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Sample records for predicting clinical response

  1. Predicting clinical responses in major depression using intrinsic functional connectivity.

    PubMed

    Qin, Jian; Shen, Hui; Zeng, Ling-Li; Jiang, Weixiong; Liu, Li; Hu, Dewen

    2015-08-19

    There has been increasing interest in multivariate pattern analysis (MVPA) as a means of distinguishing psychiatric patients from healthy controls using brain imaging. However, it remains unclear whether MVPA methods can accurately estimate the medication status of psychiatric patients. This study aims to develop an MVPA approach to accurately predict the antidepressant medication status of individuals with major depression on the basis of whole-brain resting-state functional connectivity MRI (rs-fcMRI). We investigated data from rs-fcMRI of 24 medication-naive depressed patients, 16 out of whom subsequently underwent antidepressant treatment and achieved clinical recovery, and 29 demographically similar controls. By training a linear support vector machine classifier and combining it with principal component analysis, the medication-naive patients were identified from the healthy controls with 100% accuracy. In addition, we found reliable correlations between MVPA prediction scores and clinical symptom severity. Moreover, the most discriminative functional connections were located within or across the cerebellum and default mode, affective, and sensorimotor networks, indicating that these networks may play important roles in major depression. Most importantly, only ∼30% of these discriminative connections were normalized in clinically recovered patients after antidepressant treatment. The current study may not only show the feasibility of estimating medication status by MVPA of whole-brain rs-fcMRI data in major depression but also shed new light on the pathological mechanism of this disorder. PMID:26164454

  2. Early treatment response predicted subsequent clinical response in patients with schizophrenia taking paliperidone extended-release.

    PubMed

    Yeh, En-Chi; Huang, Ming-Chyi; Tsai, Chang-Jer; Chen, Chun-Tse; Chen, Kuan-Yu; Chiu, Chih-Chiang

    2015-11-30

    This 6-week open-labeled study investigated whether early treatment response in patients receiving paliperidone extended-release (paliperidone ER) can facilitate prediction of responses at Week 6. Patients with schizophrenia or schizoaffective disorder were administered 9mg/day of paliperidone ER during the first 2 weeks, after which the dose was adjusted clinically. They were assessed on Days 0, 4, 7, 14, 28, and 42 by the Positive and Negative Syndrome Scale (PANSS). The serum concentrations of 9-hydroxyrisperidone were examined on Days 14 and 42. Among the 41 patients enrolled, 26 were classified as responders (≧50% improvement on total PANSS scores at Week 6). In the receiver-operator curves (ROC) analyses, the changes in total PANSS scores at Week 2 appeared to show more accurate predictability compared to Day 4 and Day 7. At Week 6, no significant correlation was observed between blood 9-hydroxyrisperidone concentration and the total score or changes of PANSS scores. The results suggest that early treatment response to paliperidone ER, particularly at Week 2, can serve as a suitable outcome predictor at Week 6. Using 9mg/day paliperidone ER as an initial dose for schizophrenia treatment exhibited relatively favorable tolerability and feasibility. PMID:26319696

  3. High-throughput screening using patient-derived tumor xenografts to predict clinical trial drug response.

    PubMed

    Gao, Hui; Korn, Joshua M; Ferretti, Stéphane; Monahan, John E; Wang, Youzhen; Singh, Mallika; Zhang, Chao; Schnell, Christian; Yang, Guizhi; Zhang, Yun; Balbin, O Alejandro; Barbe, Stéphanie; Cai, Hongbo; Casey, Fergal; Chatterjee, Susmita; Chiang, Derek Y; Chuai, Shannon; Cogan, Shawn M; Collins, Scott D; Dammassa, Ernesta; Ebel, Nicolas; Embry, Millicent; Green, John; Kauffmann, Audrey; Kowal, Colleen; Leary, Rebecca J; Lehar, Joseph; Liang, Ying; Loo, Alice; Lorenzana, Edward; Robert McDonald, E; McLaughlin, Margaret E; Merkin, Jason; Meyer, Ronald; Naylor, Tara L; Patawaran, Montesa; Reddy, Anupama; Röelli, Claudia; Ruddy, David A; Salangsang, Fernando; Santacroce, Francesca; Singh, Angad P; Tang, Yan; Tinetto, Walter; Tobler, Sonja; Velazquez, Roberto; Venkatesan, Kavitha; Von Arx, Fabian; Wang, Hui Qin; Wang, Zongyao; Wiesmann, Marion; Wyss, Daniel; Xu, Fiona; Bitter, Hans; Atadja, Peter; Lees, Emma; Hofmann, Francesco; Li, En; Keen, Nicholas; Cozens, Robert; Jensen, Michael Rugaard; Pryer, Nancy K; Williams, Juliet A; Sellers, William R

    2015-11-01

    Profiling candidate therapeutics with limited cancer models during preclinical development hinders predictions of clinical efficacy and identifying factors that underlie heterogeneous patient responses for patient-selection strategies. We established ∼1,000 patient-derived tumor xenograft models (PDXs) with a diverse set of driver mutations. With these PDXs, we performed in vivo compound screens using a 1 × 1 × 1 experimental design (PDX clinical trial or PCT) to assess the population responses to 62 treatments across six indications. We demonstrate both the reproducibility and the clinical translatability of this approach by identifying associations between a genotype and drug response, and established mechanisms of resistance. In addition, our results suggest that PCTs may represent a more accurate approach than cell line models for assessing the clinical potential of some therapeutic modalities. We therefore propose that this experimental paradigm could potentially improve preclinical evaluation of treatment modalities and enhance our ability to predict clinical trial responses. PMID:26479923

  4. Predicting Ovarian Cancer Patients' Clinical Response to Platinum-Based Chemotherapy by Their Tumor Proteomic Signatures.

    PubMed

    Yu, Kun-Hsing; Levine, Douglas A; Zhang, Hui; Chan, Daniel W; Zhang, Zhen; Snyder, Michael

    2016-08-01

    Ovarian cancer is the deadliest gynecologic malignancy in the United States with most patients diagnosed in the advanced stage of the disease. Platinum-based antineoplastic therapeutics is indispensable to treating advanced ovarian serous carcinoma. However, patients have heterogeneous responses to platinum drugs, and it is difficult to predict these interindividual differences before administering medication. In this study, we investigated the tumor proteomic profiles and clinical characteristics of 130 ovarian serous carcinoma patients analyzed by the Clinical Proteomic Tumor Analysis Consortium (CPTAC), predicted the platinum drug response using supervised machine learning methods, and evaluated our prediction models through leave-one-out cross-validation. Our data-driven feature selection approach indicated that tumor proteomics profiles contain information for predicting binarized platinum response (P < 0.0001). We further built a least absolute shrinkage and selection operator (LASSO)-Cox proportional hazards model that stratified patients into early relapse and late relapse groups (P = 0.00013). The top proteomic features indicative of platinum response were involved in ATP synthesis pathways and Ran GTPase binding. Overall, we demonstrated that proteomic profiles of ovarian serous carcinoma patients predicted platinum drug responses as well as provided insights into the biological processes influencing the efficacy of platinum-based therapeutics. Our analytical approach is also extensible to predicting response to other antineoplastic agents or treatment modalities for both ovarian and other cancers. PMID:27312948

  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. Biomarkers for predicting clinical response to immunosuppressive therapy in aplastic anemia.

    PubMed

    Narita, Atsushi; Kojima, Seiji

    2016-08-01

    The decision to select hematopoietic stem cell transplantation (HSCT) or immunosuppressive therapy (IST) as initial therapy in acquired aplastic anemia (AA) is currently based on patient age and the availability of a human leukocyte antigen (HLA)-matched donor. Although IST is a promising treatment option, the ability to predict its long-term outcomes remains poor due to refractoriness, relapses, and the risk of clonal evolution. Several predictive biomarkers for response to IST have been posited, including age, gender, pre-treatment blood cell counts, cytokines, gene mutations, paroxysmal nocturnal hemoglobinuria (PNH), and telomere length (TL). While previous studies have provided substantial biological insights into the utility of IST, the prognostic power of the reported biomarkers is currently insufficient to contribute to clinical decision making. Recently, a large retrospective analysis proposed the combination of minor PNH clones and TL as an efficient predictor of IST response. Identification of a reliable predictor would provide a useful tool for determining the most appropriate treatment choice for AA patients, including up-front HSCT from HLA-matched unrelated donor. The present review summarizes studies evaluating the utility of biomarkers in predicting the clinical response to IST of patients with AA, and provides a baseline for prospective studies aimed at validating previously reported biomarkers. PMID:27091471

  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. Future clinical uses of neurophysiological biomarkers to predict and monitor treatment response for schizophrenia

    PubMed Central

    Light, Gregory A.; Swerdlow, Neal R.

    2015-01-01

    Advances in psychiatric neuroscience have transformed our understanding of impaired and spared brain functions in psychotic illnesses. Despite substantial progress, few if any laboratory tests have graduated to clinics to inform diagnoses, guide treatments, and monitor treatment response. Providers must rely on careful behavioral observation and interview techniques to make inferences about patients’ inner experiences and then secondary deductions about impacted neural systems. Development of more effective treatments has also been hindered by a lack of translational quantitative biomarkers that can span the brain–behavior–treatment knowledge gap. Here, we describe an example of a simple, low-cost, and translatable electroencephalography (EEG) measure that offers promise for improving our understanding and treatment of psychotic illnesses: mismatch negativity (MMN). MMN is sensitive to and/or predicts response to some pharmacologic and non-pharmacologic interventions and accounts for substantial portions of variance in clinical, cognitive, and psychosocial functioning in schizophrenia. This measure has recently been validated for use in large-scale multisite clinical studies of schizophrenia. Lastly, MMN greatly improves our ability to forecast which individuals at high clinical risk actually develop a psychotic illness. These attributes suggest that MMN can contribute to personalized biomarker-guided treatment strategies aimed at ameliorating or even preventing the onset of psychosis. PMID:25752648

  10. 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

  11. Plasma genetic and genomic abnormalities predict treatment response and clinical outcome in advanced prostate cancer.

    PubMed

    Xia, Shu; Kohli, Manish; Du, Meijun; Dittmar, Rachel L; Lee, Adam; Nandy, Debashis; Yuan, Tiezheng; Guo, Yongchen; Wang, Yuan; Tschannen, Michael R; Worthey, Elizabeth; Jacob, Howard; See, William; Kilari, Deepak; Wang, Xuexia; Hovey, Raymond L; Huang, Chiang-Ching; Wang, Liang

    2015-06-30

    Liquid biopsies, examinations of tumor components in body fluids, have shown promise for predicting clinical outcomes. To evaluate tumor-associated genomic and genetic variations in plasma cell-free DNA (cfDNA) and their associations with treatment response and overall survival, we applied whole genome and targeted sequencing to examine the plasma cfDNAs derived from 20 patients with advanced prostate cancer. Sequencing-based genomic abnormality analysis revealed locus-specific gains or losses that were common in prostate cancer, such as 8q gains, AR amplifications, PTEN losses and TMPRSS2-ERG fusions. To estimate tumor burden in cfDNA, we developed a Plasma Genomic Abnormality (PGA) score by summing the most significant copy number variations. Cox regression analysis showed that PGA scores were significantly associated with overall survival (p < 0.04). After androgen deprivation therapy or chemotherapy, targeted sequencing showed significant mutational profile changes in genes involved in androgen biosynthesis, AR activation, DNA repair, and chemotherapy resistance. These changes may reflect the dynamic evolution of heterozygous tumor populations in response to these treatments. These results strongly support the feasibility of using non-invasive liquid biopsies as potential tools to study biological mechanisms underlying therapy-specific resistance and to predict disease progression in advanced prostate cancer. PMID:25915538

  12. Plasma genetic and genomic abnormalities predict treatment response and clinical outcome in advanced prostate cancer

    PubMed Central

    Du, Meijun; Dittmar, Rachel L.; Lee, Adam; Nandy, Debashis; Yuan, Tiezheng; Guo, Yongchen; Wang, Yuan; Tschannen, Michael R.; Worthey, Elizabeth; Jacob, Howard; See, William; Kilari, Deepak; Wang, Xuexia; Hovey, Raymond L.; Huang, Chiang-Ching; Wang, Liang

    2015-01-01

    Liquid biopsies, examinations of tumor components in body fluids, have shown promise for predicting clinical outcomes. To evaluate tumor-associated genomic and genetic variations in plasma cell-free DNA (cfDNA) and their associations with treatment response and overall survival, we applied whole genome and targeted sequencing to examine the plasma cfDNAs derived from 20 patients with advanced prostate cancer. Sequencing-based genomic abnormality analysis revealed locus-specific gains or losses that were common in prostate cancer, such as 8q gains, AR amplifications, PTEN losses and TMPRSS2-ERG fusions. To estimate tumor burden in cfDNA, we developed a Plasma Genomic Abnormality (PGA) score by summing the most significant copy number variations. Cox regression analysis showed that PGA scores were significantly associated with overall survival (p < 0.04). After androgen deprivation therapy or chemotherapy, targeted sequencing showed significant mutational profile changes in genes involved in androgen biosynthesis, AR activation, DNA repair, and chemotherapy resistance. These changes may reflect the dynamic evolution of heterozygous tumor populations in response to these treatments. These results strongly support the feasibility of using non-invasive liquid biopsies as potential tools to study biological mechanisms underlying therapy-specific resistance and to predict disease progression in advanced prostate cancer. PMID:25915538

  13. 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…

  14. Mucosal Immune Responses Predict Clinical Outcomes during Influenza Infection Independently of Age and Viral Load

    PubMed Central

    Oshansky, Christine M.; Gartland, Andrew J.; Wong, Sook-San; Jeevan, Trushar; Wang, David; Roddam, Philippa L.; Caniza, Miguela A.; Hertz, Tomer; DeVincenzo, John P.; Webby, Richard J.

    2014-01-01

    Rationale: Children are an at-risk population for developing complications following influenza infection, but immunologic correlates of disease severity are not understood. We hypothesized that innate cellular immune responses at the site of infection would correlate with disease outcome. Objectives: To test the immunologic basis of severe illness during natural influenza virus infection of children and adults at the site of infection. Methods: An observational cohort study with longitudinal sampling of peripheral and mucosal sites in 84 naturally influenza-infected individuals, including infants. Cellular responses, viral loads, and cytokines were quantified from nasal lavages and blood, and correlated to clinical severity. Measurements and Main Results: We show for the first time that although viral loads in children and adults were similar, innate responses in the airways were stronger in children and varied considerably between plasma and site of infection. Adjusting for age and viral load, an innate immune profile characterized by increased nasal lavage monocyte chemotactic protein-3, IFN-α2, and plasma IL-10 levels at enrollment predicted progression to severe disease. Increased plasma IL-10, monocyte chemotactic protein-3, and IL-6 levels predicted hospitalization. This inflammatory cytokine production correlated significantly with monocyte localization from the blood to the site of infection, with conventional monocytes positively correlating with inflammation. Increased frequencies of CD14lo monocytes were in the airways of participants with lower inflammatory cytokine levels. Conclusions: An innate profile was identified that correlated with disease progression independent of viral dynamics and age. The airways and blood displayed dramatically different immune profiles emphasizing the importance of cellular migration and localized immune phenotypes. PMID:24308446

  15. 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

  16. Clinical validity: Combinatorial pharmacogenomics predicts antidepressant responses and healthcare utilizations better than single gene phenotypes.

    PubMed

    Altar, C A; Carhart, J M; Allen, J D; Hall-Flavin, D K; Dechairo, B M; Winner, J G

    2015-10-01

    In four previous studies, a combinatorial multigene pharmacogenomic test (GeneSight) predicted those patients whose antidepressant treatment for major depressive disorder resulted in poorer efficacy and increased health-care resource utilizations. Here, we extended the analysis of clinical validity to the combined data from these studies. We also compared the outcome predictions of the combinatorial use of allelic variations in genes for four cytochrome P450 (CYP) enzymes (CYP2D6, CYP2C19, CYP2C9 and CYP1A2), the serotonin transporter (SLC6A4) and serotonin 2A receptor (HTR2A) with the outcome predictions for the very same subjects using traditional, single-gene analysis. Depression scores were measured at baseline and 8-10 weeks later for the 119 fully blinded subjects who received treatment as usual (TAU) with antidepressant standard of care, without the benefit of pharmacogenomic medication guidance. For another 96 TAU subjects, health-care utilizations were recorded in a 1-year, retrospective chart review. All subjects were genotyped after the clinical study period, and phenotype subgroups were created among those who had been prescribed a GeneSight panel medication that is a substrate for either CYP enzyme or serotonin effector protein. On the basis of medications prescribed for each subject at baseline, the combinatorial pharmacogenomic (CPGx™) GeneSight method categorized each subject into either a green ('use as directed'), yellow ('use with caution') or red category ('use with increased caution and with more frequent monitoring') phenotype, whereas the single-gene method categorized the same subjects with the traditional phenotype (for example, poor, intermediate, extensive or ultrarapid CYP metabolizer). The GeneSight combinatorial categorization approach discriminated and predicted poorer outcomes for red category patients prescribed medications metabolized by CYP2D6, CYP2C19 and CYP1A2 (P=0.0034, P=0.04 and P=0.03, respectively), whereas the single

  17. 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. PMID:26757216

  18. New Perspectives on Predictive Biomarkers of Tumor Response and Their Clinical Application in Preoperative Chemoradiation Therapy for Rectal Cancer.

    PubMed

    Kim, Nam Kyu; Hur, Hyuk

    2015-11-01

    Preoperative chemoradiation therapy (CRT) is the standard treatment for patients with locally advanced rectal cancer (LARC) and can improve local control and survival outcomes. However, the responses of individual tumors to CRT are not uniform and vary widely, from complete response to disease progression. Patients with resistant tumors can be exposed to irradiation and chemotherapy that are both expensive and at times toxic without benefit. In contrast, about 60% of tumors show tumor regression and T and N down-staging. Furthermore, a pathologic complete response (pCR), which is characterized by sterilization of all tumor cells, leads to an excellent prognosis and is observed in approximately 10-30% of cases. This variety in tumor response has lead to an increased need to develop a model predictive of responses to CRT in order to identify patients who will benefit from this multimodal treatment. Endoscopy, magnetic resonance imaging, positron emission tomography, serum carcinoembryonic antigen, and molecular biomarkers analyzed using immunohistochemistry and gene expression profiling are the most commonly used predictive models in preoperative CRT. Such modalities guide clinicians in choosing the best possible treatment options and the extent of surgery for each individual patient. However, there are still controversies regarding study outcomes, and a nomogram of combined models of future trends is needed to better predict patient response. The aim of this article was to review currently available tools for predicting tumor response after preoperative CRT in rectal cancer and to explore their applicability in clinical practice for tailored treatment. PMID:26446626

  19. New Perspectives on Predictive Biomarkers of Tumor Response and Their Clinical Application in Preoperative Chemoradiation Therapy for Rectal Cancer

    PubMed Central

    Hur, Hyuk

    2015-01-01

    Preoperative chemoradiation therapy (CRT) is the standard treatment for patients with locally advanced rectal cancer (LARC) and can improve local control and survival outcomes. However, the responses of individual tumors to CRT are not uniform and vary widely, from complete response to disease progression. Patients with resistant tumors can be exposed to irradiation and chemotherapy that are both expensive and at times toxic without benefit. In contrast, about 60% of tumors show tumor regression and T and N down-staging. Furthermore, a pathologic complete response (pCR), which is characterized by sterilization of all tumor cells, leads to an excellent prognosis and is observed in approximately 10-30% of cases. This variety in tumor response has lead to an increased need to develop a model predictive of responses to CRT in order to identify patients who will benefit from this multimodal treatment. Endoscopy, magnetic resonance imaging, positron emission tomography, serum carcinoembryonic antigen, and molecular biomarkers analyzed using immunohistochemistry and gene expression profiling are the most commonly used predictive models in preoperative CRT. Such modalities guide clinicians in choosing the best possible treatment options and the extent of surgery for each individual patient. However, there are still controversies regarding study outcomes, and a nomogram of combined models of future trends is needed to better predict patient response. The aim of this article was to review currently available tools for predicting tumor response after preoperative CRT in rectal cancer and to explore their applicability in clinical practice for tailored treatment. PMID:26446626

  20. Neurobiological markers predicting treatment response in anxiety disorders: A systematic review and implications for clinical application.

    PubMed

    Lueken, Ulrike; Zierhut, Kathrin C; Hahn, Tim; Straube, Benjamin; Kircher, Tilo; Reif, Andreas; Richter, Jan; Hamm, Alfons; Wittchen, Hans-Ulrich; Domschke, Katharina

    2016-07-01

    Anxiety disorders constitute the largest group of mental disorders with a high individual and societal burden. Neurobiological markers of treatment response bear potential to improve response rates by informing stratified medicine approaches. A systematic review was performed on the current evidence of the predictive value of genetic, neuroimaging and other physiological markers for treatment response (pharmacological and/or psychotherapeutic treatment) in anxiety disorders. Studies published until March 2015 were selected through search in PubMed, Web of Science, PsycINFO, Embase, and CENTRAL. Sixty studies were included, among them 27 on genetic, 17 on neuroimaging and 16 on other markers. Preliminary evidence was found for the functional 5-HTTLPR/rs25531 genotypes, anterior cingulate cortex function and cardiovascular flexibility to modulate treatment outcome. Studies varied considerably in methodological quality. Application of more stringent study methodology, predictions on the individual patient level and cross-validation in independent samples are recommended to set the next stage of biomarker research and to avoid flawed conclusions in the emerging field of "Mental Health Predictomics". PMID:27168345

  1. The use of computational models to predict response to HIV therapy for clinical cases in Romania

    PubMed Central

    Revell, Andrew D; Ene, Luminiţa; Duiculescu, Dan; Wang, Dechao; Youle, Mike; Pozniak, Anton; Montaner, Julio; Larder, Brendan A

    2012-01-01

    Introduction A major challenge in Romania is the optimisation of antiretroviral therapy for the many HIV-infected adults with, on average, a decade of treatment experience. The RDI has developed computational models that predict virological response to therapy but these require a genotype, which is not routinely available in Romania. Moreover the models, which were trained without any Romanian data, have proved most accurate for patients from the healthcare settings that contributed the training data. Here we develop and test a novel model that does not require a genotype, with test data from Romania. Methods A random forest (RF) model was developed to predict the probability of the HIV viral load (VL) being reduced to <50 copies/ml following therapy change. The input variables were baseline VL, CD4 count, treatment history and time to follow-up. The model was developed with 3188 treatment changes episodes (TCEs) from North America, Western Europe and Australia. The model’s predictions for 100 independent TCEs from the RDI database were compared to those of a model trained with the same data plus genotypes and then tested using 39 TCEs from Romania in terms of the area under the ROC curve (AUC). Results When tested with the 100 independent RDI TCEs, the AUC values for the models with and without genotypes were 0.88 and 0.86 respectively. For the 39 Romanian TCEs the AUC was 0.60. However, when 14 cases with viral loads that may have been between 50 and 400 copies were removed, the AUC increased to 0.83. Discussion Despite having been trained without data from Romania, the model predicted treatment responses in treatment-experienced Romanian patients with clade F virus accurately without the need for a genotype. The results suggest that this approach might be generalisable and useful in helping design optimal salvage regimens for treatment-experienced patients in countries with limited resources where genotyping is not always available. PMID:24432257

  2. 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

  3. Near-Infrared Spectroscopy in Schizophrenia: A Possible Biomarker for Predicting Clinical Outcome and Treatment Response

    PubMed Central

    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

  4. Can a physician predict the clinical response to first-line immunomodulatory treatment in relapsing–remitting multiple sclerosis?

    PubMed Central

    Mezei, Zsolt; Bereczki, Daniel; Racz, Lilla; Csiba, Laszlo; Csepany, Tünde

    2012-01-01

    Background Decreased relapse rate and slower disease progression have been reported with long-term use of immunomodulatory treatments (IMTs, interferon beta or glatiramer acetate) in relapsing–remitting multiple sclerosis. There are, however, patients who do not respond to such treatments, and they can be potential candidates for alternative therapeutic approaches. Objective To identify clinical factors as possible predictors of poor long-term response. Methods A 9-year prospective, continuous follow-up at a single center in Hungary to assess clinical efficacy of IMT. Results In a patient group of 81 subjects with mean IMT duration of 54 ± 33 months, treatment efficacy expressed as annual relapse rate and change in clinical severity from baseline did not depend on the specific IMT (any of the interferon betas or glatiramer acetate), and on mono- or multifocal features of the initial appearance of the disease. Responders had shorter disease duration and milder clinical signs at the initiation of treatment. Relapse-rate reduction in the initial 2 years of treatment predicted clinical efficacy in subsequent years. Conclusion Based on these observations, we suggest that a 2-year trial period is sufficient to decide on the efficacy of a specific IMT. For those with insufficient relapse reduction in the first 2 years of treatment, a different IMT or other therapeutic approaches should be recommended. PMID:23118540

  5. A Personalized BEST: Characterization of Latent Clinical Classes of Nonischemic Heart Failure That Predict Outcomes and Response to Bucindolol

    PubMed Central

    Kao, David P.; Wagner, Brandie D.; Robertson, Alastair D.; Bristow, Michael R.; Lowes, Brian D.

    2012-01-01

    Background Heart failure patients with reduced ejection fraction (HFREF) are heterogenous, and our ability to identify patients likely to respond to therapy is limited. We present a method of identifying disease subtypes using high-dimensional clinical phenotyping and latent class analysis that may be useful in personalizing prognosis and treatment in HFREF. Methods A total of 1121 patients with nonischemic HFREF from the β-blocker Evaluation of Survival Trial were categorized according to 27 clinical features. Latent class analysis was used to generate two latent class models, LCM A and B, to identify HFREF subtypes. LCM A consisted of features associated with HF pathogenesis, whereas LCM B consisted of markers of HF progression and severity. The Seattle Heart Failure Model (SHFM) Score was also calculated for all patients. Mortality, improvement in left ventricular ejection fraction (LVEF) defined as an increase in LVEF ≥5% and a final LVEF of 35% after 12 months, and effect of bucindolol on both outcomes were compared across HFREF subtypes. Performance of models that included a combination of LCM subtypes and SHFM scores towards predicting mortality and LVEF response was estimated and subsequently validated using leave-one-out cross-validation and data from the Multicenter Oral Carvedilol Heart Failure Assessment Trial. Results A total of 6 subtypes were identified using LCM A and 5 subtypes using LCM B. Several subtypes resembled familiar clinical phenotypes. Prognosis, improvement in LVEF, and the effect of bucindolol treatment differed significantly between subtypes. Prediction improved with addition of both latent class models to SHFM for both 1-year mortality and LVEF response outcomes. Conclusions The combination of high-dimensional phenotyping and latent class analysis identifies subtypes of HFREF with implications for prognosis and response to specific therapies that may provide insight into mechanisms of disease. These subtypes may facilitate

  6. Prediction of clinical and endoscopic responses to anti-tumor necrosis factor-α antibodies in ulcerative colitis.

    PubMed

    Morita, Yukihiro; Bamba, Shigeki; Takahashi, Kenichiro; Imaeda, Hirotsugu; Nishida, Atsushi; Inatomi, Osamu; Sasaki, Masaya; Tsujikawa, Tomoyuki; Sugimoto, Mitsushige; Andoh, Akira

    2016-08-01

    Objective In patients with ulcerative colitis (UC), the relationship between the initial endoscopic findings and the response to anti-tumor necrosis factor (TNF)-α antibodies remains unclear. We herein evaluated the potential of endoscopic assessment using the ulcerative colitis endoscopic index of severity (UCEIS) to predict the response to anti-TNF-α antibodies. Methods We enrolled 64 patients with UC undergoing anti-TNF-α maintenance therapy with infliximab (IFX) or adalimumab (ADA) between April 2010 and March 2015. Anti-TNF-α trough levels were determined by ELISA. Endoscopic disease activity was assessed using the UCEIS. Results The clinical response rate at 8 weeks was 77.4% for IFX and 66.7% for ADA. Serum albumin levels were significantly higher and the UCEIS bleeding descriptor before treatment was significantly lower in the responders than in the non-responders (p < 0.05 each). The CRP levels at 2 weeks were significantly lower in the responders (p < 0.001). The serum albumin levels before treatment were significantly higher and the UCEIS erosions and ulcers descriptor was significantly lower in the mucosal healing group than in the non-mucosal healing group (p < 0.05 each). A significant and negative correlation between the trough levels of anti-TNF-α antibodies and the UCEIS descriptors was observed. The trough levels of anti-TNF-α antibodies to achieve mucosal healing were 2.7 μg/mL for IFX and 10.3 μg/mL for ADA. Conclusions The UCEIS score, as well as some clinical markers (serum albumin and CRP levels), is useful for the prediction of the treatment outcome of UC patients in response to anti-TNF-α antibodies. PMID:26888161

  7. Cultural Responsivity in Clinical Psychology Graduate Students: A Developmental Approach to the Prediction of Learning

    ERIC Educational Resources Information Center

    Berrin, Sebastian Everett

    2010-01-01

    This study used a mixed-method approach to examine students' experiences in multicultural training and their opinions about various aspects of their course(s). A developmental model of learning was employed to analyze results. More specifically, this study explored the relationship between clinical psychology doctoral students' self-reported…

  8. A Personalized Approach to Biological Therapy Using Prediction of Clinical Response Based on MRP8/14 Serum Complex Levels in Rheumatoid Arthritis Patients

    PubMed Central

    Nair, S. C.; Welsing, P. M. J.; Choi, I. Y. K.; Roth, J.; Holzinger, D.; Bijlsma, J. W. J.; van Laar, J. M.; Gerlag, D. M.; Lafeber, F. P. J. G.; Tak, P. P.

    2016-01-01

    Objectives Measurement of MRP8/14 serum levels has shown potential in predicting clinical response to different biological agents in rheumatoid arthritis (RA). We aimed to develop a treatment algorithm based on a prediction score using MRP8/14 measurements and clinical parameters predictive for response to different biological agents. Methods Baseline serum levels of MRP8/14 were measured in 170 patients starting treatment with infliximab, adalimumab or rituximab. We used logistic regression analysis to develop a predictive score for clinical response at 16 weeks. MRP8/14 levels along with clinical variables at baseline were investigated. We also investigated how the predictive effect of MRP8/14 was modified by drug type. A treatment algorithm was developed based on categorizing the expected response per drug type as high, intermediate or low for each patient and optimal treatment was defined. Finally, we present the utility of using this treatment algorithm in clinical practice. Results The probability of response increased with higher baseline MRP8/14 complex levels (OR = 1.39), differentially between the TNF-blockers and rituximab (OR of interaction term = 0.78), and also increased with higher DAS28 at baseline (OR = 1.28). Rheumatoid factor positivity, functional disability (a higher HAQ), and previous use of a TNF-inhibitor decreased the probability of response. Based on the treatment algorithm 80 patients would have been recommended for anti-TNF treatment, 8 for rituximab, 13 for another biological treatment (other than TNFi or rituximab) and for 69 no recommendation was made. The predicted response rates matched the observed response in the cohort well. On group level the predicted response based on the algorithm resulted in a modest 10% higher response rate in our cohort with much higher differences in response probability in individual patients treated contrary to treatment recommendation. Conclusions Prediction of response using MRP8/14 levels along with

  9. Systolic pressure response to voluntary apnea predicts sympathetic tone in obstructive sleep apnea as a clinically useful index.

    PubMed

    Jouett, Noah P; Hardisty, Janelle M; Mason, J Ryan; Niv, Dorene; Romano, James J; Watenpaugh, Donald E; Burk, John R; Smith, Michael L

    2016-01-01

    The present investigation tested the hypotheses that systolic arterial pressure (SAP) responses to voluntary apnea (a) serve as a surrogate of sympathetic nerve activity (SNA), (b) can distinguish Obstructive Sleep Apnea (OSA) patients from control subjects and (c) can document autonomic effects of treatment. 9 OSA and 10 control subjects were recruited in a laboratory study; 44 OSA subjects and 78 control subjects were recruited in a clinical study; and 21 untreated OSA subjects and 14 well-treated OSA subjects were recruited into a treatment study. Each subject performed hypoxic and room air voluntary apneas in triplicate. Muscle SNA (MSNA) and continuous AP were measured during each apnea in the laboratory study, while systolic arterial pressure (SAP) responses were measured continuously and by standard auscultation in the clinical and treatment studies. OSA subjects exhibited increased mean arterial pressure (MAP), SAP and MSNA responses to hypoxic apnea (all P<0.01) and the SAP response highly correlated with the MSNA response (R(2)=0.72, P<0.001). Clinical assessment confirmed that OSA subjects exhibited markedly elevated SAP responses (P<0.01), while treated OSA subjects had a decreased SAP response to apnea (P<0.04) compared to poorly treated subjects. These data indicate that (a) OSA subjects exhibit increased pressor and MSNA responses to apnea, and that (b) voluntary apnea may be a clinically useful assessment tool of autonomic dysregulation and treatment efficacy in OSA. PMID:26774324

  10. 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

  11. Early Clinical Response after 2 Weeks of Sorafenib Therapy Predicts Outcomes and Anti-Tumor Response in Patients with Advanced Hepatocellular Carcinoma

    PubMed Central

    Kuzuya, Teiji; Ishigami, Masatoshi; Ishizu, Yoji; Honda, Takashi; Hayashi, Kazuhiko; Katano, Yoshiaki; Hirooka, Yoshiki; Ishikawa, Tetsuya; Nakano, Isao; Goto, Hidemi

    2015-01-01

    Background & Aims We evaluated the relationship between the early clinical response after 2 weeks of sorafenib therapy and the outcomes and anti-tumor response in patients with advanced hepatocellular carcinoma. Methods Fifty-seven patients who had intrahepatic hypervascular hepatocellular carcinoma and Child-Pugh (CP) class A disease at baseline were enrolled in this prospective, multicenter, observational, non-interventional study. As an early clinical response after 2 weeks of sorafenib therapy, changes in intra-tumor blood flow on contrast-enhanced computed tomography (CE-CT), alpha-fetoprotein (AFP) levels, and remnant liver function were investigated. Results After 2 weeks of sorafenib therapy, there were 26 patients (45.6%) without disappearance of arterial tumor enhancement on CE-CT, 15 patients (26.3%) with an AFP ratio of >1.2, and seven patients (12.3%) with two or more increments in the CP score. Multivariate analysis showed that the absence of disappearance of arterial tumor enhancement on CE-CT, AFP ratio of >1.2, and two or more increments in the CP score after 2 weeks of sorafenib therapy were significant and independent predictors of worse survival. Upon scoring these three variables as "poor prognostic factors", patients with poor prognostic score 4, 3 or 2 (n = 17) had significantly worse outcomes and a significantly higher progressive disease (PD) rate based on modified Response Evaluation Criteria in Solid Tumors at 6 weeks after sorafenib therapy than those with poor prognostic score 1 or 0 (n = 40) (median overall survival: 194 days vs. 378 days; p = 0.0010, PD rate: 70.6% vs. 20.0%; p = 0.0003, respectively). Conclusions Changes in intra-tumor blood flow on CE-CT, AFP levels, and remnant liver function after 2 weeks of sorafenib therapy may be useful for predicting the outcomes and anti-tumor response to sorafenib in patients with advanced hepatocellular carcinoma. PMID:26421430

  12. Clinical course, costs and predictive factors for response to treatment in carpal tunnel syndrome: the PALMS study protocol

    PubMed Central

    2014-01-01

    Background Carpal tunnel syndrome (CTS) is the most common neuropathy of the upper limb and a significant contributor to hand functional impairment and disability. Effective treatment options include conservative and surgical interventions, however it is not possible at present to predict the outcome of treatment. The primary aim of this study is to identify which baseline clinical factors predict a good outcome from conservative treatment (by injection) or surgery in patients diagnosed with carpal tunnel syndrome. Secondary aims are to describe the clinical course and progression of CTS, and to describe and predict the UK cost of CTS to the individual, National Health Service (NHS) and society over a two year period. Methods/Design In this prospective observational cohort study patients presenting with clinical signs and symptoms typical of CTS and in whom the diagnosis is confirmed by nerve conduction studies are invited to participate. Data on putative predictive factors are collected at baseline and follow-up through patient questionnaires and include standardised measures of symptom severity, hand function, psychological and physical health, comorbidity and quality of life. Resource use and cost over the 2 year period such as prescribed medications, NHS and private healthcare contacts are also collected through patient self-report at 6, 12, 18 and 24 months. The primary outcome used to classify treatment success or failures will be a 5-point global assessment of change. Secondary outcomes include changes in clinical symptoms, functioning, psychological health, quality of life and resource use. A multivariable model of factors which predict outcome and cost will be developed. Discussion This prospective cohort study will provide important data on the clinical course and UK costs of CTS over a two-year period and begin to identify predictive factors for treatment success from conservative and surgical interventions. PMID:24507749

  13. Prediction of Response to Treatment in a Randomized Clinical Trial of Couple Therapy: A 2-Year Follow-Up

    ERIC Educational Resources Information Center

    Baucom, Brian R.; Atkins, David C.; Simpson, Lorelei E.; Christensen, Andrew

    2009-01-01

    Many studies have examined pretreatment predictors of immediate posttreatment outcome, but few studies have examined prediction of long-term treatment response to couple therapies. Four groups of predictors (demographic, intrapersonal, communication, and other interpersonal) and 2 moderators (pretreatment severity and type of therapy) were…

  14. Tumor-associated gastroparesis with esophageal carcinoma. Use of intravenous metoclopramide during radionuclide gastric emptying studies to predict clinical response

    SciTech Connect

    Choe, A.I.; Ziessman, H.A.; Fleischer, D.E.

    1989-07-01

    This case report describes a patient with esophageal carcinoma and tumor-associated gastroparesis. The radionuclide gastric emptying study diagnosed very delayed liquid and solid gastric emptying. Metoclopramide was administered intravenously during the study and was able to predict a good response to oral therapy.

  15. Expression of Heat Shock Protein 27 in Melanoma Metastases Is Associated with Overall Response to Bevacizumab Monotherapy: Analyses of Predictive Markers in a Clinical Phase II Study

    PubMed Central

    Schuster, Cornelia; Akslen, Lars A.; Straume, Oddbjørn

    2016-01-01

    The aim of this study was to identify potential predictive biomarkers in 35 patients with metastatic melanoma treated with anti-angiogenic bevacizumab monotherapy in a clinical phase II study. The immunohistochemical expression of various angiogenic factors in tissues from primary melanomas and metastases as well as their concentration in blood samples were examined. Strong expression of Heat Shock Protein 27 (HSP27) in metastases correlated significantly with complete or partial response to bevacizumab (p = 0.044). Furthermore, clinical benefit, i.e., complete or partial response or stable disease for at least 6 months, was more frequent in patients with strong expression of HSP27 in primary tumors (p = 0.046). Tissue expression of vascular endothelial growth factor (VEGF-A), its splicing variant VEGF165b or basic fibroblast growth factor (bFGF) did not correlate with response, and the concentration of HSP27, VEGF-A or bFGF measured in blood samples before treatment did not show predictive value. Further, microvessel density, proliferating microvessel density and presence of glomeruloid microvascular proliferations were assessed in sections of primary tumors and metastases. Microvessel density in primary melanomas was significantly higher in patients with clinical benefit than in non-responders (p = 0.042). In conclusion, our findings suggest that strong HSP27 expression in melanoma metastases predicts response to bevacizumab treatment. Trial Registration ClinicalTrials.gov NCT00139360 PMID:27166673

  16. Prediction of therapeutic response in steroid-treated pulmonary sarcoidosis. Evaluation of clinical parameters, bronchoalveolar lavage, gallium-67 lung scanning, and serum angiotensin-converting enzyme levels

    SciTech Connect

    Hollinger, W.M.; Staton, G.W. Jr.; Fajman, W.A.; Gilman, M.J.; Pine, J.R.; Check, I.J.

    1985-07-01

    To find a pretreatment predictor of steroid responsiveness in pulmonary sarcoidosis the authors studied 21 patients before and after steroid treatment by clinical evaluation, pulmonary function tests, bronchoalveolar lavage (BAL), gallium-67 lung scan, and serum angiotensin-converting enzyme (SACE) level. Although clinical score, forced vital capacity (FVC), BAL percent lymphocytes (% lymphs), quantitated gallium-67 lung uptake, and SACE levels all improved with therapy, only the pretreatment BAL % lymphs correlated with the improvement in FVC (r = 0.47, p less than 0.05). Pretreatment BAL % lymphs of greater than or equal to 35% predicted improvement in FVC of 10/11 patients, whereas among 10 patients with BAL % lymphs less than 35%, 5 patients improved and 5 deteriorated. Clinical score, pulmonary function parameters, quantitated gallium-67 lung uptake, and SACE level used alone, in combination with BAL % lymphs or in combination with each other, did not improve this predictive value. The authors conclude that steroid therapy improves a number of clinical and laboratory parameters in sarcoidosis, but only the pretreatment BAL % lymphs are useful in predicting therapeutic responsiveness.

  17. Early epigenetic changes and DNA damage do not predict clinical response in an overlapping schedule of 5-azacytidine and entinostat in patients with myeloid malignancies

    PubMed Central

    Fandy, Tamer E.; Herman, James G.; Kerns, Patrick; Jiemjit, Anchalee; Sugar, Elizabeth A.; Choi, Si-Ho; Yang, Allen S.; Aucott, Timothy; Dauses, Tianna; Odchimar-Reissig, Rosalie; Licht, Jonathan; McConnell, Melanie J.; Nasrallah, Chris; Kim, Marianne K. H.; Zhang, Weijia; Sun, Yezou; Murgo, Anthony; Espinoza-Delgado, Igor; Oteiza, Katharine; Owoeye, Ibitayo; Silverman, Lewis R.; Carraway, Hetty E.

    2009-01-01

    Sequential administration of DNA methyltransferase (DNMT) inhibitors and histone deacetylase (HDAC) inhibitors has demonstrated clinical efficacy in patients with hematologic malignancies. However, the mechanism behind their clinical efficacy remains controversial. In this study, the methylation dynamics of 4 TSGs (p15INK4B, CDH-1, DAPK-1, and SOCS-1) were studied in sequential bone marrow samples from 30 patients with myelodysplastic syndrome (MDS) or acute myeloid leukemia (AML) who completed a minimum of 4 cycles of therapy with 5-azacytidine and entinostat. Reversal of promoter methylation after therapy was observed in both clinical responders and nonresponders across all genes. There was no association between clinical response and either baseline methylation or methylation reversal in the bone marrow or purified CD34+ population, nor was there an association with change in gene expression. Transient global hypomethylation was observed in samples after treatment but was not associated with clinical response. Induction of histone H3/H4 acetylation and the DNA damage–associated variant histone γ-H2AX was observed in peripheral blood samples across all dose cohorts. In conclusion, methylation reversal of candidate TSGs during cycle 1 of therapy was not predictive of clinical response to combination “epigenetic” therapy. This trial is registered with http://www.clinicaltrials.gov under NCT00101179. PMID:19546476

  18. Integrating in vitro sensitivity and dose-response slope is predictive of clinical response to ABL kinase inhibitors in chronic myeloid leukemia.

    PubMed

    Vainstein, Vladimir; Eide, Christopher A; O'Hare, Thomas; Shukron, Ofir; Druker, Brian J

    2013-11-01

    BCR-ABL mutations result in clinical resistance to ABL tyrosine kinase inhibitors (TKIs) in chronic myeloid leukemia (CML). Although in vitro 50% inhibitory concentration (IC(50)) values for specific mutations have been suggested to guide TKI choice in the clinic, the quantitative relationship between IC(50) and clinical response has never been demonstrated. We used Hill's equation for in vitro response of Ba/F3 cells transduced with various BCR-ABL mutants to determine IC(50) and the slope of the dose-response curve. We found that slope variability between mutants tracked with in vitro TKI resistance, provides particular additional interpretive value in cases where in vitro IC(50) and clinical response are disparate. Moreover, unlike IC(50) alone, higher inhibitory potential at peak concentration (IPP), which integrates IC(50), slope, and peak concentration (Cmax), correlated with improved complete cytogenetic response (CCyR) rates in CML patients treated with dasatinib. Our findings suggest a metric integrating in vitro and clinical data may provide an improved tool for BCR-ABL mutation-guided TKI selection. PMID:24062017

  19. Epidemiological and Clinical Baseline Characteristics as Predictive Biomarkers of Response to Anti-VEGF Treatment in Patients with Neovascular AMD.

    PubMed

    Tsilimbaris, Miltiadis K; López-Gálvez, Maria I; Gallego-Pinazo, Roberto; Margaron, Philippe; Lambrou, George N

    2016-01-01

    Purpose. To review the current literature investigating patient response to antivascular endothelial growth factor-A (VEGF) therapy in the treatment of neovascular age-related macular degeneration (nAMD) and to identify baseline characteristics that might predict response. Method. A literature search of the PubMed database was performed, using the keywords: AMD, anti-VEGF, biomarker, optical coherence tomography, treatment outcome, and predictor. The search was limited to articles published from 2006 to date. Exclusion criteria included phase 1 trials, case reports, studies focusing on indications other than nAMD, and oncology. Results. A total of 1467 articles were identified, of which 845 were excluded. Of the 622 remaining references, 47 met all the search criteria and were included in this review. Conclusion. Several baseline characteristics correlated with anti-VEGF treatment response, including best-corrected visual acuity, age, lesion size, and retinal thickness. The majority of factors were associated with disease duration, suggesting that longer disease duration before treatment results in worse treatment outcomes. This highlights the need for early treatment for patients with nAMD to gain optimal treatment outcomes. Many of the identified baseline characteristics are interconnected and cannot be evaluated in isolation; therefore multivariate analyses will be required to determine any specific relationship with treatment response. PMID:27073691

  20. Epidemiological and Clinical Baseline Characteristics as Predictive Biomarkers of Response to Anti-VEGF Treatment in Patients with Neovascular AMD

    PubMed Central

    López-Gálvez, Maria I.; Margaron, Philippe; Lambrou, George N.

    2016-01-01

    Purpose. To review the current literature investigating patient response to antivascular endothelial growth factor-A (VEGF) therapy in the treatment of neovascular age-related macular degeneration (nAMD) and to identify baseline characteristics that might predict response. Method. A literature search of the PubMed database was performed, using the keywords: AMD, anti-VEGF, biomarker, optical coherence tomography, treatment outcome, and predictor. The search was limited to articles published from 2006 to date. Exclusion criteria included phase 1 trials, case reports, studies focusing on indications other than nAMD, and oncology. Results. A total of 1467 articles were identified, of which 845 were excluded. Of the 622 remaining references, 47 met all the search criteria and were included in this review. Conclusion. Several baseline characteristics correlated with anti-VEGF treatment response, including best-corrected visual acuity, age, lesion size, and retinal thickness. The majority of factors were associated with disease duration, suggesting that longer disease duration before treatment results in worse treatment outcomes. This highlights the need for early treatment for patients with nAMD to gain optimal treatment outcomes. Many of the identified baseline characteristics are interconnected and cannot be evaluated in isolation; therefore multivariate analyses will be required to determine any specific relationship with treatment response. PMID:27073691

  1. Presence of Systemic Inflammatory Response Syndrome Predicts a Poor Clinical Outcome in Dogs with a Primary Hepatitis.

    PubMed

    Kilpatrick, Scott; Dreistadt, Margaret; Frowde, Polly; Powell, Roger; Milne, Elspeth; Smith, Sionagh; Morrison, Linda; Gow, Adam G; Handel, Ian; Mellanby, Richard J

    2016-01-01

    Primary hepatopathies are a common cause of morbidity and mortality in dogs. The underlying aetiology of most cases of canine hepatitis is unknown. Consequently, treatments are typically palliative and it is difficult to provide accurate prognostic information to owners. In human hepatology there is accumulating data which indicates that the presence of systemic inflammatory response syndrome (SIRS) is a common and debilitating event in patients with liver diseases. For example, the presence of SIRS has been linked to the development of complications such as hepatic encephalopathy (HE) and is associated with a poor clinical outcome in humans with liver diseases. In contrast, the relationship between SIRS and clinical outcome in dogs with a primary hepatitis is unknown. Seventy dogs with histologically confirmed primary hepatitis were enrolled into the study. Additional clinical and clinicopathological information including respiratory rate, heart rate, temperature, white blood cell count, sodium, potassium, sex, presence of ascites, HE score, alanine aminotransferase (ALT), alkaline phosphatase (ALP), bilirubin and red blood cell concentration were available in all cases. The median survival of dogs with a SIRS score of 0 or 1 (SIRS low) was 231 days compared to a median survival of 7 days for dogs with a SIRS score of 2, 3 or 4 (SIRS high) (p<0.001). A Cox proportional hazard model, which included all other co-variables, revealed that a SIRS high score was an independent predictor of a poor clinical outcome. The effect of modulating inflammation on treatment outcomes in dogs with a primary hepatitis is deserving of further study. PMID:26808672

  2. Predictors of primary breast cancers responsiveness to preoperative Epirubicin/Cyclophosphamide-based chemotherapy: translation of microarray data into clinically useful predictive signatures

    PubMed Central

    Modlich, Olga; Prisack, Hans-Bernd; Munnes, Marc; Audretsch, Werner; Bojar, Hans

    2005-01-01

    PLS-DA models were implemented to discriminate all three response classes in one step. Conclusion In this study signatures were identified capable to predict clinical outcome in an independent set of primary breast cancer patients undergoing PST-EC. PMID:16091131

  3. Clinical Utility of a New Automated Hepatitis C Virus Core Antigen Assay for Prediction of Treatment Response in Patients with Chronic Hepatitis C.

    PubMed

    Kim, Mi Na; Kim, Hyon Suk; Kim, Ja Kyung; Kim, Beom Kyung; Kim, Seung Up; Park, Jun Yong; Kim, Do Young; Ahn, Sang Hoon; Han, Kwang Hyub

    2016-09-01

    Hepatitis C virus core antigen (HCV Ag) is a recently developed marker of hepatitis C virus (HCV) infection. We investigated the clinical utility of the new HCV Ag assay for prediction of treatment response in HCV infection. We analyzed serum from 92 patients with HCV infection who had been treated with pegylated interferon and ribavirin. HCV Ag levels were determined at baseline in all enrolled patients and at week 4 in 15 patients. Baseline HCV Ag levels showed good correlations with HCV RNA (r = 0.79, P < 0.001). Mean HCV Ag levels at baseline were significantly lower in patients with a sustained virologic response (SVR) than in those with a non SVR (relapse plus non responder) based on HCV RNA analysis (2.8 log₁₀fmol/L vs. 3.27 log₁₀fmol/L, P = 0.023). Monitoring of the viral kinetics by determination of HCV RNA and HCV Ag levels resulted in similarly shaped curves. Patients with undetectable HCV Ag levels at week 4 had a 92.3% probability of achieving SVR based on HCV RNA assay results. The HCV Ag assay may be used as a supplement for predicting treatment response in HCV infection, but not as an alternative to the HCV RNA assay. PMID:27510387

  4. Clinical Utility of a New Automated Hepatitis C Virus Core Antigen Assay for Prediction of Treatment Response in Patients with Chronic Hepatitis C

    PubMed Central

    2016-01-01

    Hepatitis C virus core antigen (HCV Ag) is a recently developed marker of hepatitis C virus (HCV) infection. We investigated the clinical utility of the new HCV Ag assay for prediction of treatment response in HCV infection. We analyzed serum from 92 patients with HCV infection who had been treated with pegylated interferon and ribavirin. HCV Ag levels were determined at baseline in all enrolled patients and at week 4 in 15 patients. Baseline HCV Ag levels showed good correlations with HCV RNA (r = 0.79, P < 0.001). Mean HCV Ag levels at baseline were significantly lower in patients with a sustained virologic response (SVR) than in those with a non SVR (relapse plus non responder) based on HCV RNA analysis (2.8 log10fmol/L vs. 3.27 log10fmol/L, P = 0.023). Monitoring of the viral kinetics by determination of HCV RNA and HCV Ag levels resulted in similarly shaped curves. Patients with undetectable HCV Ag levels at week 4 had a 92.3% probability of achieving SVR based on HCV RNA assay results. The HCV Ag assay may be used as a supplement for predicting treatment response in HCV infection, but not as an alternative to the HCV RNA assay. PMID:27510387

  5. Clinical Predictive Modeling Development and Deployment through FHIR Web Services

    PubMed Central

    Khalilia, Mohammed; Choi, Myung; Henderson, Amelia; Iyengar, Sneha; Braunstein, Mark; Sun, Jimeng

    2015-01-01

    Clinical predictive modeling involves two challenging tasks: model development and model deployment. In this paper we demonstrate a software architecture for developing and deploying clinical predictive models using web services via the Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) standard. The services enable model development using electronic health records (EHRs) stored in OMOP CDM databases and model deployment for scoring individual patients through FHIR resources. The MIMIC2 ICU dataset and a synthetic outpatient dataset were transformed into OMOP CDM databases for predictive model development. The resulting predictive models are deployed as FHIR resources, which receive requests of patient information, perform prediction against the deployed predictive model and respond with prediction scores. To assess the practicality of this approach we evaluated the response and prediction time of the FHIR modeling web services. We found the system to be reasonably fast with one second total response time per patient prediction. PMID:26958207

  6. Clinical Predictive Modeling Development and Deployment through FHIR Web Services.

    PubMed

    Khalilia, Mohammed; Choi, Myung; Henderson, Amelia; Iyengar, Sneha; Braunstein, Mark; Sun, Jimeng

    2015-01-01

    Clinical predictive modeling involves two challenging tasks: model development and model deployment. In this paper we demonstrate a software architecture for developing and deploying clinical predictive models using web services via the Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) standard. The services enable model development using electronic health records (EHRs) stored in OMOP CDM databases and model deployment for scoring individual patients through FHIR resources. The MIMIC2 ICU dataset and a synthetic outpatient dataset were transformed into OMOP CDM databases for predictive model development. The resulting predictive models are deployed as FHIR resources, which receive requests of patient information, perform prediction against the deployed predictive model and respond with prediction scores. To assess the practicality of this approach we evaluated the response and prediction time of the FHIR modeling web services. We found the system to be reasonably fast with one second total response time per patient prediction. PMID:26958207

  7. Early changes in biochemical markers of bone turnover predict the long-term response to alendronate therapy in representative elderly women: a randomized clinical trial.

    PubMed

    Greenspan, S L; Parker, R A; Ferguson, L; Rosen, H N; Maitland-Ramsey, L; Karpf, D B

    1998-09-01

    Although the antiresorptive agent alendronate has been shown to increase bone mineral density (BMD) at the hip and spine and decrease the incidence of osteoporotic fractures in older women, few data are available regarding early prediction of long-term response to therapy, particularly with regard to increases in hip BMD. Examining short-term changes in biochemical markers incorporates physiologic response with therapeutic compliance and should provide useful prognostic information for patients. The objective of this study was to examine whether early changes in biochemical markers of bone turnover predict long-term changes in hip BMD in elderly women. The study was a double-blind, placebo-controlled, randomized clinical trial which took place in a community-based academic hospital. One hundred and twenty community-dwelling, ambulatory women 65 years of age and older participated in the study. Intervention consisted of alendronate versus placebo for 2.5 years. All patients received appropriate calcium and vitamin D supplementation. The principal outcome measures included BMD of the hip (total hip, femoral neck, trochanter, and intertrochanter), spine (posteroanterior [PA] and lateral), total body, and radius. Biochemical markers of bone resorption included urinary N-telopeptide cross-linked collagen type I and free deoxypyridinoline; markers of bone formation included serum osteocalcin and bone-specific alkaline phosphatase. Long-term alendronate therapy was associated with increased BMD at the total hip (4.0%), femoral neck (3.1%), trochanter (5.5%), intertrochanter (3.8%), PA spine (7.8%), lateral spine (10.6%), total body (2.2%), and one-third distal radius (1.3%) in elderly women (all p < 0.01). In the placebo group, bone density increased 1.9-2.1% at the spine (p < 0.05) and remained stable at all other sites. At 6 months, there were significant decreases in all markers of bone turnover (-10% to -53%, p < 0.01) in women on alendronate. The changes in urinary

  8. 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

  9. How to Establish Clinical Prediction Models

    PubMed Central

    Bang, Heejung

    2016-01-01

    A clinical prediction model can be applied to several challenging clinical scenarios: screening high-risk individuals for asymptomatic disease, predicting future events such as disease or death, and assisting medical decision-making and health education. Despite the impact of clinical prediction models on practice, prediction modeling is a complex process requiring careful statistical analyses and sound clinical judgement. Although there is no definite consensus on the best methodology for model development and validation, a few recommendations and checklists have been proposed. In this review, we summarize five steps for developing and validating a clinical prediction model: preparation for establishing clinical prediction models; dataset selection; handling variables; model generation; and model evaluation and validation. We also review several studies that detail methods for developing clinical prediction models with comparable examples from real practice. After model development and vigorous validation in relevant settings, possibly with evaluation of utility/usability and fine-tuning, good models can be ready for the use in practice. We anticipate that this framework will revitalize the use of predictive or prognostic research in endocrinology, leading to active applications in real clinical practice. PMID:26996421

  10. How to Establish Clinical Prediction Models.

    PubMed

    Lee, Yong Ho; Bang, Heejung; Kim, Dae Jung

    2016-03-01

    A clinical prediction model can be applied to several challenging clinical scenarios: screening high-risk individuals for asymptomatic disease, predicting future events such as disease or death, and assisting medical decision-making and health education. Despite the impact of clinical prediction models on practice, prediction modeling is a complex process requiring careful statistical analyses and sound clinical judgement. Although there is no definite consensus on the best methodology for model development and validation, a few recommendations and checklists have been proposed. In this review, we summarize five steps for developing and validating a clinical prediction model: preparation for establishing clinical prediction models; dataset selection; handling variables; model generation; and model evaluation and validation. We also review several studies that detail methods for developing clinical prediction models with comparable examples from real practice. After model development and vigorous validation in relevant settings, possibly with evaluation of utility/usability and fine-tuning, good models can be ready for the use in practice. We anticipate that this framework will revitalize the use of predictive or prognostic research in endocrinology, leading to active applications in real clinical practice. PMID:26996421

  11. 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

  12. 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. PMID:27249550

  13. Can site response be predicted?

    USGS Publications Warehouse

    Boore, D.M.

    2004-01-01

    Large modifications of seismic waves are produced by variations of material properties near the Earth's surface and by both surface and buried topography. These modifications, usually referred to as "site response", in general lead to larger motions on soil sites than on rock-like sites. Because the soil amplifications can be as large as a factor of ten, they are important in engineering applications that require the quantitative specification of ground motions. This has been recognised for years by both seismologists and engineers, and it is hard to open an earthquake journal these days without finding an article on site response. What is often missing in these studies, however, are discussions of the uncertainty of the predicted response. A number of purely observational studies demonstrate that ground motions have large site-to-site variability for a single earthquake and large earthquake-location- dependent variability for a single site. This variability makes site-specific, earthquake-specific predictions of site response quite uncertain, even if detailed geotechnical and geological information is available near the site. Predictions of site response for average classes of sites exposed to the motions from many earthquakes can be made with much greater certainty if sufficient empirical observations are available.

  14. 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. PMID:26573645

  15. Combining clinical variables to optimize prediction of antidepressant treatment outcomes.

    PubMed

    Iniesta, Raquel; Malki, Karim; Maier, Wolfgang; Rietschel, Marcella; Mors, Ole; Hauser, Joanna; Henigsberg, Neven; Dernovsek, Mojca Zvezdana; Souery, Daniel; Stahl, Daniel; Dobson, Richard; Aitchison, Katherine J; Farmer, Anne; Lewis, Cathryn M; McGuffin, Peter; Uher, Rudolf

    2016-07-01

    The outcome of treatment with antidepressants varies markedly across people with the same diagnosis. A clinically significant prediction of outcomes could spare the frustration of trial and error approach and improve the outcomes of major depressive disorder through individualized treatment selection. It is likely that a combination of multiple predictors is needed to achieve such prediction. We used elastic net regularized regression to optimize prediction of symptom improvement and remission during treatment with escitalopram or nortriptyline and to identify contributing predictors from a range of demographic and clinical variables in 793 adults with major depressive disorder. A combination of demographic and clinical variables, with strong contributions from symptoms of depressed mood, reduced interest, decreased activity, indecisiveness, pessimism and anxiety significantly predicted treatment outcomes, explaining 5-10% of variance in symptom improvement with escitalopram. Similar combinations of variables predicted remission with area under the curve 0.72, explaining approximately 15% of variance (pseudo R(2)) in who achieves remission, with strong contributions from body mass index, appetite, interest-activity symptom dimension and anxious-somatizing depression subtype. Escitalopram-specific outcome prediction was more accurate than generic outcome prediction, and reached effect sizes that were near or above a previously established benchmark for clinical significance. Outcome prediction on the nortriptyline arm did not significantly differ from chance. These results suggest that easily obtained demographic and clinical variables can predict therapeutic response to escitalopram with clinically meaningful accuracy, suggesting a potential for individualized prescription of this antidepressant drug. PMID:27089522

  16. Predicting Clinical Outcomes Using Molecular Biomarkers

    PubMed Central

    Burke, Harry B.

    2016-01-01

    Over the past 20 years, there has been an exponential increase in the number of biomarkers. At the last count, there were 768,259 papers indexed in PubMed.gov directly related to biomarkers. Although many of these papers claim to report clinically useful molecular biomarkers, embarrassingly few are currently in clinical use. It is suggested that a failure to properly understand, clinically assess, and utilize molecular biomarkers has prevented their widespread adoption in treatment, in comparative benefit analyses, and their integration into individualized patient outcome predictions for clinical decision-making and therapy. A straightforward, general approach to understanding how to predict clinical outcomes using risk, diagnostic, and prognostic molecular biomarkers is presented. In the future, molecular biomarkers will drive advances in risk, diagnosis, and prognosis, they will be the targets of powerful molecular therapies, and they will individualize and optimize therapy. Furthermore, clinical predictions based on molecular biomarkers will be displayed on the clinician’s screen during the physician–patient interaction, they will be an integral part of physician–patient-shared decision-making, and they will improve clinical care and patient outcomes. PMID:27279751

  17. The value of lateral spread response monitoring in predicting the clinical outcome after microvascular decompression in hemifacial spasm: a prospective study on 100 patients.

    PubMed

    El Damaty, Ahmed; Rosenstengel, Christian; Matthes, Marc; Baldauf, Joerg; Schroeder, Henry W S

    2016-07-01

    Microvascular decompression represents an effective treatment for hemifacial spasm. The use of lateral spread response (LSR) monitoring remains a useful intraoperative tool to ensure adequate decompression of the facial nerve. The aim of this study was to assess the value of LSRs intraoperative monitoring as a prognostic indicator for the outcome of microvascular decompression in hemifacial spasm. Our study included 100 patients prospectively. The patients were classified into four groups whether LSRs were totally, partially, not relieved, or not detected from the start. According to clinical outcome, the patients were classified into four groups depending on the clinical course after surgery and the residual symptoms if any. Then, correlations were made between LSR events and treatment outcome to detect its reliability as a prognostic indicator. LSRs were relieved totally in 56 % of the patients, partially relieved in 14 %, not relieved in 10 %, and were not detected in 20 % of the patients from the start. HFS was relieved directly after operation in 62 % with clinical improvement of 90-100 %. Thirty-one percent described 50-90 % improvement over the next 3 months after surgery. Almost all of these 31 % (28 out of 31 patients) reported further clinical improvement of 90-100 % within 1 year after surgery. Three percent suffered from a relapse after a HFS-free period, and 4 % reported minimal or no improvement describing 0-50 % of the preoperative state. The percentage of the satisfied patients with the clinical outcome who reported after 1 year a clinical improvement of 90-100 % was 90 %. Statistical analysis did not find a significant correlation between the relief of LSRs and clinical outcome. LSRs may only represent an intraoperative tool to guide for an adequate decompression but failed to represent a reliable prognostic indicator for treatment outcome. PMID:27053220

  18. Prediction of earthquake response spectra

    USGS Publications Warehouse

    Joyner, W.B.; Boore, David M.

    1982-01-01

    We have developed empirical equations for predicting earthquake response spectra in terms of magnitude, distance, and site conditions, using a two-stage regression method similar to the one we used previously for peak horizontal acceleration and velocity. We analyzed horizontal pseudo-velocity response at 5 percent damping for 64 records of 12 shallow earthquakes in Western North America, including the recent Coyote Lake and Imperial Valley, California, earthquakes. We developed predictive equations for 12 different periods between 0.1 and 4.0 s, both for the larger of two horizontal components and for the random horizontal component. The resulting spectra show amplification at soil sites compared to rock sites for periods greater than or equal to 0.3 s, with maximum amplification exceeding a factor of 2 at 2.0 s. For periods less than 0.3 s there is slight deamplification at the soil sites. These results are generally consistent with those of several earlier studies. A particularly significant aspect of the predicted spectra is the change of shape with magnitude (confirming earlier results by McGuire and by Irifunac and Anderson). This result indicates that the conventional practice of scaling a constant spectral shape by peak acceleration will not give accurate answers. The Newmark and Hall method of spectral scaling, using both peak acceleration and peak velocity, largely avoids this error. Comparison of our spectra with the Nuclear Regulatory Commission's Regulatory Guide 1.60 spectrum anchored at the same value at 0.1 s shows that the Regulatory Guide 1.60 spectrum is exceeded at soil sites for a magnitude of 7.5 at all distances for periods greater than about 0.5 s. Comparison of our spectra for soil sites with the corresponding ATC-3 curve of lateral design force coefficient for the highest seismic zone indicates that the ATC-3 curve is exceeded within about 7 km of a magnitude 6.5 earthquake and within about 15 km of a magnitude 7.5 event. The amount by

  19. Identifying and managing clinical responsibility.

    PubMed

    Armitage, Mary; Shaw, Kirstyn; Drew, Peter

    2008-04-01

    Clinical responsibility is an area that requires extensive consideration and development to align with other changes in healthcare in the NHS. Increasing levels of litigation and investigation into the practice of medical practitioners have highlighted the need for clearer guidance for doctors. Using hypothetical case studies, this project explored the understandings and experiences of physicians and potential solutions in areas where there was ambiguity in clinical responsibility. In addition, existing policy and practice within trusts throughout the UK was analysed. The output from the focus group discussion and policy analysis led to the recommendations and guidance for doctors outlined in this paper, with the aim of illustrating the central themes that both doctors and trusts need to address in the future. PMID:18478855

  20. Improved tumour response prediction with equivalent uniform dose in pre-clinical study using direct intratumoural infusion of liposome-encapsulated 186Re radionuclides

    NASA Astrophysics Data System (ADS)

    Hrycushko, Brian A.; Ware, Steve; Li, Shihong; Bao, Ande

    2011-09-01

    Crucial to all cancer therapy modalities is a strong correlation between treatment and effect. Predictability of therapy success/failure allows for the optimization of treatment protocol and aids in the decision of whether additional treatment is necessary to prevent tumour progression. This work evaluated the relationship between cancer treatment and effect for intratumoural infusions of liposome-encapsulated 186Re to head and neck squamous cell carcinoma xenografts of nude rats. Absorbed dose calculations using a dose-point kernel convolution technique showed significant intratumoural dose heterogeneity due to the short range of the beta-particle emissions. The use of three separate tumour infusion locations improved dose homogeneity compared to a single infusion location as a result of a more uniform radioactivity distribution. An improved dose-response correlation was obtained when using effective uniform dose (EUD) calculations based on a generic set of radiobiological parameters (R2 = 0.84) than when using average tumour absorbed dose (R2 = 0.22). Varying radiobiological parameter values over ranges commonly used for all types of tumours showed little effect on EUD calculations, which suggests that individualized parameter use is of little significance as long as the intratumoural dose heterogeneity is taken into consideration in the dose-response relationship. The improved predictability achieved when using EUD calculations for this cancer therapy modality may be useful for treatment planning and evaluation.

  1. What predicts performance during clinical psychology training?

    PubMed Central

    Scior, Katrina; Bradley, Caroline E; Potts, Henry W W; Woolf, Katherine; de C Williams, Amanda C

    2014-01-01

    Objectives While the question of who is likely to be selected for clinical psychology training has been studied, evidence on performance during training is scant. This study explored data from seven consecutive intakes of the UK's largest clinical psychology training course, aiming to identify what factors predict better or poorer outcomes. Design Longitudinal cross-sectional study using prospective and retrospective data. Method Characteristics at application were analysed in relation to a range of in-course assessments for 274 trainee clinical psychologists who had completed or were in the final stage of their training. Results Trainees were diverse in age, pre-training experience, and academic performance at A-level (advanced level certificate required for university admission), but not in gender or ethnicity. Failure rates across the three performance domains (academic, clinical, research) were very low, suggesting that selection was successful in screening out less suitable candidates. Key predictors of good performance on the course were better A-levels and better degree class. Non-white students performed less well on two outcomes. Type and extent of pre-training clinical experience on outcomes had varied effects on outcome. Research supervisor ratings emerged as global indicators and predicted nearly all outcomes, but may have been biased as they were retrospective. Referee ratings predicted only one of the seven outcomes examined, and interview ratings predicted none of the outcomes. Conclusions Predicting who will do well or poorly in clinical psychology training is complex. Interview and referee ratings may well be successful in screening out unsuitable candidates, but appear to be a poor guide to performance on the course. Practitioner points While referee and selection interview ratings did not predict performance during training, they may be useful in screening out unsuitable candidates at the application stage High school final academic performance

  2. Early sorafenib-related adverse events predict therapy response of TACE plus sorafenib: A multicenter clinical study of 606 HCC patients.

    PubMed

    Zhao, Yan; Li, Hailiang; Bai, Wei; Liu, Jueshi; Lv, Weifu; Sahu, Sonia; Guan, Sheng; Qin, Xiao; Wang, Wenhui; Ren, Weixin; Mu, Wei; Guo, Weidong; Gu, Shanzhi; Ma, Yilong; Yin, Zhanxin; Guo, Wengang; Wang, Wenjun; Wang, Yongji; Duran, Rafael; Fan, Daiming; Zhang, Zhuoli; Han, Guohong

    2016-08-15

    The purpose of our study was to test the hypothesis that sorafenib-related dermatologic adverse events (AEs) as an early biomarker can predict the long-term outcomes following the combination therapy of transarterial chemoembolization (TACE) plus sorafenib (TACE-S). The intermediate-stage hepatocellular carcinoma patients who received either TACE-S or TACE-alone treatment were consecutively included into analysis. In the TACE-S group, patients with ≥ grade 2 dermatologic AEs within the first month of sorafenib initiation were defined as responders; whereas those with < grade 2 were defined as nonresponders. In the TACE-S group, the median overall survival (OS) of the responders was significantly longer than that of nonresponders (28.9 months vs. 16.8 months, respectively; p = 0.004). Multivariate analysis demonstrated that nonresponders were significantly associated with an increased risk of death compared with responders (HR = 1.9; 95% confidence Interval-CI: 1.3-2.7; p = 0.001). The survival analysis showed that the median OS was 27.9 months (95% CI: 25.0-30.8) among responders treated with TACE-S vs.18.3 months (95% CI: 14.5-22.1) among those who received TACE-alone (p = 0.046). The median time to progression was 13.1 months (95% CI: 4.4-21.8) in the TACE-S group, a duration that was significantly longer than that in the TACE-alone group [5 months (95% CI: 6.4-13.3), p = 0.014]. This study demonstrated that sorafenib-related dermatologic AEs are clinical biomarkers to identify responders from all of the patients for TACE-S therapy. Sorafenib-related dermatologic AEs, clinical biomarkers, can predict the efficacy of TACE-S in future randomized controlled trials. PMID:27038145

  3. (18)F-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. PMID:26758726

  4. Systemic IFNγ predicts local implant macrophage response.

    PubMed

    Hoene, Andreas; Patrzyk, Maciej; Walschus, Uwe; Finke, Birgit; Lucke, Silke; Nebe, Barbara; Schröder, Karsten; Schlosser, Michael

    2015-03-01

    Implantation of biomaterials can cause complications often associated with inflammatory reactions. However, repeated evaluation of the implant site would be burdening for patients. Alternatively, blood examinations with analysis of inflammatory serum markers could potentially be useful to reflect the local cellular response for detection and/or prediction of inflammation-related complications. Therefore, following intramuscular implantation of surface-modified Ti implants in rats, this study aimed at examining possible associations between the post-implantation time course of pro-inflammatory (INFγ, IL-2) and anti-inflammatory (IL-4, IL-10) cytokine serum concentrations and the local peri-implant tissue response after 56 days (pro-inflammatory CD68-positive monocytes/macrophages, anti-inflammatory CD163-positive macrophages, MHC class II-positive cells, activated natural killer cells and mast cells). Multivariate correlation analysis revealed a significant interaction between serum IFNγ and peri-implant tissue CD68-positive monocytes/macrophages (p = 0.001) while no interactions were found for other cytokines and cell types. Additional Pearson correlation analysis of IFNγ serum concentrations on each experimental day vs. the CD68-positive monocytes/macrophages response on day 56 demonstrated a consistently positive correlation that was strongest during the first three weeks. Thus, high early pro-inflammatory IFNγ serum concentration was associated with high late number of pro-inflammatory CD68-positive monocyte/macrophages and low early serum IFNγ with low late CD68-positive monocyte/macrophage numbers. Further studies aimed at examination of patient samples could establish the relevance of this association to predict clinical complications. After implantation of titanium samples, high early IFNγ serum concentrations were associated with a pronounced late pro-inflammatory CD68-positive monocyte/ macrophage (red circle) response, while no correlation was found

  5. Clinical trial designs incorporating predictive biomarkers.

    PubMed

    Renfro, Lindsay A; Mallick, Himel; An, Ming-Wen; Sargent, Daniel J; Mandrekar, Sumithra J

    2016-02-01

    Development of oncologic therapies has traditionally been performed in a sequence of clinical trials intended to assess safety (phase I), preliminary efficacy (phase II), and improvement over the standard of care (phase III) in homogeneous (in terms of tumor type and disease stage) patient populations. As cancer has become increasingly understood on the molecular level, newer "targeted" drugs that inhibit specific cancer cell growth and survival mechanisms have increased the need for new clinical trial designs, wherein pertinent questions on the relationship between patient biomarkers and response to treatment can be answered. Herein, we review the clinical trial design literature from initial to more recently proposed designs for targeted agents or those treatments hypothesized to have enhanced effectiveness within patient subgroups (e.g., those with a certain biomarker value or who harbor a certain genetic tumor mutation). We also describe a number of real clinical trials where biomarker-based designs have been utilized, including a discussion of their respective advantages and challenges. As cancers become further categorized and/or reclassified according to individual patient and tumor features, we anticipate a continued need for novel trial designs to keep pace with the changing frontier of clinical cancer research. PMID:26827695

  6. 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. PMID:20351444

  7. 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

  8. Usefulness of plasma renin activity in predicting haemodynamic and clinical responses and survival during long term converting enzyme inhibition in severe chronic heart failure. Experience in 100 consecutive patients.

    PubMed Central

    Packer, M; Medina, N; Yushak, M; Lee, W H

    1985-01-01

    The relation between plasma renin activity before treatment and the haemodynamic and clinical responses to converting enzyme inhibition was determined in 100 consecutive patients with severe chronic heart failure who were treated with captopril or enalapril. Initial doses of captopril produced significant increases in cardiac index and decreases in left ventricular filling pressure, mean arterial pressure, mean right atrial pressure, heart rate, and systemic vascular resistance that varied linearly with the pretreatment value for plasma renin activity. In contrast, there was no relation between the pretreatment activity and the magnitude of haemodynamic improvement after 1-3 months of treatment with the converting enzyme inhibitors, and, consequently, a similar proportion of patients with a high (greater than 6 ng/ml/h; greater than 4.62 mmol/l/h), intermediate (2-6 ng/ml/h; 1.54-4.62 mmol/l/h), and low (less than 2 ng/ml/h; less than 1.54 mmol/l/h) pretreatment value improved clinically during long term treatment (64%, 60%, and 64% respectively). Long term survival after one, two, and three years was similar in the three groups. Estimating the degree of activation of the renin-angiotensin system by measuring pretreatment plasma renin activity fails to predict the long term haemodynamic or clinical responses to converting enzyme inhibitors in patients with severe chronic heart failure, and thus appears to be of limited value in selecting those patients likely to benefit from treatment with these drugs. PMID:2994697

  9. Clinical Relevance of Prognostic and Predictive Molecular Markers in Gliomas.

    PubMed

    Siegal, Tali

    2016-01-01

    Sorting and grading of glial tumors by the WHO classification provide clinicians with guidance as to the predicted course of the disease and choice of treatment. Nonetheless, histologically identical tumors may have very different outcome and response to treatment. Molecular markers that carry both diagnostic and prognostic information add useful tools to traditional classification by redefining tumor subtypes within each WHO category. Therefore, molecular markers have become an integral part of tumor assessment in modern neuro-oncology and biomarker status now guides clinical decisions in some subtypes of gliomas. The routine assessment of IDH status improves histological diagnostic accuracy by differentiating diffuse glioma from reactive gliosis. It carries a favorable prognostic implication for all glial tumors and it is predictive for chemotherapeutic response in anaplastic oligodendrogliomas with codeletion of 1p/19q chromosomes. Glial tumors that contain chromosomal codeletion of 1p/19q are defined as tumors of oligodendroglial lineage and have favorable prognosis. MGMT promoter methylation is a favorable prognostic marker in astrocytic high-grade gliomas and it is predictive for chemotherapeutic response in anaplastic gliomas with wild-type IDH1/2 and in glioblastoma of the elderly. The clinical implication of other molecular markers of gliomas like mutations of EGFR and ATRX genes and BRAF fusion or point mutation is highlighted. The potential of molecular biomarker-based classification to guide future therapeutic approach is discussed and accentuated. PMID:26508407

  10. Brain connectomics predict response to treatment in social anxiety disorder.

    PubMed

    Whitfield-Gabrieli, S; Ghosh, S S; Nieto-Castanon, A; Saygin, Z; Doehrmann, O; Chai, X J; Reynolds, G O; Hofmann, S G; Pollack, M H; Gabrieli, J D E

    2016-05-01

    We asked whether brain connectomics can predict response to treatment for a neuropsychiatric disorder better than conventional clinical measures. Pre-treatment resting-state brain functional connectivity and diffusion-weighted structural connectivity were measured in 38 patients with social anxiety disorder (SAD) to predict subsequent treatment response to cognitive behavioral therapy (CBT). We used a priori bilateral anatomical amygdala seed-driven resting connectivity and probabilistic tractography of the right inferior longitudinal fasciculus together with a data-driven multivoxel pattern analysis of whole-brain resting-state connectivity before treatment to predict improvement in social anxiety after CBT. Each connectomic measure improved the prediction of individuals' treatment outcomes significantly better than a clinical measure of initial severity, and combining the multimodal connectomics yielded a fivefold improvement in predicting treatment response. Generalization of the findings was supported by leave-one-out cross-validation. After dividing patients into better or worse responders, logistic regression of connectomic predictors and initial severity combined with leave-one-out cross-validation yielded a categorical prediction of clinical improvement with 81% accuracy, 84% sensitivity and 78% specificity. Connectomics of the human brain, measured by widely available imaging methods, may provide brain-based biomarkers (neuromarkers) supporting precision medicine that better guide patients with neuropsychiatric diseases to optimal available treatments, and thus translate basic neuroimaging into medical practice. PMID:26260493

  11. Predicting fish population response to instream flows

    SciTech Connect

    Studley, T.K.; Baldridge, J.E.; Railsback, S.F.

    1996-10-01

    A cooperative research program initiated by Pacific Gas and Electric is described. The goals of the project are to determine if trout populations respond to changes in base streamflows in a predictible manner, and to evaluate and improve the methods used to predict rainbow and brown trout population responses under altered flow regimes. Predictive methods based on computer models of the Physical Habitat Simulation System are described, and predictions generated for four diversions and creeks are tabulated. Baseline data indicates that instream flow assessments can be improved by using guild criteria in streams with competing species and including additional limiting factors (low recruitment, high winter flow, and high stream temperatures) in the analyses.

  12. Biomarkers in systemic sclerosis: Their potential to predict clinical courses.

    PubMed

    Hasegawa, Minoru

    2016-01-01

    The concept of a biomarker was defined as "a characteristic marker that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention" by the National Institutes of Health Biomarkers Definitions Working group in 2001. Clinical features, disease progress, therapeutic response and prognosis are heterogeneous among patients with systemic sclerosis (SSc). Therefore, biomarkers that can predict these matters are required for the progress of clinical practice. At present, SSc-specific autoantibodies are the most useful biomarkers for diagnosis and predicting clinical features. Otherwise, biomarkers specific only for SSc have not been identified yet. The glycoprotein krebs von den Lungen-6, surfactant protein-D and CCL18 are promising serum biomarkers of SSc-related interstitial lung diseases. Serum/plasma levels of brain natriuretic peptide and serum N-terminal pro-brain natriuretic peptide have been used as biomarkers for SSc-related pulmonary arterial hypertension. Other potential serum/plasma biomarkers for fibrosis and vascular involvement of SSc are connective tissue growth factor, interleukin-6, CCL2, CXCL4, intercellular adhesion molecule (ICAM)-1, P-selectin, vascular endothelial growth factor, von Willebrand factor, endostatin, endoglin and endothelin-1. In our multicenter prospective studies of Japanese early SSc, serum ICAM-1 levels were predictive for subsequent respiratory dysfunction and serum levels of CXCL8 and P-selectin were predictive for subsequent physical disability. Further large, multicenter, prospective, longitudinal studies will be needed to identify and validate critical biomarkers of SSc. PMID:26782004

  13. [Prognostic predictive factors of the clinical response to immunotherapy with subcutaneous interleukin-2, in patients with metastatic renal carcinoma: analysis of 60 cases].

    PubMed

    Lissoni, P; Scardino, E; Favini, P; Barni, S; Tancini, G; Baccalin, A; Verweij, F; Strada, G; Musci, R; Rocco, F

    1995-04-01

    The intravenous immunotherapy with high-dose interleukin-2 (IL-2) would constitute one of the most effective treatments of metastatic renal cell carcinoma (RCC). More recently, IL-2 subcutaneous therapy has also appeared active, either alone or in association with interferon, with results comparable to those found with the intravenous route of injection, but with a lower toxicity. On this basis, we have designed a protocol of treatment with low-dose IL-2 alone given subcutaneously as a first or a second line therapy in metastatic RCC. The study included 60 consecutive patients (pts) (M/F: 39/21, median age 56 years, range 26/74). IL-2 was given at a dose of 3 millions IU twice/day for 5 days/week, for 6 weeks, corresponding to one cycle. In non progressed pts a second cycle was repeated after a 28-day rest period. Dominant metastasis sites were, as follows: soft tissues: 8; bone: 11; lung: 29; liver: 3; liver plus lung: 7; adrenal: 2. The minimum follow-up was 18 months and the median follow-up was 34 months (range 18-48). A complete response (CR) was achieved in 2/60 (3%) pts. A partial response (PR) was obtained in 15/60 (25%). Therefore, tumor objective rate (CR + PR) was 17/60 (28%). The median duration of response was 13 months (4-33).(ABSTRACT TRUNCATED AT 250 WORDS) PMID:7787857

  14. 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

  15. Ethical responsibilities of the clinical engineer.

    PubMed

    Saha, P; Saha, S

    1986-01-01

    Because of the growth of medical technology, Clinical Engineers have increased responsibilities in respect to this new technology and so to modern medicine itself. This results in a need to ensure that an ethical consciousness of responsibilities to patients, physicians, and institutions grows within Clinical Engineers as they move into evermore important roles within the health care system. Clinical Engineers must have clearly defined roles, as well as authority acknowledged and supported by other health care professionals. Most importantly, Clinical Engineers themselves must recognize the seriousness of their professional responsibilities as they contribute to the maintenance of equipment, use and design instrumentation, and fulfill roles in administration, management, and research. As members of the health care team, Clinical Engineers must be prepared to face ethical issues arising from defective or inadequate equipment, hazards and incidents, scarcity and resources, conflict of interest, confidentiality, clinical research, "truth-telling," and care of the terminally ill. PMID:10275911

  16. A New Clinical Conceptualization: Prediction Therapy.

    ERIC Educational Resources Information Center

    DiMattia, Dominic J.; Huber, Charles H.

    1983-01-01

    Describes prediction therapy, a cognitive behavioral approach which suggests that individuals seek situations that are optimally predictable. Outlines a five-step protocol of prediction which includes establishing a climate for change, analysis and assessment, identification of therapeutic goals, reorientation and reeducation, and provision of…

  17. miR-155 Drives Metabolic Reprogramming of ER+ Breast Cancer Cells Following Long-Term Estrogen Deprivation and Predicts Clinical Response to Aromatase Inhibitors.

    PubMed

    Bacci, Marina; Giannoni, Elisa; Fearns, Antony; Ribas, Ricardo; Gao, Qiong; Taddei, Maria Letizia; Pintus, Gianfranco; Dowsett, Mitch; Isacke, Clare M; Martin, Lesley-Ann; Chiarugi, Paola; Morandi, Andrea

    2016-03-15

    Aromatase inhibitors (AI) have become the first-line endocrine treatment of choice for postmenopausal estrogen receptor-positive (ER(+)) breast cancer patients, but resistance remains a major challenge. Metabolic reprogramming is a hallmark of cancer and may contribute to drug resistance. Here, we investigated the link between altered breast cancer metabolism and AI resistance using AI-resistant and sensitive breast cancer cells, patient tumor samples, and AI-sensitive human xenografts. We found that long-term estrogen deprivation (LTED), a model of AI resistance, was associated with increased glycolysis dependency. Targeting the glycolysis-priming enzyme hexokinase-2 (HK2) in combination with the AI, letrozole, synergistically reduced cell viability in AI-sensitive models. Conversely, MCF7-LTED cells, which displayed a high degree of metabolic plasticity, switched to oxidative phosphorylation when glycolysis was impaired. This effect was ER dependent as breast cancer cells with undetectable levels of ER failed to exhibit metabolic plasticity. MCF7-LTED cells were also more motile than their parental counterparts and assumed amoeboid-like invasive abilities upon glycolysis inhibition with 2-deoxyglucose (2-DG). Mechanistic investigations further revealed an important role for miR-155 in metabolic reprogramming. Suppression of miR-155 resulted in sensitization of MCF7-LTED cells to metformin treatment and impairment of 2-DG-induced motility. Notably, high baseline miR-155 expression correlated with poor response to AI therapy in a cohort of ER(+) breast cancers treated with neoadjuvant anastrozole. These findings suggest that miR-155 represents a biomarker potentially capable of identifying the subset of breast cancers most likely to adapt to and relapse on AI therapy. PMID:26795347

  18. 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.

  19. Molecular Markers Predictive of Chemotherapy Response in Colorectal Cancer

    PubMed Central

    Shiovitz, Stacey; Grady, William M.

    2015-01-01

    Recognition of the molecular heterogeneity of colorectal cancer (CRC) has led to the classification of CRC based on a variety of clinical and molecular characteristics. Although the clinical significance of the majority of these molecular alterations is still being ascertained, it is widely anticipated that these characteristics will improve the accuracy of our ability to determine the prognosis and therapeutic response of CRC patients. A few of these markers, such as microsatellite instability and the CpG island methylator phenotype (CIMP), show promise as predictive markers for cytotoxic chemotherapy. KRAS is a validated biomarker for EGFR-targeted therapy, while NRAS and PI3KCA are evolving markers for targeted therapies. Multiple new actionable drug targets are being identified on a regular basis, but most are not ready for clinical use at this time. This review focuses on key molecular features of CRCs and the application of these molecular alterations as predictive biomarkers for CRC. PMID:25663616

  20. Clinical and EEG response to anticonvulsants in neonatal seizures.

    PubMed Central

    Connell, J; Oozeer, R; de Vries, L; Dubowitz, L M; Dubowitz, V

    1989-01-01

    During a two year period prospective continuous electroencephalographic (EEG) monitoring of 275 infants identified seizure activity in 55 cases, 31 of whom were treated with anticonvulsant drugs on clinical grounds. EEG and clinical response was complete in only two and equivocal in another six. Clinical response with persistent EEG seizures occurred in 13 and neither clinical nor EEG response in 10. There was no significant improvement in the generally poor neurological outcome compared with that in 24 infants whose seizures were not treated because of limited or absent clinical manifestations. Background EEG abnormality (as an index of associated cerebral dysfunction) was a guide to potential lack of response to anticonvulsant drugs; it was also predictive of subsequent clinical outcome irrespective of treatment. This study shows that commonly used anticonvulsant drugs (phenobarbitone, paraldehyde, phenytoin, and diazepam) have little effect on seizure control or neurological outcome in neonatal seizures associated with haemorrhagic, hypoxic, or ischaemic cerebral lesions. In view of the variable clinical appearance of EEG seizure activity, continuous EEG monitoring should be an essential feature of further study of neonatal anticonvulsant treatment. PMID:2730114

  1. Prediction of Achievement in Clinical Pharmacy Courses

    ERIC Educational Resources Information Center

    Simon, Lee S.

    1978-01-01

    A study sought to identify student characteristics which account for academic achievement in clinical pharmacy courses. Preclinical grade point average was the best predictor. Subscales of the California Personality Inventory and the Myers-Briggs Type Indicator, work experience, sex, and age were the other predictor variables. (SW)

  2. Personalized Nutrition by Prediction of Glycemic Responses.

    PubMed

    Zeevi, David; Korem, Tal; Zmora, Niv; Israeli, David; Rothschild, Daphna; Weinberger, Adina; Ben-Yacov, Orly; Lador, Dar; Avnit-Sagi, Tali; Lotan-Pompan, Maya; Suez, Jotham; Mahdi, Jemal Ali; Matot, Elad; Malka, Gal; Kosower, Noa; Rein, Michal; Zilberman-Schapira, Gili; Dohnalová, Lenka; Pevsner-Fischer, Meirav; Bikovsky, Rony; Halpern, Zamir; Elinav, Eran; Segal, Eran

    2015-11-19

    Elevated postprandial blood glucose levels constitute a global epidemic and a major risk factor for prediabetes and type II diabetes, but existing dietary methods for controlling them have limited efficacy. Here, we continuously monitored week-long glucose levels in an 800-person cohort, measured responses to 46,898 meals, and found high variability in the response to identical meals, suggesting that universal dietary recommendations may have limited utility. We devised a machine-learning algorithm that integrates blood parameters, dietary habits, anthropometrics, physical activity, and gut microbiota measured in this cohort and showed that it accurately predicts personalized postprandial glycemic response to real-life meals. We validated these predictions in an independent 100-person cohort. Finally, a blinded randomized controlled dietary intervention based on this algorithm resulted in significantly lower postprandial responses and consistent alterations to gut microbiota configuration. Together, our results suggest that personalized diets may successfully modify elevated postprandial blood glucose and its metabolic consequences. VIDEO ABSTRACT. PMID:26590418

  3. Predicting Methylphenidate Response in ADHD Using Machine Learning Approaches

    PubMed Central

    Kim, Jae-Won; Sharma, Vinod

    2015-01-01

    Background: There are no objective, biological markers that can robustly predict methylphenidate response in attention deficit hyperactivity disorder. This study aimed to examine whether applying machine learning approaches to pretreatment demographic, clinical questionnaire, environmental, neuropsychological, neuroimaging, and genetic information can predict therapeutic response following methylphenidate administration. Methods: The present study included 83 attention deficit hyperactivity disorder youth. At baseline, parents completed the ADHD Rating Scale-IV and Disruptive Behavior Disorder rating scale, and participants undertook the continuous performance test, Stroop color word test, and resting-state functional MRI scans. The dopamine transporter gene, dopamine D4 receptor gene, alpha-2A adrenergic receptor gene (ADRA2A) and norepinephrine transporter gene polymorphisms, and blood lead and urine cotinine levels were also measured. The participants were enrolled in an 8-week, open-label trial of methylphenidate. Four different machine learning algorithms were used for data analysis. Results: Support vector machine classification accuracy was 84.6% (area under receiver operating characteristic curve 0.84) for predicting methylphenidate response. The age, weight, ADRA2A MspI and DraI polymorphisms, lead level, Stroop color word test performance, and oppositional symptoms of Disruptive Behavior Disorder rating scale were identified as the most differentiating subset of features. Conclusions: Our results provide preliminary support to the translational development of support vector machine as an informative method that can assist in predicting treatment response in attention deficit hyperactivity disorder, though further work is required to provide enhanced levels of classification performance. PMID:25964505

  4. Predicting outcome in clinically isolated syndrome using machine learning

    PubMed Central

    Wottschel, V.; Alexander, D.C.; Kwok, P.P.; Chard, D.T.; Stromillo, M.L.; De Stefano, N.; Thompson, A.J.; Miller, D.H.; Ciccarelli, O.

    2014-01-01

    We aim to determine if machine learning techniques, such as support vector machines (SVMs), can predict the occurrence of a second clinical attack, which leads to the diagnosis of clinically-definite Multiple Sclerosis (CDMS) in patients with a clinically isolated syndrome (CIS), on the basis of single patient's lesion features and clinical/demographic characteristics. Seventy-four patients at onset of CIS were scanned and clinically reviewed after one and three years. CDMS was used as the gold standard against which SVM classification accuracy was tested. Radiological features related to lesional characteristics on conventional MRI were defined a priori and used in combination with clinical/demographic features in an SVM. Forward recursive feature elimination with 100 bootstraps and a leave-one-out cross-validation was used to find the most predictive feature combinations. 30 % and 44 % of patients developed CDMS within one and three years, respectively. The SVMs correctly predicted the presence (or the absence) of CDMS in 71.4 % of patients (sensitivity/specificity: 77 %/66 %) at 1 year, and in 68 % (60 %/76 %) at 3 years on average over all bootstraps. Combinations of features consistently gave a higher accuracy in predicting outcome than any single feature. Machine-learning-based classifications can be used to provide an “individualised” prediction of conversion to MS from subjects' baseline scans and clinical characteristics, with potential to be incorporated into routine clinical practice. PMID:25610791

  5. Predicting and Monitoring Cancer Treatment Response with DW-MRI

    PubMed Central

    Thoeny, Harriet C.; Ross, Brian D.

    2010-01-01

    An imaging biomarker that would provide for an early quantitative metric of clinical treatment response in cancer patients would provide for a paradigm shift in cancer care. Currently, non-image based clinical outcome metrics include morphology, clinical and laboratory parameters however, these are obtained relatively late following treatment. Diffusion-weighted MRI (DW-MRI) holds promise for use as a cancer treatment response biomarker as it is sensitive to macromolecular and microstructural changes which can occur at the cellular level earlier than anatomical changes during therapy. Studies have shown that successful treatment of a many tumor types can be detected using DW-MRI as an early increase in the apparent diffusion coefficient (ADC) values. Additionally, low pre-treatment ADC values of various tumors are often predictive of better outcome. These capabilities, once validated, could provide for an important opportunity to individualize therapy thereby minimizing unnecessary systemic toxicity associated with ineffective therapies with the additional advantage of improving overall patient health care and associated costs. In this report, we provide a brief technical overview of DW-MRI acquisition protocols, quantitative image analysis approaches and review studies which have implemented DW-MRI for the purpose of early prediction of cancer treatment response. PMID:20575076

  6. 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. PMID:19339691

  7. 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

  8. Prediction of clinical behaviour and treatment for cancers.

    PubMed

    Futschik, Matthias E; Sullivan, Mike; Reeve, Anthony; Kasabov, Nikola

    2003-01-01

    Prediction of clinical behaviour and treatment for cancers is based on the integration of clinical and pathological parameters. Recent reports have demonstrated that gene expression profiling provides a powerful new approach for determining disease outcome. If clinical and microarray data each contain independent information then it should be possible to combine these datasets to gain more accurate prognostic information. Here, we have used existing clinical information and microarray data to generate a combined prognostic model for outcome prediction for diffuse large B-cell lymphoma (DLBCL). A prediction accuracy of 87.5% was achieved. This constitutes a significant improvement compared to the previously most accurate prognostic model with an accuracy of 77.6%. The model introduced here may be generally applicable to the combination of various types of molecular and clinical data for improving medical decision support systems and individualising patient care. PMID:15130817

  9. Roles and Responsibilities of Clinical Nurse Researchers.

    ERIC Educational Resources Information Center

    Kirchhoff, Karin T.; Mateo, Magdelena A.

    1996-01-01

    A follow-up survey of 142 nurse researchers employed in clinical settings (75% response) found that fewer than half have a budget, 52% have secretarial support, 82% have a research committee, and 71% report to the chief nurse executive. Although their positions were primarily research, the average time spent on research was 50%. (JOW)

  10. [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 else--that is,…

  11. 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

  12. 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.

  13. Biosocial processes predicting multisystemic therapy treatment response.

    PubMed

    Ryan, Stacy R; Brennan, Patricia A; Cunningham, Phillippe B; Foster, Sharon L; Brock, Rebecca L; Whitmore, Elizabeth

    2013-02-01

    This study examined biological (testosterone) and social (deviant peer affiliation) factors early in treatment as predictors of treatment outcome among adolescent boys receiving Multisystemic Therapy (MST) in community settings. Outcome variables included changes in youth aggression and delinquency as reported by the primary caregiver. Testosterone and deviant peer affiliation were assessed at treatment onset; and outcome variables (aggression and delinquency) were assessed at treatment onset, mid-treatment and end-of-treatment. Participants were 112 adolescent boys (M age=15.42, SD=1.31) and their caregivers. Growth curve analyses revealed that the combination of high testosterone and high deviant peer affiliation early in treatment were significantly associated with less of a decline in aggression and delinquency over the course of treatment. Results provide novel evidence for the role of testosterone in the prediction of future externalizing behaviors. Clinical and theoretical implications are discussed. PMID:23247043

  14. Clinical Trials for Predictive Medicine—New Challenges and Paradigms*

    PubMed Central

    Simon, Richard

    2014-01-01

    Background Developments in biotechnology and genomics have increased the focus of biostatisticians on prediction problems. This has led to many exciting developments for predictive modeling where the number of variables is larger than the number of cases. Heterogeneity of human diseases and new technology for characterizing them presents new opportunities and challenges for the design and analysis of clinical trials. Purpose In oncology, treatment of broad populations with regimens that do not benefit most 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[1, 2]. Results We have reviewed prospective designs for the development of new therapeutics with candidate predictive biomarkers. We have also 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. Conclusions Developing new treatments with predictive biomarkers for identifying the patients who are most likely or least likely to benefit makes drug development more complex. But for many new oncology drugs it is the only science based approach and should increase the chance of success. It may also lead to more consistency in results among trials and has obvious benefits for reducing the number of patients who ultimately receive expensive drugs which expose them risks of adverse events but no benefit. This approach also has great potential value for controlling societal expenditures on health care. Development of treatments with predictive biomarkers requires major changes in the standard paradigms for the design and analysis of clinical trials. Some of the key assumptions

  15. 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

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

    PubMed

    Chen, Jonathan H; Goldstein, Mary K; Asch, Steven M; Altman, Russ B

    2016-01-01

    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

  17. Predictive biomarker discovery through the parallel integration of clinical trial and functional genomics datasets.

    PubMed

    Swanton, Charles; Larkin, James M; Gerlinger, Marco; Eklund, Aron C; Howell, Michael; Stamp, Gordon; Downward, Julian; Gore, Martin; Futreal, P Andrew; Escudier, Bernard; Andre, Fabrice; Albiges, Laurence; Beuselinck, Benoit; Oudard, Stephane; Hoffmann, Jens; Gyorffy, Balázs; Torrance, Chris J; Boehme, Karen A; Volkmer, Hansjuergen; Toschi, Luisella; Nicke, Barbara; Beck, Marlene; Szallasi, Zoltan

    2010-01-01

    The European Union multi-disciplinary Personalised RNA interference to Enhance the Delivery of Individualised Cytotoxic and Targeted therapeutics (PREDICT) consortium has recently initiated a framework to accelerate the development of predictive biomarkers of individual patient response to anti-cancer agents. The consortium focuses on the identification of reliable predictive biomarkers to approved agents with anti-angiogenic activity for which no reliable predictive biomarkers exist: sunitinib, a multi-targeted tyrosine kinase inhibitor and everolimus, a mammalian target of rapamycin (mTOR) pathway inhibitor. Through the analysis of tumor tissue derived from pre-operative renal cell carcinoma (RCC) clinical trials, the PREDICT consortium will use established and novel methods to integrate comprehensive tumor-derived genomic data with personalized tumor-derived small hairpin RNA and high-throughput small interfering RNA screens to identify and validate functionally important genomic or transcriptomic predictive biomarkers of individual drug response in patients. PREDICT's approach to predictive biomarker discovery differs from conventional associative learning approaches, which can be susceptible to the detection of chance associations that lead to overestimation of true clinical accuracy. These methods will identify molecular pathways important for survival and growth of RCC cells and particular targets suitable for therapeutic development. Importantly, our results may enable individualized treatment of RCC, reducing ineffective therapy in drug-resistant disease, leading to improved quality of life and higher cost efficiency, which in turn should broaden patient access to beneficial therapeutics, thereby enhancing clinical outcome and cancer survival. The consortium will also establish and consolidate a European network providing the technological and clinical platform for large-scale functional genomic biomarker discovery. Here we review our current understanding

  18. Predicting Clinical Scores from Magnetic Resonance Scans in Alzheimer's Disease

    PubMed Central

    Stonnington, Cynthia M.; Chu, Carlton; Klöppel, Stefan; Jack, Clifford R; Ashburner, John; Frackowiak, Richard S.J.

    2010-01-01

    Machine learning and pattern recognition methods have been used to diagnose Alzheimer's disease (AD) and Mild Cognitive Impairment (MCI) from individual MRI scans. Another application of such methods is to predict clinical scores from individual scans. Using relevance vector regression (RVR), we predicted individuals' performances on established tests from their MRI T1 weighted image in two independent datasets. From Mayo Clinic, 73 probable AD patients and 91 cognitively normal (CN) controls completed the Mini-Mental State Examination (MMSE), Dementia Rating Scale (DRS), and Auditory Verbal Learning Test (AVLT) within 3 months of their scan. Baseline MRI's from the Alzheimer's disease Neuroimaging Initiative (ADNI) comprised the other dataset; 113 AD, 351 MCI, and 122 CN subjects completed the MMSE and Alzheimer's Disease Assessment Scale—Cognitive subtest (ADAS-cog) and 39 AD, 92 MCI, and 32 CN ADNI subjects completed MMSE, ADAS-cog, and AVLT. Predicted and actual clinical scores were highly correlated for the MMSE, DRS, and ADAS-cog tests (P<.0001). Training with one dataset and testing with another demonstrated stability between datasets. DRS, MMSE, and ADAS-Cog correlated better than AVLT with whole brain grey matter changes associated with AD. This result underscores their utility for screening and tracking disease. RVR offers a novel way to measure interactions between structural changes and neuropsychological tests beyond that of univariate methods. In clinical practice, we envision using RVR to aid in diagnosis and predict clinical outcome. PMID:20347044

  19. Elevated EBNA1 Immune Responses Predict Conversion to Multiple Sclerosis

    PubMed Central

    Lünemann, Jan D.; Tintoré, Mar; Messmer, Brady; Strowig, Till; Rovira, Álex; Perkal, Héctor; Caballero, Estrella; Münz, Christian; Montalban, Xavier; Comabella, Manuel

    2009-01-01

    Objective The aims of the study were to determine the immune responses to candidate viral triggers of multiple sclerosis (MS) in patients with clinically isolated syndromes (CIS), and to evaluate their potential value in predicting conversion to MS. Methods Immune responses to Epstein-Barr virus (EBV), human herpesvirus 6, cytomegalovirus (HCMV), and measles were determined in a cohort of 147 CIS patients with a mean follow-up of 7 years and compared with 50 demographically matched controls. Results Compared to controls, CIS patients showed increased humoral (p<0.0001) and cellular (p=0.007) immune responses to the EBV-encoded nuclear antigen-1 (EBNA1), but not to other EBV-derived proteins. IgG responses to other virus antigens and frequencies of T cells specific for HCMV and influenza virus gene products were unchanged in CIS patients. EBNA1 was the only viral antigen towards which immune responses correlated with number of T2 lesions (p=0.006) and number of Barkhof criteria (p=0.001) at baseline, and with number of T2 lesions (p=0.012 both at 1 and 5 years), presence of new T2 lesions (p=0.003 and p=0.028 at 1 and 5 years), and EDSS (p=0.015 and p=0.010 at 1 and 5 years) during follow-up. In a univariate Cox regression model, increased EBNA1-specific IgG responses predicted conversion to MS based on McDonald criteria [hazard ratio (95% confidence interval), 2.2 (1.2–4.3); p=0.003]. Interpretation Our results indicate that elevated immune responses towards EBNA1 are selectively increased in CIS patients and suggest that EBNA1-specific IgG titers could be used as a prognostic marker for disease conversion and disability progression. PMID:20225269

  20. On-time clinical phenotype prediction based on narrative reports

    PubMed Central

    Bejan, Cosmin A.; Vanderwende, Lucy; Evans, Heather L.; Wurfel, Mark M.; Yetisgen-Yildiz, Meliha

    2013-01-01

    In this paper we describe a natural language processing system which is able to predict whether or not a patient exhibits a specific phenotype using the information extracted from the narrative reports associated with the patient. Furthermore, the phenotypic annotations from our report dataset were performed at the report level which allows us to perform the prediction of the clinical phenotype at any point in time during the patient hospitalization period. Our experiments indicate that an important factor in achieving better results for this problem is to determine how much information to extract from the patient reports in the time interval between the patient admission time and the current prediction time. PMID:24551325

  1. Roles and responsibilities of clinical nurse researchers.

    PubMed

    Kirchhoff, K T; Mateo, M A

    1996-01-01

    A follow-up survey of 142 nurse researchers employed in clinical settings (NRECS) was conducted 10 years after the first one conducted by Knafl, Bevis, and Kirchhoff in which only 34 individuals qualified for inclusion. An 80-item questionnaire included items about the structure of the position, processes used, variables that may influence outcomes, and outside activities. When ineligible persons were excluded, the response rate was 75 per cent. Most commonly NRECS had positions in clinical settings only (55.7 per cent), offices (75.5 per cent), some staff (72.6 per cent), and secretarial support (52.8 per cent), and they usually reported to the chief nurse executives (71.7 per cent). Although the majority of NRECS reported responsibility for research activities, the average time spent on research is only 50 per cent. Most (82 per cent) have a nursing research committee, but NRECS also sit on other research-related committees in the department or hospital. Details about salary, responsibilities, and processes will be helpful to those preparing themselves or others for this role, for those who wish to start such a position for themselves or another, or for those in the role wanting to know how other NRECS perform. PMID:8632106

  2. 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

  3. Analysis of factors that predict clinical performance in medical school.

    PubMed

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

    2009-10-01

    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 predict performance in medical school consistently across students, and whether either predicts clinical performance in clerkships. A path model was developed to examine relationships among indices of medical student performance during the first three years of medical school for five cohorts of medical students. A structural equation approach was used to calculate the coefficients hypothesized in the model for majority and minority students. Significant differences between majority and minority students were observed. MCAT scores, for example, did not predict performance of minority students in the first year of medical school but did predict performance of majority students. This information may be of use to medical school admissions and resident selection committees. PMID:18030590

  4. 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.

  5. Clinical Prediction Rule of Drug Resistant Epilepsy in Children

    PubMed Central

    Boonluksiri, Pairoj; Visuthibhan, Anannit; Katanyuwong, Kamornwan

    2015-01-01

    Background and Purpose: Clinical prediction rules (CPR) are clinical decision-making tools containing variables such as history, physical examination, diagnostic tests by developing scoring model from potential risk factors. This study is to establish clinical prediction scoring of drug-resistant epilepsy (DRE) in children using clinical manifestationa and only basic electroencephalography (EEG). Methods: Retrospective cohort study was conducted. A total of 308 children with diagnosed epilepsy were recruited. Primary outcome was the incidence of DRE. Independent determinants were patient characteristics, clinical manifestations and electroencephalography. CPR was performed based on multiple logistic regression. Results: The incidence of DRE was 42%. Risk factors were age onset, prior neurological deficits, and abnormal EEG. CPR can be established and stratified the prediction using scores into 3 levels such as low risk (score<6), moderate risk (score 6–12) and high risk (score>12) with positive likelihood ratio of 0.5, 1.8 and 12.5 respectively. Conclusions: CPR with scoring risks were stratified into 3 levels. The strongest risk is prior global neurological deficits. PMID:26819940

  6. Clinical predictors of therapeutic response to antipsychotics in schizophrenia

    PubMed Central

    Carbon, Maren; Correll, Christoph U.

    2014-01-01

    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. PMID:25733955

  7. Prediction and diagnosis of clinical outcomes affecting restoration margins.

    PubMed

    Dennison, J B; Sarrett, D C

    2012-04-01

    The longevity of dental restorations is largely dependent on the continuity at the interface between the restorative material and adjacent tooth structure (the restoration margin). Clinical decisions on restoration repair or replacement are usually based upon the weakest point along that margin interface. Physical properties of a restorative material, such as polymerisation shrinkage, water sorption, solubility, elastic modulus and shear strength, all have an effect on stress distribution and can significantly affect margin integrity. This review will focus on two aspects of margin deterioration in the oral environment: the in vitro testing of margin seal using emersion techniques to simulate the oral environment and to predict clinical margin failure and the relationship between clinically observable microleakage and secondary caries. The many variables associated with in vitro testing of marginal leakage and the interpretation of the data are presented in detail. The most recent studies of marginal leakage mirror earlier methodology and lack validity and reliability. The lack of standardised testing procedures makes it impossible to compare studies or to predict the clinical performance of adhesive materials. Continual repeated in vitro studies contribute little to the science in this area. Clinical evidence is cited to refute earlier conclusions that clinical microleakage (penetrating margin discoloration) leads to caries development and is an indication for restoration replacement. Margin defects, without visible evidence of soft dentin on the wall or base of the defect, should be monitored, repaired or resealed, in lieu of total restoration replacement. PMID:22066463

  8. Clinical Risk Prediction by Exploring High-Order Feature Correlations

    PubMed Central

    Wang, Fei; Zhang, Ping; Wang, Xiang; Hu, Jianying

    2014-01-01

    Clinical risk prediction is one important problem in medical informatics, and logistic regression is one of the most widely used approaches for clinical risk prediction. In many cases, the number of potential risk factors is fairly large and the actual set of factors that contribute to the risk is small. Therefore sparse logistic regression is proposed, which can not only predict the clinical risk but also identify the set of relevant risk factors. The inputs of logistic regression and sparse logistic regression are required to be in vector form. This limits the applicability of these models in the problems when the data cannot be naturally represented vectors (e.g., medical images are two-dimensional matrices). To handle the cases when the data are in the form of multi-dimensional arrays, we propose HOSLR: High-Order Sparse Logistic Regression, which can be viewed as a high order extension of sparse logistic regression. Instead of solving one classification vector as in conventional logistic regression, we solve for K classification vectors in HOSLR (K is the number of modes in the data). A block proximal descent approach is proposed to solve the problem and its convergence is guaranteed. Finally we validate the effectiveness of HOSLR on predicting the onset risk of patients with Alzheimer’s disease and heart failure. PMID:25954428

  9. 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

  10. 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

  11. 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…

  12. Gambling severity predicts midbrain response to near-miss outcomes

    PubMed Central

    Chase, Henry W.; Clark, Luke

    2010-01-01

    Gambling is a common recreational activity that becomes dysfunctional in a subset of individuals, with DSM ‘pathological gambling’ regarded as the most severe form. During gambling, players experience a range of cognitive distortions that promote an over-estimation of the chances of winning. Near-miss outcomes are thought to fuel these distortions. We observed previously that near-misses recruited overlapping circuitry to monetary wins in a study in healthy volunteers (Clark et al. 2009). The present study sought to extend these observations in regular gamblers and relate brain responses to an index of gambling severity. Twenty regular gamblers, who varied in their involvement from recreational players to probable pathological gamblers, were scanned whilst performing a simplified slot-machine task that delivered occasional monetary wins, as well as near-miss and full-miss non-win outcomes. In the overall group, near-miss outcomes were associated with a significant response in the ventral striatum, which was also recruited by monetary wins. Gambling severity, measured with the South Oaks Gambling Screen, predicted a greater response in the dopaminergic midbrain to near-miss outcomes. This effect survived controlling for clinical co-morbidities that were present in the regular gamblers. Gambling severity did not predict win-related responses in the midbrain or elsewhere. These results demonstrate that near-miss events during gambling recruit reward-related brain circuitry in regular players. An association with gambling severity in the midbrain suggests that near-miss outcomes may enhance dopamine transmission in disordered gambling, which extends neurobiological similarities between pathological gambling and drug addiction. PMID:20445043

  13. 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

  14. 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

  15. Excessive Cytolytic Responses Predict Tuberculosis Relapse After Apparently Successful Treatment

    PubMed Central

    Cliff, Jacqueline M.; Cho, Jang-Eun; Lee, Ji-Sook; Ronacher, Katharina; King, Elizabeth C.; van Helden, Paul; Walzl, Gerhard; Dockrell, Hazel M.

    2016-01-01

    Background. Currently, there are no tools to accurately predict tuberculosis relapse. This study aimed to determine whether patients who experience tuberculosis relapse have different immune responses to mycobacteria in vitro than patients who remain cured for 2 years. Methods. Patients with an initial episode of pulmonary tuberculosis were recruited in South Africa. Diluted blood, collected at diagnosis and after 2 and 4 weeks of treatment, was cultured with live Mycobacterium tuberculosis for 6 days, and cellular RNA was frozen. Gene expression in samples from 10 patients who subsequently experienced relapse, confirmed by strain genotyping, was compared to that in samples from patients who remained cured, using microarrays. Results. At diagnosis, expression of 668 genes was significantly different in samples from patients who experienced relapse, compared with expression in patients who remained successfully cured; these differences persisted for at least 4 weeks. Gene ontology and biological pathways analyses revealed significant upregulation of genes involved in cytotoxic cell-mediated killing. Results were confirmed by real-time quantitative reverse-transcription polymerase chain reaction analysis in a wider patient cohort. Conclusions. These data show that patients who will subsequently experience relapse exhibit altered immune responses, including excessively robust cytolytic responses to M. tuberculosis in vitro, at the time of diagnosis, compared with patients who will achieve durable cure. Together with microbiological and clinical indices, these differences could be exploited in drug development. PMID:26351358

  16. 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.

  17. 42 CFR 493.1457 - Standard; Clinical consultant responsibilities.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... 42 Public Health 5 2011-10-01 2011-10-01 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...

  18. 42 CFR 493.1419 - Standard; Clinical consultant responsibilities.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... 42 Public Health 5 2011-10-01 2011-10-01 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...

  19. 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

  20. Prediction of nuclear hormone receptor response elements.

    PubMed

    Sandelin, Albin; Wasserman, Wyeth W

    2005-03-01

    The nuclear receptor (NR) class of transcription factors controls critical regulatory events in key developmental processes, homeostasis maintenance, and medically important diseases and conditions. Identification of the members of a regulon controlled by a NR could provide an accelerated understanding of development and disease. New bioinformatics methods for the analysis of regulatory sequences are required to address the complex properties associated with known regulatory elements targeted by the receptors because the standard methods for binding site prediction fail to reflect the diverse target site configurations. We have constructed a flexible Hidden Markov Model framework capable of predicting NHR binding sites. The model allows for variable spacing and orientation of half-sites. In a genome-scale analysis enabled by the model, we show that NRs in Fugu rubripes have a significant cross-regulatory potential. The model is implemented in a web interface, freely available for academic researchers, available at http://mordor.cgb.ki.se/NHR-scan. PMID:15563547

  1. Clinical Prediction Rules That Don't Hold Up-Where to Go From Here?

    PubMed

    Stanton, Tasha R

    2016-07-01

    Clinical prediction rules (CPRs) are created to help guide clinical decision making. To do this, they use the presence or absence of certain factors that have been shown to meaningfully predict a patient's prognosis, diagnosis, or response to treatment. While representing a seminal methodological step forward in individualized care, one of the main drawbacks of CPRs continues to be validation studies that do not support the initially derived CPR. This is particularly important because validation of CPRs in an independent patient population prior to clinical implementation is essential. Why is it quite common for existing CPRs to fall down at the validation stage? And what does this mean for research that aims to individualize treatment? J Orthop Sports Phys Ther 2016;46(7):502-505. doi:10.2519/jospt.2016.0606. PMID:27363570

  2. 18F-FDG PET/CT Prediction of an Aggressive Clinical Course for Dermatofibrosarcoma Protuberans.

    PubMed

    Basu, Sandip; Goliwale, Fahim

    2016-06-01

    The ability to assess tumor biology is a benefit of molecular imaging with (18)F-FDG PET/CT, which performs better than anatomic imaging in evaluating malignancies. We present an unusual case of fatal dermatofibrosarcoma protuberans, a usually indolent entity for which high-grade (18)F-FDG uptake was predictive of an aggressive clinical course unabated by tyrosine kinase inhibitor imatinib mesylate, to which the patient showed a poor response. PMID:26338485

  3. Predictable Outcomes with Porcelain Laminate Veneers: A Clinical Report.

    PubMed

    Pimentel, Welson; Teixeira, Marcelo Lucchesi; Costa, Priscila Paganini; Jorge, Mônica Zacharias; Tiossi, Rodrigo

    2016-06-01

    This clinical report describes how to achieve predictable outcomes for anterior teeth esthetic restorations with porcelain laminate veneers by associating the digital planning and design of the restoration with interim restorations. The previous digital smile design of the restoration eliminates the communication barrier with the patient and assists the clinician throughout patient treatment. Interim restorations (diagnostic mock-ups) further enhance communication with the patient and prevent unnecessary tooth reduction for conservative tooth preparation. Adequate communication between patient and clinician contributes to successful definitive restorations and patient satisfaction with the final esthetic outcome. PMID:26633080

  4. Clinical presentation and outcome prediction of clinical, serological, and histopathological classification schemes in ANCA-associated vasculitis with renal involvement.

    PubMed

    Córdova-Sánchez, Bertha M; Mejía-Vilet, Juan M; Morales-Buenrostro, Luis E; Loyola-Rodríguez, Georgina; Uribe-Uribe, Norma O; Correa-Rotter, Ricardo

    2016-07-01

    Several classification schemes have been developed for anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV), with actual debate focusing on their clinical and prognostic performance. Sixty-two patients with renal biopsy-proven AAV from a single center in Mexico City diagnosed between 2004 and 2013 were analyzed and classified under clinical (granulomatosis with polyangiitis [GPA], microscopic polyangiitis [MPA], renal limited vasculitis [RLV]), serological (proteinase 3 anti-neutrophil cytoplasmic antibodies [PR3-ANCA], myeloperoxidase anti-neutrophil cytoplasmic antibodies [MPO-ANCA], ANCA negative), and histopathological (focal, crescenteric, mixed-type, sclerosing) categories. Clinical presentation parameters were compared at baseline between classification groups, and the predictive value of different classification categories for disease and renal remission, relapse, renal, and patient survival was analyzed. Serological classification predicted relapse rate (PR3-ANCA hazard ratio for relapse 2.93, 1.20-7.17, p = 0.019). There were no differences in disease or renal remission, renal, or patient survival between clinical and serological categories. Histopathological classification predicted response to therapy, with a poorer renal remission rate for sclerosing group and those with less than 25 % normal glomeruli; in addition, it adequately delimited 24-month glomerular filtration rate (eGFR) evolution, but it did not predict renal nor patient survival. On multivariate models, renal replacement therapy (RRT) requirement (HR 8.07, CI 1.75-37.4, p = 0.008) and proteinuria (HR 1.49, CI 1.03-2.14, p = 0.034) at presentation predicted renal survival, while age (HR 1.10, CI 1.01-1.21, p = 0.041) and infective events during the induction phase (HR 4.72, 1.01-22.1, p = 0.049) negatively influenced patient survival. At present, ANCA-based serological classification may predict AAV relapses, but neither clinical nor serological

  5. Personalized approach to growth hormone treatment: clinical use of growth prediction models.

    PubMed

    Wit, J M; Ranke, M B; Albertsson-Wikland, K; Carrascosa, A; Rosenfeld, R G; Van Buuren, S; Kristrom, B; Schoenau, E; Audi, L; Hokken-Koelega, A C S; Bang, P; Jung, H; Blum, W F; Silverman, L A; Cohen, P; Cianfarani, S; Deal, C; Clayton, P E; de Graaff, L; Dahlgren, J; Kleintjens, J; Roelants, M

    2013-01-01

    The goal of growth hormone (GH) treatment in a short child is to attain a fast catch-up growth toward the target height (TH) standard deviation score (SDS), followed by a maintenance phase, a proper pubertal height gain, and an adult height close to TH. The short-term response variable of GH treatment, first-year height velocity (HV) (cm/year or change in height SDS), can either be compared with GH response charts for diagnosis, age and gender, or with predicted HV based on prediction models. Three types of prediction models have been described: the Kabi International Growth Hormone Study models, the Gothenburg models and the Cologne model. With these models, 50-80% of the variance could be explained. When used prospectively, individualized dosing reduces the variation in growth response in comparison with a fixed dose per body weight. Insulin-like growth factor-I-based dose titration also led to a decrease in the variation. It is uncertain whether adding biochemical, genetic or proteomic markers may improve the accuracy of the prediction. Prediction models may lead to a more evidence-based approach to determine the GH dose regimen and may reduce the drug costs for GH treatment. There is a need for user-friendly software programs to make prediction models easily available in the clinic. PMID:23735882

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

    PubMed Central

    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. PMID:26885438

  7. A Novel, Noninvasive, Predictive Epilepsy Biomarker with Clinical Potential

    PubMed Central

    Choy, ManKin; Dubé, Celine M.; Patterson, Katelin; Barnes, Samuel R.; Maras, Pamela; Blood, Arlin B.; Hasso, Anton N.; Obenaus, Andre

    2014-01-01

    A significant proportion of temporal lobe epilepsy (TLE), a common, intractable brain disorder, arises in children with febrile status epilepticus (FSE). Preventative therapy development is hampered by our inability to identify early the FSE individuals who will develop TLE. In a naturalistic rat model of FSE, we used high-magnetic-field MRI and long-term video EEG to seek clinically relevant noninvasive markers of epileptogenesis and found that reduced amygdala T2 relaxation times in high-magnetic-field MRI hours after FSE predicted experimental TLE. Reduced T2 values likely represented paramagnetic susceptibility effects derived from increased unsaturated venous hemoglobin, suggesting augmented oxygen utilization after FSE termination. Indeed, T2 correlated with energy-demanding intracellular translocation of the injury-sensor high-mobility group box 1 (HMGB1), a trigger of inflammatory cascades implicated in epileptogenesis. Use of deoxyhemoglobin-sensitive MRI sequences enabled visualization of the predictive changes on lower-field, clinically relevant scanners. This novel MRI signature delineates the onset and suggests mechanisms of epileptogenesis that follow experimental FSE. PMID:24966369

  8. 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

  9. Clinical neuroprediction: Amygdala reactivity predicts depressive symptoms 2 years later.

    PubMed

    Mattson, Whitney I; Hyde, Luke W; Shaw, Daniel S; Forbes, Erika E; Monk, Christopher S

    2016-06-01

    Depression is linked to increased amygdala activation to neutral and negatively valenced facial expressions. Amygdala activation may be predictive of changes in depressive symptoms over time. However, most studies in this area have focused on small, predominantly female and homogenous clinical samples. Studies are needed to examine how amygdala reactivity relates to the course of depressive symptoms dimensionally, prospectively and in populations diverse in gender, race and socioeconomic status. A total of 156 men from predominately low-income backgrounds completed an fMRI task where they viewed emotional facial expressions. Left and right amygdala reactivity to neutral, but not angry or fearful, facial expressions relative to a non-face baseline at age 20 predicted greater depressive symptoms 2 years later, controlling for age 20 depressive symptoms. Heightened bilateral amygdala reactivity to neutral facial expressions predicted increases in depressive symptoms 2 years later in a large community sample. Neutral facial expressions are affectively ambiguous and a tendency to interpret these stimuli negatively may reflect to cognitive biases that lead to increases in depressive symptoms over time. Individual differences in amygdala reactivity to neutral facial expressions appear to identify those at most risk for a more problematic course of depressive symptoms across time. PMID:26865423

  10. PREDICTIVE BAYESIAN PATHOGEN DOSE-RESPONSE MODEL FORMS

    EPA Science Inventory

    The use of predictive Bayesian methods in dose-response assessment will be investigated. The predictive Bayesian approach offers an alternative to current approaches in that it does not require the selection of a specific confidence limit, yet provides an answer that is more cons...

  11. Psychological hardiness predicts neuroimmunological responses to stress.

    PubMed

    Sandvik, Asle M; Bartone, Paul T; Hystad, Sigurd William; Phillips, Terry M; Thayer, Julian F; Johnsen, Bjørn Helge

    2013-01-01

    Psychological hardiness characterizes people who remain healthy under psychosocial stress. The present exploratory study investigates possible links between hardiness and several immune and neuroendocrine markers: IL-6, IL-12, IL-4, IL-10, & neuropeptide-Y. A total of 21 Norwegian navy cadets were studied in the context of a highly stressful military field exercise. Blood samples were collected midway, and again late in the exercise when stress levels were highest. Psychological hardiness (including commitment, control, and challenge) was measured two days before the exercise. While all subjects scored high in hardiness, some were high only in commitment and control, but relatively low in challenge. These "unbalanced" hardiness subjects were also more stress reactive, showing suppressed proinflammatory cytokines (IL-12), increased anti-inflammatory cytokines (IL-4, IL-10), and lower neuropeptide-Y levels as compared to the hardiness-balanced group. This study thus shows that being high in hardiness with a balanced profile is linked to more moderate and healthy immune and neuroendocrine responses to stress. PMID:23458268

  12. 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.

  13. 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. PMID:25112173

  14. 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

  15. A Model of Placebo Response in Antidepressant Clinical Trials

    PubMed Central

    Rutherford, Bret R; Roose, Steven P.

    2012-01-01

    Placebo response in clinical trials of antidepressant medications is substantial and increasing. High placebo response rates hamper efforts to detect signals of efficacy for new antidepressant medications, contributing to more failed trials 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. This review examines contributors to placebo response in antidepressant clinical trials and proposes 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 that enhances treatment response. PMID:23318413

  16. Predicting the clinical effect of a short acting bronchodilator in individual patients using artificial neural networks.

    PubMed

    de Matas, Marcel; Shao, Qun; Biddiscombe, Martyn F; Meah, Sally; Chrystyn, Henry; Usmani, Omar S

    2010-12-23

    Artificial neural networks were used in this study to model the relationships between in vitro data, subject characteristics and in vivo outcomes from N=18 mild-moderate asthmatics receiving monodisperse salbutamol sulphate aerosols of 1.5, 3 and 6 μm mass median aerodynamic diameter in a cumulative dosing schedule of 10, 20, 40 and 100 μg. Input variables to the model were aerodynamic particle size (APS), body surface area (BSA), age, pre-treatment forced expiratory volume in one-second (FEV(1)), forced vital capacity, cumulative emitted drug dose and bronchodilator reversibility to a standard salbutamol sulphate 200 μg dose MDI (REV(%)). These factors were used by the model to predict the bronchodilator response at 10 (T10) and 20 (T20) min after receiving each of the 4 doses for each of the 3 different particle sizes. Predictability was assessed using data from selected patients in this study, which were set aside and not used in model generation. Models reliably predicted ΔFEV(1)(%) in individual subjects with non-linear determinants (R(2)) of ≥ 0.8. The average error between predicted and observed ΔFEV(1)(%) for individual subjects was <4% across the cumulative dosing regimen. Increases in APS and drug dose gave improved ΔFEV(1)(%). Models also showed trends towards improved responses in younger patients and those having greater REV(%), whilst BSA was also shown to influence clinical effect. These data show that APS can be used to discriminate predictably between aerosols giving different bronchodilator responses across a cumulative dosing schedule, whilst patient characteristics can be used to reliably estimate clinical response in individual subjects. PMID:20932900

  17. 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

  18. 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.

  19. Clinical and laboratory predictive markers for acute dengue infection

    PubMed Central

    2013-01-01

    Background Early diagnosis of dengue virus infection during the febrile stage is essential for adjusting appropriate management. This study is to identify the predictive markers of clinical and laboratory findings in the acute stage of dengue infection during a major outbreak of dengue virus type 1 that occurred in southern Taiwan during 2007. A retrospective, hospital-based study was conducted at a university hospital in southern Taiwan from January to December, 2007. Patient who was reported for clinically suspected dengue infection was enrolled. Laboratory-positive dengue cases are confirmed by enzyme-linked immunosorbent assay of specific dengue IgM, fourfold increase of dengue-specific IgG titers in convalescent serum, or by reverse transcription-polymerase chain reaction (RT-PCR) of dengue virus. Results The suspected dengue cases consist of 100 children (≤ 18 years) and 481 adults. Among the 581 patients, 67 (67%) children and 309 (64.2%) adults were laboratory-confirmed. Patients who had laboratory indeterminate were excluded. Most cases were uncomplicated and 3.8% of children and 2.9% of adults developed dengue hemorrhagic fever or dengue shock syndrome (DHF/DSS). The overall mortality rate in those with DHF/DSS was 7.1%, and the average duration of hospitalization was 20 days. The most common symptoms/signs at admission were myalgia (46.8%), petechiae (36.9%) and nausea/vomiting (33.5%). The most notable laboratory findings included leukopenia (2966 ± 1896/cmm), thrombocytopenia (102 ± 45 × 103/cmm), prolonged activated partial thromboplastin time (aPTT) (45 ± 10 s), and elevated serum levels of aminotransferase (AST, 166 ± 208 U/L; ALT, 82 ± 103 U/L) and low C - reactive protein (CRP) (6 ± 11 mg/L). Based on the clinical features for predicting laboratory-confirmed dengue infection, the sensitivities of typical rash, myalgia, and positive tourniquet test are 59.2%, 46.8%, and 34.2%, while the specificities for

  20. Improving Clinical Prediction of Bipolar Spectrum Disorders in Youth

    PubMed Central

    Frazier, Thomas W.; Youngstrom, Eric A.; Fristad, Mary A.; Demeter, Christine; Birmaher, Boris; Kowatch, Robert A.; Arnold, L. Eugene; Axelson, David; Gill, Mary K.; Horwitz, Sarah M.; Findling, Robert L.

    2014-01-01

    This report evaluates whether classification tree algorithms (CTA) may improve the identification of individuals at risk for bipolar spectrum disorders (BPSD). Analyses used the Longitudinal Assessment of Manic Symptoms (LAMS) cohort (629 youth, 148 with BPSD and 481 without BPSD). Parent ratings of mania symptoms, stressful life events, parenting stress, and parental history of mania were included as risk factors. Comparable overall accuracy was observed for CTA (75.4%) relative to logistic regression (77.6%). However, CTA showed increased sensitivity (0.28 vs. 0.18) at the expense of slightly decreased specificity and positive predictive power. The advantage of CTA algorithms for clinical decision making is demonstrated by the combinations of predictors most useful for altering the probability of BPSD. The 24% sample probability of BPSD was substantially decreased in youth with low screening and baseline parent ratings of mania, negative parental history of mania, and low levels of stressful life events (2%). High screening plus high baseline parent-rated mania nearly doubled the BPSD probability (46%). Future work will benefit from examining additional, powerful predictors, such as alternative data sources (e.g., clinician ratings, neurocognitive test data); these may increase the clinical utility of CTA models further. PMID:25143826

  1. Urate predicts rate of clinical decline in Parkinson disease

    PubMed Central

    Ascherio, Alberto; LeWitt, Peter A.; Xu, Kui; Eberly, Shirley; Watts, Arthur; Matson, Wayne R.; Marras, Connie; Kieburtz, Karl; Rudolph, Alice; Bogdanov, Mikhail B.; Schwid, Steven R.; Tennis, Marsha; Tanner, Caroline M.; Beal, M. Flint; Lang, Anthony E.; Oakes, David; Fahn, Stanley; Shoulson, Ira; Schwarzschild, Michael A.

    2009-01-01

    Context The risk of Parkinson disease (PD) and its rate of progression may decline with increasing blood urate, a major antioxidant. Objective To determine whether serum and cerebrospinal fluid (CSF) concentrations of urate predict clinical progression in patients with PD. Design, Setting, and Participants 800 subjects with early PD enrolled in the DATATOP trial. Pre-treatment urate was measured in serum for 774 subjects and in CSF for 713. Main Outcome Measures Treatment-, age- and sex-adjusted hazard ratios (HRs) for clinical disability requiring levodopa therapy, the pre-specified primary endpoint. Results The HR of progressing to endpoint decreased with increasing serum urate (HR for 1 standard deviation increase = 0.82; 95% CI = 0.73 to 0.93). In analyses stratified by α-tocopherol treatment (2,000 IU/day), a decrease in the HR for the primary endpoint was seen only among subjects not treated with α-tocopherol (HR = 0.75; 95% CI = 0.62 to 0.89, versus those treated HR = 0.90; 95% CI = 0.75 to 1.08). Results were similar for the rate of change in the United Parkinson Disease Rating Scale (UPDRS). CSF urate was also inversely related to both the primary endpoint (HR for highest versus lowest quintile = 0.65; 95% CI: 0.54 to 0.96) and to the rate of change in UPDRS. As with serum urate, these associations were present only among subjects not treated with α-tocopherol. Conclusion Higher serum and CSF urate at baseline were associated with slower rates of clinical decline. The findings strengthen the link between urate and PD and the rationale for considering CNS urate elevation as a potential strategy to slow PD progression. PMID:19822770

  2. Autoantibody Epitope Spreading in the Pre-Clinical Phase Predicts Progression to Rheumatoid Arthritis

    PubMed Central

    Deane, Kevin D.; Lahey, Lauren J.; Derber, Lezlie A.; Chandra, Piyanka E.; Edison, Jess D.; Gilliland, William R.; Tibshirani, Robert J.; Norris, Jill M.; Holers, V. Michael; Robinson, William H.

    2012-01-01

    Rheumatoid arthritis (RA) is a prototypical autoimmune arthritis affecting nearly 1% of the world population and is a significant cause of worldwide disability. Though prior studies have demonstrated the appearance of RA-related autoantibodies years before the onset of clinical RA, the pattern of immunologic events preceding the development of RA remains unclear. To characterize the evolution of the autoantibody response in the preclinical phase of RA, we used a novel multiplex autoantigen array to evaluate development of the anti-citrullinated protein antibodies (ACPA) and to determine if epitope spread correlates with rise in serum cytokines and imminent onset of clinical RA. To do so, we utilized a cohort of 81 patients with clinical RA for whom stored serum was available from 1–12 years prior to disease onset. We evaluated the accumulation of ACPA subtypes over time and correlated this accumulation with elevations in serum cytokines. We then used logistic regression to identify a profile of biomarkers which predicts the imminent onset of clinical RA (defined as within 2 years of testing). We observed a time-dependent expansion of ACPA specificity with the number of ACPA subtypes. At the earliest timepoints, we found autoantibodies targeting several innate immune ligands including citrullinated histones, fibrinogen, and biglycan, thus providing insights into the earliest autoantigen targets and potential mechanisms underlying the onset and development of autoimmunity in RA. Additionally, expansion of the ACPA response strongly predicted elevations in many inflammatory cytokines including TNF-α, IL-6, IL-12p70, and IFN-γ. Thus, we observe that the preclinical phase of RA is characterized by an accumulation of multiple autoantibody specificities reflecting the process of epitope spread. Epitope expansion is closely correlated with the appearance of preclinical inflammation, and we identify a biomarker profile including autoantibodies and cytokines which

  3. Model-Based Prediction of the Patient-Specific Response to Adrenaline

    PubMed Central

    Chase, J. Geoffrey; Starfinger, Christina; Hann, Christopher E; Revie, James A; Stevenson, Dave; Shaw, Geoffrey M; Desaive, Thomas

    2010-01-01

    A model for the cardiovascular and circulatory systems has previously been validated in simulated cardiac and circulatory disease states. It has also been shown to accurately capture the main hemodynamic trends in porcine models of pulmonary embolism and PEEP (positive end-expiratory pressure) titrations at different volemic levels. In this research, the existing model and parameter identification process are used to study the effect of different adrenaline doses in healthy and critically ill patient populations, and to develop a means of predicting the hemodynamic response to adrenaline. The hemodynamic effects on arterial blood pressures and stroke volume (cardiac index) are simulated in the model and adrenaline-specific parameters are identified. The dose dependent changes in these parameters are then related to adrenaline dose using data from studies published in the literature. These relationships are then used to predict the future, patient-specific response to a change in dose or over time periods from 1-12 hours. The results are compared to data from 3 published adrenaline dosing studies comprising a total of 37 data sets. Absolute percentage errors for the identified model are within 10% when re-simulated and compared to clinical data for all cases. All identified parameter trends match clinically expected changes. Absolute percentage errors for the predicted hemodynamic responses (N=15) are also within 10% when re-simulated and compared to clinical data. Clinically accurate prediction of the effect of inotropic circulatory support drugs, such as adrenaline, offers significant potential for this type of model-based application. Overall, this work represents a further clinical, proof of concept, of the underlying fundamental mathematical model, methods and approach, as well as providing a template for using the model in clinical titration of adrenaline in a decision support role in critical care. They are thus a further justification in support of upcoming

  4. 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. PMID:25576352

  5. Factors predictive of a beneficial response to therapy of hepatitis C.

    PubMed

    Davis, G L; Lau, J Y

    1997-09-01

    Alpha interferon is the only drug that has been shown to be effective in the treatment of chronic hepatitis C, but only half of patients respond, either transiently or permanently. Pretreatment features that are associated with a greater likelihood of response to short courses of interferon include low hepatitis C virus (HCV) RNA levels, viral genotypes 2 or 3, and the absence of fibrosis or cirrhosis on liver biopsy. Each of these features is more predictive of sustained response (SR) than the end-of-treatment response (ETR). However, the accuracy of these features in predicting response in individual patients is poor. Furthermore, there are several limitations to using these factors in the clinical management of patients. Most importantly, they were identified in 6-month treatment trials. Longer treatment or combination of interferon with ribavirin reduces relapses and will therefore lessen the association of these factors with long-term response. In addition, changes in the definition of treatment end points and the technology used to measure HCV RNA might change the association between these predictive factors and response. The best predictor of a treatment response is the early normalization of the serum alanine aminotransferase (ALT) level during interferon treatment. HCV RNA loss during treatment may also be helpful in predicting response, but it is probably no better than serum ALT levels and is expensive. In summary, several clinical and virological features are associated with higher response rates to interferon treatment. Although pretreatment factors do not accurately predict treatment outcome in individuals, they may be helpful in counseling patients and making treatment decisions. PMID:9305676

  6. Predicting olfactory receptor neuron responses from odorant structure

    PubMed Central

    Schmuker, Michael; de Bruyne, Marien; Hähnel, Melanie; Schneider, Gisbert

    2007-01-01

    Background Olfactory receptors work at the interface between the chemical world of volatile molecules and the perception of scent in the brain. Their main purpose is to translate chemical space into information that can be processed by neural circuits. Assuming that these receptors have evolved to cope with this task, the analysis of their coding strategy promises to yield valuable insight in how to encode chemical information in an efficient way. Results We mimicked olfactory coding by modeling responses of primary olfactory neurons to small molecules using a large set of physicochemical molecular descriptors and artificial neural networks. We then tested these models by recording in vivo receptor neuron responses to a new set of odorants and successfully predicted the responses of five out of seven receptor neurons. Correlation coefficients ranged from 0.66 to 0.85, demonstrating the applicability of our approach for the analysis of olfactory receptor activation data. The molecular descriptors that are best-suited for response prediction vary for different receptor neurons, implying that each receptor neuron detects a different aspect of chemical space. Finally, we demonstrate that receptor responses themselves can be used as descriptors in a predictive model of neuron activation. Conclusion The chemical meaning of molecular descriptors helps understand structure-response relationships for olfactory receptors and their "receptive fields". Moreover, it is possible to predict receptor neuron activation from chemical structure using machine-learning techniques, although this is still complicated by a lack of training data. PMID:17880742

  7. Predicted ball grid array thermal response during reflow soldering

    SciTech Connect

    Voth, T.E.; Bergman, T.L.

    1995-12-31

    A numerical model is developed to predict the detailed thermomechanical response of a BGA assembly during reflow soldering. The governing coupled solid mechanics and heat diffusion equations are solved using a commercially available finite element package. Reported predictions illustrate the system`s sensitivity to both thermal and mechanical processing conditions, as well as component thermal properties. Specifically, assemblies with components of high thermal conductivity show the greatest sensitivity to mechanical loading conditions.

  8. A Systematic Review of Studies Comparing Diagnostic Clinical Prediction Rules with Clinical Judgment

    PubMed Central

    Sanders, Sharon; Doust, Jenny; Glasziou, Paul

    2015-01-01

    Background Diagnostic clinical prediction rules (CPRs) are developed to improve diagnosis or decrease diagnostic testing. Whether, and in what situations diagnostic CPRs improve upon clinical judgment is unclear. Methods and Findings We searched MEDLINE, Embase and CINAHL, with supplementary citation and reference checking for studies comparing CPRs and clinical judgment against a current objective reference standard. We report 1) the proportion of study participants classified as not having disease who hence may avoid further testing and or treatment and 2) the proportion, among those classified as not having disease, who do (missed diagnoses) by both approaches. 31 studies of 13 medical conditions were included, with 46 comparisons between CPRs and clinical judgment. In 2 comparisons (4%), CPRs reduced the proportion of missed diagnoses, but this was offset by classifying a larger proportion of study participants as having disease (more false positives). In 36 comparisons (78%) the proportion of diagnoses missed by CPRs and clinical judgment was similar, and in 9 of these, the CPRs classified a larger proportion of participants as not having disease (fewer false positives). In 8 comparisons (17%) the proportion of diagnoses missed by the CPRs was greater. This was offset by classifying a smaller proportion of participants as having the disease (fewer false positives) in 2 comparisons. There were no comparisons where the CPR missed a smaller proportion of diagnoses than clinical judgment and classified more participants as not having the disease. The design of the included studies allows evaluation of CPRs when their results are applied independently of clinical judgment. The performance of CPRs, when implemented by clinicians as a support to their judgment may be different. Conclusions In the limited studies to date, CPRs are rarely superior to clinical judgment and there is generally a trade-off between the proportion classified as not having disease and the

  9. 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. PMID:21877962

  10. 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

  11. 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

  12. 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.

  13. 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.

  14. 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.

  15. 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... HUMAN SERVICES (CONTINUED) STANDARDS AND CERTIFICATION LABORATORY REQUIREMENTS Personnel for Nonwaived Testing Laboratories Performing High Complexity Testing § 493.1457 Standard; Clinical...

  16. 42 CFR 493.1419 - Standard; Clinical consultant responsibilities.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ... clinical consultation to the laboratory's clients; (b) Be available to assist the laboratory's clients in... 42 Public Health 5 2010-10-01 2010-10-01 false Standard; Clinical consultant responsibilities. 493... HUMAN SERVICES (CONTINUED) STANDARDS AND CERTIFICATION LABORATORY REQUIREMENTS Personnel for...

  17. Multifractal analysis of tumour microscopic images in the prediction of breast cancer chemotherapy response.

    PubMed

    Vasiljevic, Jelena; Pribic, Jelena; Kanjer, Ksenija; Jonakowski, Wojtek; Sopta, Jelena; Nikolic-Vukosavljevic, Dragica; Radulovic, Marko

    2015-10-01

    Due to the individual heterogeneity, highly accurate predictors of chemotherapy response in invasive breast cancer are needed for effective chemotherapeutic management. However, predictive molecular determinants for conventional chemotherapy are only emerging and still incorporate a high degree of predictive variability. Based on such pressing need for predictive performance improvement, we explored the value of pre-therapy tumour histology image analysis to predict chemotherapy response. Fractal analysis was applied to hematoxylin/eosin stained archival tissue of diagnostic biopsies derived from 106 patients diagnosed with invasive breast cancer. The tissue was obtained prior to neoadjuvant anthracycline-based chemotherapy and patients were subsequently divided into three groups according to their actual chemotherapy response: partial pathological response (pPR), pathological complete response (pCR) and progressive/stable disease (PD/SD). It was shown that multifractal analysis of breast tumour tissue prior to chemotherapy indeed has the capacity to distinguish between histological images of the different chemotherapy responder groups with accuracies of 91.4% for pPR, 82.9% for pCR and 82.1% for PD/SD. F(α)max was identified as the most important predictive parameter. It represents the maximum of multifractal spectrum f(α), where α is the Hölder's exponent. This is the first study investigating the predictive value of multifractal analysis as a simple and cost-effective tool to predict the chemotherapy response. Improvements in chemotherapy prediction provide clinical benefit by enabling more optimal chemotherapy decisions, thus directly affecting the quality of life and survival. PMID:26303582

  18. An Oracle: Antituberculosis Pharmacokinetics-Pharmacodynamics, Clinical Correlation, and Clinical Trial Simulations To Predict the Future▿

    PubMed Central

    Pasipanodya, Jotam; Gumbo, Tawanda

    2011-01-01

    Antimicrobial pharmacokinetic-pharmacodynamic (PK/PD) science and clinical trial simulations have not been adequately applied to the design of doses and dose schedules of antituberculosis regimens because many researchers are skeptical about their clinical applicability. We compared findings of preclinical PK/PD studies of current first-line antituberculosis drugs to findings from several clinical publications that included microbiologic outcome and pharmacokinetic data or had a dose-scheduling design. Without exception, the antimicrobial PK/PD parameters linked to optimal effect were similar in preclinical models and in tuberculosis patients. Thus, exposure-effect relationships derived in the preclinical models can be used in the design of optimal antituberculosis doses, by incorporating population pharmacokinetics of the drugs and MIC distributions in Monte Carlo simulations. When this has been performed, doses and dose schedules of rifampin, isoniazid, pyrazinamide, and moxifloxacin with the potential to shorten antituberculosis therapy have been identified. In addition, different susceptibility breakpoints than those in current use have been identified. These steps outline a more rational approach than that of current methods for designing regimens and predicting outcome so that both new and older antituberculosis agents can shorten therapy duration. PMID:20937778

  19. Variability in Multisensory Responses Predicts the Self-Space.

    PubMed

    Serino, Andrea

    2016-03-01

    Our brains distinguish between stimuli that are close enough to interact with our bodies and those that are further away by generating a multisensory representation of space near the self, termed peripersonal space. Recent findings show that variability in neuronal response to audio-tactile stimuli predicts the location of the peripersonal space boundary at the individual level. PMID:26833067

  20. 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.

  1. 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

  2. Simplifying clinical use of the genetic risk prediction model BRCAPRO.

    PubMed

    Biswas, Swati; Atienza, Philamer; Chipman, Jonathan; Hughes, Kevin; Barrera, Angelica M Gutierrez; Amos, Christopher I; Arun, Banu; Parmigiani, Giovanni

    2013-06-01

    Health care providers need simple tools to identify patients at genetic risk of breast and ovarian cancers. Genetic risk prediction models such as BRCAPRO could fill this gap if incorporated into Electronic Medical Records or other Health Information Technology solutions. However, BRCAPRO requires potentially extensive information on the counselee and her family history. Thus, it may be useful to provide simplified version(s) of BRCAPRO for use in settings that do not require exhaustive genetic counseling. We explore four simplified versions of BRCAPRO, each using less complete information than the original model. BRCAPROLYTE uses information on affected relatives only up to second degree. It is in clinical use but has not been evaluated. BRCAPROLYTE-Plus extends BRCAPROLYTE by imputing the ages of unaffected relatives. BRCAPROLYTE-Simple reduces the data collection burden associated with BRCAPROLYTE and BRCAPROLYTE-Plus by not collecting the family structure. BRCAPRO-1Degree only uses first-degree affected relatives. We use data on 2,713 individuals from seven sites of the Cancer Genetics Network and MD Anderson Cancer Center to compare these simplified tools with the Family History Assessment Tool (FHAT) and BRCAPRO, with the latter serving as the benchmark. BRCAPROLYTE retains high discrimination; however, because it ignores information on unaffected relatives, it overestimates carrier probabilities. BRCAPROLYTE-Plus and BRCAPROLYTE-Simple provide better calibration than BRCAPROLYTE, so they have higher specificity for similar values of sensitivity. BRCAPROLYTE-Plus performs slightly better than BRCAPROLYTE-Simple. The Areas Under the ROC curve are 0.783 (BRCAPRO), 0.763 (BRCAPROLYTE), 0.772 (BRCAPROLYTE-Plus), 0.773 (BRCAPROLYTE-Simple), 0.728 (BRCAPRO-1Degree), and 0.745 (FHAT). The simpler versions, especially BRCAPROLYTE-Plus and BRCAPROLYTE-Simple, lead to only modest loss in overall discrimination compared to BRCAPRO in this dataset. Thus, we conclude that

  3. The Pupillary Orienting Response Predicts Adaptive Behavioral Adjustment after Errors

    PubMed Central

    Murphy, Peter R.; van Moort, Marianne L.; Nieuwenhuis, Sander

    2016-01-01

    Reaction time (RT) is commonly observed to slow down after an error. This post-error slowing (PES) has been thought to arise from the strategic adoption of a more cautious response mode following deployment of cognitive control. Recently, an alternative account has suggested that PES results from interference due to an error-evoked orienting response. We investigated whether error-related orienting may in fact be a pre-cursor to adaptive post-error behavioral adjustment when the orienting response resolves before subsequent trial onset. We measured pupil dilation, a prototypical measure of autonomic orienting, during performance of a choice RT task with long inter-stimulus intervals, and found that the trial-by-trial magnitude of the error-evoked pupil response positively predicted both PES magnitude and the likelihood that the following response would be correct. These combined findings suggest that the magnitude of the error-related orienting response predicts an adaptive change of response strategy following errors, and thereby promote a reconciliation of the orienting and adaptive control accounts of PES. PMID:27010472

  4. Predicting aquifer response time for application in catchment modeling.

    PubMed

    Walker, Glen R; Gilfedder, Mat; Dawes, Warrick R; Rassam, David W

    2015-01-01

    It is well established that changes in catchment land use can lead to significant impacts on water resources. Where land-use changes increase evapotranspiration there is a resultant decrease in groundwater recharge, which in turn decreases groundwater discharge to streams. The response time of changes in groundwater discharge to a change in recharge is a key aspect of predicting impacts of land-use change on catchment water yield. Predicting these impacts across the large catchments relevant to water resource planning can require the estimation of groundwater response times from hundreds of aquifers. At this scale, detailed site-specific measured data are often absent, and available spatial data are limited. While numerical models can be applied, there is little advantage if there are no detailed data to parameterize them. Simple analytical methods are useful in this situation, as they allow the variability in groundwater response to be incorporated into catchment hydrological models, with minimal modeling overhead. This paper describes an analytical model which has been developed to capture some of the features of real, sloping aquifer systems. The derived groundwater response timescale can be used to parameterize a groundwater discharge function, allowing groundwater response to be predicted in relation to different broad catchment characteristics at a level of complexity which matches the available data. The results from the analytical model are compared to published field data and numerical model results, and provide an approach with broad application to inform water resource planning in other large, data-scarce catchments. PMID:24842053

  5. 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.

  6. Etiology of Cellulitis and Clinical Prediction of Streptococcal Disease: A Prospective Study

    PubMed Central

    Bruun, Trond; Oppegaard, Oddvar; Kittang, Bård R.; Mylvaganam, Haima; Langeland, Nina; Skrede, Steinar

    2016-01-01

    Background. The importance of bacteria other than group A streptococci (GAS) in different clinical presentations of cellulitis is unclear, commonly leading to treatment with broad-spectrum antibiotics. The aim of this study was to describe the etiological and clinical spectrum of cellulitis and identify clinical features predicting streptococcal etiology. Methods. We prospectively enrolled 216 patients hospitalized with cellulitis. Clinical details were registered. Bacterial culture was performed from blood, cutaneous or subcutaneous tissue, and/or swabs from skin lesions. Paired serum samples were analyzed for anti-streptolysin O and anti-deoxyribonuclease B antibodies. Results. Serology or blood or tissue culture confirmed β-hemolytic streptococcal (BHS) etiology in 72% (146 of 203) of cases. An additional 13% (27 of 203) of cases had probable BHS infection, indicated by penicillin response or BHS cultured from skin swabs. β-hemolytic streptococcal etiology was predominant in all clinical subgroups, including patients without sharply demarcated erythema. β-hemolytic group C or G streptococci (GCS/GGS) were more commonly isolated than GAS (36 vs 22 cases). This predominance was found in the lower extremity infections. Group C or G streptococci in swabs were associated with seropositivity just as often as GAS. Staphylococcus aureus was cultured from swabs as a single pathogen in 24 cases, 14 (64%) of which had confirmed BHS etiology. Individual BHS-associated clinical characteristics increased the likelihood of confirmed BHS disease only slightly; positive likelihood ratios did not exceed 2.1. Conclusions. β-hemolytic streptococci were the dominating cause of cellulitis in all clinical subgroups and among cases with S aureus in cutaneous swabs. Group C or G streptococci were more frequently detected than GAS. No single clinical feature substantially increased the probability of confirmed BHS etiology. PMID:26734653

  7. 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.

  8. Clinical Prediction of Fall Risk and White Matter Abnormalities

    PubMed Central

    Koo, Bang-Bon; Bergethon, Peter; Qiu, Wei Qiao; Scott, Tammy; Hussain, Mohammed; Rosenberg, Irwin; Caplan, Louis R.; Bhadelia, Rafeeque A.

    2015-01-01

    Background 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 determined by the Tinetti scale, have specific patterns of WM abnormalities on diffusion tensor imaging. Design, Setting, and Patients Community-based cohort of 125 homebound elderly individuals. Main Outcome Measures Diffusion tensor imaging scans were analyzed using tract-based spatial statistics analysis to determine the location of WM abnormalities in subjects with Tinetti scale scores of 25 or higher (without risk of falls) and lower than 25 (with risk of falls). Multivariate linear least squares correlation analysis was performed to determine the association between Tinetti scale scores and local fractional anisotropy values on each skeletal voxel controlling for possible confounders. Results In subjects with risk of falls (Tinetti scale score <25), clusters of abnormal WM were seen in the medial frontal and parietal subcortical pathways, genu and splenium of corpus callosum, posterior cingulum, prefrontal and orbitofrontal pathways, and longitudinal pathways that connect frontal-parietal-temporal lobes. Among these abnormalities, those in medial frontal and parietal subcortical pathways correlated with Mini-Mental State Examination scores, while the other locations were unrelated to these scores. Conclusions Elderly individuals at risk for falls as determined by the Tinetti scale have WM abnormalities in specific locations on diffusion tensor imaging, some of which correlate with cognitive function scores. PMID:22332181

  9. 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. PMID:26569560

  10. A novel embryonic plasticity gene signature that predicts metastatic competence and clinical outcome

    PubMed Central

    Soundararajan, Rama; Paranjape, Anurag N.; Barsan, Valentin; Chang, Jeffrey T.; Mani, Sendurai A.

    2015-01-01

    Currently, very few prognosticators accurately predict metastasis in cancer patients. In order to complete the metastatic cascade and successfully colonize distant sites, carcinoma cells undergo dynamic epithelial-mesenchymal-transition (EMT) and its reversal, mesenchymal-epithelial-transition (MET). While EMT-centric signatures correlate with response to therapy, they are unable to predict metastatic outcome. One reason is due to the wide range of transient phenotypes required for a tumor cell to disseminate and recreate a similar histology at distant sites. Since such dynamic cellular processes are also seen during embryo development (epithelial-like epiblast cells undergo transient EMT to generate the mesoderm, which eventually redifferentiates into epithelial tissues by MET), we sought to utilize this unique and highly conserved property of cellular plasticity to predict metastasis. Here we present the identification of a novel prognostic gene expression signature derived from mouse embryonic day 6.5 that is representative of extensive cellular plasticity, and predicts metastatic competence in human breast tumor cells. This signature may thus complement conventional clinical parameters to offer accurate prediction for outcome among multiple classes of breast cancer patients. PMID:26123483

  11. 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

  12. 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

  13. Rorschach Prediction of Success in Clinical Training: A Second Look

    ERIC Educational Resources Information Center

    Carlson, Rae

    1969-01-01

    A Rorschach Index based on ego-psychological conceptualization of an optimal personality picture predicted for 155 trainees was compared with predictions from the Miller Analogies Test (MAT) and the Strong Vocational Interest Blank (SVIB). The Index predicted success and failure more effectively. (Author)

  14. 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.

  15. Modeling HIV Immune Response and Validation with Clinical Data

    PubMed Central

    Banks, H. T.; Davidian, M.; Hu, Shuhua; Kepler, Grace M.; Rosenberg, E.S.

    2009-01-01

    A system of ordinary differential equations is formulated to describe the pathogenesis of HIV infection, wherein certain features that have been shown to be important by recent experimental research are incorporated in the model. These include the role of CD4+ memory cells that serve as a major reservoir of latently infected cells, a critical role for T-helper cells in the generation of CD8 memory cells capable of efficient recall response, and stimulation by antigens other than HIV. A stability analysis illustrates the capability of this model in admitting multiple locally asymptotically stable (locally a.s.) off-treatment equilibria. We show that this more biologically-detailed model can exhibit the phenomenon of transient viremia experienced by some patients on therapy with viral load levels suppressed below the detection limit. We also show that the loss of CD4+ T-cell help in the generation of CD8+ memory cells leads to larger peak values for the viral load during transient viremia. Censored clinical data is used to obtain parameter estimates. We demonstrate that using a reduced set of 16 free parameters, obtained by fixing some parameters at their population averages, the model provides reasonable fits to the patient data and, moreover, that it exhibits good predictive capability. We further show that parameter values obtained for most clinical patients do not admit multiple locally a.s off-treatment equilibria. This suggests that treatment to move from a high viral load equilibrium state to an equilibrium state with a lower (or zero) viral load is not possible for these patients. PMID:19495424

  16. 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.

  17. 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

  18. Predicting the shock compression response of heterogeneous powder mixtures

    NASA Astrophysics Data System (ADS)

    Fredenburg, D. A.; Thadhani, N. N.

    2013-06-01

    A model framework for predicting the dynamic shock-compression response of heterogeneous powder mixtures using readily obtained measurements from quasi-static tests is presented. Low-strain-rate compression data are first analyzed to determine the region of the bulk response over which particle rearrangement does not contribute to compaction. This region is then fit to determine the densification modulus of the mixture, σD, an newly defined parameter describing the resistance of the mixture to yielding. The measured densification modulus, reflective of the diverse yielding phenomena that occur at the meso-scale, is implemented into a rate-independent formulation of the P-α model, which is combined with an isobaric equation of state to predict the low and high stress dynamic compression response of heterogeneous powder mixtures. The framework is applied to two metal + metal-oxide (thermite) powder mixtures, and good agreement between the model and experiment is obtained for all mixtures at stresses near and above those required to reach full density. At lower stresses, rate-dependencies of the constituents, and specifically those of the matrix constituent, determine the ability of the model to predict the measured response in the incomplete compaction regime.

  19. 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.

  20. Operating Outpatient Clinics in 1990.

    ERIC Educational Resources Information Center

    Bohannan, Harry M.

    1984-01-01

    The future of dental school clinic operations is discussed including change within dental education, factors influencing change, and some predicted changes. Fundamental change can be predicted in educational philosophy, responsibility for clinical care, clinic facilities, clinic operation, and faculty. (MLW)

  1. 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.

  2. Expression of Estrogen-Related Gene Markers in Breast Cancer Tissue Predicts Aromatase Inhibitor Responsiveness

    PubMed Central

    Moy, Irene; Lin, Zhihong; Rademaker, Alfred W.; Reierstad, Scott; Khan, Seema A.; Bulun, Serdar E.

    2013-01-01

    Aromatase inhibitors (AIs) are the most effective class of drugs in the endocrine treatment of breast cancer, with an approximate 50% treatment response rate. Our objective was to determine whether intratumoral expression levels of estrogen-related genes are predictive of AI responsiveness in postmenopausal women with breast cancer. Primary breast carcinomas were obtained from 112 women who received AI therapy after failing adjuvant tamoxifen therapy and developing recurrent breast cancer. Tumor ERα and PR protein expression were analyzed by immunohistochemistry (IHC). Messenger RNA (mRNA) levels of 5 estrogen-related genes–AKR1C3, aromatase, ERα, and 2 estradiol/ERα target genes, BRCA1 and PR–were measured by real-time PCR. Tumor protein and mRNA levels were compared with breast cancer progression rates to determine predictive accuracy. Responsiveness to AI therapy–defined as the combined complete response, partial response, and stable disease rates for at least 6 months–was 51%; rates were 56% in ERα-IHC-positive and 14% in ERα-IHC-negative tumors. Levels of ERα, PR, or BRCA1 mRNA were independently predictive for responsiveness to AI. In cross-validated analyses, a combined measurement of tumor ERα and PR mRNA levels yielded a more superior specificity (36%) and identical sensitivity (96%) to the current clinical practice (ERα/PR-IHC). In patients with ERα/PR-IHC-negative tumors, analysis of mRNA expression revealed either non-significant trends or statistically significant positive predictive values for AI responsiveness. In conclusion, expression levels of estrogen-related mRNAs are predictive for AI responsiveness in postmenopausal women with breast cancer, and mRNA expression analysis may improve patient selection. PMID:24223121

  3. Mothers' labeling responses to infants' gestures predict vocabulary outcomes.

    PubMed

    Olson, Janet; Masur, Elise Frank

    2015-11-01

    Twenty-nine infants aged 1;1 and their mothers were videotaped while interacting with toys for 18 minutes. Six experimental stimuli were presented to elicit infant communicative bids in two communicative intent contexts - proto-declarative and proto-imperative. Mothers' verbal responses to infants' gestural and non-gestural communicative bids were coded for object and action labels. Relations between maternal labeling responses and infants' vocabularies at 1;1 and 1;5 were examined. Mothers' labeling responses to infants' gestural communicative bids were concurrently and predictively related to infants' vocabularies, whereas responses to non-gestural communicative bids were not. Mothers' object labeling following gestures in the proto-declarative context mediated the association from infants' gesturing in the proto-declarative context to concurrent noun lexicons and was the strongest predictor of subsequent noun lexicons. Mothers' action labeling after infants' gestural bids in the proto-imperative context predicted infants' acquisition of action words at 1;5. Findings show that mothers' responsive labeling explain specific relations between infants' gestures and their vocabulary development. PMID:25643656

  4. Creating and evaluating genetic tests predictive of drug response

    PubMed Central

    Weiss, Scott T.; McLeod, Howard L.; Flockhart, David A.; Dolan, M. Eileen; Benowitz, Neal L.; Johnson, Julie A.; Ratain, Mark J.; Giacomini, Kathleen M.

    2009-01-01

    A key goal of pharmacogenetics — the use of genetic variation to elucidate inter-individual variation in drug treatment response — is to aid the development of predictive genetic tests that could maximize drug efficacy and minimize drug toxicity. The completion of the Human Genome Project and the associated HapMap Project, together with advances in technologies for investigating genetic variation, have greatly advanced the potential to develop such tests; however, many challenges remain. With the aim of helping to address some of these challenges, this article discusses the steps that are involved in the development of predictive tests for drug treatment response based on genetic variation, and factors that influence the development and performance of these tests. PMID:18587383

  5. 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.

  6. 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. PMID:19054506

  7. Predicting Response to Hormonal Therapy and Survival in Men with Hormone Sensitive Metastatic Prostate Cancer

    PubMed Central

    Grivas, Petros D.; Robins, Diane M.; Hussain, Maha

    2014-01-01

    Androgen deprivation is the cornerstone of the management of metastatic prostate cancer. Despite several decades of clinical experience with this therapy there are no standard predictive biomarkers for response. Although several candidate genetic, hormonal, inflammatory, biochemical, metabolic biomarkers have been suggested as potential predictors of response and outcome, none has been prospectively validated nor has proven clinical utility to date. There is significant heterogeneity in the depth and duration of hormonal response and in the natural history of advanced disease; therefore to better optimize/individualize therapy and for future development, identification of biomarkers is critical. This review summarizes the current data on the role of several candidate biomarkers that have been evaluated in the advanced/metastatic disease setting. PMID:22705096

  8. 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

  9. Predictive Value of IL-8 for Sepsis and Severe Infections after Burn Injury - A Clinical Study

    PubMed Central

    Kraft, Robert; Herndon, David N; Finnerty, Celeste C; Cox, Robert A; Song, Juquan; Jeschke, Marc G

    2014-01-01

    The inflammatory response induced by burn injury contributes to increased incidence of infections, sepsis, organ failure, and mortality. Thus, monitoring post-burn inflammation is of paramount importance but so far there are no reliable biomarkers available to monitor and/or predict infectious complications after burn. As IL-8 is a major mediator for inflammatory responses, the aim of our study was to determine whether IL-8 expression can be used to predict post-burn sepsis, infections, and mortality other outcomes post-burn. Plasma cytokines, acute phase proteins, constitutive proteins, and hormones were analyzed during the first 60 days post injury from 468 pediatric burn patients. Demographics and clinical outcome variables (length of stay, infection, sepsis, multiorgan failure (MOF), and mortality were recorded. A cut-off level for IL-8 was determined using receiver operating characteristic (ROC) analysis. Statistical significance is set at (p<0.05). ROC analysis identified a cut-off level of 234 pg/ml for IL-8 for survival. Patients were grouped according to their average IL-8 levels relative to this cut off and stratified into high (H) (n=133) and low (L) (n=335) groups. In the L group, regression analysis revealed a significant predictive value of IL-8 to percent of total body surface area (TBSA) burned and incidence of MOF (p<0.001). In the H group IL-8 levels were able to predict sepsis (p<0.002). In the H group, elevated IL-8 was associated with increased inflammatory and acute phase responses compared to the L group (p<0.05). High levels of IL-8 correlated with increased MOF, sepsis, and mortality. These data suggest that serum levels of IL-8 may be a valid biomarker for monitoring sepsis, infections, and mortality in burn patients. PMID:25514427

  10. 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

  11. Can quantitative sensory testing predict responses to analgesic treatment?

    PubMed

    Grosen, K; Fischer, I W D; Olesen, A E; Drewes, A M

    2013-10-01

    The role of quantitative sensory testing (QST) in prediction of analgesic effect in humans is scarcely investigated. This updated review assesses the effectiveness in predicting analgesic effects in healthy volunteers, surgical patients and patients with chronic pain. A systematic review of English written, peer-reviewed articles was conducted using PubMed and Embase (1980-2013). Additional studies were identified by chain searching. Search terms included 'quantitative sensory testing', 'sensory testing' and 'analgesics'. Studies on the relationship between QST and response to analgesic treatment in human adults were included. Appraisal of the methodological quality of the included studies was based on evaluative criteria for prognostic studies. Fourteen studies (including 720 individuals) met the inclusion criteria. Significant correlations were observed between responses to analgesics and several QST parameters including (1) heat pain threshold in experimental human pain, (2) electrical and heat pain thresholds, pressure pain tolerance and suprathreshold heat pain in surgical patients, and (3) electrical and heat pain threshold and conditioned pain modulation in patients with chronic pain. Heterogeneity among studies was observed especially with regard to application of QST and type and use of analgesics. Although promising, the current evidence is not sufficiently robust to recommend the use of any specific QST parameter in predicting analgesic response. Future studies should focus on a range of different experimental pain modalities rather than a single static pain stimulation paradigm. PMID:23658120

  12. 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.

  13. 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

  14. Prognostic prediction through biclustering-based classification of clinical gene expression time series.

    PubMed

    Carreiro, André V; Anunciação, Orlando; Carriço, João A; Madeira, Sara C

    2011-01-01

    The constant drive towards a more personalized medicine led to an increasing interest in temporal gene expression analyzes. It is now broadly accepted that considering a temporal perpective represents a great advantage to better understand disease progression and treatment results at a molecular level. In this context, biclustering algorithms emerged as an important tool to discover local expression patterns in biomedical applications, and CCC-Biclustering arose as an efficient algorithm relying on the temporal nature of data to identify all maximal temporal patterns in gene expression time series. In this work, CCC-Biclustering was integrated in new biclustering-based classifiers for prognostic prediction. As case study we analyzed multiple gene expression time series in order to classify the response of Multiple Sclerosis patients to the standard treatment with Interferon-β, to which nearly half of the patients reveal a negative response. In this scenario, using an effective predictive model of a patient's response would avoid useless and possibly harmful therapies for the non-responder group. The results revealed interesting potentialities to be further explored in classification problems involving other (clinical) time series. PMID:21926438

  15. Predictive factors associated with hepatitis C antiviral therapy response.

    PubMed

    Cavalcante, Lourianne Nascimento; Lyra, André Castro

    2015-06-28

    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

  16. Depression and anxiety predict sex-specific cortisol responses to interpersonal stress.

    PubMed

    Powers, Sally I; Laurent, Heidemarie K; Gunlicks-Stoessel, Meredith; Balaban, Susan; Bent, Eileen

    2016-07-01

    Clinical theories posit interpersonal stress as an important factor in the emergence and exacerbation of depression and anxiety, while neuroendocrine research confirms the association of these syndromes with dysregulation in a major stress response system, the hypothalamic-pituitary-adrenal (HPA) axis. However, the proposal that depression and anxiety symptoms and diagnoses are associated with problematic HPA responses to close relationship stress has not been directly tested. We examined 196 heterosexual dating couples' depression and anxiety symptoms and diagnoses, assessed with questionnaires and diagnostic interviews, in relation to cortisol responses to discussion of an unresolved relationship conflict. Participants provided seven salivary samples in anticipation of and directly following the discussion, and throughout an hour-long recovery period, which were assayed for cortisol. Multilevel models of the HPA response predicted by symptoms or diagnoses showed that women's depressive symptoms predicted attenuated cortisol levels, with a flatter response curve. In contrast, men's depression symptoms and women's anxiety symptoms and diagnoses predicted higher cortisol levels. These findings highlight the importance of examining sex differences in responses to interpersonal stressors for understanding HPA dysregulation in internalizing psychopathology. PMID:27107208

  17. 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. PMID:26671961

  18. Lipocalin-2 as an Infection-Related Biomarker to Predict Clinical Outcome in Ischemic Stroke

    PubMed Central

    Hochmeister, Sonja; Engel, Odilo; Adzemovic, Milena Z.; Pekar, Thomas; Kendlbacher, Paul; Zeitelhofer, Manuel; Haindl, Michaela; Meisel, Andreas; Fazekas, Franz; Seifert-Held, Thomas

    2016-01-01

    Objectives From previous data in animal models of cerebral ischemia, lipocalin-2 (LCN2), a protein related to neutrophil function and cellular iron homeostasis, is supposed to have a value as a biomarker in ischemic stroke patients. Therefore, we examined LCN2 expression in the ischemic brain in an animal model and measured plasma levels of LCN2 in ischemic stroke patients. Methods In the mouse model of transient middle cerebral artery occlusion (tMCAO), LCN2 expression in the brain was analyzed by immunohistochemistry and correlated to cellular nonheme iron deposition up to 42 days after tMCAO. In human stroke patients, plasma levels of LCN2 were determined one week after ischemic stroke. In addition to established predictive parameters such as age, National Institutes of Health Stroke Scale and thrombolytic therapy, LCN2 was included into linear logistic regression modeling to predict clinical outcome at 90 days after stroke. Results Immunohistochemistry revealed expression of LCN2 in the mouse brain already at one day following tMCAO, and the amount of LCN2 subsequently increased with a maximum at 2 weeks after tMCAO. Accumulation of cellular nonheme iron was detectable one week post tMCAO and continued to increase. In ischemic stroke patients, higher plasma levels of LCN2 were associated with a worse clinical outcome at 90 days and with the occurrence of post-stroke infections. Conclusions LCN2 is expressed in the ischemic brain after temporary experimental ischemia and paralleled by the accumulation of cellular nonheme iron. Plasma levels of LCN2 measured in patients one week after ischemic stroke contribute to the prediction of clinical outcome at 90 days and reflect the systemic response to post-stroke infections. PMID:27152948

  19. Can traits predict the competitive response of herbaceous Mediterranean species?

    NASA Astrophysics Data System (ADS)

    Navas, Marie-Laure; Moreau-Richard, Julie

    2005-03-01

    This study tested whether (i) functional traits, measured on individually grown plants, can be used to predict species response to competition; (ii) species competitive response depends on the standing biomass of neighbouring vegetation. Nine herbaceous Mediterranean species (targets) were grown as isolated plants or in mixture with one of three grasses (neighbours) differing in RGR, in pots during 4 months. Ten traits of targets, related to plant size, resource acquisition and conservation ability, and the above-ground biomass of neighbours were measured. Species with large basal area when individually grown were least suppressed by competition. No relationship was found between species competitive response and other traits measured on plants grown in isolation, including height and RGR. This result suggests that plant basal area could be used to predict the competitive ability of species in herbaceous communities. The competitive response of targets varied with the standing biomass of neighbours: it decreased with increasing neighbour biomass for low-productive mixtures, then reached a minimal value as neighbour biomass became larger, whatever the identity of neighbours. The implications of this relationship are discussed.

  20. Baseline Factors Predicting Placebo Response to Treatment in Children and Adolescents With Autism Spectrum Disorders

    PubMed Central

    King, Bryan H.; Dukes, Kimberly; Donnelly, Craig L.; Sikich, Linmarie; McCracken, James T.; Scahill, Lawrence; Hollander, Eric; Bregman, Joel D.; Anagnostou, Evdokia; Robinson, Fay; Sullivan, Lisa; Hirtz, Deborah

    2016-01-01

    IMPORTANCE The finding of factors that differentially predict the likelihood of response to placebo over that of an active drug could have a significant impact on study design in this population. OBJECTIVE To identify possible nonspecific, baseline predictors of response to intervention in a large randomized clinical trial of children and adolescents with autism spectrum disorders. DESIGN, SETTING, AND PARTICIPANTS Randomized clinical trial of citalopram hydrobromide for children and adolescents with autism spectrum disorders and prominent repetitive behavior. Baseline data at study entry were examined with respect to final outcome to determine if response predictors could be identified. A total of 149 children and adolescents 5 to 17 years of age (mean [SD] age, 9.4 [3.1] years) from 6 academic centers were randomly assigned to citalopram (n = 73) or placebo (n = 76). Participants had autistic disorder, Asperger syndrome, or pervasive developmental disorder, not otherwise specified; had illness severity ratings that were moderate or more than moderate on the Clinical Global Impression–Severity scale; and scored moderate or more than moderate on compulsive behaviors measured with the modified Children’s Yale-Brown Obsessive-Compulsive Scale. INTERVENTIONS Twelve weeks of treatment with citalopram (10 mg/5 mL) or placebo. The mean (SD) maximum dose of citalopram was 16.5 (6.5) mg by mouth daily (maximum dose, 20 mg/d). MAIN OUTCOMES AND MEASURES A positive response was defined as having a score of at least much improved on the Clinical Global Impression–Improvement scale at week 12. Baseline measures included demographic (sex, age, weight, and pubertal status), clinical, and family measures. Clinical variables included baseline illness severity ratings (the Aberrant Behavior Checklist, the Child and Adolescent Symptom Inventory, the Vineland Adaptive Behavior Scales, the Repetitive Behavior Scale–Revised, and the Children’s Yale-Brown Obsessive

  1. 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.

  2. 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.

  3. A metabolomics approach for predicting the response to neoadjuvant chemotherapy in cervical cancer patients.

    PubMed

    Hou, Yan; Yin, Mingzhu; Sun, Fengyu; Zhang, Tao; Zhou, Xiaohua; Li, Huiyan; Zheng, Jian; Chen, Xiuwei; Li, Cong; Ning, Xiaoming; Lou, Ge; Li, Kang

    2014-08-01

    Cervical cancer is a clinical and pathological heterogeneity disease, which requires different types of treatments and leads to a variety of outcomes. In clinical practice, only some patients benefit from chemotherapy treatment. Identifying patients who will be responsive to chemotherapy could increase their survival time, which has important implications in personalized treatment and outcomes, while identifying non-responders may reduce the likelihood for these patients to receive ineffective treatment and thereby enable them to receive other potentially effective treatments. Plasma metabolite profiling was performed in this study to identify the potential biomarkers that could predict the response to neoadjuvant chemotherapy (NACT) for cervical cancer patients. The metabolic profiles of plasma from 38 cervical cancer patients with a complete, partial and non-response to NACT were studied using a combination of liquid chromatography coupled with mass spectrometry (LC/MS) and multivariate analysis methods. L-Valine and L-tryptophan were finally identified and verified as the potential biomarkers. A prediction model constructed with L-valine and L-tryptophan correctly identified approximately 80% of patients who were non-response to chemotherapy and 87% of patients who were had a pathologically complete response to chemotherapy. The model has an excellent discriminant performance with an AUC of 0.9407. These results show promise for larger studies that could produce more personalized treatment protocols for cervical cancer patients. PMID:24865370

  4. 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.

  5. 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…

  6. 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

  7. 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

  8. Pain catastrophizing predicts poor response to topical analgesics in patients with neuropathic pain

    PubMed Central

    Mankovsky, Tsipora; Lynch, Mary E; Clark, AJ; Sawynok, J; Sullivan, Michael JL

    2012-01-01

    BACKGROUND: Previous research suggests that high levels of pain catastrophizing might predict poorer response to pharmacological interventions for neuropathic pain. OBJECTIVE: The present study sought to examine the clinical relevance of the relation between catastrophizing and analgesic response in individuals with neuropathic pain. Clinically meaningful reductions were defined in terms of the magnitude of reductions in pain through the course of treatment, and in terms of the number of patients whose end-of-treatment pain ratings were below 4/10. METHODS: Patients (n=82) with neuropathic pain conditions completed a measure of pain catastrophizing at the beginning of a three-week trial examining the efficacy of topical analgesics for neuropathic pain. RESULTS: Consistent with previous research, high scores on the measure of pain catastrophizing prospectively predicted poorer response to treatment. Fewer catastrophizers than noncatastrophizers showed moderate (≥2 points) or substantial reductions in pain ratings through the course of treatment. Fewer catastrophizers than noncatastrophizers achieved end-of-treatment pain ratings below 4/10. CONCLUSIONS: The results of the present study suggest that the development of brief interventions specifically targeting catastrophic thinking might be useful for enhancing the effects of pharmacological interventions for neuropathic pain. Furthermore, failure to account for the level of catastrophizing might contribute to null findings in clinical trials of analgesic medication. PMID:22518362

  9. 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. PMID:26156867

  10. Prognostic and Predictive Biomarkers in Colorectal Cancer. From the Preclinical Setting to Clinical Practice.

    PubMed

    Maurel, Joan; Postigo, Antonio

    2015-01-01

    Colorectal cancer (CRC) is the second largest cause of cancer mortality in Western countries, mostly due to metastasis. Understanding the natural history and prognostic factors in patients with metastatic CRC (mCRC) is essential for the optimal design of clinical trials. The main prognostic factors currently used in clinical practice are related to tumor behavior (e.g., white blood counts, levels of lactate dehydrogenase, levels of alkaline phosphatase) disease extension (e.g., presence of extrahepatic spread, number of organs affected) and general functional status (e.g., performance status as defined by the Eastern Cooperative Oncology Group). However, these parameters are not always sufficient to establish appropriate therapeutic strategies. First-line therapy in mCRC combines conventional chemotherapy (CHT) (e.g., FOLFOX, FOLFIRI, CAPOX) with a number of agents targeted to specific signaling pathways (TA) (e.g., panitumumab and cetuximab for cases KRAS/NRAS WT, and bevacizumab). Although the response rate to this combination regime exceeds 50%, progression of the disease is almost universal and only less than 10% of patients are free of disease at 2 years. Current clinical trials with second and third line therapy include new TA, such as tyrosin-kinase receptors inhibitors (MET, HER2, IGF-1R), inhibitors of BRAF, MEK, PI3K, AKT, mTORC, NOTCH and JAK1/JAK2, immunotherapy modulators and check point inhibitors (anti-PD-L1 and anti- PD1). Despite the identification of multiple prognostic and predictive biomarkers and signatures, it is still unclear how expression of many of these biomarkers is modulated by CHT and/or TA, thus potentially affecting response to treatment. In this review we analyzed how certain biomarkers in tumor cells and microenvironment influence the response to new TA and immune-therapies strategies in mCRC pre-treated patients. PMID:26452385

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

    PubMed Central

    Thase, Michael E.

    2014-01-01

    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. PMID:25733957

  12. 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. PMID:27353036

  13. 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

  14. Predicting cardiovascular disease from handgrip strength: the potential clinical implications.

    PubMed

    Leong, Darryl P; Teo, Koon K

    2015-12-01

    The measurement of handgrip strength has proven prognostic value for all-cause and cardiovascular death, and for cardiovascular disease. It is also an important indicator of frailty and vulnerability. The measurement of handgrip strength may be most useful in the context of multi-morbidity, where it may be a simple tool to identify the individual at particularly high risk of adverse outcomes, who may benefit from closer clinical attention. Research into dietary, exercise, and pharmacologic strategies to increase muscle strength is ongoing. Important issues will be the feasibility and sustainability of increases in muscle strength, and whether these increases translate into clinical benefit. PMID:26513210

  15. Uncontrolled diabetes predicts poor response to novel antiandrogens.

    PubMed

    Karantanos, Theodoros; Karanika, Styliani; Gignac, Gretchen

    2016-09-01

    Metabolic abnormalities including hyperglycemia and hyperlipidemia have been associated with worse prognosis of prostate cancer (PCa), but there are limited data regarding their impact on the prognosis of castrate-resistant prostate cancer (CRPC) and the response of novel antiandrogens, namely abiraterone acetate (AA) and enzalutamide. Retrospective analysis of 61 patients with CRPC on AA or enzalutamide, treated at the Boston Medical Center, was performed. We evaluated hemoglobin A1c (HbA1c), HDL, LDL, Triglycerides and BMI within 2months before the initiation of treatment with AA or enzalutamide and progression-free survival (PFS) under this treatment. Regression analysis and analysis of variance were used to evaluate the data. HbA1c levels were found to predict adversely the PFS on the novel agents (df (1, 37), P=0.00, R(2)=0.40, coeff=-3.28). The Kaplan-Meier analysis showed that there is significant difference in survival between the HbA1c 4.7-5.9% compared with patients with HbA1c 7.8-11.6% (6.72±1.3months, log rank test P<0.0001) LDL (P=0.07), HDL (P=0.14), and triglycerides (P=0.33) were not found to predict PFS. BMI predicted PFS positively (df (1.59), P=0.02, R(2)=0.09, coeff=0.03), but not independently of HbA1c (P=0.07). No significant implications of social and family history, previous chemotherapy regimen, and Gleason score with PFS were found. Multiple markers of patients' health state were not associated with HbA1c values. Uncontrolled diabetes can predict for poor response of CRPC patients to AA and enzalutamide determining PFS under this treatment. Elevated BMI can positively affect PFS at this stage of disease. PMID:27515296

  16. The Prediction of Academic and Clinical Performance in Medical School

    ERIC Educational Resources Information Center

    Gough, Harrison G.; Hall, Wallace B.

    1975-01-01

    A study of medical student performance showed the clinical performance factor more or less unpredictable from aptitude and premedical academic achievement indices while the academic performance factor was forecast with acceptable accuracy by equations based on the Medical College Admissions Test and premedical grade point average. (JT)

  17. Human arm posture prediction in response to isometric endpoint forces.

    PubMed

    Davoudabadi Farahani, Saeed; Andersen, Michael Skipper; de Zee, Mark; Rasmussen, John

    2015-11-26

    The ability to predict the musculoskeletal response to external loads has multiple applications for the design of machines with a human interface and for the prediction of outcomes of musculoskeletal interventions. In this study, we applied an inverse-inverse dynamics technique to investigate its ability to predict arm posture in response to isometric hand forces. For each subject, we made a three-dimensional musculoskeletal model using the AnyBody Modelling System (AMS). Then, we had each subject-specific model hold a weight anteriorly to the right shoulder joint at a distance of half of the arm length. We selected the glenohumeral abduction angle (GHAA) as the only free parameter. Subsequently, we used inverse-inverse dynamics to find the optimal GHAA that minimised a performance criterion with physiological constraints. In this study, we investigated the performance of two different objective functions: summation of squared muscle activity (SSMA) and summation of squared normalised joint torques (SSNJT). To validate the simulation results, arm posture responses to different isometric downward hand forces were measured for six healthy male subjects. Five trials were performed for each loading condition. The results showed that, with an increase in hand load, there was a reduced GHAA in all subjects. Another interesting finding was that self-selected postures for lighter tasks varied more than postures for heavier tasks for all subjects. To understand this, we investigated the curvature of the objective function as a function of the load and observed an increased curvature with increased load. This may explain the reduced intra-subject variations observed for increasing loads. PMID:26482735

  18. FTO predicts weight regain in the Look AHEAD clinical trial

    Technology Transfer Automated Retrieval System (TEKTRAN)

    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 obe...

  19. 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…

  20. 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.

  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. 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. PMID:24844652

  3. [Legal responsibility in the exercising of the neurology clinical practice].

    PubMed

    Siso Martín, J

    2004-12-01

    The importance of responsibility in the clinical practice is derived from the transcendency of what they affect (life and health) and the risk implicit to it. The clinical performance does not require curing. The obligations that are derived from them are means and not results. It is also not correct to associate error and responsibility. Responsibility of the professional may be claimed by civil, patrimony, corporative, disciplinary and penal routes based on the reasons and according to who is making the claim. These claims may be presented individually or jointly based on whether the modality of the professional practice is free or carried out by others, whether in public health or private health care. The professional has different alternatives to respond to the possible lawsuits that are presented, both penal and civil action or protection have the common problem of the difficulty of proof. PMID:15719285

  4. 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. PMID:26772957

  5. 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

  6. 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

  7. Predicting the Naturalistic Course of Major Depressive Disorder Using Clinical and Multimodal Neuroimaging Information: A Multivariate Pattern Recognition Study

    PubMed Central

    Schmaal, Lianne; Marquand, Andre F.; Rhebergen, Didi; van Tol, Marie-José; Ruhé, Henricus G.; van der Wee, Nic J.A.; Veltman, Dick J.; Penninx, Brenda W.J.H.

    2015-01-01

    Background A chronic course of major depressive disorder (MDD) is associated with profound alterations in brain volumes and emotional and cognitive processing. However, no neurobiological markers have been identified that prospectively predict MDD course trajectories. This study evaluated the prognostic value of different neuroimaging modalities, clinical characteristics, and their combination to classify MDD course trajectories. Methods One hundred eighteen MDD patients underwent structural and functional magnetic resonance imaging (MRI) (emotional facial expressions and executive functioning) and were clinically followed-up at 2 years. Three MDD trajectories (chronic n = 23, gradual improving n = 36, and fast remission n = 59) were identified based on Life Chart Interview measuring the presence of symptoms each month. Gaussian process classifiers were employed to evaluate prognostic value of neuroimaging data and clinical characteristics (including baseline severity, duration, and comorbidity). Results Chronic patients could be discriminated from patients with more favorable trajectories from neural responses to various emotional faces (up to 73% accuracy) but not from structural MRI and functional MRI related to executive functioning. Chronic patients could also be discriminated from remitted patients based on clinical characteristics (accuracy 69%) but not when age differences between the groups were taken into account. Combining different task contrasts or data sources increased prediction accuracies in some but not all cases. Conclusions Our findings provide evidence that the prediction of naturalistic course of depression over 2 years is improved by considering neuroimaging data especially derived from neural responses to emotional facial expressions. Neural responses to emotional salient faces more accurately predicted outcome than clinical data. PMID:25702259

  8. Rapid response predicts treatment outcomes in binge eating disorder: implications for stepped care.

    PubMed

    Masheb, Robin M; Grilo, Carlos M

    2007-08-01

    The authors examined rapid response in 75 overweight patients with binge eating disorder (BED) who participated in a randomized clinical trial of guided self-help treatments (cognitive-behavioral therapy [CBTgsh] and behavioral weight loss [BWLgsh]). Rapid response, defined as a 65% or greater reduction in binge eating by the 4th treatment week, occurred in 62% of CBTgsh and 47% of BWLgsh participants. Rapid response was unrelated to most patient characteristics except for eating psychopathology and depressive symptoms. Participants with rapid response were more likely to achieve binge remission and had greater improvements in overall eating pathology and depressive symptomatology than participants without rapid response. Rapid response had different prognostic significance for the 2 treatments. In terms of binge eating, participants receiving CBTgsh, but not BWLgsh, did equally well regardless of whether they experienced rapid response. In terms of increasing restraint and weight loss, participants with rapid response receiving BWLgsh had greater restraint and weight loss than participants receiving CBTgsh. Rapid response has utility for predicting outcomes, provides evidence for specificity of treatment effects, and has implications for stepped care treatment models of BED. PMID:17663617

  9. Exploring the clinical validity of predicted TRE in navigation

    NASA Astrophysics Data System (ADS)

    Bickel, M.; Güler, Ö.; Kral, F.; Schwarm, F.; Freysinger, W.

    2010-02-01

    In a detailed laboratory investigation we performed a series of experiments in order to assess the validity of the widely used TRE concept to predict the application accuracy. On base of 1mm CT scan a plastic skull, a cadaver head and a volunteer were registered to an in house navigation system. We stored the position data of an optical camera (NDI Polaris) for registration with pre-defined CT coordinates. For every specimen we choose 3, 5, 7 and 9 registration and 10 evaluation points, respectively, performing 10 registrations. The data were evaluated both with the Arun and the Horn approaches. The vectorial difference between actual and predefined position in the CT data set was stored and evaluated for FRE and TRE. Evaluation and visualization was implemented in Matlab. The data were analyzed, specifically for normal distribution, with MS Excel and SPSS Version 15.0. For the plastic skull and the anatomic specimen submillimetric application accuracy was found experimentally and confirmed by the calculated TRE. Since for the volunteer no Titanium screws were implanted anatomic landmarks had to be used for registration and evaluation; an application accuracy in the low millimeter regime was found in all approaches. However, the detailed statistical analysis of the data revealed that the model predictions and the actual measurements do not exhibit a strong statistical correlation (p < 0.05). These data suggest that the TRE predictions are too optimistic and should be used with caution intraoperatively.

  10. 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

  11. 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. PMID:25122193

  12. Prediction of high frequency gust response with airfoil thickness effects

    NASA Astrophysics Data System (ADS)

    Lysak, Peter D.; Capone, Dean E.; Jonson, Michael L.

    2013-05-01

    The unsteady lift forces that act on an airfoil in turbulent flow are an undesirable source of vibration and noise in many industrial applications. Methods to predict these forces have traditionally treated the airfoil as a flat plate. At higher frequencies, where the relevant turbulent length scales are comparable to the airfoil thickness, the flat plate approximation becomes invalid and results in overprediction of the unsteady force spectrum. This work provides an improved methodology for the prediction of the unsteady lift forces that accounts for the thickness of the airfoil. An analytical model was developed to calculate the response of the airfoil to high frequency gusts. The approach is based on a time-domain calculation with a sharp-edged gust and accounts for the distortion of the gust by the mean flow around the airfoil leading edge. The unsteady lift is calculated from a weighted integration of the gust vorticity, which makes the model relatively straightforward to implement and verify. For routine design calculations of turbulence-induced forces, a closed-form gust response thickness correction factor was developed for NACA 65 series airfoils.

  13. 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.

  14. 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. PMID

  15. Developing an International Register of Clinical Prediction Rules for Use in Primary Care: A Descriptive Analysis

    PubMed Central

    Keogh, Claire; Wallace, Emma; O’Brien, Kirsty K.; Galvin, Rose; Smith, Susan M.; Lewis, Cliona; Cummins, Anthony; Cousins, Grainne; Dimitrov, Borislav D.; Fahey, Tom

    2014-01-01

    PURPOSE We describe the methodology used to create a register of clinical prediction rules relevant to primary care. We also summarize the rules included in the register according to various characteristics. METHODS To identify relevant articles, we searched the MEDLINE database (PubMed) for the years 1980 to 2009 and supplemented the results with searches of secondary sources (books on clinical prediction rules) and personal resources (eg, experts in the field). The rules described in relevant articles were classified according to their clinical domain, the stage of development, and the clinical setting in which they were studied. RESULTS Our search identified clinical prediction rules reported between 1965 and 2009. The largest share of rules (37.2%) were retrieved from PubMed. The number of published rules increased substantially over the study decades. We included 745 articles in the register; many contained more than 1 clinical prediction rule study (eg, both a derivation study and a validation study), resulting in 989 individual studies. In all, 434 unique rules had gone through derivation; however, only 54.8% had been validated and merely 2.8% had undergone analysis of their impact on either the process or outcome of clinical care. The rules most commonly pertained to cardiovascular disease, respiratory, and musculoskeletal conditions. They had most often been studied in the primary care or emergency department settings. CONCLUSIONS Many clinical prediction rules have been derived, but only about half have been validated and few have been assessed for clinical impact. This lack of thorough evaluation for many rules makes it difficult to retrieve and identify those that are ready for use at the point of patient care. We plan to develop an international web-based register of clinical prediction rules and computer-based clinical decision support systems. PMID:25024245

  16. The effect of the stromal component of breast tumours on prediction of clinical outcome using gene expression microarray analysis

    PubMed Central

    Cleator, Susan J; Powles, Trevor J; Dexter, Tim; Fulford, Laura; Mackay, Alan; Smith, Ian E; Valgeirsson, Haukur; Ashworth, Alan; Dowsett, Mitch

    2006-01-01

    Introduction The aim of this study was to examine the effect of the cellular composition of biopsies on the error rates of multigene predictors of response of breast tumours to neoadjuvant adriamycin and cyclophosphamide (AC) chemotherapy. Materials and methods Core biopsies were taken from primary breast tumours of 43 patients prior to AC, and subsequent clinical response was recorded. Post-chemotherapy (day 21) samples were available for 16 of these samples. Frozen sections of each core were used to estimate the proportion of invasive cancer and other tissue components at three levels. Transcriptional profiling was performed using a cDNA array containing 4,600 elements. Results Twenty-three (53%) patients demonstrated a 'good' and 20 (47%) a 'poor' clinical response. The percentage invasive tumour in core biopsies collected from these patients varied markedly. Despite this, agglomerative clustering of sample expression profiles showed that almost all biopsies from the same tumour aggregated as nearest neighbours. SAM (significance analysis of microarrays) regression analysis identified 144 genes which distinguished high- and low-percentage invasive tumour biopsies at a false discovery rate of not more than 5%. The misclassification error of prediction of clinical response using microarray data from pre-treatment biopsies (on leave-one-out cross-validation) was 28%. When prediction was performed on subsets of samples which were more homogeneous in their proportions of malignant and stromal cells, the misclassification error was considerably lower (8%–13%, p < 0.05 on permutation). Conclusion The non-tumour content of breast cancer samples has a significant effect on gene expression profiles. Consideration of this factor improves accuracy of response prediction by expression array profiling. Future gene expression array prediction studies should be planned taking this into account. PMID:16790077

  17. 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. PMID:25249375

  18. Plasma Biomarkers Can Predict Treatment Response in Tuberculosis Patients

    PubMed Central

    Lee, Meng-Rui; Tsai, Chia-Jung; Wang, Wei-Jie; Chuang, Tzu-Yi; Yang, Chih-Mann; Chang, Lih-Yu; Lin, Ching-Kai; Wang, Jann-Yuan; Shu, Chin-Chong; Lee, Li-Na; Yu, Chong-Jen

    2015-01-01

    Abstract Despite numerous studies, there has been little progress in the use of biomarkers for predicting treatment response in patients with tuberculosis (TB). Patients with culture-confirmed pulmonary TB between 2010 and 2014 were prospectively recruited. Blood samples were taken upon diagnosis and 2 months after the start of standard anti-TB treatment. A pilot study utilizing measurement of TB-antigen-stimulated cytokines was conducted to select potential biomarkers for further testing. Outcome was defined as persistent culture positivity at 2 months into treatment. Of 167 enrolled patients, 26 had persistent culture positivity. RANTES, IL-22, MMP-8, IL-18, MIG, and Granzyme A were selected as potential biomarkers. For predicting persistent culture positivity, receiver-operating characteristics (ROC) analysis showed that initial RANTES (AUC: 0.725 [0.624–0.827]) and 2-month MMP-8 (AUC: 0.632 [0.512–0.713]) had good discriminative ability. Using a logistic regression model, low initial RANTES level (<440 pg/mL), initial smear positivity, and high 2-month MMP-8 level (>3000 pg/mL) were associated with persistent culture positivity. Low initial RANTES level and initial smear positivity had a positive predictive value of 60% (12/20) for persistent culture positivity, compared with 4% (3/75) among patients with high RANTES level and smear negativity upon diagnosis. In the 72 patients with either low RANTES/smear negativity or high RANTES/smear positivity upon diagnosis, the 2-month MMP-8 level had a positive and negative predictive value of 24 and 94%, respectively, for 2-month culture status. Aside from an initial sputum smear status, serum RANTES level at diagnosis and MMP-8 level at 2 months of treatment may be used to stratify risk for culture persistence. PMID:26426648

  19. Prediction of placebo responses: a systematic review of the literature

    PubMed Central

    Horing, Bjoern; Weimer, Katja; Muth, Eric R.; Enck, Paul

    2014-01-01

    Objective: Predicting who responds to placebo treatment—and under which circumstances—has been a question of interest and investigation for generations. However, the literature is disparate and inconclusive. This review aims to identify publications that provide high quality data on the topic of placebo response (PR) prediction. Methods: To identify studies concerned with PR prediction, independent searches were performed in an expert database (for all symptom modalities) and in PubMed (for pain only). Articles were selected when (a) they assessed putative predictors prior to placebo treatment and (b) an adequate control group was included when the associations of predictors and PRs were analyzed. Results: Twenty studies were identified, most with pain as dependent variable. Most predictors of PRs were psychological constructs related to actions, expected outcomes and the emotional valence attached to these events (goal-seeking, self-efficacy/-esteem, locus of control, optimism). Other predictors involved behavioral control (desire for control, eating restraint), personality variables (fun seeking, sensation seeking, neuroticism), or biological markers (sex, a single nucleotide polymorphism related to dopamine metabolism). Finally, suggestibility and beliefs in expectation biases, body consciousness, and baseline symptom severity were found to be predictive. Conclusions: While results are heterogeneous, some congruence of predictors can be identified. PRs mainly appear to be moderated by expectations of how the symptom might change after treatment, or expectations of how symptom repetition can be coped with. It is suggested to include the listed constructs in future research. Furthermore, a closer look at variables moderating symptom change in control groups seems warranted. PMID:25324797

  20. 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

  1. 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.

  2. Behavioral variant of frontotemporal dementia: Fundamental clinical issues associated with prediction of pathological bases.

    PubMed

    Miki, Tomoko; Yokota, Osamu; Ishizu, Hideki; Kuroda, Shigetoshi; Oshima, Etsuko; Terada, Seishi; Yamada, Norihito

    2016-08-01

    Behavioral variant of frontotemporal dementia (bvFTD) is a clinical syndrome characterized mainly by behavioral symptoms due to frontal dysfunction. Major neurodegenerative bases of bvFTD include Pick's disease, frontotemporal lobar degeneration with trans-activation response DNA protein 43-positive inclusions, corticobasal degeneration, and progressive supranuclear palsy. Early disinhibition characterized by socially inappropriate behaviors, loss of manners, and impulsive, rash and careless actions is the most important clinical feature of bvFTD. On the other hand, it was reported that clinical presentations of some Alzheimer's disease cases and patients with psychiatric disorders (e.g., addictive disorders, gambling disorder and kleptomania) often resemble that of bvFTD. Although clinical differentiation of 'true' bvFTD cases with frontotemporal lobar degeneration (FTLD) pathology from mimicking cases without it is not always easy, evaluation of the following features, which were noted in autopsy-confirmed FTLD cases and/or clinical bvFTD cases with circumscribed lobar atrophy, may often provide clues for the diagnosis. (i) The initial symptoms frequently develop at 65 years or younger, and (ii) 'socially inappropriate behaviors' can be frequently interpreted as contextually inappropriate behaviors prompted by environmental visual and auditory stimuli. Taking a detailed history usually reveals various kinds of such behaviors in various situations in everyday life rather than the repetition of a single kind of behavior (e.g., repeated shoplifting). (iii) A correlation between the distribution of cerebral atrophy and neurological and behavioral symptoms is usually observed, and the proportion of FTLD cases with right side-predominant cerebral atrophy may be higher in a psychiatric setting than a neurological setting. Finally, (iv) whether the previous course and the combination of symptoms observed at the first medical visit can be explained by major evolution

  3. In Vitro Drug Sensitivity Tests to Predict Molecular Target Drug Responses in Surgically Resected Lung Cancer

    PubMed Central

    Miyazaki, Ryohei; Anayama, Takashi; Hirohashi, Kentaro; Okada, Hironobu; Kume, Motohiko; Orihashi, Kazumasa

    2016-01-01

    Background Epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) and anaplastic lymphoma kinase (ALK) inhibitors have dramatically changed the strategy of medical treatment of lung cancer. Patients should be screened for the presence of the EGFR mutation or echinoderm microtubule-associated protein-like 4 (EML4)-ALK fusion gene prior to chemotherapy to predict their clinical response. The succinate dehydrogenase inhibition (SDI) test and collagen gel droplet embedded culture drug sensitivity test (CD-DST) are established in vitro drug sensitivity tests, which may predict the sensitivity of patients to cytotoxic anticancer drugs. We applied in vitro drug sensitivity tests for cyclopedic prediction of clinical responses to different molecular targeting drugs. Methods The growth inhibitory effects of erlotinib and crizotinib were confirmed for lung cancer cell lines using SDI and CD-DST. The sensitivity of 35 cases of surgically resected lung cancer to erlotinib was examined using SDI or CD-DST, and compared with EGFR mutation status. Results HCC827 (Exon19: E746-A750 del) and H3122 (EML4-ALK) cells were inhibited by lower concentrations of erlotinib and crizotinib, respectively than A549, H460, and H1975 (L858R+T790M) cells were. The viability of the surgically resected lung cancer was 60.0 ± 9.8 and 86.8 ± 13.9% in EGFR-mutants vs. wild types in the SDI (p = 0.0003). The cell viability was 33.5 ± 21.2 and 79.0 ± 18.6% in EGFR mutants vs. wild-type cases (p = 0.026) in CD-DST. Conclusions In vitro drug sensitivity evaluated by either SDI or CD-DST correlated with EGFR gene status. Therefore, SDI and CD-DST may be useful predictors of potential clinical responses to the molecular anticancer drugs, cyclopedically. PMID:27070423

  4. The Use of Clinical Parameters to Predict Obstructive Sleep Apnea Syndrome Severity in Children

    PubMed Central

    Mitchell, Ron B.; Garetz, Suzan; Moore, Reneé H.; Rosen, Carol L.; Marcus, Carole L.; Katz, Eliot S.; Arens, Raanan; Chervin, Ronald D.; Paruthi, Shalini; Amin, Raouf; Elden, Lisa; Ellenberg, Susan S.; Redline, Susan

    2016-01-01

    IMPORTANCE It is important to distinguish children with different levels of severity of obstructive sleep apnea syndrome (OSAS) preoperatively using clinical parameters. This can identify children who most need polysomnography (PSG) prior to adenotonsillectomy (AT). OBJECTIVE To assess whether a combination of factors, including demographics, physical examination findings, and caregiver reports from questionnaires, can predict different levels of OSAS severity in children. DESIGN, SETTING, AND PARTICIPANTS Baseline data from 453 children from the Childhood Adenotonsillectomy (CHAT) study were analyzed. Children 5.0 to 9.9 years of age with PSG-diagnosed OSAS, who were considered candidates for AT, were included. INTERVENTIONS Polysomnography for diagnosis of OSAS. MAIN OUTCOMES AND MEASURES Linear or logistic regression models were fitted to identify which demographic, clinical, and caregiver reports were significantly associated with the apnea hypopnea index (AHI) and oxygen desaturation index (ODI). RESULTS Race (African American), obesity (body mass index z score > 2), and the Pediatric Sleep Questionnaire (PSQ) total score were associated with higher levels of AHI and ODI (P = .05). A multivariable model that included the most significant variables explained less than 3% of the variance in OSAS severity as measured by PSG outcomes. Tonsillar size and Friedman palate position were not associated with increased AHI or ODI. Models that tested for potential effect modification by race or obesity showed no evidence of interactions with any clinical measure, AHI, or ODI (P > .20 for all comparisons). CONCLUSIONS AND RELEVANCE This study of more than 450 children with OSAS identifies a number of clinical parameters that are associated with OSAS severity. However, information on demographics, physical findings, and questionnaire responses does not robustly discriminate different levels of OSAS severity. TRIAL REGISTRATION clinicaltrials.gov Identifier: NCT00560859 PMID

  5. 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.; Carroll, Martin

    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

  6. 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

  7. Planning and predictability of clinical outcomes in esthetic rehabilitation.

    PubMed

    Kurbad, A

    2015-01-01

    In esthetic rehabilitation, it is a challenge to meet the needs of patients with growing expectations. Creating predictable results is the key to success. This can be accomplished by performing a comprehensive esthetic diagnosis, elaborating treatment proposals that satisfy today's esthetic standards, and using modern computer-assisted methods. The diagnostic wax-up and mock-up are effective tools that allow patients to visualize treatment proposals without invasive procedures. Once the patient has approved the proposals, they provide the basis for the fabrication of the final restoration. The use of modern ceramic materials makes it possible to achieve a good esthetic outcome, even in restorations with extremely thin layer thicknesses. Esthetic cementation is the final step of restorative treatment. PMID:25911830

  8. Early predictors of a clinical response at 8 weeks in patients with first-episode psychosis treated with paliperidone ER.

    PubMed

    Chung, Young-Chul; Cui, Yin; Kim, Min-Gul; Kim, Yun-Jeong; Lee, Keon-Hak; Chae, Soo-Wan

    2016-08-01

    Identification of early clinical markers that predict later treatment outcomes in first-episode psychosis is highly valuable. The present study was conducted to determine the best time at which to predict the late treatment response in first-episode psychosis patients treated with paliperidone extended release (ER), the factors predicting early treatment responses (at Week 2 and Week 3) and the relationships between the paliperidone ER plasma concentrations at Week 2 and Week 3, and the treatment responses at Week 2, Week 3 and Week 8. Various criteria for assessing treatment response were employed. We determined the plasma paliperidone concentrations at Week 2 and Week 3, using validated high-performance liquid chromatography/tandem mass spectrometry (HPLC-MS/MS). The treatment response at Week 3 optimally predicted the later (Week 8) response, in terms of negative predictive value (NPV). Independent predictors for good treatment responses at Week 2 and Week 3 were: Female gender, a higher educational level, a higher Positive and Negative Syndrome Scale (PANSS) excited score, and/or a shorter duration of untreated psychosis (DUP). The plasma paliperidone concentration at Week 3, but not Week 2, was a significant predictor of the late treatment response at Week 8. These results may help appropriate clinical decision-making for early non-responders after having their first episode of psychosis. PMID:27334812

  9. Do in-vivo behaviors predict early response in family-based treatment for anorexia nervosa?

    PubMed Central

    Darcy, Alison M; Bryson, Susan W.; Agras, W. Stewart; Fitzpatrick, Kathleen Kara; Le Grange, Daniel; Lock, James

    2014-01-01

    The aim of the study is to explore whether identified parental and patient behaviors observed in the first few sessions of family-based treatment (FBT) predict early response (weight gain of 1.8 kg by session four) to treatment. Therapy film recordings from 21 adolescent participants recruited into the FBT arm of a multi-site randomized clinical trial were coded for the presence of behaviors (length of observed behavior divided by length of session recording) in the first, second and fourth sessions. Behaviors that differed between early responders and non-early responders on univariate analysis were entered into discriminant class analyses. Participants with fewer negative verbal behaviors in the first session and were away from table during the meal session less had the greatest rates of early response. Parents who made fewer critical statements and who did not repeatedly present food during the meal session had children who had the greatest rates of early response. In-vivo behaviors in early sessions of FBT may predict early response to FBT. Adaptations to address participant resistance and to decrease the numbers of critical comments made by parents while encouraging their children to eat might improve early response to FBT. PMID:24091274

  10. Do in-vivo behaviors predict early response in family-based treatment for anorexia nervosa?

    PubMed

    Darcy, Alison M; Bryson, Susan W; Agras, W Stewart; Fitzpatrick, Kathleen Kara; Le Grange, Daniel; Lock, James

    2013-11-01

    The aim of the study is to explore whether identified parental and patient behaviors observed in the first few sessions of family-based treatment (FBT) predict early response (weight gain of 1.8 kg by session four) to treatment. Therapy film recordings from 21 adolescent participants recruited into the FBT arm of a multi-site randomized clinical trial were coded for the presence of behaviors (length of observed behavior divided by length of session recording) in the first, second and fourth sessions. Behaviors that differed between early responders and non-early responders on univariate analysis were entered into discriminant class analyses. Participants with fewer negative verbal behaviors in the first session and were away from table during the meal session less had the greatest rates of early response. Parents who made fewer critical statements and who did not repeatedly present food during the meal session had children who had the greatest rates of early response. In-vivo behaviors in early sessions of FBT may predict early response to FBT. Adaptations to address participant resistance and to decrease the numbers of critical comments made by parents while encouraging their children to eat might improve early response to FBT. PMID:24091274