to predict child language outcomes in clinical samples of children with language delay and non-representative samples of typically developing children. An effective and timely measure of maternal responsiveness for use at the population level has not yet been established. Aims: To determine…

  12. A Nomogram for Predicting the Pathological Response of Axillary Lymph Node Metastasis in Breast Cancer Patients

    PubMed Central

    Jin, Xi; Jiang, Yi-Zhou; Chen, Sheng; Shao, Zhi-Ming; Di, Gen-Hong

    2016-01-01

    The value of sentinel lymph node biopsy (SLNB) in post-neoadjuvant chemotherapy (NCT) patients is still controversial. We aimed to identify predictors and construct a nomogram for predicting the pathologically complete response (pCR) of axillary lymph nodes (ALNs) after NCT in node positive breast cancer patients. In total, 426 patients with pathologically proven ALN metastasis before NCT were enrolled, randomized 1:1 and divided into a training set and a validation set. We developed a nomogram based on independent predictors for ALN pCR identified by multivariate logistic regression as well as clinical significant predictors. The multivariate logistic regression analysis showed that hormone receptor (HR) status, human epidermal growth factor 2 (HER2) status and Ki67 index were independent predictors. The nomogram was thereby constructed by those independent predictors as well as tumor size and NCT regimens. The areas under the receiver operating characteristic curve of the training set and the validation set were 0.804 and 0.749, respectively. We constructed a nomogram for predicting ALN pCR in patients who received NCT. Our nomogram can improve risk stratification, accurately predict post-NCT ALN status and avoid unnecessary ALN dissection. PMID:27576704

  13. Predicting Out-of-Office Blood Pressure in the Clinic (PROOF-BP)

    PubMed Central

    Stevens, Richard; Gill, Paramjit; Martin, Una; Godwin, Marshall; Hanley, Janet; Heneghan, Carl; Hobbs, F.D. Richard; Mant, Jonathan; McKinstry, Brian; Myers, Martin; Nunan, David; Ward, Alison; Williams, Bryan; McManus, Richard J.

    2016-01-01

    Patients often have lower (white coat effect) or higher (masked effect) ambulatory/home blood pressure readings compared with clinic measurements, resulting in misdiagnosis of hypertension. The present study assessed whether blood pressure and patient characteristics from a single clinic visit can accurately predict the difference between ambulatory/home and clinic blood pressure readings (the home–clinic difference). A linear regression model predicting the home–clinic blood pressure difference was derived in 2 data sets measuring automated clinic and ambulatory/home blood pressure (n=991) using candidate predictors identified from a literature review. The model was validated in 4 further data sets (n=1172) using area under the receiver operator characteristic curve analysis. A masked effect was associated with male sex, a positive clinic blood pressure change (difference between consecutive measurements during a single visit), and a diagnosis of hypertension. Increasing age, clinic blood pressure level, and pulse pressure were associated with a white coat effect. The model showed good calibration across data sets (Pearson correlation, 0.48–0.80) and performed well-predicting ambulatory hypertension (area under the receiver operator characteristic curve, 0.75; 95% confidence interval, 0.72–0.79 [systolic]; 0.87; 0.85–0.89 [diastolic]). Used as a triaging tool for ambulatory monitoring, the model improved classification of a patient’s blood pressure status compared with other guideline recommended approaches (93% [92% to 95%] classified correctly; United States, 73% [70% to 75%]; Canada, 74% [71% to 77%]; United Kingdom, 78% [76% to 81%]). This study demonstrates that patient characteristics from a single clinic visit can accurately predict a patient’s ambulatory blood pressure. Usage of this prediction tool for triaging of ambulatory monitoring could result in more accurate diagnosis of hypertension and hence more appropriate treatment. PMID:27001299

  14. Early Prediction of the Response of Breast Tumors to Neoadjuvant Chemotherapy using Quantitative MRI and Machine Learning

    PubMed Central

    Mani, Subramani; Chen, Yukun; Arlinghaus, Lori R.; Li, Xia; Chakravarthy, A. Bapsi; Bhave, Sandeep R.; Welch, E. Brian; Levy, Mia A.; Yankeelov, Thomas E.

    2011-01-01

    The ability to predict early in the course of treatment the response of breast tumors to neoadjuvant chemotherapy can stratify patients based on response for patient-specific treatment strategies. Currently response to neoadjuvant chemotherapy is evaluated based on physical exam or breast imaging (mammogram, ultrasound or conventional breast MRI). There is a poor correlation among these measurements and with the actual tumor size when measured by the pathologist during definitive surgery. We tested the feasibility of using quantitative MRI as a tool for early prediction of tumor response. Between 2007 and 2010 twenty consecutive patients diagnosed with Stage II/III breast cancer and receiving neoadjuvant chemotherapy were enrolled on a prospective imaging study. Our study showed that quantitative MRI parameters along with routine clinical measures can predict responders from non-responders to neoadjuvant chemotherapy. The best predictive model had an accuracy of 0.9, a positive predictive value of 0.91 and an AUC of 0.96. PMID:22195145

  15. Early Prediction of Long-Term Response to Cabergoline in Patients with Macroprolactinomas

    PubMed Central

    Lee, Youngki; Ku, Cheol Ryong; Kim, Eui-Hyun; Lee, Eun Jig; Kim, Sun Ho

    2014-01-01

    Background Cabergoline is typically effective for treating prolactinomas; however, some patients display cabergoline resistance, and the early characteristics of these patients remain unclear. We analyzed early indicators predicting long-term response to cabergoline. Methods We retrospectively reviewed the cases of 44 patients with macroprolactinomas who received cabergoline as first-line treatment; the patients were followed for a median of 16 months. The influence of various clinical parameters on outcomes was evaluated. Results Forty patients (90.9%) were treated medically and displayed tumor volume reduction (TVR) of 74.7%, a prolactin normalization (NP) rate of 81.8%, and a complete response (CR; TVR >50% with NP, without surgery) rate of 70.5%. Most patients (93.1%) with TVR ≥25% and NP at 3 months eventually achieved CR, whereas only 50% of patients with TVR ≥25% without NP and no patients with TVR <25% achieved CR. TVR at 3 months was strongly correlated with final TVR (R=0.785). Patients with large macroadenomas exhibited a low NP rate at 3 months, but eventually achieved TVR and NP rates similar to those of patients with smaller tumors. Surgery independently reduced the final dose of cabergoline (β=-1.181 mg/week), and two of four patients who underwent surgery were able to discontinue cabergoline. Conclusion Determining cabergoline response using TVR and NP 3 months after treatment is useful for predicting later outcomes. However, further cabergoline administration should be considered for patients with TVR >25% at 3 months without NP, particularly those with huge prolactinomas, because a delayed response may be achieved. As surgery can reduce the cabergoline dose necessary for successful disease control, it should be considered for cabergoline-resistant patients. PMID:25309786

  16. Pre-Clinical Assessment of Immune Responses to Adeno-Associated Virus (AAV) Vectors.

    PubMed

    Basner-Tschakarjan, Etiena; Bijjiga, Enoch; Martino, Ashley T

    2014-01-01

    Transitioning to human trials from pre-clinical models resulted in the emergence of inhibitory AAV vector immune responses which has become a hurdle for sustained correction. Early animal studies did not predict the full range of host immunity to the AAV vector in human studies. While pre-existing antibody titers against AAV vectors has been a lingering concern, cytotoxic T-cell (CTL) responses against the input capsid can prevent long-term therapy in humans. These discoveries spawned more thorough profiling of immune response to rAAV in pre-clinical models, which have assessed both innate and adaptive immunity and explored methods for bypassing these responses. Many efforts toward measuring innate immunity have utilized Toll-like receptor deficient models and have focused on differential responses to viral capsid and genome. From adaptive studies, it is clear that humoral responses are relevant for initial vector transduction efficiency while cellular responses impact long-term outcomes of gene transfer. Measuring humoral responses to AAV vectors has utilized in vitro neutralizing antibody assays and transfer of seropositive serum to immunodeficient mice. Overcoming antibodies using CD20 inhibitors, plasmapheresis, altering route of delivery and using different capsids have been explored. CTL responses were measured using in vitro and in vivo models. In in vitro assays expansion of antigen-specific T-cells as well as cytotoxicity toward AAV transduced cells can be shown. Many groups have successfully mimicked antigen-specific T-cell proliferation, but actual transgene level reduction and parameters of cytotoxicity toward transduced target cells have only been shown in one model. The model utilized adoptive transfer of capsid-specific in vitro expanded T-cells isolated from immunized mice with LPS as an adjuvant. Finally, the development of immune tolerance to AAV vectors by enriching regulatory T-cells as well as modulating the response pharmacologically has also

  17. Neuroimaging biomarkers to predict treatment response in schizophrenia: the end of 30 years of solitude?

    PubMed

    Dazzan, Paola

    2014-12-01

    Studies that have used structural magnetic resonance imaging (MRI) suggest that individuals with psychoses have brain alterations, particularly in frontal and temporal cortices, and in the white matter tracts that connect them. Furthermore, these studies suggest that brain alterations may be particularly prominent, already at illness onset, in those individuals more likely to have poorer outcomes (eg, higher number of hospital admissions, and poorer symptom remission, level of functioning, and response to the first treatment with antipsychotic drugs). The fact that, even when present, these brain alterations are subtle and distributed in nature, has limited, until now, the utility of MRI in the clinical management of these disorders. More recently, MRI approaches, such as machine learning, have suggested that these neuroanatomical biomarkers can be used for direct clinical benefits. For example, using support vector machine, MRI data obtained at illness onset have been used to predict, with significant accuracy, whether a specific individual is likely to experience a remission of symptoms later on in the course of the illness. Taken together, this evidence suggests that validated, strong neuroanatomical markers could be used not only to inform tailored intervention strategies in a single individual, but also to allow patient stratification in clinical trials for new treatments.

  18. Appraisals and Responses to Experimental Symptom Analogues in Clinical and Nonclinical Individuals With Psychotic Experiences

    PubMed Central

    Ward, Thomas A.; Gaynor, Keith J.; Hunter, Mike D.; Woodruff, Peter W. R.; Garety, Philippa A.; Peters, Emmanuelle R.

    2014-01-01

    Objective: Cognitive models of psychosis suggest that anomalous experiences alone do not always lead to clinical psychosis, with appraisals and responses to experiences being central to understanding the transition to “need for care”. Methods: The appraisals and response styles of Clinical (C; n = 28) and Nonclinical (NC; n = 34) individuals with psychotic experiences were compared following experimental analogues of thought interference (Cards Task) and auditory hallucinations (Virtual Acoustic Space Paradigm). Results: The groups were matched in terms of their psychotic experiences. As predicted, the C group scored higher than the NC group on maladaptive appraisals following both tasks, rated the experience as more personally significant, and was more likely to incorporate the experimental setup into their ongoing experiences. The C group also appraised the Cards Task as more salient, distressing, and threatening; this group scored higher on maladaptive—and lower on adaptive—response styles, than the NC group on both tasks. Conclusions: The findings are consistent with cognitive models of psychosis, with maladaptive appraisals and response styles characterizing the C group only. Clinical applications of both tasks are suggested to facilitate the identification and modification of maladaptive appraisals. PMID:23858493

  19. Prediction and set-dependent scaling of early postural responses in cerebellar patients.

    PubMed

    Timmann, D; Horak, F B

    1997-02-01

    We reported previously that patients with cerebellar deficits were unable to scale the magnitude of their early automatic postural responses to the predicted amplitudes of surface translations based on central set from prior experience. The present study investigated whether this deficit in set-dependent amplitude scaling was based predominantly on the cerebellar patient's disability (i) to predict perturbation amplitudes on the basis of prior experience, (ii) to scale the gain or magnitude of upcoming postural responses or (iii) to habituate postural responses. The increase in size of the early postural response when a larger than actual platform amplitude was expected and decrease when a smaller one was expected was defined as a measure of set-dependent amplitude prediction. The suppression of the postural response when the same platform velocity was repeated was used as a measure of habituation. The correlation between the size of early postural responses and platform amplitudes when presented serially, but not randomly, tested the ability to scale the gain of postural responses based on prior experience. Results show that although cerebellar patients could predict perturbation amplitudes based on prior experience, they could not use this prediction to modify precisely the gain of responses. The ability to habituate the magnitude of postural responses was not affected by cerebellar lesions. Thus, the cerebellum might not be critical for predicting upcoming events or for habituating to repeated postural stimuli, although it is important for accurate tuning of response gain based on prediction.

  20. Predicting response to epidermal growth factor receptor-targeted therapy in colorectal cancer.

    PubMed

    Adams, Richard; Maughan, Tim

    2007-04-01

    The discovery over 20 years ago by the Nobel Laureate Stanley Cohen of epidermal growth factor and its receptor, followed by the recognition that this receptor is overexpressed in multiple cancer types, has been of phenomenal significance. From these events the 'Holy Grail' of targeted therapy has looked increasingly realistic. Over the last 5 years this work has come of age with the licensing of multiple agents targeting this important mitogenic pathway in multiple tumor types. However, these agents and the technology behind them, while impressive, have resulted in lower clinical response rates than anticipated. In this review we will focus on the epidermal growth factor receptor-targeted therapies in colorectal cancer, why our expectations from these therapies have not yet been fulfilled and how we may predict those cancers that are likely to respond or be resistant to these therapies through a greater appreciation of the intricacy, diversity and dynamism of cellular signaling mechanisms.

  1. HIV-specific Th2 and Th17 responses predict HIV vaccine protection efficacy

    PubMed Central

    Sauce, Delphine; Gorochov, Guy; Larsen, Martin

    2016-01-01

    Understanding the factors that delineate the efficacy of T-cell responses towards pathogens is crucial for our ability to develop potent therapies and vaccines against infectious diseases, such as HIV. Here we show that a recently developed analytical tool, the polyfunctionality index (PI), not only enables prediction of protection after vaccination against HIV, but also allows identification of the immunological pathways involved. Our data suggest that induction of a synergistic network of CD4+ T-cell subsets is implicated in HIV-protection. Accordingly, we provide evidence that vaccine-induced protection is associated with CD40L expressing Th2 cells and IL-2 secreting Th17 cells. In conclusion, we describe a novel approach that is widely applicable and readily interpretable in a biological and clinical context. This approach could greatly impact our fundamental understanding of T-cell immunity as well as the search for effective vaccines. PMID:27324186

  2. Clinical Data Predict Neurodevelopmental Outcome Better than Head Ultrasound in Extremely Low Birth Weight Infants

    PubMed Central

    Broitman, Eduardo; Ambalavanan, Namasivayam; Higgins, Rosemary D.; Vohr, Betty R.; Das, Abhik; Bhaskar, Brinda; Murray, Kennan; Hintz, Susan R.; Carlo, Waldemar A.

    2007-01-01

    Objective To determine the relative contribution of clinical data versus head ultrasound (HUS) in predicting neurodevelopmental impairment (NDI) in extremely low birth weight (ELBW) infants. Study design 2103 ELBW infants (<1000g) admitted to a National Institute of Child Health and Human Development Neonatal Research Network center who had a HUS within the first 28 days, a repeat one around 36 weeks’ post-menstrual age, and neurodevelopmental assessment at 18–22 months corrected age were selected. Multivariate logistic regression models were developed using clinical and/or HUS variables. The primary outcome was the predictive abilities of the HUS done before 28 days after birth and closer to 36 weeks post-menstrual age, either alone or in combination with “Early” and “Late” clinical variables. Results Models using clinical variables alone predicted NDI better than models with only HUS variables at both 28 days and 36 weeks (both p < 0.001), and addition of the HUS data did not improve prediction. NDI was absent in 30% and 28% of the infants with grade IV intracranial hemorrhage or periventricular leukomalacia, respectively, but was present in 39% of the infants with a normal head ultrasound. Conclusions Clinical models were better than head ultrasound models in predicting neurodevelopment. PMID:17961693

  3. Response to clozapine in a clinically identifiable subtype of schizophrenia

    PubMed Central

    Butcher, Nancy J.; Fung, Wai Lun Alan; Fitzpatrick, Laura; Guna, Alina; Andrade, Danielle M.; Lang, Anthony E.; Chow, Eva W. C.; Bassett, Anne S.

    2015-01-01

    Background Genetic testing in psychiatry promises to improve patient care through advances in personalised medicine. However, there are few clinically relevant examples. Aims To determine whether patients with a well-established genetic subtype of schizophrenia show a different response profile to the antipsychotic clozapine than those with idiopathic schizophrenia. Method We retrospectively studied the long-term safety and efficacy of clozapine in 40 adults with schizophrenia, half with a 22q11.2 deletion (22q11.2DS group) and half matched for age and clinical severity but molecularly confirmed to have no pathogenic copy number variant (idiopathic group). Results Both groups showed similar clinical improvement and significant reductions in hospitalisations, achieved at a lower median dose for those in the 22q11.2DS group. Most common side-effects were similarly prevalent between the two groups, however, half of the 22q11.2DS group experienced at least one rare serious adverse event compared with none of the idiopathic group. Many were successfully retried on clozapine. Conclusions Individuals with 22q11.2DS-schizophrenia respond as well to clozapine treatment as those with other forms of schizophrenia, but may represent a disproportionate number of those with serious adverse events, primarily seizures. Lower doses and prophylactic (for example anticonvulsant) management strategies can help ameliorate side-effect risks. This first systematic evaluation of antipsychotic response in a genetic subtype of schizophrenia provides a proof-of-principle for personalised medicine and supports the utility of clinical genetic testing in schizophrenia. PMID:25745132

  4. Using clinical information to make individualized prognostic predictions in people at ultra high risk for psychosis.

    PubMed

    Mechelli, Andrea; Lin, Ashleigh; Wood, Stephen; McGorry, Patrick; Amminger, Paul; Tognin, Stefania; McGuire, Philip; Young, Jonathan; Nelson, Barnaby; Yung, Alison

    2016-12-04

    Recent studies have reported an association between psychopathology and subsequent clinical and functional outcomes in people at ultra-high risk (UHR) for psychosis. This has led to the suggestion that psychopathological information could be used to make prognostic predictions in this population. However, because the current literature is based on inferences at group level, the translational value of the findings for everyday clinical practice is unclear. Here we examined whether psychopathological information could be used to make individualized predictions about clinical and functional outcomes in people at UHR. Participants included 416 people at UHR followed prospectively at the Personal Assessment and Crisis Evaluation (PACE) Clinic in Melbourne, Australia. The data were analysed using Support Vector Machine (SVM), a supervised machine learning technique that allows inferences at the individual level. SVM predicted transition to psychosis with a specificity of 60.6%, a sensitivity of 68.6% and an accuracy of 64.6% (p<0.001). In addition, SVM predicted functioning with a specificity of 62.5%, a sensitivity of 62.5% and an accuracy of 62.5% (p=0.008). Prediction of transition was driven by disorder of thought content, attenuated positive symptoms and functioning, whereas functioning was best predicted by attention disturbances, anhedonia-asociality and disorder of thought content. These results indicate that psychopathological information allows individualized prognostic predictions with statistically significant accuracy. However, this level of accuracy may not be sufficient for clinical translation in real-world clinical practice. Accuracy might be improved by combining psychopathological information with other types of data using a multivariate machine learning framework.

  5. Predicting the influence of the electronic health record on clinical coding practice in hospitals.

    PubMed

    Robinson, Kerin; Shepheard, Jennie

    2004-01-01

    The key drivers of change to clinical coding practice are identified and examined, and a major shift is predicted. The traditional purposes of the coding function have been the provision of data for research and epidemiology, in morbidity data reporting and, latterly, for casemix-based funding. It is contended that, as the development of electronic health records progresses, the need for an embedded nomenclature will force major change in clinical coding practice. Clinical coders must become expert in information technology and analysis, change their work practices, and become an integral part of the clinical team.

  6. Predictive capacity of risk assessment scales and clinical judgment for pressure ulcers: a meta-analysis.

    PubMed

    García-Fernández, Francisco Pedro; Pancorbo-Hidalgo, Pedro L; Agreda, J Javier Soldevilla

    2014-01-01

    A systematic review with meta-analysis was completed to determine the capacity of risk assessment scales and nurses' clinical judgment to predict pressure ulcer (PU) development. Electronic databases were searched for prospective studies on the validity and predictive capacity of PUs risk assessment scales published between 1962 and 2010 in English, Spanish, Portuguese, Korean, German, and Greek. We excluded gray literature sources, integrative review articles, and retrospective or cross-sectional studies. The methodological quality of the studies was assessed according to the guidelines of the Critical Appraisal Skills Program. Predictive capacity was measured as relative risk (RR) with 95% confidence intervals. When 2 or more valid original studies were found, a meta-analysis was conducted using a random-effect model and sensitivity analysis. We identified 57 studies, including 31 that included a validation study. We also retrieved 4 studies that tested clinical judgment as a risk prediction factor. Meta-analysis produced the following pooled predictive capacity indicators: Braden (RR = 4.26); Norton (RR = 3.69); Waterlow (RR = 2.66); Cubbin-Jackson (RR = 8.63); EMINA (RR = 6.17); Pressure Sore Predictor Scale (RR = 21.4); and clinical judgment (RR = 1.89). Pooled analysis of 11 studies found adequate risk prediction capacity in various clinical settings; the Braden, Norton, EMINA (mEntal state, Mobility, Incontinence, Nutrition, Activity), Waterlow, and Cubbin-Jackson scales showed the highest predictive capacity. The clinical judgment of nurses was found to achieve inadequate predictive capacity when used alone, and should be used in combination with a validated scale.

  7. Interactive Voice/Web Response System in clinical research

    PubMed Central

    Ruikar, Vrishabhsagar

    2016-01-01

    Emerging technologies in computer and telecommunication industry has eased the access to computer through telephone. An Interactive Voice/Web Response System (IxRS) is one of the user friendly systems for end users, with complex and tailored programs at its backend. The backend programs are specially tailored for easy understanding of users. Clinical research industry has experienced revolution in methodologies of data capture with time. Different systems have evolved toward emerging modern technologies and tools in couple of decades from past, for example, Electronic Data Capture, IxRS, electronic patient reported outcomes, etc. PMID:26952178

  8. Interactive Voice/Web Response System in clinical research.

    PubMed

    Ruikar, Vrishabhsagar

    2016-01-01

    Emerging technologies in computer and telecommunication industry has eased the access to computer through telephone. An Interactive Voice/Web Response System (IxRS) is one of the user friendly systems for end users, with complex and tailored programs at its backend. The backend programs are specially tailored for easy understanding of users. Clinical research industry has experienced revolution in methodologies of data capture with time. Different systems have evolved toward emerging modern technologies and tools in couple of decades from past, for example, Electronic Data Capture, IxRS, electronic patient reported outcomes, etc.

  9. An Integrated Analysis of Heterogeneous Drug Responses in Acute Myeloid Leukemia That Enables the Discovery of Predictive Biomarkers.

    PubMed

    Chen, Weihsu C; Yuan, Julie S; Xing, Yan; Mitchell, Amanda; Mbong, Nathan; Popescu, Andreea C; McLeod, Jessica; Gerhard, Gitte; Kennedy, James A; Bogdanoski, Goce; Lauriault, Stevan; Perdu, Sofie; Merkulova, Yulia; Minden, Mark D; Hogge, Donna E; Guidos, Cynthia; Dick, John E; Wang, Jean C Y

    2016-03-01

    Many promising new cancer drugs proceed through preclinical testing and early-phase trials only to fail in late-stage clinical testing. Thus, improved models that better predict survival outcomes and enable the development of biomarkers are needed to identify patients most likely to respond to and benefit from therapy. Here, we describe a comprehensive approach in which we incorporated biobanking, xenografting, and multiplexed phospho-flow (PF) cytometric profiling to study drug response and identify predictive biomarkers in acute myeloid leukemia (AML) patients. To test the efficacy of our approach, we evaluated the investigational JAK2 inhibitor fedratinib (FED) in 64 patient samples. FED robustly reduced leukemia in mouse xenograft models in 59% of cases and was also effective in limiting the protumorigenic activity of leukemia stem cells as shown by serial transplantation assays. In parallel, PF profiling identified FED-mediated reduction in phospho-STAT5 (pSTAT5) levels as a predictive biomarker of in vivo drug response with high specificity (92%) and strong positive predictive value (93%). Unexpectedly, another JAK inhibitor, ruxolitinib (RUX), was ineffective in 8 of 10 FED-responsive samples. Notably, this outcome could be predicted by the status of pSTAT5 signaling, which was unaffected by RUX treatment. Consistent with this observed discrepancy, PF analysis revealed that FED exerted its effects through multiple JAK2-independent mechanisms. Collectively, this work establishes an integrated approach for testing novel anticancer agents that captures the inherent variability of response caused by disease heterogeneity and in parallel, facilitates the identification of predictive biomarkers that can help stratify patients into appropriate clinical trials.

  10. Neural response imaging (NRI) cochlear mapping: prospects for clinical application.

    PubMed

    Arnold, L; Lindsey, P; Hacking, C; Boyle, P

    2007-12-01

    The objective of the study was to investigate the potential for clinical application of neural response imaging (NRI) cochlear mapping. Cochlear mapping was performed at each fitting session up to at least six months following initial fitting. Stimulation was delivered to one electrode site. NRI was recorded from each of the remaining sites. The procedure was repeated for apical, medial and basal stimulation sites, stimulating at subjective threshold and most comfortable levels. Responses were obtained in five out of six subjects and are discussed in terms of: reproducibility, quality, changes over time. Cochlear mapping provided repeatable data that gave interesting insights into the implanted cochlea. Further work is required to determine whether this approach could contribute to programme optimisation.

  11. Rapid-Response Impulsivity: Definitions, Measurement Issues, and Clinical Implications

    PubMed Central

    Hamilton, Kristen R.; Littlefield, Andrew K.; Anastasio, Noelle C.; Cunningham, Kathryn A.; Fink, Latham H.; Wing, Victoria C.; Mathias, Charles W.; Lane, Scott D.; Schutz, Christian; Swann, Alan C.; Lejuez, C.W.; Clark, Luke; Moeller, F. Gerard; Potenza, Marc N.

    2015-01-01

    Impulsivity is a multi-faceted construct that is a core feature of multiple psychiatric conditions and personality disorders. However, progress in understanding and treating impulsivity in the context of these conditions is limited by a lack of precision and consistency in its definition and assessment. Rapid-response-impulsivity (RRI) represents a tendency toward immediate action that occurs with diminished forethought and is out of context with the present demands of the environment. Experts from the International Society for Research on Impulsivity (InSRI) met to discuss and evaluate RRI-measures in terms of reliability, sensitivity, and validity with the goal of helping researchers and clinicians make informed decisions about the use and interpretation of findings from RRI-measures. Their recommendations are described in this manuscript. Commonly-used clinical and preclinical RRI-tasks are described, and considerations are provided to guide task selection. Tasks measuring two conceptually and neurobiologically distinct types of RRI, “refraining from action initiation” (RAI) and “stopping an ongoing action” (SOA) are described. RAI and SOA-tasks capture distinct aspects of RRI that may relate to distinct clinical outcomes. The InSRI group recommends that: 1) selection of RRI-measures should be informed by careful consideration of the strengths, limitations, and practical considerations of the available measures; 2) researchers use both RAI and SOA tasks in RRI studies to allow for direct comparison of RRI types and examination of their associations with clinically relevant measures; and, 3) similar considerations should be made for human and non-human studies in an effort to harmonize and integrate pre-clinical and clinical research. PMID:25867840

  12. Use of clinical movement screening tests to predict injury in sport.

    PubMed

    Chimera, Nicole J; Warren, Meghan

    2016-04-18

    Clinical movement screening tests are gaining popularity as a means to determine injury risk and to implement training programs to prevent sport injury. While these screens are being used readily in the clinical field, it is only recently that some of these have started to gain attention from a research perspective. This limits applicability and poses questions to the validity, and in some cases the reliability, of the clinical movement tests as they relate to injury prediction, intervention, and prevention. This editorial will review the following clinical movement screening tests: Functional Movement Screen™, Star Excursion Balance Test, Y Balance Test, Drop Jump Screening Test, Landing Error Scoring System, and the Tuck Jump Analysis in regards to test administration, reliability, validity, factors that affect test performance, intervention programs, and usefulness for injury prediction. It is important to review the aforementioned factors for each of these clinical screening tests as this may help clinicians interpret the current body of literature. While each of these screening tests were developed by clinicians based on what appears to be clinical practice, this paper brings to light that this is a need for collaboration between clinicians and researchers to ensure validity of clinically meaningful tests so that they are used appropriately in future clinical practice. Further, this editorial may help to identify where the research is lacking and, thus, drive future research questions in regards to applicability and appropriateness of clinical movement screening tools.

  13. Use of clinical movement screening tests to predict injury in sport

    PubMed Central

    Chimera, Nicole J; Warren, Meghan

    2016-01-01

    Clinical movement screening tests are gaining popularity as a means to determine injury risk and to implement training programs to prevent sport injury. While these screens are being used readily in the clinical field, it is only recently that some of these have started to gain attention from a research perspective. This limits applicability and poses questions to the validity, and in some cases the reliability, of the clinical movement tests as they relate to injury prediction, intervention, and prevention. This editorial will review the following clinical movement screening tests: Functional Movement Screen™, Star Excursion Balance Test, Y Balance Test, Drop Jump Screening Test, Landing Error Scoring System, and the Tuck Jump Analysis in regards to test administration, reliability, validity, factors that affect test performance, intervention programs, and usefulness for injury prediction. It is important to review the aforementioned factors for each of these clinical screening tests as this may help clinicians interpret the current body of literature. While each of these screening tests were developed by clinicians based on what appears to be clinical practice, this paper brings to light that this is a need for collaboration between clinicians and researchers to ensure validity of clinically meaningful tests so that they are used appropriately in future clinical practice. Further, this editorial may help to identify where the research is lacking and, thus, drive future research questions in regards to applicability and appropriateness of clinical movement screening tools. PMID:27114928

  14. Predicting Semantic Changes in Abstraction in Tutor Responses to Students

    ERIC Educational Resources Information Center

    Lipschultz, Michael; Litman, Diane; Katz, Sandra; Albacete, Patricia; Jordan, Pamela

    2014-01-01

    Post-problem reflective tutorial dialogues between human tutors and students are examined to predict when the tutor changed the level of abstraction from the student's preceding turn (i.e., used more general terms or more specific terms); such changes correlate with learning. Prior work examined lexical changes in abstraction. In this work, we…

  15. Mechanistic Modeling Framework for Predicting Extreme Battery Response

    SciTech Connect

    Geller, Anthony S.

    2014-11-01

    The objectives of this project are to address the root cause implications of thermal runaway of Li-ion batteries by delivering a software architecture solution that can lead to the development of predictive mechanisms that are based on identification of species.

  16. Predicting fertilizer nitrogen response in corn following alfalfa

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Correct prediction and application of alfalfa nitrogen (N) credits to first-year corn can reduce fertilizer N costs for growers, reduce over-application of N, and reduce the potential for water contamination. For decades, researchers have found that first-year corn following alfalfa often requires n...

  17. High-Level Clonal FGFR Amplification and Response to FGFR Inhibition in a Translational Clinical Trial

    PubMed Central

    Babina, Irina S.; Herrera-Abreu, Maria Teresa; Tarazona, Noelia; Peckitt, Clare; Kilgour, Elaine; Smith, Neil R.; Geh, Catherine; Rooney, Claire; Cutts, Ros; Campbell, James; Ning, Jian; Fenwick, Kerry; Swain, Amanda; Brown, Gina; Chua, Sue; Thomas, Anne; Johnston, Stephen R.D.; Ajaz, Mazhar; Sumpter, Katherine; Gillbanks, Angela; Watkins, David; Chau, Ian; Popat, Sanjay; Cunningham, David; Turner, Nicholas C.

    2017-01-01

    FGFR1 and FGFR2 are amplified in many tumor types, yet what determines response to FGFR inhibition in amplified cancers is unknown. In a translational clinical trial, we show that gastric cancers with high-level clonal FGFR2 amplification have a high response rate to the selective FGFR inhibitor AZD4547, whereas cancers with subclonal or low-level amplification did not respond. Using cell lines and patient-derived xenograft models, we show that high-level FGFR2 amplification initiates a distinct oncogene addiction phenotype, characterized by FGFR2-mediated transactivation of alternative receptor kinases, bringing PI3K/mTOR signaling under FGFR control. Signaling in low-level FGFR1-amplified cancers is more restricted to MAPK signaling, limiting sensitivity to FGFR inhibition. Finally, we show that circulating tumor DNA screening can identify high-level clonally amplified cancers. Our data provide a mechanistic understanding of the distinct pattern of oncogene addiction seen in highly amplified cancers and demonstrate the importance of clonality in predicting response to targeted therapy. Significance Robust single-agent response to FGFR inhibition is seen only in high-level FGFR-amplified cancers, with copy-number level dictating response to FGFR inhibition in vitro, in vivo, and in the clinic. High-level amplification of FGFR2 is relatively rare in gastric and breast cancers, and we show that screening for amplification in circulating tumor DNA may present a viable strategy to screen patients. PMID:27179038

  18. Sadness prediction and response: effects of age and agreeableness.

    PubMed

    Pearman, Ann; Andreoletti, Carrie; Isaacowitz, Derek M

    2010-04-01

    Research has suggested that both age and personality play a role in emotional experience and regulation, but these variables have not been considered together to determine the relative contribution of each. This study simultaneously examined age and agreeableness differences in the experience of sad stimuli. Participants were 46 younger adults (age, M = 22.04 years, SD = 5.41 years) and 48 older adults (age, M = 74.23, SD = 7.82 years). Participants were asked to predict how sad stimuli (i.e., sad photos) would make them feel and were then measured on their actual reaction to the stimuli (reactivity) as well as on their emotional recovery. Agreeableness, but not age, was related to predicted levels of sadness, such that the more agreeable, the higher the predicted sadness (beta = 0.37). In contrast to expectations, prediction accuracy was not related to age or agreeableness. For emotional reactivity, agreeableness (beta = 0.16), but not age, was related to reactivity to sad stimuli (i.e., more agreeable, higher reactivity). Finally, age (beta = 0.14) was significantly related to emotional recovery such that the older adults reported lower levels of sadness at posttest than did the younger adults. Similarly, people who were more agreeable also reported better emotional recovery (beta = 0.15). These relationships were not affected by depression or pretest sadness ratings. Overall, these findings suggest distinct roles for age and agreeableness in predicting different components of the emotion regulation process. An individual with advanced age, high levels of agreeableness, or both may be well-positioned for resilience throughout the emotion regulation process.

  19. Interleukin-28b CC genotype predicts early treatment response and CT/TT genotypes predicts non-response in patients infected with HCV genotype 3.

    PubMed

    Gupta, Abhishak Chander; Trehanpati, Nirupma; Sukriti, Sukriti; Hissar, Syed; Midha, Vandana; Sood, Ajit; Sarin, Shiv K

    2014-04-01

    Response to antiviral therapy for hepatitis C virus (HCV) depends upon the genotype and host immune response. IL28b gene mutations have been shown to modulate host antiviral immune response against genotype 1. However, the predictive value of IL28b polymorphism in genotype 3 HCV patients is largely unknown. The association of IL28b polymorphism with virological response was studied in 356 patients with genotype 3 chronic HCV undergoing treatment with peg-interferon and ribavirin and was compared with matched controls. IL28b genotyping followed by DNA sequencing was performed to identify the CC, CT, or TT genotypes. Two log reduction of HCV RNA at Day 7 (Quick Viral Response, QVR) and HCV RNA negativity at Day 28 (Rapid Viral Response, RVR) were analyzed with CC and non-CC genotypes in addition to other predictors of response. The associations of alleles with the response patterns were predicted. Sustained viral response was seen in 250 (70.2%) patients and the IL28b genotype CC/CT/TT distribution was 61.1%; 30.5%; and 8.4%, respectively. The non-CC genotypes were significantly higher in non-responders when compared to responders (67.6% vs. 38.9%, P < 0.001). Interestingly, the rapid viral response in responders was observed in 72.7% with the CC genotype and in 27.2% with the non-CC genotype (P < 0.001). Multivariate analysis showed CC genotype as an independent factor predicting the sustained viral response in patients infected with HCV genotype 3. In conclusion, the IL28b CT/TT genotype strongly correlates with treatment non-response in patients infected with HCV genotype 3 and CC genotype of IL28b is associated with higher quick viral response.

  20. Comparison of MCNPX and Geant4 proton energy deposition predictions for clinical use

    PubMed Central

    Titt, U.; Bednarz, B.; Paganetti, H.

    2012-01-01

    Several different Monte Carlo codes are currently being used at proton therapy centers to improve upon dose predictions over standard methods using analytical or semi-empirical dose algorithms. There is a need to better ascertain the differences between proton dose predictions from different available Monte Carlo codes. In this investigation Geant4 and MCNPX, the two most-utilized Monte Carlo codes for proton therapy applications, were used to predict energy deposition distributions in a variety of geometries, comprising simple water phantoms, water phantoms with complex inserts and in a voxelized geometry based on clinical CT data. The gamma analysis was used to evaluate the differences of the predictions between the codes. The results show that in the all cases the agreement was better than clinical acceptance criteria. PMID:22996039

  1. Label Propagation Prediction of Drug-Drug Interactions Based on Clinical Side Effects.

    PubMed

    Zhang, Ping; Wang, Fei; Hu, Jianying; Sorrentino, Robert

    2015-07-21

    Drug-drug interaction (DDI) is an important topic for public health, and thus attracts attention from both academia and industry. Here we hypothesize that clinical side effects (SEs) provide a human phenotypic profile and can be translated into the development of computational models for predicting adverse DDIs. We propose an integrative label propagation framework to predict DDIs by integrating SEs extracted from package inserts of prescription drugs, SEs extracted from FDA Adverse Event Reporting System, and chemical structures from PubChem. Experimental results based on hold-out validation demonstrated the effectiveness of the proposed algorithm. In addition, the new algorithm also ranked drug information sources based on their contributions to the prediction, thus not only confirming that SEs are important features for DDI prediction but also paving the way for building more reliable DDI prediction models by prioritizing multiple data sources. By applying the proposed algorithm to 1,626 small-molecule drugs which have one or more SE profiles, we obtained 145,068 predicted DDIs. The predicted DDIs will help clinicians to avoid hazardous drug interactions in their prescriptions and will aid pharmaceutical companies to design large-scale clinical trial by assessing potentially hazardous drug combinations. All data sets and predicted DDIs are available at http://astro.temple.edu/~tua87106/ddi.html.

  2. Predicting future clinical changes of MCI patients using longitudinal and multimodal biomarkers.

    PubMed

    Zhang, Daoqiang; Shen, Dinggang

    2012-01-01

    Accurate prediction of clinical changes of mild cognitive impairment (MCI) patients, including both qualitative change (i.e., conversion to Alzheimer's disease (AD)) and quantitative change (i.e., cognitive scores) at future time points, is important for early diagnosis of AD and for monitoring the disease progression. In this paper, we propose to predict future clinical changes of MCI patients by using both baseline and longitudinal multimodality data. To do this, we first develop a longitudinal feature selection method to jointly select brain regions across multiple time points for each modality. Specifically, for each time point, we train a sparse linear regression model by using the imaging data and the corresponding clinical scores, with an extra 'group regularization' to group the weights corresponding to the same brain region across multiple time points together and to allow for selection of brain regions based on the strength of multiple time points jointly. Then, to further reflect the longitudinal changes on the selected brain regions, we extract a set of longitudinal features from the original baseline and longitudinal data. Finally, we combine all features on the selected brain regions, from different modalities, for prediction by using our previously proposed multi-kernel SVM. We validate our method on 88 ADNI MCI subjects, with both MRI and FDG-PET data and the corresponding clinical scores (i.e., MMSE and ADAS-Cog) at 5 different time points. We first predict the clinical scores (MMSE and ADAS-Cog) at 24-month by using the multimodality data at previous time points, and then predict the conversion of MCI to AD by using the multimodality data at time points which are at least 6-month ahead of the conversion. The results on both sets of experiments show that our proposed method can achieve better performance in predicting future clinical changes of MCI patients than the conventional methods.

  3. Childhood trauma predicts antidepressant response in adults with major depression: data from the randomized international study to predict optimized treatment for depression

    PubMed Central

    Williams, L M; Debattista, C; Duchemin, A-M; Schatzberg, A F; Nemeroff, C B

    2016-01-01

    Few reliable predictors indicate which depressed individuals respond to antidepressants. Several studies suggest that a history of early-life trauma predicts poorer response to antidepressant therapy but results are variable and limited in adults. The major goal of the present study was to evaluate the role of early-life trauma in predicting acute response outcomes to antidepressants in a large sample of well-characterized patients with major depressive disorder (MDD). The international Study to Predict Optimized Treatment for Depression (iSPOT-D) is a randomized clinical trial with enrollment from December 2008 to January 2012 at eight academic and nine private clinical settings in five countries. Patients (n=1008) meeting DSM-IV criteria for MDD and 336 matched healthy controls comprised the study sample. Six participants withdrew due to serious adverse events. Randomization was to 8 weeks of treatment with escitalopram, sertraline or venlafaxine with dosage adjusted by the participant's treating clinician per routine clinical practice. Exposure to 18 types of traumatic events before the age of 18 was assessed using the Early-Life Stress Questionnaire. Impact of early-life stressors—overall trauma ‘load' and specific type of abuse—on treatment outcomes measures: response: (⩾50% improvement on the 17-item Hamilton Rating Scale for Depression, HRSD17 or on the 16-item Quick Inventory of Depressive Symptomatology—Self-Rated, QIDS_SR16) and remission (score ⩽7 on the HRSD17 and ⩽5 on the QIDS_SR16). Trauma prevalence in MDD was compared with controls. Depressed participants were significantly more likely to report early-life stress than controls; 62.5% of MDD participants reported more than two traumatic events compared with 28.4% of controls. The higher rate of early-life trauma was most apparent for experiences of interpersonal violation (emotional, sexual and physical abuses). Abuse and notably abuse occurring at ⩽7 years of age predicted poorer

  4. Childhood trauma predicts antidepressant response in adults with major depression: data from the randomized international study to predict optimized treatment for depression.

    PubMed

    Williams, L M; Debattista, C; Duchemin, A-M; Schatzberg, A F; Nemeroff, C B

    2016-05-03

    Few reliable predictors indicate which depressed individuals respond to antidepressants. Several studies suggest that a history of early-life trauma predicts poorer response to antidepressant therapy but results are variable and limited in adults. The major goal of the present study was to evaluate the role of early-life trauma in predicting acute response outcomes to antidepressants in a large sample of well-characterized patients with major depressive disorder (MDD). The international Study to Predict Optimized Treatment for Depression (iSPOT-D) is a randomized clinical trial with enrollment from December 2008 to January 2012 at eight academic and nine private clinical settings in five countries. Patients (n=1008) meeting DSM-IV criteria for MDD and 336 matched healthy controls comprised the study sample. Six participants withdrew due to serious adverse events. Randomization was to 8 weeks of treatment with escitalopram, sertraline or venlafaxine with dosage adjusted by the participant's treating clinician per routine clinical practice. Exposure to 18 types of traumatic events before the age of 18 was assessed using the Early-Life Stress Questionnaire. Impact of early-life stressors-overall trauma 'load' and specific type of abuse-on treatment outcomes measures: response: (⩾50% improvement on the 17-item Hamilton Rating Scale for Depression, HRSD17 or on the 16-item Quick Inventory of Depressive Symptomatology-Self-Rated, QIDS_SR16) and remission (score ⩽7 on the HRSD17 and ⩽5 on the QIDS_SR16). Trauma prevalence in MDD was compared with controls. Depressed participants were significantly more likely to report early-life stress than controls; 62.5% of MDD participants reported more than two traumatic events compared with 28.4% of controls. The higher rate of early-life trauma was most apparent for experiences of interpersonal violation (emotional, sexual and physical abuses). Abuse and notably abuse occurring at ⩽7 years of age predicted poorer outcomes

  5. Clinical iron deficiency disturbs normal human responses to hypoxia

    PubMed Central

    Frise, Matthew C.; Cheng, Hung-Yuan; Nickol, Annabel H.; Curtis, M. Kate; Pollard, Karen A.; Roberts, David J.; Ratcliffe, Peter J.; Dorrington, Keith L.; Robbins, Peter A.

    2016-01-01

    BACKGROUND. Iron bioavailability has been identified as a factor that influences cellular hypoxia sensing, putatively via an action on the hypoxia-inducible factor (HIF) pathway. We therefore hypothesized that clinical iron deficiency would disturb integrated human responses to hypoxia. METHODS. We performed a prospective, controlled, observational study of the effects of iron status on hypoxic pulmonary hypertension. Individuals with absolute iron deficiency (ID) and an iron-replete (IR) control group were exposed to two 6-hour periods of isocapnic hypoxia. The second hypoxic exposure was preceded by i.v. infusion of iron. Pulmonary artery systolic pressure (PASP) was serially assessed with Doppler echocardiography. RESULTS. Thirteen ID individuals completed the study and were age- and sex-matched with controls. PASP did not differ by group or study day before each hypoxic exposure. During the first 6-hour hypoxic exposure, the rise in PASP was 6.2 mmHg greater in the ID group (absolute rises 16.1 and 10.7 mmHg, respectively; 95% CI for difference, 2.7–9.7 mmHg, P = 0.001). Intravenous iron attenuated the PASP rise in both groups; however, the effect was greater in ID participants than in controls (absolute reductions 11.1 and 6.8 mmHg, respectively; 95% CI for difference in change, –8.3 to –0.3 mmHg, P = 0.035). Serum erythropoietin responses to hypoxia also differed between groups. CONCLUSION. Clinical iron deficiency disturbs normal responses to hypoxia, as evidenced by exaggerated hypoxic pulmonary hypertension that is reversed by subsequent iron administration. Disturbed hypoxia sensing and signaling provides a mechanism through which iron deficiency may be detrimental to human health. TRIAL REGISTRATION. ClinicalTrials.gov (NCT01847352). FUNDING. M.C. Frise is the recipient of a British Heart Foundation Clinical Research Training Fellowship (FS/14/48/30828). K.L. Dorrington is supported by the Dunhill Medical Trust (R178/1110). D.J. Roberts was

  6. A Probabilistic Reasoning Method for Predicting the Progression of Clinical Findings from Electronic Medical Records

    PubMed Central

    Goodwin, Travis; Harabagiu, Sanda M.

    2015-01-01

    In this paper, we present a probabilistic reasoning method capable of generating predictions of the progression of clinical findings (CFs) reported in the narrative portion of electronic medical records. This method benefits from a probabilistic knowledge representation made possible by a graphical model. The knowledge encoded in the graphical model considers not only the CFs extracted from the clinical narratives, but also their chronological ordering (CO) made possible by a temporal inference technique described in this paper. Our experiments indicate that the predictions about the progression of CFs achieve high performance given the COs induced from patient records. PMID:26306238

  7. MRE11 and ATM Expression Levels Predict Rectal Cancer Survival and Their Association with Radiotherapy Response

    PubMed Central

    Revoltar, Maxine; Lim, Stephanie H.; Tut, Thein-Ga; Abubakar, Askar; Henderson, Chris J.; Chua, Wei; Ng, Weng; Lee, Mark; De Souza, Paul; Morgan, Matthew; Lee, C. Soon; Shin, Joo-Shik

    2016-01-01

    Background Aberrant expression of DNA repair proteins is associated with poor survival in cancer patients. We investigated the combined expression of MRE11 and ATM as a predictive marker of response to radiotherapy in rectal cancer. Methods MRE11 and ATM expression were examined in tumor samples from 262 rectal cancer patients who underwent surgery for rectal cancer, including a sub-cohort of 54 patients who were treated with neoadjuvant radiotherapy. The relationship between expression of the two-protein panel and tumor regression grade (TRG) was assessed by Mann–Whitney U test and receiver operating characteristics area under curve (ROC-AUC) analysis. The association between expression of the two-protein panel and clinicopathologic variables and survival was examined by Kaplan-Meier methods and Cox regression analysis. Results A high score for two-protein combined expression in the tumor center (TC) was significantly associated with worse disease-free survival (DFS) (P = 0.035) and overall survival (OS) (P = 0.003) in the whole cohort, and with DFS (P = 0.028) and OS (P = 0.024) in the neoadjuvant subgroup (n = 54). In multivariate analysis, the two-protein combination panel (HR = 2.178, 95% CI 1.115–4.256, P = 0.023) and perineural invasion (HR = 2.183, 95% CI 1.222–3.899, P = 0.008) were significantly associated with DFS. Using ROC-AUC analysis of good versus poor histological tumor response among patients treated preoperatively with radiotherapy, the average ROC-AUC was 0.745 for the combined panel, 0.618 for ATM alone, and 0.711 for MRE11 alone. Conclusions The MRE11/ATM two-protein panel developed in this study may have clinical value as a predictive marker of tumor response to neoadjuvant radiotherapy, and a prognostic marker for disease-free and overall survival. PMID:27930716

  8. Predicting Treatment Response for Oppositional Defiant and Conduct Disorder Using Pre-treatment Adrenal and Gonadal Hormones.

    PubMed

    Shenk, Chad E; Dorn, Lorah D; Kolko, David J; Susman, Elizabeth J; Noll, Jennie G; Bukstein, Oscar G

    2012-12-01

    Variations in adrenal and gonadal hormone profiles have been linked to increased rates of oppositional defiant disorder (ODD) and conduct disorder (CD). These relationships suggest that certain hormone profiles may be related to how well children respond to psychological treatments for ODD and CD. The current study assessed whether pre-treatment profiles of adrenal and gonadal hormones predicted response to psychological treatment of ODD and CD. One hundred five children, 6 - 11 years old, participating in a randomized, clinical trial provided samples for cortisol, testosterone, dehydroepiandrosterone, and androstenedione. Diagnostic interviews of ODD and CD were administered up to three years post-treatment to track treatment response. Group-based trajectory modeling identified two trajectories of treatment response: 1) a High-response trajectory where children demonstrated lower rates of an ODD or CD diagnosis throughout follow-up, and 2) a Low-response trajectory where children demonstrated higher rates of an ODD or CD diagnosis throughout follow-up. Hierarchical logistic regression predicting treatment response demonstrated that children with higher pre-treatment concentrations of testosterone were four times more likely to be in the Low-response trajectory. No other significant relationship existed between pre-treatment hormone profiles and treatment response. These results suggest that higher concentrations of testosterone are related to how well children diagnosed with ODD or CD respond to psychological treatment over the course of three years.

  9. Predictive biomarkers in colorectal cancer: usage, validation, and design in clinical trials.

    PubMed

    Shi, Qian; Mandrekar, Sumithra J; Sargent, Daniel J

    2012-03-01

    As cancer treatment development has shifted its attention to targeted therapies, it is becoming increasingly important to provide tools for selecting the right treatment for an individual patient to achieve optimal clinical benefit. Biomarkers, identified and studied in the process of understanding the nature of the disease at the molecular pathogenesis level, have been increasingly recognized as a critical aspect in more accurate diagnosis, prognosis assessment, and therapeutic targeting. Predictive biomarkers, which can aid treatment decisions, require extensive data for validation. In this article, we discuss the definition, clinical usages, and more extensively the clinical trial designs for the validation of predictive biomarkers. Predictive biomarker validation methods can be broadly grouped into retrospective and prospective designs. Retrospective validation utilizes data from previously conducted prospective randomized controlled trials. Prospective designs include enrichment designs, treatment-by-marker interaction designs, marker-based strategy designs, and adaptive designs. We discuss each design with examples and provide comparisons of the advantages and disadvantages among the different designs. We conclude that the combination of scientific, clinical, statistical, ethical, and practical considerations provides guidance for the choice of the clinical trial design for validation of each proposed predictive biomarker.

  10. Prediction of clinical phenotypes in invasive breast carcinomas from the integration of radiomics and genomics data.

    PubMed

    Guo, Wentian; Li, Hui; Zhu, Yitan; Lan, Li; Yang, Shengjie; Drukker, Karen; Morris, Elizabeth; Burnside, Elizabeth; Whitman, Gary; Giger, Maryellen L; Ji, Yuan

    2015-10-01

    Genomic and radiomic imaging profiles of invasive breast carcinomas from The Cancer Genome Atlas and The Cancer Imaging Archive were integrated and a comprehensive analysis was conducted to predict clinical outcomes using the radiogenomic features. Variable selection via LASSO and logistic regression were used to select the most-predictive radiogenomic features for the clinical phenotypes, including pathological stage, lymph node metastasis, and status of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). Cross-validation with receiver operating characteristic (ROC) analysis was performed and the area under the ROC curve (AUC) was employed as the prediction metric. Higher AUCs were obtained in the prediction of pathological stage, ER, and PR status than for lymph node metastasis and HER2 status. Overall, the prediction performances by genomics alone, radiomics alone, and combined radiogenomics features showed statistically significant correlations with clinical outcomes; however, improvement on the prediction performance by combining genomics and radiomics data was not found to be statistically significant, most likely due to the small sample size of 91 cancer cases with 38 radiomic features and 144 genomic features.

  11. Prediction of clinical phenotypes in invasive breast carcinomas from the integration of radiomics and genomics data

    PubMed Central

    Guo, Wentian; Li, Hui; Zhu, Yitan; Lan, Li; Yang, Shengjie; Drukker, Karen; Morris, Elizabeth; Burnside, Elizabeth; Whitman, Gary; Giger, Maryellen L.; Ji, Yuan; TCGA Breast Phenotype Research Group

    2015-01-01

    Abstract. Genomic and radiomic imaging profiles of invasive breast carcinomas from The Cancer Genome Atlas and The Cancer Imaging Archive were integrated and a comprehensive analysis was conducted to predict clinical outcomes using the radiogenomic features. Variable selection via LASSO and logistic regression were used to select the most-predictive radiogenomic features for the clinical phenotypes, including pathological stage, lymph node metastasis, and status of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). Cross-validation with receiver operating characteristic (ROC) analysis was performed and the area under the ROC curve (AUC) was employed as the prediction metric. Higher AUCs were obtained in the prediction of pathological stage, ER, and PR status than for lymph node metastasis and HER2 status. Overall, the prediction performances by genomics alone, radiomics alone, and combined radiogenomics features showed statistically significant correlations with clinical outcomes; however, improvement on the prediction performance by combining genomics and radiomics data was not found to be statistically significant, most likely due to the small sample size of 91 cancer cases with 38 radiomic features and 144 genomic features. PMID:26835491

  12. MicroRNAs as tools to predict glucocorticoid response in inflammatory bowel diseases

    PubMed Central

    De Iudicibus, Sara; Lucafò, Marianna; Martelossi, Stefano; Pierobon, Chiara; Ventura, Alessandro; Decorti, Giuliana

    2013-01-01

    In spite of the introduction in therapy of highly effective biological agents, glucocorticoids (GCs) are still employed to induce remission in moderate to severe inflammatory bowel diseases (IBD), but considerable inter-individual differences in their efficacy and side effects have been reported. The effectiveness of these drugs is indeed very variable and side effects, particularly severe in pediatric patients, are common and often unpredictable: the understanding of the complex gene regulation mediated by GCs could shed light on the causes of this variability. In this context, microRNAs (miRNAs) represent a new and promising field of research. miRNAs are small non-coding RNA molecules that suppress gene expression at post-transcriptional level, and are fine-tuning regulators of diverse biological processes, including the development and function of the immune system, apoptosis, metabolism and inflammation. Emerging data have implicated the deregulated expression of certain miRNA networks in the pathogenesis of autoimmune and inflammatory diseases, such as IBD. There is a great interest in the identification of the role of miRNAs in the modulation of pharmacological response; however, the association between miRNA and GC response in patients with IBD has not yet been evaluated in a prospective clinical study. The identification of miRNAs differently expressed as a consequence of GC treatment in comparison to diagnosis, represents an important innovative approach that could be translated into clinical practice. In this review we highlight the altered regulation of proteins involved in GC molecular mechanism by miRNAs, and their potential role as molecular markers useful for predicting in advance GC response. PMID:24307788

  13. HIV-specific cytolytic CD4 T cell responses during acute HIV infection predict disease outcome

    PubMed Central

    Soghoian, Damien Z.; Jessen, Heiko; Flanders, Michael; Sierra-Davidson, Kailan; Cutler, Sam; Pertel, Thomas; Ranasinghe, Srinika; Lindqvist, Madelene; Davis, Isaiah; Lane, Kimberly; Rychert, Jenna; Rosenberg, Eric S.; Piechocka-Trocha, Alicja; Brass, Abraham L.; Brenchley, Jason M.; Walker, Bruce D.; Streeck, Hendrik

    2013-01-01

    Early immunological events during acute HIV infection are thought to fundamentally influence long-term disease outcome. Whereas the contribution of HIV-specific CD8 T cell responses to early viral control is well established, the role of HIV-specific CD4 T cell responses in the control of viral replication following acute infection is unknown. A growing body of evidence suggests that CD4 T cells - besides their helper function - have the capacity to directly recognize and kill virally infected cells. In a longitudinal study of a cohort of individuals acutely infected with HIV, we observed that subjects able to spontaneously control HIV replication in the absence of antiretroviral therapy showed a significant expansion of HIV-specific CD4 T cell responses—but not CD8 T cell responses–compared to subjects who progressed to a high viral set point (p=0.038). Strikingly, this expansion occurred prior to differences in viral load or CD4 T cell count and was characterized by robust cytolytic activity and expression of a distinct profile of perforin and granzymes at the earliest time point. Kaplan-Meier analysis revealed that the emergence of Granzyme A+ HIV-specific CD4 T cell responses at baseline was highly predictive of slower disease progression and clinical outcome (average days to CD4 T cell count <350/μl was 575 versus 306, p=0.001). These data demonstrate that HIV-specific CD4 T cell responses can be used during the earliest phase of HIV infection as an immunological predictor of subsequent viral set point and disease outcome. Moreover, these data suggest that expansion of Granzyme A+ HIV-specific cytolytic CD4 T cell responses early during acute HIV infection contributes substantially to the control of viral replication. PMID:22378925

  14. An Endotoxin Tolerance Signature Predicts Sepsis and Organ Dysfunction at Initial Clinical Presentation

    PubMed Central

    Pena, Olga M.; Hancock, David G.; Lyle, Ngan H.; Linder, Adam; Russell, James A.; Xia, Jianguo; Fjell, Christopher D.; Boyd, John H.; Hancock, Robert E.W.

    2014-01-01

    Background Sepsis involves aberrant immune responses to infection, but the exact nature of this immune dysfunction remains poorly defined. Bacterial endotoxins like lipopolysaccharide (LPS) are potent inducers of inflammation, which has been associated with the pathophysiology of sepsis, but repeated exposure can also induce a suppressive effect known as endotoxin tolerance or cellular reprogramming. It has been proposed that endotoxin tolerance might be associated with the immunosuppressive state that was primarily observed during late-stage sepsis. However, this relationship remains poorly characterised. Here we clarify the underlying mechanisms and timing of immune dysfunction in sepsis. Methods We defined a gene expression signature characteristic of endotoxin tolerance. Gene-set test approaches were used to correlate this signature with early sepsis, both newly and retrospectively analysing microarrays from 593 patients in 11 cohorts. Then we recruited a unique cohort of possible sepsis patients at first clinical presentation in an independent blinded controlled observational study to determine whether this signature was associated with the development of confirmed sepsis and organ dysfunction. Findings All sepsis patients presented an expression profile strongly associated with the endotoxin tolerance signature (p < 0.01; AUC 96.1%). Importantly, this signature further differentiated between suspected sepsis patients who did, or did not, go on to develop confirmed sepsis, and predicted the development of organ dysfunction. Interpretation Our data support an updated model of sepsis pathogenesis in which endotoxin tolerance-mediated immune dysfunction (cellular reprogramming) is present throughout the clinical course of disease and related to disease severity. Thus endotoxin tolerance might offer new insights guiding the development of new therapies and diagnostics for early sepsis. PMID:25685830

  15. Fc gamma receptor 3A and 2A polymorphisms do not predict response to rituximab in follicular lymphoma

    PubMed Central

    Kenkre, Vaishalee P.; Hong, Fangxin; Cerhan, James R.; Lewis, Marcia; Sullivan, Leslie; Williams, Michael E.; Gascoyne, Randy D.; Horning, Sandra J.; Kahl, Brad S.

    2015-01-01

    Purpose Pre-clinical studies suggest that single nucleotide polymorphisms (SNPs) in the Fcγ receptor (FCGR) genes influence response to rituximab, but the clinical relevance of this is uncertain. Experimental Design We prospectively obtained specimens for genotyping in the RESORT study, where 408 previously untreated, low tumor burden follicular lymphoma (FL) patients were treated with single agent rituximab. Patients received rituximab in 4 weekly doses and responders were randomized to rituximab re-treatment (RR) upon progression versus maintenance rituximab (MR). SNP genotyping was performed in 321 consenting patients. Results Response rates to initial therapy and response duration were correlated with the FCGR3A SNP at position 158 (rs396991) and the FCGR2A SNP at position 131 (rs1801274). The response rate to initial rituximab was 71%. No FCGR genotypes or grouping of genotypes were predictive of initial response. 289 patients were randomized to RR (n = 143) or to MR (n = 146). With a median follow up of 5.5 years, the 3-yr response duration in the RR arm and the MR arm was 50% and 78%, respectively. Genotyping was available in 235 of 289 randomized patients. In patients receiving RR (n = 115) or MR (n =120), response duration was not associated with any FCGR genotypes or genotype combinations. Conclusions Based on this analysis of treatment-naïve, low tumor burden FL, we conclude that the FCGR3A and FCGR2A SNPs do not confer differential responsiveness to rituximab. PMID:26510856

  16. Baseline Factors Predictive of SLT Response: A Prospective Study.

    PubMed

    Bruen, Robin; Lesk, Mark R; Harasymowycz, Paul

    2012-01-01

    Purpose. To study the response to Selective Laser Trabeculoplasty (SLT) according to baseline medical treatment, angle pigmentation, age, diagnosis (open-angle glaucoma or ocular hypertension), and baseline intraocular pressure (IOP). Methods. 74 eyes of 74 patients were enrolled in this study. Baseline characteristics were recorded for each patient. IOP in the treated and fellow eyes was measured at baseline, and 1 month, 6 months, and 12 months following SLT. IOP changes in the different groups were compared using two-way ANOVA and Pearson's correlation. Results. The mean age of our cohort was 71 ± 10 years. The mean baseline IOP was 21.5 ± 5 mmHg, and the mean change in IOP from baseline in the treated eye at one year was -4.67 ± 3.40 mmHg. Higher baseline IOP was highly correlated with greater absolute IOP decrease. Prostaglandin analogue use at baseline was shown to be associated with a statistically decreased IOP-lowering response following SLT when corrected for baseline IOP. No significant differences in IOP response were found when comparing groups stratified for age, angle pigmentation, phakic status, gender, or diagnosis. Discussion. The results of this study confirm the finding that higher baseline IOP is a predictor of greater IOP response following SLT, and that pretreatment with prostaglandin analogue therapy is associated with a decreased IOP-lowering response following SLT. The study is limited by the small number of eyes with data available for complete case analysis.

  17. Early changes in emotional processing as a marker of clinical response to SSRI treatment in depression.

    PubMed

    Godlewska, B R; Browning, M; Norbury, R; Cowen, P J; Harmer, C J

    2016-11-22

    Antidepressant treatment reduces behavioural and neural markers of negative emotional bias early in treatment and has been proposed as a mechanism of antidepressant drug action. Here, we provide a critical test of this hypothesis by assessing whether neural markers of early emotional processing changes predict later clinical response in depression. Thirty-five unmedicated patients with major depression took the selective serotonin re-uptake inhibitor (SSRI), escitalopram (10 mg), over 6 weeks, and were classified as responders (22 patients) versus non-responders (13 patients), based on at least a 50% reduction in symptoms by the end of treatment. The neural response to fearful and happy emotional facial expressions was assessed before and after 7 days of treatment using functional magnetic resonance imaging. Changes in the neural response to these facial cues after 7 days of escitalopram were compared in patients as a function of later clinical response. A sample of healthy controls was also assessed. At baseline, depressed patients showed greater activation to fear versus happy faces than controls in the insula and dorsal anterior cingulate. Depressed patients who went on to respond to the SSRI had a greater reduction in neural activity to fearful versus happy facial expressions after just 7 days of escitalopram across a network of regions including the anterior cingulate, insula, amygdala and thalamus. Mediation analysis confirmed that the direct effect of neural change on symptom response was not mediated by initial changes in depressive symptoms. These results support the hypothesis that early changes in emotional processing with antidepressant treatment are the basis of later clinical improvement. As such, early correction of negative bias may be a key mechanism of antidepressant drug action and a potentially useful predictor of therapeutic response.

  18. Early changes in emotional processing as a marker of clinical response to SSRI treatment in depression

    PubMed Central

    Godlewska, B R; Browning, M; Norbury, R; Cowen, P J; Harmer, C J

    2016-01-01

    Antidepressant treatment reduces behavioural and neural markers of negative emotional bias early in treatment and has been proposed as a mechanism of antidepressant drug action. Here, we provide a critical test of this hypothesis by assessing whether neural markers of early emotional processing changes predict later clinical response in depression. Thirty-five unmedicated patients with major depression took the selective serotonin re-uptake inhibitor (SSRI), escitalopram (10 mg), over 6 weeks, and were classified as responders (22 patients) versus non-responders (13 patients), based on at least a 50% reduction in symptoms by the end of treatment. The neural response to fearful and happy emotional facial expressions was assessed before and after 7 days of treatment using functional magnetic resonance imaging. Changes in the neural response to these facial cues after 7 days of escitalopram were compared in patients as a function of later clinical response. A sample of healthy controls was also assessed. At baseline, depressed patients showed greater activation to fear versus happy faces than controls in the insula and dorsal anterior cingulate. Depressed patients who went on to respond to the SSRI had a greater reduction in neural activity to fearful versus happy facial expressions after just 7 days of escitalopram across a network of regions including the anterior cingulate, insula, amygdala and thalamus. Mediation analysis confirmed that the direct effect of neural change on symptom response was not mediated by initial changes in depressive symptoms. These results support the hypothesis that early changes in emotional processing with antidepressant treatment are the basis of later clinical improvement. As such, early correction of negative bias may be a key mechanism of antidepressant drug action and a potentially useful predictor of therapeutic response. PMID:27874847

  19. Using unknown knowns to predict coastal response to future climate

    NASA Astrophysics Data System (ADS)

    Plant, N. G.; Lentz, E. E.; Gutierrez, B.; Thieler, E. R.; Passeri, D. L.

    2015-12-01

    The coastal zone, including its bathymetry, topography, ecosystem, and communities, depends on and responds to a wide array of natural and engineered processes associated with climate variability. Climate affects the frequency of coastal storms, which are only resolved probabilistically for future conditions, as well as setting the pace for persistent processes (e.g., waves driving daily alongshore transport; beach nourishment). It is not clear whether persistent processes or extreme events contribute most to the integrated evolution of the coast. Yet, observations of coastal change record the integration of persistent and extreme processes. When these observations span a large spatial domain and/or temporal range they may reflect a wide range of forcing and boundary conditions that include different levels of sea-level rise, storminess, sediment input, engineering activities, and elevation distributions. We have been using a statistical approach to characterize the interrelationships between oceanographic, ecological, and geomorphic processes—including the role played by human activities via coastal protection, beach nourishment, and other forms of coastal management. The statistical approach, Bayesian networks, incorporates existing information to establish underlying prior expectations for the distributions and inter-correlations of variables most relevant to coastal geomorphic evolution. This underlying information can then be used to make predictions. We demonstrate several examples of the utility of this approach using data as constraints and then propagating the constraints and uncertainty to make predictions of unobserved variables that include changes in shorelines, dunes, and overwash deposits. We draw on data from the Gulf and Atlantic Coasts of the United States, resolving time scales of years to a century. The examples include both short-term storm impacts and long-term evolution associated with sea-level rise. We show that the Bayesian network can

  20. Post-Exercise Heart Rate Recovery Independently Predicts Clinical Outcome in Patients with Acute Decompensated Heart Failure

    PubMed Central

    Youn, Jong-Chan; Lee, Hye Sun; Choi, Suk-Won; Han, Seong-Woo; Ryu, Kyu-Hyung; Shin, Eui-Cheol; Kang, Seok-Min

    2016-01-01

    Background Post-exercise heart rate recovery (HRR) is an index of parasympathetic function associated with clinical outcome in patients with chronic heart failure. However, its relationship with the pro-inflammatory response and prognostic value in consecutive patients with acute decompensated heart failure (ADHF) has not been investigated. Methods We measured HRR and pro-inflammatory markers in 107 prospectively and consecutively enrolled, recovered ADHF patients (71 male, 59 ± 15 years, mean ejection fraction 28.9 ± 14.2%) during the pre-discharge period. The primary endpoint included cardiovascular (CV) events defined as CV mortality, cardiac transplantation, or rehospitalization due to HF aggravation. Results The CV events occurred in 30 (28.0%) patients (5 cardiovascular deaths and 7 cardiac transplantations) during the follow-up period (median 214 days, 11–812 days). When the patients with ADHF were grouped by HRR according to the Contal and O’Quigley’s method, low HRR was shown to be associated with significantly higher levels of serum monokine-induced by gamma interferon (MIG) and poor clinical outcome. Multivariate Cox regression analysis revealed that low HRR was an independent predictor of CV events in both enter method and stepwise method. The addition of HRR to a model significantly increased predictability for CV events across the entire follow-up period. Conclusion Impaired post-exercise HRR is associated with a pro-inflammatory response and independently predicts clinical outcome in patients with ADHF. These findings may explain the relationship between autonomic dysfunction and clinical outcome in terms of the inflammatory response in these patients. PMID:27135610

  1. Short‐Term Efficacy Reliably Predicts Long‐Term Clinical Benefit in Rheumatoid Arthritis Clinical Trials as Demonstrated by Model‐Based Meta‐Analysis

    PubMed Central

    Zhu, Rui; Xiao, Jim; Davis, John C.; Mandema, Jaap W.; Jin, Jin Y.; Tang, Meina T.

    2015-01-01

    Abstract The objective of this study was to assess the relationship between short‐term and long‐term treatment effects measured by the American College of Rheumatology (ACR) 50 responses and to assess the feasibility of predicting 6‐month efficacy from short‐term data. A rheumatoid arthritis (RA) database was constructed from 68 reported trials. We focused on the relationship between 3‐ and 6‐month ACR50 treatment effects and developed a generalized nonlinear model to quantify the relationship and test the impact of covariates. The ΔACR50 at 6 months strongly correlated with that at 3 months, moderately correlated with that at 2 months, and only weakly correlated with results obtained at <2 months. A scaling factor that reflected the ratio of 6‐ to 3‐month treatment effects was estimated to be 0.997, suggesting that the treatment effects at 3 months are approaching a “plateau.” Drug classes, baseline Disease Activity Score in 28 Joints, and the magnitude of control arm response did not show significant impacts on the scaling factor. This work quantitatively supports the empirical clinical development paradigm of using 3‐month efficacy data to predict long‐term efficacy and to inform the probability of clinical success based on early efficacy readout. PMID:26517752

  2. Dynamic arterial elastance to predict arterial pressure response to volume loading in preload-dependent patients

    PubMed Central

    2011-01-01

    Introduction Hemodynamic resuscitation should be aimed at achieving not only adequate cardiac output but also sufficient mean arterial pressure (MAP) to guarantee adequate tissue perfusion pressure. Since the arterial pressure response to volume expansion (VE) depends on arterial tone, knowing whether a patient is preload-dependent provides only a partial solution to the problem. The objective of this study was to assess the ability of a functional evaluation of arterial tone by dynamic arterial elastance (Eadyn), defined as the pulse pressure variation (PPV) to stroke volume variation (SVV) ratio, to predict the hemodynamic response in MAP to fluid administration in hypotensive, preload-dependent patients with acute circulatory failure. Methods We performed a prospective clinical study in an adult medical/surgical intensive care unit in a tertiary care teaching hospital, including 25 patients with controlled mechanical ventilation who were monitored with the Vigileo® monitor, for whom the decision to give fluids was made because of the presence of acute circulatory failure, including arterial hypotension (MAP ≤65 mmHg or systolic arterial pressure <90 mmHg) and preserved preload responsiveness condition, defined as a SVV value ≥10%. Results Before fluid infusion, Eadyn was significantly different between MAP responders (MAP increase ≥15% after VE) and MAP nonresponders. VE-induced increases in MAP were strongly correlated with baseline Eadyn (r2 = 0.83; P < 0.0001). The only predictor of MAP increase was Eadyn (area under the curve, 0.986 ± 0.02; 95% confidence interval (CI), 0.84-1). A baseline Eadyn value >0.89 predicted a MAP increase after fluid administration with a sensitivity of 93.75% (95% CI, 69.8%-99.8%) and a specificity of 100% (95% CI, 66.4%-100%). Conclusions Functional assessment of arterial tone by Eadyn, measured as the PVV to SVV ratio, predicted arterial pressure response after volume loading in hypotensive, preload-dependent patients

  3. Alternate virtual populations elucidate the type I interferon signature predictive of the response to rituximab in rheumatoid arthritis

    PubMed Central

    2013-01-01

    Background Mechanistic biosimulation can be used in drug development to form testable hypotheses, develop predictions of efficacy before clinical trial results are available, and elucidate clinical response to therapy. However, there is a lack of tools to simultaneously (1) calibrate the prevalence of mechanistically distinct, large sets of virtual patients so their simulated responses statistically match phenotypic variability reported in published clinical trial outcomes, and (2) explore alternate hypotheses of those prevalence weightings to reflect underlying uncertainty in population biology. Here, we report the development of an algorithm, MAPEL (Mechanistic Axes Population Ensemble Linkage), which utilizes a mechanistically-based weighting method to match clinical trial statistics. MAPEL is the first algorithm for developing weighted virtual populations based on biosimulation results that enables the rapid development of an ensemble of alternate virtual population hypotheses, each validated by a composite goodness-of-fit criterion. Results Virtual patient cohort mechanistic biosimulation results were successfully calibrated with an acceptable composite goodness-of-fit to clinical populations across multiple therapeutic interventions. The resulting virtual populations were employed to investigate the mechanistic underpinnings of variations in the response to rituximab. A comparison between virtual populations with a strong or weak American College of Rheumatology (ACR) score in response to rituximab suggested that interferon β (IFNβ) was an important mechanistic contributor to the disease state, a signature that has previously been identified though the underlying mechanisms remain unclear. Sensitivity analysis elucidated key anti-inflammatory properties of IFNβ that modulated the pathophysiologic state, consistent with the observed prognostic correlation of baseline type I interferon measurements with clinical response. Specifically, the effects of IFN

  4. CAsubtype: An R Package to Identify Gene Sets Predictive of Cancer Subtypes and Clinical Outcomes.

    PubMed

    Kong, Hualei; Tong, Pan; Zhao, Xiaodong; Sun, Jielin; Li, Hua

    2017-01-21

    In the past decade, molecular classification of cancer has gained high popularity owing to its high predictive power on clinical outcomes as compared with traditional methods commonly used in clinical practice. In particular, using gene expression profiles, recent studies have successfully identified a number of gene sets for the delineation of cancer subtypes that are associated with distinct prognosis. However, identification of such gene sets remains a laborious task due to the lack of tools with flexibility, integration and ease of use. To reduce the burden, we have developed an R package, CAsubtype, to efficiently identify gene sets predictive of cancer subtypes and clinical outcomes. By integrating more than 13,000 annotated gene sets, CAsubtype provides a comprehensive repertoire of candidates for new cancer subtype identification. For easy data access, CAsubtype further includes the gene expression and clinical data of more than 2000 cancer patients from TCGA. CAsubtype first employs principal component analysis to identify gene sets (from user-provided or package-integrated ones) with robust principal components representing significantly large variation between cancer samples. Based on these principal components, CAsubtype visualizes the sample distribution in low-dimensional space for better understanding of the distinction between samples and classifies samples into subgroups with prevalent clustering algorithms. Finally, CAsubtype performs survival analysis to compare the clinical outcomes between the identified subgroups, assessing their clinical value as potentially novel cancer subtypes. In conclusion, CAsubtype is a flexible and well-integrated tool in the R environment to identify gene sets for cancer subtype identification and clinical outcome prediction. Its simple R commands and comprehensive data sets enable efficient examination of the clinical value of any given gene set, thus facilitating hypothesis generating and testing in biological and

  5. Inspiratory duty cycle responses to flow limitation predict nocturnal hypoventilation.

    PubMed

    Schneider, H; Krishnan, V; Pichard, L E; Patil, S P; Smith, P L; Schwartz, A R

    2009-05-01

    Upper airway obstruction (UAO) can elicit neuromuscular responses that mitigate and/or compensate for the obstruction. It was hypothesised that flow-limited breathing elicits specific timing responses that can preserve ventilation due to increases in inspiratory duty cycle rather than respiratory rate. By altering nasal pressure during non-rapid eye movement (non-REM) sleep, similar degrees of UAO were induced in healthy males and females (n = 10 each). Inspiratory duty cycle, respiratory rate and minute ventilation were determined for each degree of UAO during non-REM sleep and compared with the baseline nonflow-limited condition. A dose-dependent increase in the inspiratory duty cycle and respiratory rate was observed in response to increasing severity of UAO. Increases in the inspiratory duty cycle, but not respiratory rate, helped to acutely maintain ventilation. Heterogeneity in these responses was associated with variable degrees of ventilatory compensation, allowing for the segregation of individuals at risk for hypoventilation during periods of inspiratory airflow limitation. Upper airway obstruction constitutes a unique load on the respiratory system. The inspiratory duty cycle, but not the respiratory rate, determine the individual's ability to compensate for inspiratory airflow limitation during sleep, and may represent a quantitative phenotype for obstructive sleep apnoea susceptibility.

  6. Uninvolved immunoglobulins predicting hematological response in newly diagnosed AL amyloidosis.

    PubMed

    Muchtar, Eli; Magen, Hila; Itchaki, Gilad; Cohen, Amos; Rosenfeld, Ra'ama; Shochat, Tzippy; Kornowski, Ran; Iakobishvili, Zaza; Raanani, Pia

    2016-02-01

    Immunoparesis serves as a marker for elevated risk for progression in plasma cell proliferative disorders. However, the impact of immunoparesis in AL amyloidosis has not been addressed. Immunoparesis was defined qualitatively as any decrease below the low reference levels of the uninvolved immunoglobulins and quantitatively, as the relative difference between the uninvolved immunoglobulins and the lower reference values. Forty-one newly diagnosed AL amyloidosis patients were included. Sixty-six percent of patients had a suppression of the uninvolved immunoglobulins. The median relative difference of the uninvolved immunoglobulins was 18% above the low reference levels [range (-71%)-210%]. Ninety percent of the patients were treated with novel agents-based regimens, mostly bortezomib-containing regimens. Nineteen percent of the patients did not attain response to first line treatment. Patients with relative difference of uninvolved immunoglobulins below -25% of the low reference levels were less likely to respond to first line treatment compared to patients with a relative difference of -25% and above [odds ratio for no response vs. partial response and better 30 [(95% CI 4.1-222.2), P=0.0004]. Patients who failed first line treatment were successfully salvaged with lenalidomide-based treatment. Immunoparesis, if assessed quantitatively, may serve as a predictor of response in AL amyloidosis patients treated with bortezomib-containing regimens.

  7. Relationship-Based Infant Care: Responsive, on Demand, and Predictable

    ERIC Educational Resources Information Center

    Petersen, Sandra; Wittmer, Donna

    2008-01-01

    Young babies are easily overwhelmed by the pain of hunger or gas. However, when an infant's day is filled with caregiving experiences characterized by quick responses to his cries and accurate interpretations of the meaning of his communication, the baby learns that he can count on being fed and comforted. He begins to develop trust in his teacher…

  8. Maternal Oxytocin Response Predicts Mother-to-Infant Gaze

    PubMed Central

    Kim, Sohye; Fonagy, Peter; Koos, Orsolya; Dorsett, Kimberly; Strathearn, Lane

    2014-01-01

    The neuropeptide oxytocin is importantly implicated in the emergence and maintenance of maternal behavior that forms the basis of the mother-infant bond. However, no research has yet examined the specific association between maternal oxytocin and maternal gaze, a key modality through which the mother makes social contact and engages with her infant. Furthermore, prior oxytocin studies have assessed maternal engagement primarily during episodes free of infant distress, while maternal engagement during infant distress is considered to be uniquely relevant to the formation of secure mother-infant attachment. Two patterns of maternal gaze, maternal gaze toward and gaze shifts away from the infant, were micro-coded while 50 mothers interacted with their 7-month-old infants during a modified still-face procedure. Maternal oxytocin response was defined as a change in the mother’s plasma oxytocin level following interaction with her infant as compared to baseline. The mother’s oxytocin response was positively associated with the duration of time her gaze was directed toward her infant, while negatively associated with the frequency with which her gaze shifted away from her infant. Importantly, mothers who showed low/average oxytocin response demonstrated a significant decrease in their gaze toward their infants during periods of infant distress, while such change was not observed in mothers with high oxytocin response. The findings underscore the involvement of oxytocin in regulating the mother’s responsive engagement with her infant, particularly in times when the infant’s need for access to the mother is greatest. PMID:24184574

  9. A-line, bispectral index, and estimated effect-site concentrations: a prediction of clinical end-points of anesthesia.

    PubMed

    Kreuer, Sascha; Bruhn, Jörgen; Larsen, Reinhard; Buchinger, Heiko; Wilhelm, Wolfram

    2006-04-01

    Autoregressive modeling with exogenous input of middle-latency auditory evoked potentials (A-Line AEP index, AAI) has been developed for monitoring depth of anesthesia. We investigated the prediction of recovery and dose-response relationship of desflurane and AAI or bispectral index (BIS) values. Twenty adult men scheduled for radical prostatectomy were recruited. To minimize opioid effects, analgesia was provided by a concurrent epidural in addition to the general anesthetic. Electrodes for AAI and BIS monitoring and a headphone for auditory stimuli were applied. Propofol and remifentanil were used for anesthetic induction. Maintenance of anesthesia was with desflurane only. For comparison to AAI and BIS monitor parameters, pharmacokinetic models for desflurane and propofol distribution and effect-site concentrations were used to predict clinical end-points (Prediction probability P(K)). Patients opened their eyes at an AAI value of 47 +/- 20 and a BIS value of 77 +/- 14 (mean +/- sd), and the prediction probability for eye opening was P(K) = 0.81 for AAI, P(K) = 0.89 for BIS, and P(K) = 0.91 for desflurane effect-site concentration. The opening of eyes was best predicted by the calculated desflurane effect-site concentration. The relationship between predicted desflurane effect-site concentration versus AAI and BIS was calculated by nonlinear regression analysis (r = 0.75 for AAI and r = 0.80 for BIS). The correlation between BIS and clinical end-points of anesthesia or the desflurane effect-compartment concentration is better than for the AAI.

  10. Molecular profiling of prostate cancer derived exosomes may reveal a predictive signature for response to docetaxel

    PubMed Central

    Kharaziha, Pedram; Chioureas, Dimitris; Rutishauser, Dorothea; Baltatzis, George; Lennartsson, Lena; Fonseca, Pedro; Azimi, Alireza; Hultenby, Kjell; Zubarev, Roman; Ullén, Anders; Yachnin, Jeffrey; Nilsson, Sten; Panaretakis, Theocharis

    2015-01-01

    Docetaxel is a cornerstone treatment for metastatic, castration resistant prostate cancer (CRPC) which remains a leading cause of cancer-related deaths, worldwide. The clinical usage of docetaxel has resulted in modest gains in survival, primarily due to the development of resistance. There are currently no clinical biomarkers available that predict whether a CRPC patient will respond or acquire resistance to this therapy. Comparative proteomics analysis of exosomes secreted from DU145 prostate cancer cells that are sensitive (DU145 Tax-Sen) or have acquired resistance (DU145 Tax-Res) to docetaxel, demonstrated significant differences in the amount of exosomes secreted and in their molecular composition. A panel of proteins was identified by proteomics to be differentially enriched in DU145 Tax-Res compared to DU145 Tax-Sen exosomes and was validated by western blotting. Importantly, we identified MDR-1, MDR-3, Endophilin-A2 and PABP4 that were enriched only in DU145 Tax-Res exosomes. We validated the presence of these proteins in the serum of a small cohort of patients. DU145 cells that have uptaken DU145 Tax-Res exosomes show properties of increased matrix degradation. In summary, exosomes derived from DU145 Tax-Res cells may be a valuable source of biomarkers for response to therapy. PMID:25844599

  11. Dose-response curve slope helps predict therapeutic potency and breadth of HIV broadly neutralizing antibodies.

    PubMed

    Webb, Nicholas E; Montefiori, David C; Lee, Benhur

    2015-09-29

    A new generation of HIV broadly neutralizing antibodies (bnAbs) with remarkable potency, breadth and epitope diversity has rejuvenated interest in immunotherapeutic strategies. Potencies defined by in vitro IC50 and IC80 values (50 and 80% inhibitory concentrations) figure prominently into the selection of clinical candidates; however, much higher therapeutic levels will be required to reduce multiple logs of virus and impede escape. Here we predict bnAb potency at therapeutic levels by analysing dose-response curve slopes, and show that slope is independent of IC50/IC80 and specifically relates to bnAb epitope class. With few exceptions, CD4-binding site and V3-glycan bnAbs exhibit slopes >1, indicative of higher expected therapeutic effectiveness, whereas V2-glycan, gp41 membrane-proximal external region (MPER) and gp120-gp41 bnAbs exhibit less favourable slopes <1. Our results indicate that slope is one major predictor of both potency and breadth for bnAbs at clinically relevant concentrations, and may better coordinate the relationship between bnAb epitope structure and therapeutic expectations.

  12. Predicting nonlinear properties of metamaterials from the linear response.

    PubMed

    O'Brien, Kevin; Suchowski, Haim; Rho, Junsuk; Salandrino, Alessandro; Kante, Boubacar; Yin, Xiaobo; Zhang, Xiang

    2015-04-01

    The discovery of optical second harmonic generation in 1961 started modern nonlinear optics. Soon after, R. C. Miller found empirically that the nonlinear susceptibility could be predicted from the linear susceptibilities. This important relation, known as Miller's Rule, allows a rapid determination of nonlinear susceptibilities from linear properties. In recent years, metamaterials, artificial materials that exhibit intriguing linear optical properties not found in natural materials, have shown novel nonlinear properties such as phase-mismatch-free nonlinear generation, new quasi-phase matching capabilities and large nonlinear susceptibilities. However, the understanding of nonlinear metamaterials is still in its infancy, with no general conclusion on the relationship between linear and nonlinear properties. The key question is then whether one can determine the nonlinear behaviour of these artificial materials from their exotic linear behaviour. Here, we show that the nonlinear oscillator model does not apply in general to nonlinear metamaterials. We show, instead, that it is possible to predict the relative nonlinear susceptibility of large classes of metamaterials using a more comprehensive nonlinear scattering theory, which allows efficient design of metamaterials with strong nonlinearity for important applications such as coherent Raman sensing, entangled photon generation and frequency conversion.

  13. Tumour-infiltrating regulatory T cell density before neoadjuvant chemoradiotherapy for rectal cancer does not predict treatment response.

    PubMed

    McCoy, Melanie J; Hemmings, Chris; Anyaegbu, Chidozie C; Austin, Stephanie J; Lee-Pullen, Tracey F; Miller, Timothy J; Bulsara, Max K; Zeps, Nikolajs; Nowak, Anna K; Lake, Richard A; Platell, Cameron F

    2017-02-03

    Neoadjuvant (preoperative) chemoradiotherapy (CRT) decreases the risk of rectal cancer recurrence and reduces tumour volume prior to surgery. However, response to CRT varies considerably between individuals and factors associated with response are poorly understood. Foxp3+ regulatory T cells (Tregs) inhibit anti-tumour immunity and may limit any response to chemotherapy and radiotherapy. We have previously reported that a low density of Tregs in the tumour stroma following neoadjuvant CRT for rectal cancer is associated with improved tumour regression. Here we have examined the association between Treg density in pre-treatment diagnostic biopsy specimens and treatment response, in this same patient cohort. We aimed to determine whether pre-treatment tumour-infiltrating Treg density predicts subsequent response to neoadjuvant CRT. Foxp3+, CD8+ and CD3+ cell densities in biopsy samples from 106 patients were assessed by standard immunohistochemistry (IHC) and evaluated for their association with tumour regression grade and survival. We found no association between the density of any T cell subset pre-treatment and clinical outcome, indicating that tumour-infiltrating Treg density does not predict response to neoadjuvant CRT in rectal cancer. Taken together with the findings of the previous study, these data suggest that in the context of neoadjuvant CRT for rectal cancer, the impact of chemotherapy and/or radiotherapy on anti-tumour immunity may be more important than the state of the pre-existing local immune response.

  14. Predictive Validity of DSM-IV Oppositional Defiant and Conduct Disorders in Clinically Referred Preschoolers

    ERIC Educational Resources Information Center

    Keenan, Kate; Boeldt, Debra; Chen, Diane; Coyne, Claire; Donald, Radiah; Duax, Jeanne; Hart, Katherine; Perrott, Jennifer; Strickland, Jennifer; Danis, Barbara; Hill, Carri; Davis, Shante; Kampani, Smita; Humphries, Marisha

    2011-01-01

    Background: Diagnostic validity of oppositional defiant and conduct disorders (ODD and CD) for preschoolers has been questioned based on concerns regarding the ability to differentiate normative, transient disruptive behavior from clinical symptoms. Data on concurrent validity have accumulated, but predictive validity is limited. Predictive…

  15. Clinical prediction of fall risk and white matter abnormalities: a diffusion tensor imaging study

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The Tinetti scale is a simple clinical tool designed to predict risk of falling by focusing on gait and stance impairment in elderly persons. Gait impairment is also associated with white matter (WM) abnormalities. Objective: To test the hypothesis that elderly subjects at risk for falling, as deter...

  16. Clinical and Actuarial Prediction of Physical Violence in a Forensic Intellectual Disability Hospital: A Longitudinal Study

    ERIC Educational Resources Information Center

    McMillan, Dean; Hastings, Richard P.; Coldwell, Jon

    2004-01-01

    Background: There is a high rate of physical violence in populations with intellectual disabilities, and this has been linked to problems for the victim, the assailant, members of staff and services. Despite the clinical significance of this behaviour, few studies have assessed methods of predicting its occurrence. The present study examined…

  17. Synergistic combination of clinical and imaging features predicts abnormal imaging patterns of pulmonary infections

    PubMed Central

    Bagci, Ulas; Jaster-Miller, Kirsten; Olivier, Kenneth N.; Yao, Jianhua; Mollura, Daniel J.

    2013-01-01

    We designed and tested a novel hybrid statistical model that accepts radiologic image features and clinical variables, and integrates this information in order to automatically predict abnormalities in chest computed-tomography (CT) scans and identify potentially important infectious disease biomarkers. In 200 patients, 160 with various pulmonary infections and 40 healthy controls, we extracted 34 clinical variables from laboratory tests and 25 textural features from CT images. From the CT scans, pleural effusion (PE), linear opacity (or thickening) (LT), tree-in-bud (TIB), pulmonary nodules, ground glass opacity (GGO), and consolidation abnormality patterns were analyzed and predicted through clinical, textural (imaging), or combined attributes. The presence and severity of each abnormality pattern was validated by visual analysis of the CT scans. The proposed biomarker identification system included two important steps: (i) a coarse identification of an abnormal imaging pattern by adaptively selected features (AmRMR), and (ii) a fine selection of the most important features from the previous step, and assigning them as biomarkers, depending on the prediction accuracy. Selected biomarkers were used to classify normal and abnormal patterns by using a boosted decision tree (BDT) classifier. For all abnormal imaging patterns, an average prediction accuracy of 76.15% was obtained. Experimental results demonstrated that our proposed biomarker identification approach is promising and may advance the data processing in clinical pulmonary infection research and diagnostic techniques. PMID:23930819

  18. Predicting Performance during Clinical Years from the New Medical College Admission Test.

    ERIC Educational Resources Information Center

    Caroline, Jan D.; And Others

    1983-01-01

    The results of a predictive validity study of the new Medical College Admission Test (MCAT) using criteria from the clinical years of undergraduate medical education are discussed. The criteria included course grades and faculty ratings of clerks in internal medicine, surgery, obstetrics and gynecology, pediatrics, and psychiatry. (Author/MLW)

  19. Sensory input attenuation allows predictive sexual response in yeast

    PubMed Central

    Banderas, Alvaro; Koltai, Mihaly; Anders, Alexander; Sourjik, Victor

    2016-01-01

    Animals are known to adjust their sexual behaviour depending on mate competition. Here we report similar regulation for mating behaviour in a sexual unicellular eukaryote, the budding yeast Saccharomyces cerevisiae. We demonstrate that pheromone-based communication between the two mating types, coupled to input attenuation by recipient cells, enables yeast to robustly monitor relative mate abundance (sex ratio) within a mixed population and to adjust their commitment to sexual reproduction in proportion to their estimated chances of successful mating. The mechanism of sex-ratio sensing relies on the diffusible peptidase Bar1, which is known to degrade the pheromone signal produced by mating partners. We further show that such a response to sexual competition within a population can optimize the fitness trade-off between the costs and benefits of mating response induction. Our study thus provides an adaptive explanation for the known molecular mechanism of pheromone degradation in yeast. PMID:27557894

  20. Conceptualizing, Understanding, and Predicting Responsible Decisions and Quality Input

    NASA Astrophysics Data System (ADS)

    Wall, N.; PytlikZillig, L. M.

    2012-12-01

    In areas such as climate change, where uncertainty is high, it is arguably less difficult to tell when efforts have resulted in changes in knowledge, than when those efforts have resulted in responsible decisions. What is a responsible decision? More broadly, when it comes to citizen input, what is "high quality" input? And most importantly, how are responsible decisions and quality input enhanced? The aim of this paper is to contribute to the understanding of the different dimensions of "responsible" or "quality" public input and citizen decisions by comparing and contrasting the different predictors of those different dimensions. We first present different possibilities for defining, operationalizing and assessing responsible or high quality decisions. For example, responsible decisions or quality input might be defined as using specific content (e.g., using climate change information in decisions appropriately), as using specific processes (e.g., investing time and effort in learning about and discussing the issues prior to making decisions), or on the basis of some judgment of the decision or input itself (e.g., judgments of the rationale provided for the decisions, or number of issues considered when giving input). Second, we present results from our work engaging people with science policy topics, and the different ways that we have tried to define these two constructs. In the area of climate change specifically, we describe the development of a short survey that assesses exposure to climate information, knowledge of and attitudes toward climate change, and use of climate information in one's decisions. Specifically, the short survey was developed based on a review of common surveys of climate change related knowledge, attitudes, and behaviors, and extensive piloting and cognitive interviews. Next, we analyze more than 200 responses to that survey (data collection is currently ongoing and will be complete after the AGU deadline), and report the predictors of

  1. Clinical implementation of dose-volume histogram predictions for organs-at-risk in IMRT planning

    NASA Astrophysics Data System (ADS)

    Moore, K. L.; Appenzoller, L. M.; Tan, J.; Michalski, J. M.; Thorstad, W. L.; Mutic, S.

    2014-03-01

    True quality control (QC) of the planning process requires quantitative assessments of treatment plan quality itself, and QC in IMRT has been stymied by intra-patient anatomical variability and inherently complex three-dimensional dose distributions. In this work we describe the development of an automated system to reduce clinical IMRT planning variability and improve plan quality using mathematical models that predict achievable OAR DVHs based on individual patient anatomy. These models rely on the correlation of expected dose to the minimum distance from a voxel to the PTV surface, whereby a three-parameter probability distribution function (PDF) was used to model iso-distance OAR subvolume dose distributions. DVH models were obtained by fitting the evolution of the PDF with distance. Initial validation on clinical cohorts of 40 prostate and 24 head-and-neck plans demonstrated highly accurate model-based predictions for achievable DVHs in rectum, bladder, and parotid glands. By quantifying the integrated difference between candidate DVHs and predicted DVHs, the models correctly identified plans with under-spared OARs, validated by replanning all cases and correlating any realized improvements against the predicted gains. Clinical implementation of these predictive models was demonstrated in the PINNACLE treatment planning system by use of existing margin expansion utilities and the scripting functionality inherent to the system. To maintain independence from specific planning software, a system was developed in MATLAB to directly process DICOM-RT data. Both model training and patient-specific analyses were demonstrated with significant computational accelerations from parallelization.

  2. Prediction of Antichollintergic Drug Response Using a Thermoregulatory Exchange Index

    DTIC Science & Technology

    2000-01-01

    psychoses are those compounds which have a degree of anticholinergic action directly on eccrine sweat glands which disrupts thermoregulation. Atropine...compounds which have a degree of anticholinergic action directly on eccrine sweat glands which disrupts thermoregulation. Atropine (a potent nerve agent...Pharm. 30, 209-221. Sato, K., Sato, F., 1981. Pharmacologic responsiveness of iso- lated single eccrine sweat glands. Am. J. Physiol. (Reg. Integrative Comp. Physiol.) 240, R44-R51.

  3. Baseline BOLD correlation predicts individuals' stimulus-evoked BOLD responses.

    PubMed

    Liu, Xiao; Zhu, Xiao-Hong; Chen, Wei

    2011-02-01

    To investigate whether individuals' ongoing neuronal activity at resting state can affect their response to brain stimulation, fMRI BOLD signals were imaged from the human visual cortex of fifteen healthy subjects in the absence and presence of visual stimulation. It was found that the temporal correlation strength but not amplitude of baseline BOLD signal fluctuations acquired under the eyes-fixed condition is positively correlated with the amplitude of stimulus-evoked BOLD responses across subjects. Moreover, the spatiotemporal correlations of baseline BOLD signals imply a coherent network covering the visual system, which is topographically indistinguishable from the "resting-state visual network" observed under the eyes-closed condition. The overall findings suggest that the synchronization of ongoing brain activity plays an important role in determining stimulus-evoked brain activity even at an early stage of the sensory system. The tight relationship between baseline BOLD correlation and stimulus-evoked BOLD amplitude provides an essential basis for understanding and interpreting the large inter-subject BOLD variability commonly observed in numerous fMRI studies and potentially for improving group fMRI analysis. This study highlights the importance to integrate the information from both resting-state coherent networks and task-evoked neural responses for a better understanding of how the brain functions.

  4. VO2 prediction and cardiorespiratory responses during underwater treadmill exercise.

    PubMed

    Greene, Nicholas P; Greene, Elizabeth S; Carbuhn, Aaron F; Green, John S; Crouse, Stephen F

    2011-06-01

    We compared cardiorespiratory responses to exercise on an underwater treadmill (UTM) and land treadmill (LTM) and derived an equation to estimate oxygen consumption (VO2) during UTM exercise. Fifty-five men and women completed one LTM and five UTM exercise sessions on separate days. The UTM sessions consisted of chest-deep immersion, with 0, 25, 50, 75, and 100% water-jet resistance. All session treadmill velocities increased every 3 min from 53.6 to 187.8 m x min(-1). Cardiorespiratory responses were similar between LTM and UTM when jet resistance for UTM was 50%. Using multiple regression analysis, weight-relative VO2 could be estimated as: VO2 (mLO2 c kg(-1) x min(-1)) = 0.19248 x height (cm) + 0.17422 x jet resistance (% max) + 0.14092 x velocity (m x min(-1)) -0.12794 x weight (kg)-27.82849, R2 = .82. Our data indicate that similar LTM and UTM cardiorespiratory responses are achievable, and we provide a reasonable estimate of UTM VO2.

  5. Anger responses to psychosocial stress predict heart rate and cortisol stress responses in men but not women.

    PubMed

    Lupis, Sarah B; Lerman, Michelle; Wolf, Jutta M

    2014-11-01

    While previous research has suggested that anger and fear responses to stress are linked to distinct sympathetic nervous system (SNS) stress responses, little is known about how these emotions predict hypothalamus-pituitary-adrenal (HPA) axis reactivity. Further, earlier research primarily relied on retrospective self-report of emotion. The current study aimed at addressing both issues in male and female individuals by assessing the role of anger and fear in predicting heart rate and cortisol stress responses using both self-report and facial coding analysis to assess emotion responses. We exposed 32 healthy students (18 female; 19.6±1.7 yr) to an acute psychosocial stress paradigm (TSST) and measured heart rate and salivary cortisol levels throughout the protocol. Anger and fear before and after stress exposure was assessed by self-report, and video recordings of the TSST were assessed by a certified facial coder to determine emotion expression (FACS). Self-reported emotions and emotion expressions did not correlate (all p>.23). Increases in self-reported fear predicted blunted cortisol responses in men (β=0.41, p=.04). Also for men, longer durations of anger expression predicted exaggerated cortisol responses (β=0.67 p=.004), and more anger incidences predicted exaggerated cortisol and heart rate responses (β=0.51, p=.033; β=0.46, p=.066, resp.). Anger and fear did not predict SNS or HPA activity for females (all p>.23). The current differential self-report and facial coding findings support the use of multiple modes of emotion assessment. Particularly, FACS but not self-report revealed a robust anger-stress association that could have important downstream health effects for men. For women, future research may clarify the role of other emotions, such as self-conscious expressions of shame, for physiological stress responses. A better understanding of the emotion-stress link may contribute to behavioral interventions targeting health-promoting ways of

  6. Clinical neuroimaging markers of response to treatment in mood disorders.

    PubMed

    Porcu, Michele; Balestrieri, Antonella; Siotto, Paolo; Lucatelli, Pierleone; Anzidei, Michele; Suri, Jasjit S; Zaccagna, Fulvio; Argiolas, Giovanni Maria; Saba, Luca

    2016-10-11

    Mood disorders (MD) are important and frequent psychiatric pathologies, and the management of the patients affected by thes conditions represent an important factor of disability and a huge problem in socialterms and an economic burden. The "in-vivo" studies can help researchers to understand the first events at the base of the development of the pathology and to identify the molecular and non-molecular targets of therapies, but theyhave strong limitations due to the fact that human brain circuitsthem selvesare difficult to be reproduced in animal models. Besides these challenges, they are difficult to be selectively studied with the modern imaging (such as Magnetic Resonance and Positron Emitted Tomography/Computed Tomography) and non-imaging (such as electroencephalography, magnetoencephalography, transcranial magnetic stimulation and evoked potentials) methods. In comparison with other methods, the "in-vivo" imaging investigations have higher temporal and spatial resolution compared to the "in-vivo" non-imaging techniques.All these factors make difficult to fully understand the aetiology and pathophysiology of these disorders, and consequently make difficult not only in the development, but also the monitoring of the actions of therapies,which according to clinical observations have been demonstrated effective in the treatment. In this review, we will focus our attention on the actual state-of-theart of role of imaging in monitoring of treatment of MD, underlying that up to date there are still not standardized imaging markers available in clinical practice.We will analyse briefly the actual classification of MD; then we will focus on the "in vivo" imaging modalities used in research and clinical activity, the current knowledge about the neural models at the base ofMD. Finally the last part of the review focuses on analysis of the principle markers of response to the treatment according to the type of treatment used and to the imaging techniques adopted.

  7. Predicting dynamic knee joint load with clinical measures in people with medial knee osteoarthritis.

    PubMed

    Hunt, Michael A; Bennell, Kim L

    2011-08-01

    Knee joint loading, as measured by the knee adduction moment (KAM), has been implicated in the pathogenesis of knee osteoarthritis (OA). Given that the KAM can only currently be accurately measured in the laboratory setting with sophisticated and expensive equipment, its utility in the clinical setting is limited. This study aimed to determine the ability of a combination of four clinical measures to predict KAM values. Three-dimensional motion analysis was used to calculate the peak KAM at a self-selected walking speed in 47 consecutive individuals with medial compartment knee OA and varus malalignment. Clinical predictors included: body mass; tibial angle measured using an inclinometer; walking speed; and visually observed trunk lean toward the affected limb during the stance phase of walking. Multiple linear regression was performed to predict KAM magnitudes using the four clinical measures. A regression model including body mass (41% explained variance), tibial angle (17% explained variance), and walking speed (9% explained variance) explained a total of 67% of variance in the peak KAM. Our study demonstrates that a set of measures easily obtained in the clinical setting (body mass, tibial alignment, and walking speed) can help predict the KAM in people with medial knee OA. Identifying those patients who are more likely to experience high medial knee loads could assist clinicians in deciding whether load-modifying interventions may be appropriate for patients, whilst repeated assessment of joint load could provide a mechanism to monitor disease progression or success of treatment.

  8. Application of Static Models to Predict Midazolam Clinical Interactions in the Presence of Single or Multiple Hepatitis C Virus Drugs.

    PubMed

    Cheng, Yaofeng; Ma, Li; Chang, Shu-Ying; Humphreys, W Griffith; Li, Wenying

    2016-08-01

    Asunaprevir (ASV), daclatasvir (DCV), and beclabuvir (BCV) are three drugs developed for the treatment of chronic hepatitis C virus infection. Here, we evaluated the CYP3A4 induction potential of each drug, as well as BCV-M1 (the major metabolite of BCV), in human hepatocytes by measuring CYP3A4 mRNA alteration. The induction responses were quantified as induction fold (mRNA fold change) and induction increase (mRNA fold increase), and then fitted with four nonlinear regression algorithms. Reversible inhibition and time-dependent inhibition (TDI) on CYP3A4 activity were determined to predict net drug-drug interactions (DDIs). All four compounds were CYP3A4 inducers and inhibitors, with ASV demonstrating TDI. The curve-fitting results demonstrated that fold increase is a better assessment to determine kinetic parameters for compounds inducing weak responses. By summing the contribution of each inducer, the basic static model was able to correctly predict the potential for a clinically meaningful induction signal for single or multiple perpetrators, but with over prediction of the magnitude. With the same approach, the mechanistic static model improved the prediction accuracy of DCV and BCV when including both induction and inhibition effects, but incorrectly predicted the net DDI effects for ASV alone or triple combinations. The predictions of ASV or the triple combination could be improved by only including the induction and reversible inhibition but not the ASV CYP3A4 TDI component. Those results demonstrated that static models can be applied as a tool to help project the DDI risk of multiple perpetrators using in vitro data.

  9. Predictions of cardiovascular responses during STS reentry using mathematical models

    NASA Technical Reports Server (NTRS)

    Leonard, J. I.; Srinivasan, R.

    1985-01-01

    The physiological adaptation to weightless exposure includes cardiovascular deconditioning arising in part from a loss of total circulating blood volume and resulting in a reduction of orthostatic tolerance. The crew of the Shuttle orbiter are less tolerant to acceleration forces in the head-to-foot direction during the reentry phase of the flight at a time they must function at a high level of performance. The factors that contribute to orthostatic intolerance during and following reentry and to predict the likelihood of impaired crew performance are evaluated. A computer simulation approach employing a mathematical model of the cardiovascular system is employed. It is shown that depending on the severity of blood volume loss, the reentry acceleration stress may be detrimental to physiologic function and may place the physiologic status of the crew near the borderline of some type of impairment. They are in agreement with conclusions from early ground-based experiments and from observations of early Shuttle flights.

  10. A Coupled Probabilistic Wake Vortex and Aircraft Response Prediction Model

    NASA Technical Reports Server (NTRS)

    Gloudemans, Thijs; Van Lochem, Sander; Ras, Eelco; Malissa, Joel; Ahmad, Nashat N.; Lewis, Timothy A.

    2016-01-01

    Wake vortex spacing standards along with weather and runway occupancy time, restrict terminal area throughput and impose major constraints on the overall capacity and efficiency of the National Airspace System (NAS). For more than two decades, the National Aeronautics and Space Administration (NASA) has been conducting research on characterizing wake vortex behavior in order to develop fast-time wake transport and decay prediction models. It is expected that the models can be used in the systems level design of advanced air traffic management (ATM) concepts that safely increase the capacity of the NAS. It is also envisioned that at a later stage of maturity, these models could potentially be used operationally, in groundbased spacing and scheduling systems as well as on the flight deck.

  11. Multi-scale simulations predict responses to non-invasive nerve root stimulation

    NASA Astrophysics Data System (ADS)

    Laakso, Ilkka; Matsumoto, Hideyuki; Hirata, Akimasa; Terao, Yasuo; Hanajima, Ritsuko; Ugawa, Yoshikazu

    2014-10-01

    Objective. Established biophysical neurone models have achieved limited success in reproducing electrophysiological responses to non-invasive stimulation of the human nervous system. This is related to our insufficient knowledge of the induced electric currents inside the human body. Despite the numerous research and clinical applications of non-invasive stimulation, it is still unclear which internal sites are actually affected by it. Approach. We performed multi-scale computer simulations that, by making use of advances in computing power and numerical algorithms, combine a microscopic model of electrical excitation of neurones with a macroscopic electromagnetic model of the realistic whole-body anatomy. Main results. The simulations yield responses consistent with those experimentally recorded following magnetic and electrical motor root stimulation in human subjects, and reproduce the observed amplitudes and latencies for a wide variety of stimulation parameters. Significance. Our findings demonstrate that modern computational techniques can produce detailed predictions about which and where neurones are activated, leading to improved understanding of the physics and basic mechanisms of non-invasive stimulation and enabling potential new applications that make use of improved targeting of stimulation.

  12. Factors predicting clinical nurses' willingness to care for Ebola virus disease-infected patients: A cross-sectional, descriptive survey.

    PubMed

    Kim, Ji Soo; Choi, Jeong Sil

    2016-09-01

    The purpose of this study was to identify factors predicting clinical nurses' willingness to care for Ebola virus disease (EVD)-infected patients. Data were collected from 179 nurses employed at 10 hospitals in Korea using self-reporting questionnaires. Only 26.8% of the participants were willing to care for EVD-infected patients. Factors predicting their willingness to provide care were their belief in public service, risk perception, and age. Nurses' willingness to provide care was high when their belief in public service was high, low when their risk perception was high, and low as their age increased. In order to strengthen nurses' willingness to care for EVD-infected patients, education that targets the enhancement of belief in public service should be included in nurse training. Efforts should be directed toward lowering EVD risk perception and developing systematic responses through government-led organized support.

  13. Syndecan-4 as a biomarker to predict clinical outcome for glioblastoma multiforme treated with WT1 peptide vaccine

    PubMed Central

    Takashima, Satoshi; Oka, Yoshihiro; Fujiki, Fumihiro; Morimoto, Soyoko; Nakajima, Hiroko; Nakae, Yoshiki; Nakata, Jun; Nishida, Sumiyuki; Hosen, Naoki; Tatsumi, Naoya; Mizuguchi, Kenji; Hashimoto, Naoya; Oji, Yusuke; Tsuboi, Akihiro; Kumanogoh, Atsushi; Sugiyama, Haruo

    2016-01-01

    Aim: In cancer immunotherapy, biomarkers are important for identification of responsive patients. This study was aimed to find biomarkers that predict clinical outcome of WT1 peptide vaccination. Materials & methods: Candidate genes that were expressed differentially between long- and short-term survivors were identified by cDNA microarray analysis of peripheral blood mononuclear cells that were extracted from 30 glioblastoma patients (discovery set) prior to vaccination and validated by quantitative RT-PCR using discovery set and different 23 patients (validation set). Results: SDC-4 mRNA expression levels distinguished between the long- and short-term survivors: 1-year survival rates were 64.0 and 18.5% in SDC4-low and -high patients, respectively. Conclusion: SDC-4 is a novel predictive biomarker for the efficacy of WT1 peptide vaccine. PMID:28116121

  14. Predictive models of toxicity with external radiotherapy for prostate cancer: clinical issues.

    PubMed

    Valdagni, Riccardo; Rancati, Tiziana; Fiorino, Claudio

    2009-07-01

    The objective of the current study was to analyze the state of the art and present limitations of available predictive clinical models (when available) estimating the risk of genitourinary tract and small bowel complications, erectile dysfunction, and acute and late symptoms of the rectal syndrome caused by prostate cancer external irradiation. An analysis of the literature indicated that very limited attention has been devoted to the development of "integrated," patient-tailored, user-friendly, and clinically usable tools for the prediction of external beam radiotoxicity. In this article, the authors reported on the multivariate correlation between late genitourinary and gastrointestinal toxicities and clinical/dosimetric risk factors, as well as on the first set of nomograms developed to predict acute and late rectal side effects. At the present state of knowledge, the use of nomograms as predictive instruments of radiotoxicity appears to be particularly attractive for several main reasons. They are "user friendly" and easily developed using the results of multivariate analyses, as they weigh the combined effects of multiple independent factors found to be correlated with the selected clinical endpoint. The integrated evaluation of clinical and dosimetric parameters in the single patient can help to provide a tailored probability of the specific outcome considered. Predicting a high probability of toxicity could avoid unnecessary daily costs for the individual patient in terms of quality of life modification during and after treatment, helping patients in the decision-making process of choosing the best individual, quality of life-related treatment, and clinicians in better tailoring the treatment to patient's characteristics. Cancer 2009;115(13 suppl):3141-9. (c) 2009 American Cancer Society.

  15. A Bayesian predictive sample size selection design for single-arm exploratory clinical trials.

    PubMed

    Teramukai, Satoshi; Daimon, Takashi; Zohar, Sarah

    2012-12-30

    The aim of an exploratory clinical trial is to determine whether a new intervention is promising for further testing in confirmatory clinical trials. Most exploratory clinical trials are designed as single-arm trials using a binary outcome with or without interim monitoring for early stopping. In this context, we propose a Bayesian adaptive design denoted as predictive sample size selection design (PSSD). The design allows for sample size selection following any planned interim analyses for early stopping of a trial, together with sample size determination before starting the trial. In the PSSD, we determine the sample size using the method proposed by Sambucini (Statistics in Medicine 2008; 27:1199-1224), which adopts a predictive probability criterion with two kinds of prior distributions, that is, an 'analysis prior' used to compute posterior probabilities and a 'design prior' used to obtain prior predictive distributions. In the sample size determination of the PSSD, we provide two sample sizes, that is, N and N(max) , using two types of design priors. At each interim analysis, we calculate the predictive probabilities of achieving a successful result at the end of the trial using the analysis prior in order to stop the trial in case of low or high efficacy (Lee et al., Clinical Trials 2008; 5:93-106), and we select an optimal sample size, that is, either N or N(max) as needed, on the basis of the predictive probabilities. We investigate the operating characteristics through simulation studies, and the PSSD retrospectively applies to a lung cancer clinical trial. (243)

  16. Clinical Prediction Models for Sleep Apnea: The Importance of Medical History over Symptoms

    PubMed Central

    Ustun, Berk; Westover, M. Brandon; Rudin, Cynthia; Bianchi, Matt T.

    2016-01-01

    Study Objective: Obstructive sleep apnea (OSA) is a treatable contributor to morbidity and mortality. However, most patients with OSA remain undiagnosed. We used a new machine learning method known as SLIM (Supersparse Linear Integer Models) to test the hypothesis that a diagnostic screening tool based on routinely available medical information would be superior to one based solely on patient-reported sleep-related symptoms. Methods: We analyzed polysomnography (PSG) and self-reported clinical information from 1,922 patients tested in our clinical sleep laboratory. We used SLIM and 7 state-of-the-art classification methods to produce predictive models for OSA screening using features from: (i) self-reported symptoms; (ii) self-reported medical information that could, in principle, be extracted from electronic health records (demographics, comorbidities), or (iii) both. Results: For diagnosing OSA, we found that model performance using only medical history features was superior to model performance using symptoms alone, and similar to model performance using all features. Performance was similar to that reported for other widely used tools: sensitivity 64.2% and specificity 77%. SLIM accuracy was similar to state-of-the-art classification models applied to this dataset, but with the benefit of full transparency, allowing for hands-on prediction using yes/no answers to a small number of clinical queries. Conclusion: To predict OSA, variables such as age, sex, BMI, and medical history are superior to the symptom variables we examined for predicting OSA. SLIM produces an actionable clinical tool that can be applied to data that is routinely available in modern electronic health records, which may facilitate automated, rather than manual, OSA screening. Commentary: A commentary on this article appears in this issue on page 159. Citation: Ustun B, Westover MB, Rudin C, Bianchi MT. Clinical prediction models for sleep apnea: the importance of medical history over symptoms

  17. A new clinical multivariable model that predicts postoperative acute kidney injury: impact of endogenous ouabain

    PubMed Central

    Simonini, Marco; Lanzani, Chiara; Bignami, Elena; Casamassima, Nunzia; Frati, Elena; Meroni, Roberta; Messaggio, Elisabetta; Alfieri, Ottavio; Hamlyn, John; Body, Simon C.; Collard, C. David; Zangrillo, Alberto; Manunta, Paolo; Body, Simon C.; Daniel Muehlschlegel, J.; Shernan, Stanton K.; Fox, Amanda A.; David Collard, C.

    2014-01-01

    Background Acute kidney injury (AKI) is an important complication of cardiac surgery. Recently, elevated levels of endogenous ouabain (EO), an adrenal stress hormone with haemodynamic and renal effects, have been associated with worse renal outcome after cardiac surgery. Our aim was to develop and evaluate a new risk model of AKI using simple preoperative clinical parameters and to investigate the utility of EO. Methods The primary outcome was AKI according to Acute Kidney Injury Network stage II or III. We selected the Northern New England Cardiovascular Disease Study Group (NNECDSG) as a reference model. We built a new internal predictive risk model considering common clinical variables (CLIN-RISK), compared this model with the NNECDSG model and determined whether the addition of preoperative plasma EO improved prediction of AKI. Results All models were tested on >800 patients admitted for elective cardiac surgery in our hospital. Seventy-nine patients developed AKI (9.9%). Preoperative EO levels were strongly associated with the incidence of AKI and clinical complication (total ICU stay and in-hospital mortality). The NNECDSG model was confirmed as a good predictor of AKI (AUC 0.74, comparable to the NNECDSG reference population). Our CLIN-RISK model had improved predictive power for AKI (AUC 0.79, CI 95% 0.73–0.84). Furthermore, addition of preoperative EO levels to both clinical models improved AUC to 0.79 and to 0.83, respectively (ΔAUC +0.05 and +0.04, respectively, P < 0.01). Conclusion In a population where the predictive power of the NNECDSG model was confirmed, CLIN-RISK was more powerful. Both clinical models were further improved by the addition of preoperative plasma EO levels. These new models provide improved predictability of the relative risk for the development of AKI following cardiac surgery and suggest that EO is a marker for renal vascular injury. PMID:24920842

  18. Comparison of Patient-Specific Computational Modeling Predictions and Clinical Outcomes of LASIK for Myopia

    PubMed Central

    Seven, Ibrahim; Vahdati, Ali; De Stefano, Vinicius Silbiger; Krueger, Ronald R.; Dupps, William J.

    2016-01-01

    Purpose To assess the predictive accuracy of simulation-based LASIK outcomes. Methods Preoperative and 3-month post-LASIK tomographic data from 20 eyes of 12 patients who underwent wavefront-optimized LASIK for myopia were obtained retrospectively. Patient-specific finite element models were created and case-specific treatment settings were simulated. Simulated keratometry (SimK) values and the mean tangential curvature of the central 3 mm (Kmean) were obtained from the anterior surfaces of the clinical tomographies, and computational models were compared. Correlations between Kmean prediction error and patient age, preoperative corneal hysteresis (CH), and corneal resistance factor (CRF) were assessed. Results The mean difference for Kmean between simulated and actual post-LASIK cases was not statistically significant (−0.13 ± 0.36 diopters [D], P = 0.1). The mean difference between the surgically induced clinical change in Kmean and the model-predicted change was −0.11 ± 0.34 D (P = 0.2). Kmean prediction error was correlated to CH, CRF, and patient age (r = 0.63, 0.53, and 0.5, respectively, P < 0.02), and incorporation of CH values into predictions as a linear offset increased their accuracy. Simulated changes in Kmean accounted for 97% of the variance in actual spherical equivalent refractive change. Conclusions Clinically feasible computational simulations predicted corneal curvature and manifest refraction outcomes with a level of accuracy in myopic LASIK cases that approached the limits of measurement error. Readily available preoperative biomechanical measures enhanced simulation accuracy. Patient-specific simulation may be a useful tool for clinical guidance in de novo LASIK cases. PMID:27893094

  19. Mismatch repair status may predict response to adjuvant chemotherapy in resectable pancreatic ductal adenocarcinoma.

    PubMed

    Riazy, Maziar; Kalloger, Steve E; Sheffield, Brandon S; Peixoto, Renata D; Li-Chang, Hector H; Scudamore, Charles H; Renouf, Daniel J; Schaeffer, David F

    2015-10-01

    Deficiencies in DNA mismatch repair have been associated with inferior response to 5-FU in colorectal cancer. Pancreatic ductal adenocarcinoma is similarly treated with pyrimidine analogs, yet the predictive value of mismatch repair status for response to these agents has not been examined in this malignancy. A tissue microarray with associated clinical outcome, comprising 254 resected pancreatic ductal adenocarcinoma patients was stained for four mismatch repair proteins (MLH1, MSH2, MSH6 and PMS2). Mismatch repair deficiency and proficiency was determined by the absence or presence of uniform nuclear staining in tumor cells, respectively. Cases identified as mismatch repair deficient on the tissue microarray were confirmed by immunohistochemistry on whole slide sections. Of the 265 cases, 78 (29%) received adjuvant treatment with a pyrimidine analog and 41 (15%) showed a mismatch repair-deficient immunoprofile. Multivariable disease-specific survival in the mismatch repair-proficient cohort demonstrated that adjuvant chemotherapy, regional lymph-node status, gender, and the presence of tumor budding were significant independent prognostic variables (P≤0.04); however, none of the eight clinico-pathologic covariates examined in the mismatch repair-deficient cohort were of independent prognostic significance. Univariable assessment of disease-specific survival revealed an almost identical survival profile for both treated and untreated patients with a mismatch repair-deficient profile, while treatment in the mismatch repair-proficient cohort conferred a greater than 10-month median disease-specific survival advantage over their untreated counterparts (P=0.0018). In this cohort, adjuvant chemotherapy with a pyrimidine analog conferred no survival advantage to mismatch repair-deficient pancreatic ductal adenocarcinoma patients. Mismatch repair immunoprofiling is a feasible predictive marker in pancreatic ductal adenocarcinoma patients, and further prospective

  20. Comparison of Decision-Assist and Clinical Judgment of Experts for Prediction of Lifesaving Interventions.

    PubMed

    Mackenzie, Colin F; Gao, Cheng; Hu, Peter F; Anazodo, Amechi; Chen, Hegang; Dinardo, Theresa; Imle, P Cristina; Hartsky, Lauren; Stephens, Christopher; Menaker, Jay; Fouche, Yvette; Murdock, Karen; Galvagno, Samuel; Alcorta, Richard; Shackelford, Stacy

    2015-03-01

    Early recognition of hemorrhage during the initial resuscitation of injured patients is associated with improved survival in both civilian and military casualties. We tested a transfusion and lifesaving intervention (LSI) prediction algorithm in comparison with clinical judgment of expert trauma care providers. We collected 15 min of pulse oximeter photopletysmograph waveforms and extracted features to predict LSIs. We compared this with clinical judgment of LSIs by individual categories of prehospital providers, nurses, and physicians and a combined judgment of all three providers using the Area Under Receiver Operating Curve (AUROC). We obtained clinical judgment of need for LSI from 405 expert clinicians in135 trauma patients. The pulse oximeter algorithm predicted transfusion within 6 h (AUROC, 0.92; P < 0.003) more accurately than either physicians or prehospital providers and as accurately as nurses (AUROC, 0.76; P = 0.07). For prediction of surgical procedures, the algorithm was as accurate as the three categories of clinicians. For prediction of fluid bolus, the diagnostic algorithm (AUROC, 0.9) was significantly more accurate than prehospital providers (AUROC, 0.62; P = 0.02) and nurses (AUROC, 0.57; P = 0.04) and as accurate as physicians (AUROC, 0.71; P = 0.06). Prediction of intubation by the algorithm (AUROC, 0.92) was as accurate as each of the three categories of clinicians. The algorithm was more accurate (P < 0.03) for blood and fluid prediction than the combined clinical judgment of all three providers but no different from the clinicians in the prediction of surgery (P = 0.7) or intubation (P = 0.8). Automated analysis of 15 min of pulse oximeter waveforms predicts the need for LSIs during initial trauma resuscitation as accurately as judgment of expert trauma clinicians. For prediction of emergency transfusion and fluid bolus, pulse oximetry features were more accurate than these experts. Such automated decision support could assist

  1. Breast Cancer Spatial Heterogeneity in Near-Infrared Spectra and the Prediction of Neoadjuvant Chemotherapy Response

    NASA Astrophysics Data System (ADS)

    Santoro, Ylenia

    Breast cancer accounts for more than 20% of all female cancers. Many of these patients receive neoadjuvant chemotherapy (NAC) to reduce the size of the tumor before surgery and to anticipate the efficacy of treatments for after the procedure. Breast cancer is a heterogeneous disease that comes in several clinical and histological forms. The prediction of the efficacy of chemotherapy would potentially select good candidates who would respond while excluding poor candidates who would not benefit from treatment. In this work we investigate the possibility of noninvasively predicting chemotherapy response prior to treatment based on optical biomarkers obtained from tumor spatial heterogeneities of spectral features measured using Diffuse Optical Spectroscopy. We describe an algorithm to calculate an index that characterizes spatial differences in broadband near-infrared absorption spectra of tumor-containing breast tissue. Patient-specific tumor spatial heterogeneities are visualized through a Heterogeneity Spectrum (HS). HS is a biomarker that can be attributed to different molecular distributions within the tumor. To classify lesion heterogeneities, we built a Heterogeneity Index (HI) from the HS by weighing specific absorption bands. It has been shown that NAC response is potentially related to tumor heterogeneity. Therefore, we correlate the HI obtained prior to treatment with the final response to NAC. In this thesis we also present a novel digital parallel frequency domain system for tissue imaging. The systems employs a supercontinuum laser with high brightness, and a photomultiplier with a large detection area, both allowing a deep penetration with extremely low power on the sample. The digital parallel acquisition is performed through the use of the Flimbox and it decreases the time required for standard serial systems that need to scan through all modulation frequencies. The all-digital acquisition removes analog noise, avoids the analog mixer and it does not

  2. Multiparametric magnetic resonance imaging predicts clinical outcomes in patients with chronic liver disease

    PubMed Central

    Pavlides, Michael; Banerjee, Rajarshi; Sellwood, Joanne; Kelly, Catherine J.; Robson, Matthew D.; Booth, Jonathan C.; Collier, Jane; Neubauer, Stefan; Barnes, Eleanor

    2016-01-01

    Background & Aims Multiparametric magnetic resonance (MR) imaging has been demonstrated to quantify hepatic fibrosis, iron, and steatosis. The aim of this study was to determine if MR can be used to predict negative clinical outcomes in liver disease patients. Methods Patients with chronic liver disease (n = 112) were recruited for MR imaging and data on the development of liver related clinical events were collected by medical records review. The median follow-up was 27 months. MR data were analysed blinded for the Liver Inflammation and Fibrosis score (LIF; <1, 1–1.99, 2–2.99, and ⩾3 representing normal, mild, moderate, and severe liver disease, respectively), T2∗ for liver iron content and proportion of liver fat. Baseline liver biopsy was performed in 102 patients. Results Liver disease aetiologies included non-alcoholic fatty liver disease (35%) and chronic viral hepatitis (30%). Histologically, fibrosis was mild in 54 (48%), moderate in 17 (15%), and severe in 31 (28%) patients. Overall mortality was 5%. Ten patients (11%) developed at least one liver related clinical event. The negative predictive value of LIF <2 was 100%. Two patients with LIF 2–2.99 and eight with LIF ⩾3 had a clinical event. Patients with LIF ⩾3 had a higher cumulative risk for developing clinical events, compared to those with LIF <1 (p = 0.02) and LIF 1–1.99 (p = 0.03). Cox regression analysis including all 3 variables (fat, iron, LIF) resulted in an enhanced LIF predictive value. Conclusions Non-invasive standardised multiparametric MR technology may be used to predict clinical outcomes in patients with chronic liver disease. PMID:26471505

  3. Predictive value of neurological examination for early cortical responses to somatosensory evoked potentials in patients with postanoxic coma.

    PubMed

    Bouwes, Aline; Binnekade, Jan M; Verbaan, Bart W; Zandbergen, Eveline G J; Koelman, Johannes H T M; Weinstein, Henry C; Hijdra, Albert; Horn, Janneke

    2012-03-01

    Bilateral absence of cortical N20 responses of median nerve somatosensory evoked potentials (SEP) predicts poor neurological outcome in postanoxic coma after cardiopulmonary resuscitation (CPR). Although SEP is easy to perform and available in most hospitals, it is worthwhile to know how neurological signs are associated with SEP results. The aim of this study was to investigate whether specific clinical neurological signs are associated with either an absent or a present median nerve SEP in patients after CPR. Data from the previously published multicenter prospective cohort study PROPAC (prognosis in postanoxic coma, 2000-2003) were used. Neurological examination, consisting of Glasgow Coma Score (GCS) and brain stem reflexes, and SEP were performed 24, 48, and 72 h after CPR. Positive predictive values for predicting absent and present SEP, as well as diagnostic accuracy were calculated. Data of 407 patients were included. Of the 781 SEPs performed, N20 s were present in 401, bilaterally absent in 299, and 81 SEPs were technically undeterminable. The highest positive predictive values (0.63-0.91) for an absent SEP were found for absent pupillary light responses. The highest positive predictive values (0.71-0.83) for a present SEP were found for motor scores of withdrawal to painful stimuli or better. Multivariate analyses showed a fair diagnostic accuracy (0.78) for neurological examination in predicting an absent or present SEP at 48 or 72 h after CPR. This study shows that neurological examination cannot reliably predict absent or present cortical N20 responses in median nerve SEPs in patients after CPR.

  4. Touch and personality: extraversion predicts somatosensory brain response.

    PubMed

    Schaefer, Michael; Heinze, Hans-Jochen; Rotte, Michael

    2012-08-01

    The Five-Factor-Model describes human personality in five core dimensions (extraversion, neuroticism, agreeableness, conscientiousness, and openness). These factors are supposed to have different neural substrates. For example, it has been suggested that behavioral differences between introverts and extraverts can be explained by the fact that introverts exhibit an inherent drive to compensate for overactive cortical activity in reticulo-thalamo-cortical pathways. The current study examined if responses in somatosensory cortices due to tactile stimulation are affected by personality traits. Based on previous studies and theoretical models we hypothesized a relationship of extraversion with somatosensory responses in primary somatosensory cortex (SI). In order to test this hypothesis we applied nonpainful tactile stimulation on the fingers of both hands of 23 healthy young participants (mean 25 years, standard deviation ± 2.8 years). Personality traits were assessed according to the Five-Factor-Model (NEO-FFI). Neuromagnetic source imaging revealed that the cortical activity (dipole strengths) for sources in SI were closely associated with the personality trait extraversion. Thus, the less extraverted the participants were, the higher was the cortical activity in SI. This relationship was in particular valid for the right hemisphere. We conclude that personality seems to depend on primary cortex activity. Furthermore, our results provide further evidence for an inter-hemispheric asymmetry of the social brain.

  5. Improving models to predict phenological responses to global change

    SciTech Connect

    Richardson, Andrew D.

    2015-11-25

    The term phenology describes both the seasonal rhythms of plants and animals, and the study of these rhythms. Plant phenological processes, including, for example, when leaves emerge in the spring and change color in the autumn, are highly responsive to variation in weather (e.g. a warm vs. cold spring) as well as longer-term changes in climate (e.g. warming trends and changes in the timing and amount of rainfall). We conducted a study to investigate the phenological response of northern peatland communities to global change. Field work was conducted at the SPRUCE experiment in northern Minnesota, where we installed 10 digital cameras. Imagery from the cameras is being used to track shifts in plant phenology driven by elevated carbon dioxide and elevated temperature in the different SPRUCE experimental treatments. Camera imagery and derived products (“greenness”) is being posted in near-real time on a publicly available web page (http://phenocam.sr.unh.edu/webcam/gallery/). The images will provide a permanent visual record of the progression of the experiment over the next 10 years. Integrated with other measurements collected as part of the SPRUCE program, this study is providing insight into the degree to which phenology may mediate future shifts in carbon uptake and storage by peatland ecosystems. In the future, these data will be used to develop improved models of vegetation phenology, which will be tested against ground observations collected by a local collaborator.

  6. Genetic predictive biomarkers of anti-VEGF treatment response in patients with neovascular age-related macular degeneration.

    PubMed

    Fauser, Sascha; Lambrou, George N

    2015-01-01

    Anti-vascular endothelial growth factor (anti-VEGF) therapies for neovascular age-related macular degeneration (nAMD) have proven efficacy at a study-population level, although individual patient responses vary, with most of the patients responding well to anti-VEGF therapies, while a few respond poorly. The pathogenesis of AMD is known to have a genetic component, but it is unclear if any particular genotype can predict response to anti-VEGF therapy. With the advent of less expensive genotyping technology, there have been numerous studies within this area. Here we analyze potential biomarker candidates identified that could be used in a clinical setting to predict response to anti-VEGF treatment of nAMD. We analyze single nucleotide polymorphisms (SNPs) identified from 39 publications. The SNPs that appeared to be of most importance fell into two main groups: those previously associated with AMD pathogenesis and those within the signaling pathway targeted by anti-VEGF therapies. A number of small studies found evidence supporting an association between anti-VEGF treatment response and two SNPs, CFH rs1061170 and VEGFA rs699947, but results from randomized controlled trials found no such association. It is possible that, in the future, the cumulative effect of several high-risk SNPs may prove useful in a clinical setting and that other genetic biomarkers may emerge.

  7. Comparison of Existing Clinical Scoring Systems in Predicting Severity and Prognoses of Hyperlipidemic Acute Pancreatitis in Chinese Patients

    PubMed Central

    Qiu, Lei; Sun, Rui Qing; Jia, Rong Rong; Ma, Xiu Ying; Cheng, Li; Tang, Mao Chun; Zhao, Yan

    2015-01-01

    Abstract It is important to identify the severity of acute pancreatitis (AP) in the early course of the disease. Clinical scoring systems may be helpful to predict the prognosis of patients with early AP; however, few analysts have forecast the accuracy of scoring systems for the prognosis in hyperlipidemic acute pancreatitis (HLAP). The purpose of this study was to summarize the clinical characteristics of HLAP and compare the accuracy of conventional scoring systems in predicting the prognosis of HLAP. This study retrospectively analyzed all consecutively diagnosed AP patients between September 2008 and March 2014. We compared the clinical characteristics between HLAP and nonhyperlipidemic acute pancreatitis. The bedside index for severity of acute pancreatitis (BISAP), Ranson, computed tomography severity index (CTSI), and systemic inflammatory response syndrome (SIRS) scores were applied within 48 hours following admission. Of 909 AP patients, 129 (14.2%) had HLAP, 20 were classified as severe acute pancreatitis (SAP), 8 had pseudocysts, 9 had pancreatic necrosis, 30 had pleural effusions, 33 had SIRS, 14 had persistent organ failure, and there was 1 death. Among the HLAP patients, the area under curves for BISAP, Ranson, SIRS, and CTSI in predicting SAP were 0.905, 0.938, 0.812, and 0.834, 0.874, 0.726, 0.668, and 0.848 for local complications, and 0.904, 0.917, 0.758, and 0.849 for organ failure, respectively. HLAP patients were characterized by younger age at onset, higher recurrence rate, and being more prone to pancreatic necrosis, organ failure, and SAP. BISAP, Ranson, SIRS, and CTSI all have accuracy in predicting the prognosis of HLAP patients, but each has different strengths and weaknesses. PMID:26061329

  8. De novo sequencing of circulating miRNAs identifies novel markers predicting clinical outcome of locally advanced breast cancer

    PubMed Central

    2012-01-01

    Background MicroRNAs (miRNAs) have been recently detected in the circulation of cancer patients, where they are associated with clinical parameters. Discovery profiling of circulating small RNAs has not been reported in breast cancer (BC), and was carried out in this study to identify blood-based small RNA markers of BC clinical outcome. Methods The pre-treatment sera of 42 stage II-III locally advanced and inflammatory BC patients who received neoadjuvant chemotherapy (NCT) followed by surgical tumor resection were analyzed for marker identification by deep sequencing all circulating small RNAs. An independent validation cohort of 26 stage II-III BC patients was used to assess the power of identified miRNA markers. Results More than 800 miRNA species were detected in the circulation, and observed patterns showed association with histopathological profiles of BC. Groups of circulating miRNAs differentially associated with ER/PR/HER2 status and inflammatory BC were identified. The relative levels of selected miRNAs measured by PCR showed consistency with their abundance determined by deep sequencing. Two circulating miRNAs, miR-375 and miR-122, exhibited strong correlations with clinical outcomes, including NCT response and relapse with metastatic disease. In the validation cohort, higher levels of circulating miR-122 specifically predicted metastatic recurrence in stage II-III BC patients. Conclusions Our study indicates that certain miRNAs can serve as potential blood-based biomarkers for NCT response, and that miR-122 prevalence in the circulation predicts BC metastasis in early-stage patients. These results may allow optimized chemotherapy treatments and preventive anti-metastasis interventions in future clinical applications. PMID:22400902

  9. Assessing the predictive value of the rodent neurofunctional assessment for commonly reported adverse events in phase I clinical trials.

    PubMed

    Mead, Andy N; Amouzadeh, Hamid R; Chapman, Kathryn; Ewart, Lorna; Giarola, Alessandra; Jackson, Samuel J; Jarvis, Philip; Jordaan, Pierre; Redfern, Will; Traebert, Martin; Valentin, Jean-Pierre; Vargas, Hugo M

    2016-10-01

    Central Nervous System (CNS)-related safety concerns are major contributors to delays and failure during the development of new candidate drugs (CDs). CNS-related safety data on 141 small molecule CDs from five pharmaceutical companies were analyzed to identify the concordance between rodent multi-parameter neurofunctional assessments (Functional Observational Battery: FOB, or Irwin test: IT) and the five most common adverse events (AEs) in Phase I clinical trials, namely headache, nausea, dizziness, fatigue/somnolence and pain. In the context of this analysis, the FOB/IT did not predict the occurrence of these particular AEs in man. For AEs such as headache, nausea, dizziness and pain the results are perhaps unsurprising, as the FOB/IT were not originally designed to predict these AEs. More unexpected was that the FOB/IT are not adequate for predicting 'somnolence/fatigue' nonclinically. In drug development, these five most prevalent AEs are rarely responsible for delaying or stopping further progression of CDs. More serious AEs that might stop CD development occurred at too low an incidence rate in our clinical dataset to enable translational analysis.

  10. Predicting Emotional Responses to Horror Films from Cue-Specific Affect.

    ERIC Educational Resources Information Center

    Neuendorf, Kimberly A.; Sparks, Glenn G.

    1988-01-01

    Assesses individuals' fear and enjoyment reactions to horror films, applying theories of cognition and affect that predict emotional responses to a stimulus on the basis of prior affect toward specific cues included in that stimulus. (MM)

  11. A noise level prediction method based on electro-mechanical frequency response function for capacitors.

    PubMed

    Zhu, Lingyu; Ji, Shengchang; Shen, Qi; Liu, Yuan; Li, Jinyu; Liu, Hao

    2013-01-01

    The capacitors in high-voltage direct-current (HVDC) converter stations radiate a lot of audible noise which can reach higher than 100 dB. The existing noise level prediction methods are not satisfying enough. In this paper, a new noise level prediction method is proposed based on a frequency response function considering both electrical and mechanical characteristics of capacitors. The electro-mechanical frequency response function (EMFRF) is defined as the frequency domain quotient of the vibration response and the squared capacitor voltage, and it is obtained from impulse current experiment. Under given excitations, the vibration response of the capacitor tank is the product of EMFRF and the square of the given capacitor voltage in frequency domain, and the radiated audible noise is calculated by structure acoustic coupling formulas. The noise level under the same excitations is also measured in laboratory, and the results are compared with the prediction. The comparison proves that the noise prediction method is effective.

  12. MATHEMATICAL MODEL OF STERIODOGENESIS TO PREDICT DYNAMIC RESPONSE TO ENDOCRINE DISRUPTORS

    EPA Science Inventory

    WE ARE DEVELOPING A MECHANISTIC MATHEMATICAL MODEL OF THE INTRATESTICULAR AND INTRAOVARIAN METABOLIC NETWORK THAT MEDIATES STEROID SYNTHESIS, AND THE KINETICS FOR ENZYME INHIBITION BY EDCs TO PREDICT THE TIME AND DOSE-RESPONSE.

  13. Mysid Population Responses to Resource Limitation Differ from those Predicted by Cohort Studies

    EPA Science Inventory

    Effects of anthropogenic stressors on animal populations are often evaluated by assembling vital rate responses from isolated cohort studies into a single demographic model. However, models constructed from cohort studies are difficult to translate into ecological predictions be...

  14. Predicting evolutionary responses to climate change in the sea.

    PubMed

    Munday, Philip L; Warner, Robert R; Monro, Keyne; Pandolfi, John M; Marshall, Dustin J

    2013-12-01

    An increasing number of short-term experimental studies show significant effects of projected ocean warming and ocean acidification on the performance on marine organisms. Yet, it remains unclear if we can reliably predict the impact of climate change on marine populations and ecosystems, because we lack sufficient understanding of the capacity for marine organisms to adapt to rapid climate change. In this review, we emphasise why an evolutionary perspective is crucial to understanding climate change impacts in the sea and examine the approaches that may be useful for addressing this challenge. We first consider what the geological record and present-day analogues of future climate conditions can tell us about the potential for adaptation to climate change. We also examine evidence that phenotypic plasticity may assist marine species to persist in a rapidly changing climate. We then outline the various experimental approaches that can be used to estimate evolutionary potential, focusing on molecular tools, quantitative genetics, and experimental evolution, and we describe the benefits of combining different approaches to gain a deeper understanding of evolutionary potential. Our goal is to provide a platform for future research addressing the evolutionary potential for marine organisms to cope with climate change.

  15. Adjusted Clinical Groups: Predictive Accuracy for Medicaid Enrollees in Three States

    PubMed Central

    Adams, E. Kathleen; Bronstein, Janet M.; Raskind-Hood, Cheryl

    2002-01-01

    Actuarial split-sample methods were used to assess predictive accuracy of adjusted clinical groups (ACGs) for Medicaid enrollees in Georgia, Mississippi (lagging in managed care penetration), and California. Accuracy for two non-random groups—high-cost and located in urban poor areas—was assessed. Measures for random groups were derived with and without short-term enrollees to assess the effect of turnover on predictive accuracy. ACGs improved predictive accuracy for high-cost conditions in all States, but did so only for those in Georgia's poorest urban areas. Higher and more unpredictable expenses of short-term enrollees moderated the predictive power of ACGs. This limitation was significant in Mississippi due in part, to that State's very high proportion of short-term enrollees. PMID:12545598

  16. Predicting the Response of Electricity Load to Climate Change

    SciTech Connect

    Sullivan, Patrick; Colman, Jesse; Kalendra, Eric

    2015-07-28

    Our purpose is to develop a methodology to quantify the impact of climate change on electric loads in the United States. We perform simple linear regression, assisted by geospatial smoothing, on paired temperature and load time-series to estimate the heating- and coolinginduced sensitivity to temperature across 300 transmission zones and 16 seasonal and diurnal time periods. The estimated load sensitivities can be coupled with climate scenarios to quantify the potential impact of climate change on load, with a primary application being long-term electricity scenarios. The method allows regional and seasonal differences in climate and load response to be reflected in the electricity scenarios. While the immediate product of this analysis was designed to mesh with the spatial and temporal resolution of a specific electricity model to enable climate change scenarios and analysis with that model, we also propose that the process could be applied for other models and purposes.

  17. Clinical Prediction of Suicide and Undetermined Death: A Pseudo-Prospective Clinical and Medico-Legal Study of Substance Abusers

    PubMed Central

    Brådvik, Louise; Berglund, Mats; Frank, Arne; Löwenhielm, Peter

    2017-01-01

    This study examines aspects of prediction of suicide and death of undetermined intent. We investigated all consecutive, autopsied patients between 1993 and 1997 who had been in contact with the Addiction Centre in Malmö from 1968 onwards. The staff was asked, shortly after autopsy but before they knew of the manner of death, if they thought the patient had committed suicide. The case records were blindly evaluated, and toxicological autopsy findings for alcohol in blood samples investigated. The specificity of prediction was 83% and significantly more often correct than the sensitivity, which was only 45% for suicide and for suicide/death of undetermined intent (93% versus 39%). Suicidal communication was more often considered non-serious before death of undetermined intent than before suicide. The former could be predicted by ideation but not by suicide attempt reported in case records, unlike suicide, which was predicted by both. The undetermined group also showed higher levels of alcohol in the blood at autopsy. We concluded that more serious clinical investigation of suicidal feelings, which may be hidden and not taken seriously, and treatment of alcohol use disorders with active follow-up appear urgent in the efforts to prevent suicide. PMID:28304357

  18. Serotonin modulates the effects of Pavlovian aversive predictions on response vigor.

    PubMed

    Crockett, Molly J; Clark, Luke; Apergis-Schoute, Annemieke M; Morein-Zamir, Sharon; Robbins, Trevor W

    2012-09-01

    Updated theoretical accounts of the role of serotonin (5-HT) in motivation propose that 5-HT operates at the intersection of aversion and inhibition, promoting withdrawal in the face of aversive predictions. However, the specific cognitive mechanisms through which 5-HT modulates withdrawal behavior remain poorly understood. Behavioral inhibition in response to punishments reflects at least two concurrent processes: instrumental aversive predictions linking stimuli, responses, and punishments, and Pavlovian aversive predictions linking stimuli and punishments irrespective of response. In the current study, we examined to what extent 5-HT modulates the impact of instrumental vs Pavlovian aversive predictions on behavioral inhibition. We used acute tryptophan depletion to lower central 5-HT levels in healthy volunteers, and observed behavior in a novel task designed to measure the influence of Pavlovian and instrumental aversive predictions on choice (response bias) and response vigor (response latencies). After placebo treatment, participants were biased against responding on the button that led to punishment, and they were slower to respond in a punished context, relative to a non-punished context. Specifically, participants slowed their responses in the presence of stimuli predictive of punishments. Tryptophan depletion removed the bias against responding on the punished button, and abolished slowing in the presence of punished stimuli, irrespective of response. We suggest that this set of results can be explained by a role for 5-HT in Pavlovian aversive predictions. These findings suggest additional specificity for the influence of 5-HT on aversively motivated behavioral inhibition and extend recent models of the role of 5-HT in aversive predictions.

  19. Dynamic contrast-enhanced magnetic resonance imaging for prediction of response to neoadjuvant chemotherapy in breast cancer

    NASA Astrophysics Data System (ADS)

    Fu, Juzhong; Fan, Ming; Zheng, Bin; Shao, Guoliang; Zhang, Juan; Li, Lihua

    2016-03-01

    Breast cancer is the second leading cause of women death in the United States. Currently, Neoadjuvant Chemotherapy (NAC) has become standard treatment paradigms for breast cancer patients. Therefore, it is important to find a reliable non-invasive assessment and prediction method which can evaluate and predict the response of NAC on breast cancer. The Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) approach can reflect dynamic distribution of contrast agent in tumor vessels, providing important basis for clinical diagnosis. In this study, the efficacy of DCE-MRI on evaluation and prediction of response to NAC in breast cancer was investigated. To this end, fifty-seven cases of malignant breast cancers with MRI examination both before and after two cycle of NAC were analyzed. After pre-processing approach for segmenting breast lesions and background regions, 126-dimensional imaging features were extracted from DCE-MRI. Statistical analyses were then performed to evaluate the associations between the extracted DCE-MRI features and the response to NAC. Specifically, pairwise t test was used to calculate differences of imaging features between MRI examinations before-and-after NAC. Moreover, the associations of these image features with response to NAC were assessed using logistic regression. Significant association are found between response to NAC and the features of lesion morphology and background parenchymal enhancement, especially the feature of background enhancement in normal side of breast (P=0.011). Our study indicate that DCE-MRI features can provide candidate imaging markers to predict response of NAC in breast cancer.

  20. Estimating the predictive quality of dose-response after model selection.

    PubMed

    Hu, Chuanpu; Dong, Yingwen

    2007-07-20

    Prediction of dose-response is important in dose selection in drug development. As the true dose-response shape is generally unknown, model selection is frequently used, and predictions based on the final selected model. Correctly assessing the quality of the predictions requires accounting for the uncertainties caused by the model selection process, which has been difficult. Recently, a new approach called data perturbation has emerged. It allows important predictive characteristics be computed while taking model selection into consideration. We study, through simulation, the performance of data perturbation in estimating standard error of parameter estimates and prediction errors. Data perturbation was found to give excellent prediction error estimates, although at times large Monte Carlo sizes were needed to obtain good standard error estimates. Overall, it is a useful tool to characterize uncertainties in dose-response predictions, with the potential of allowing more accurate dose selection in drug development. We also look at the influence of model selection on estimation bias. This leads to insights into candidate model choices that enable good dose-response prediction.

  1. The Utility of Situational Theory of Publics for Assessing Public Response to a Disaster Prediction.

    ERIC Educational Resources Information Center

    Major, Anne Marie

    1998-01-01

    Examines the utility of public-relations theory, specifically situational theory of publics, for assessing response to the New Madrid earthquake prediction. Finds that high personalized risk was associated with high constraint recognition regardless of belief in the prediction. Suggests development of more effective messages for communicating with…

  2. Auditory Brainstem Response to Complex Sounds Predicts Self-Reported Speech-in-Noise Performance

    ERIC Educational Resources Information Center

    Anderson, Samira; Parbery-Clark, Alexandra; White-Schwoch, Travis; Kraus, Nina

    2013-01-01

    Purpose: To compare the ability of the auditory brainstem response to complex sounds (cABR) to predict subjective ratings of speech understanding in noise on the Speech, Spatial, and Qualities of Hearing Scale (SSQ; Gatehouse & Noble, 2004) relative to the predictive ability of the Quick Speech-in-Noise test (QuickSIN; Killion, Niquette,…

  3. Prediction of postoperative liver regeneration from clinical information using a data-led mathematical model

    PubMed Central

    Yamamoto, Kimiyo N.; Ishii, Masatsugu; Inoue, Yoshihiro; Hirokawa, Fumitoshi; MacArthur, Ben D.; Nakamura, Akira; Haeno, Hiroshi; Uchiyama, Kazuhisa

    2016-01-01

    Although the capacity of the liver to recover its size after resection has enabled extensive liver resection, post-hepatectomy liver failure remains one of the most lethal complications of liver resection. Therefore, it is clinically important to discover reliable predictive factors after resection. In this study, we established a novel mathematical framework which described post-hepatectomy liver regeneration in each patient by incorporating quantitative clinical data. Using the model fitting to the liver volumes in series of computed tomography of 123 patients, we estimated liver regeneration rates. From the estimation, we found patients were divided into two groups: i) patients restored the liver to its original size (Group 1, n = 99); and ii) patients experienced a significant reduction in size (Group 2, n = 24). From discriminant analysis in 103 patients with full clinical variables, the prognosis of patients in terms of liver recovery was successfully predicted in 85–90% of patients. We further validated the accuracy of our model prediction using a validation cohort (prediction = 84–87%, n = 39). Our interdisciplinary approach provides qualitative and quantitative insights into the dynamics of liver regeneration. A key strength is to provide better prediction in patients who had been judged as acceptable for resection by current pragmatic criteria. PMID:27694914

  4. Predicting counseling psychologists attitudes and clinical judgments with respect to older adults.

    PubMed

    Tomko, Jody K; Munley, Patrick H

    2013-01-01

    The purpose of this study was to examine age, gender, training and experience in aging issues, fear of death, and multicultural competence in predicting counseling psychologists' global attitudes toward older adults and specific clinical judgments concerning a case vignette of an older client. A national sample of 364 practicing counseling psychologists participated in the study. Participants completed a demographic measure, Polizzi's refined version of the Aging Semantic Differential (Polizzi, 2003 ), a survey of professional bias based on a clinical vignette of a 70-year-old woman (James & Haley, 1995), the Collett-Lester Fear of Death Scale 3.0 (Lester, & Abdel-Khalek, 2003), the Multicultural Counseling Knowledge and Awareness Scale (MCKAS; Ponterotto, Gretchen, Utsey, Rieger, & Austin, 2002), and a Training and Experience Questionnaire. Hierarchical multiple regression analyses were used to investigate the extent to which the selected variables predicted more favorable attitudes toward older adults and less professional bias toward an older client beyond prediction by age and gender. Results revealed that older age and higher total scores on the MCKAS predicted less professional bias in clinical judgments. Gender was a significant predictor of global attitudes toward older adults. Findings suggest that multicultural knowledge, awareness, and skills are important in working with older adults.

  5. Prediction of postoperative liver regeneration from clinical information using a data-led mathematical model

    NASA Astrophysics Data System (ADS)

    Yamamoto, Kimiyo N.; Ishii, Masatsugu; Inoue, Yoshihiro; Hirokawa, Fumitoshi; MacArthur, Ben D.; Nakamura, Akira; Haeno, Hiroshi; Uchiyama, Kazuhisa

    2016-10-01

    Although the capacity of the liver to recover its size after resection has enabled extensive liver resection, post-hepatectomy liver failure remains one of the most lethal complications of liver resection. Therefore, it is clinically important to discover reliable predictive factors after resection. In this study, we established a novel mathematical framework which described post-hepatectomy liver regeneration in each patient by incorporating quantitative clinical data. Using the model fitting to the liver volumes in series of computed tomography of 123 patients, we estimated liver regeneration rates. From the estimation, we found patients were divided into two groups: i) patients restored the liver to its original size (Group 1, n = 99) and ii) patients experienced a significant reduction in size (Group 2, n = 24). From discriminant analysis in 103 patients with full clinical variables, the prognosis of patients in terms of liver recovery was successfully predicted in 85–90% of patients. We further validated the accuracy of our model prediction using a validation cohort (prediction = 84–87%, n = 39). Our interdisciplinary approach provides qualitative and quantitative insights into the dynamics of liver regeneration. A key strength is to provide better prediction in patients who had been judged as acceptable for resection by current pragmatic criteria.

  6. Predicting subtle behavioral responses of invertebrates to soil contaminants

    SciTech Connect

    Donkin, S.G.

    1995-12-31

    At concentration levels well below those which cause death and injury to soil invertebrates, a toxic chemical plume may yet effectively damage a soil ecosystem by triggering avoidance behavior among sensitive invertebrates as they move along the concentration gradient. The result may be a soil ecosystem lacking the benefits of effective nutrient cycling and mineralization which a thriving invertebrate population provides. While determining actual detection limits of invertebrates for chemical gradients in soils is experimentally difficult, theoretical calculations have suggested that such limits may be extremely low, and hence many organisms may sense and avoid concentrations of chemicals far below levels commonly considered acceptable. The minimum gradient (G) that can be detected by a receptor depends on the receptor radius (R), the chemical concentration (C), the diffusion constant of the chemical (D), the velocity of the organism (v), and the time over which the receptor integrates the chemical signal (t). In addition, the characteristics of that gradient are determined by interactions between the chemical and the soil particles (sorption/desorption), and advection through the pore spaces. The example of lead (Pb), a neurotoxic metal with demonstrated behavioral effects on the free-living nematode Caenorhabditis elegans, is used to model a chemical migrating through a soil. Based on experimentally determined Pb concentrations which elicited avoidance behavior in nematodes, and sorption characteristics of defined Pb-soil systems, the minimum detectable gradient (G) produced by a solubilized Pb plume in several soils was modeled. The results predict maximum allowable Pb levels in a soil if a healthy invertebrate community is desired, and suggest areas for further research into the subtle behavioral effects of environmental toxicants ore sensitive invertebrates.

  7. Validating genetic markers of response to recombinant human growth hormone in children with growth hormone deficiency and Turner syndrome: the PREDICT validation study

    PubMed Central

    Stevens, Adam; Murray, Philip; Wojcik, Jerome; Raelson, John; Koledova, Ekaterina; Chatelain, Pierre

    2016-01-01

    Objective Single-nucleotide polymorphisms (SNPs) associated with the response to recombinant human growth hormone (r-hGH) have previously been identified in growth hormone deficiency (GHD) and Turner syndrome (TS) children in the PREDICT long-term follow-up (LTFU) study (Nbib699855). Here, we describe the PREDICT validation (VAL) study (Nbib1419249), which aimed to confirm these genetic associations. Design and methods Children with GHD (n = 293) or TS (n = 132) were recruited retrospectively from 29 sites in nine countries. All children had completed 1 year of r-hGH therapy. 48 SNPs previously identified as associated with first year growth response to r-hGH were genotyped. Regression analysis was used to assess the association between genotype and growth response using clinical/auxological variables as covariates. Further analysis was undertaken using random forest classification. Results The children were younger, and the growth response was higher in VAL study. Direct genotype analysis did not replicate what was found in the LTFU study. However, using exploratory regression models with covariates, a consistent relationship with growth response in both VAL and LTFU was shown for four genes – SOS1 and INPPL1 in GHD and ESR1 and PTPN1 in TS. The random forest analysis demonstrated that only clinical covariates were important in the prediction of growth response in mild GHD (>4 to <10 μg/L on GH stimulation test), however, in severe GHD (≤4 μg/L) several SNPs contributed (in IGF2, GRB10, FOS, IGFBP3 and GHRHR). Conclusions The PREDICT validation study supports, in an independent cohort, the association of four of 48 genetic markers with growth response to r-hGH treatment in both pre-pubertal GHD and TS children after controlling for clinical/auxological covariates. However, the contribution of these SNPs in a prediction model of first-year response is not sufficient for routine clinical use. PMID:27651465

  8. Early postoperative tumor progression predicts clinical outcome in glioblastoma-implication for clinical trials.

    PubMed

    Merkel, Andreas; Soeldner, Dorothea; Wendl, Christina; Urkan, Dilek; Kuramatsu, Joji B; Seliger, Corinna; Proescholdt, Martin; Eyupoglu, Ilker Y; Hau, Peter; Uhl, Martin

    2017-01-18

    Molecular markers define the diagnosis of glioblastoma in the new WHO classification of 2016, challenging neuro-oncology centers to provide timely treatment initiation. The aim of this study was to determine whether a time delay to treatment initiation was accompanied by signs of early tumor progression in an MRI before the start of radiotherapy, and, if so, whether this influences the survival of glioblastoma patients. Images from 61 patients with early post-surgery MRI and a second MRI just before the start of radiotherapy were examined retrospectively for signs of early tumor progression. Survival information was analyzed using the Kaplan-Meier method, and a Cox multivariate analysis was performed to identify independent variables for survival prediction. 59 percent of patients showed signs of early tumor progression after a mean time of 24.1 days from the early post-surgery MRI to the start of radiotherapy. Compared to the group without signs of early tumor progression, which had a mean time of 23.3 days (p = 0.685, Student's t test), progression free survival was reduced from 320 to 185 days (HR 2.3; CI 95% 1.3-4.0; p = 0.0042, log-rank test) and overall survival from 778 to 329 days (HR 2.9; CI 95% 1.6-5.1; p = 0.0005). A multivariate Cox regression analysis revealed that the Karnofsky performance score, O-6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation, and signs of early tumor progression are prognostic markers of overall survival. Early tumor progression at the start of radiotherapy is associated with a worse prognosis for glioblastoma patients. A standardized baseline MRI might allow for better patient stratification.

  9. Integration of preclinical and clinical knowledge to predict intravenous PK in human: bilastine case study.

    PubMed

    Vozmediano, Valvanera; Ortega, Ignacio; Lukas, John C; Gonzalo, Ana; Rodriguez, Monica; Lucero, Maria Luisa

    2014-03-01

    Modern pharmacometrics can integrate and leverage all prior proprietary and public knowledge. Such methods can be used to scale across species or comparators, perform clinical trial simulation across alternative designs, confirm hypothesis and potentially reduce development burden, time and costs. Crucial yet typically lacking in integration is the pre-clinical stage. Prediction of PK in man, using in vitro and in vivo studies in different animal species, is increasingly well theorized but could still find wider application in drug development. The aim of the present work was to explore methods for bridging pharmacokinetic knowledge from animal species (i.v. and p.o.) and man (p.o.) into i.v. in man using the antihistamine drug bilastine as example. A model, predictive of i.v. PK in man, was developed on data from two pre-clinical species (rat and dog) and p.o. in man bilastine trials performed earlier. In the knowledge application stage, two different approaches were used to predict human plasma concentration after i.v. of bilastine: allometry (several scaling methods) and a semi-physiological method. Both approaches led to successful predict