Virginia Commonwealth University

About: Virginia Commonwealth University is a education organization based out in Richmond, Virginia, United States. It is known for research contribution in the topics: Population & Health care. The organization has 23822 authors who have published 49587 publications receiving 1787046 citations. The organization is also known as: VCU.

Schizophrenia as a complex trait: evidence from a meta-analysis of twin studies.

Abstract: Context There are many published twin studies of schizophrenia. Although these studies have been reviewed previously, to our knowledge, no review has provided quantitative summary estimates of the impact of genes and environment on liability to schizophrenia that also accounted for the different ascertainment strategies used. Objective To calculate meta-analytic estimates of heritability in liability and shared and individual-specific environmental effects from the pooled twin data. Data Sources We used a structured literature search to identify all published twin studies of schizophrenia, including MEDLINE, dissertation, and books-in-print searches. Study Selection Of the 14 identified studies, 12 met the minimal inclusion criteria of systematic ascertainment. Data Synthesis By using a multigroup twin model, we found evidence for substantial additive genetic effects—the point estimate of heritability in liability to schizophrenia was 81% (95% confidence interval, 73%-90%). Notably, there was consistent evidence across these studies for common or shared environmental influences on liability to schizophrenia—joint estimate, 11% (95% confidence interval, 3%-19%). Conclusions Despite evidence of heterogeneity across studies, these meta-analytic results from 12 published twin studies of schizophrenia are consistent with a view of schizophrenia as a complex trait that results from genetic and environmental etiological influences. These results are broadly informative in that they provide no information about the specific identity of these etiological influences, but they do provide a component of a unifying empirical basis supporting the rationality of searches for underlying genetic and common environmental etiological factors.

Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs

Abstract: Most psychiatric disorders are moderately to highly heritable. The degree to which genetic variation is unique to individual disorders or shared across disorders is unclear. To examine shared genetic etiology, we use genome-wide genotype data from the Psychiatric Genomics Consortium (PGC) for cases and controls in schizophrenia, bipolar disorder, major depressive disorder, autism spectrum disorders (ASD) and attention-deficit/hyperactivity disorder (ADHD). We apply univariate and bivariate methods for the estimation of genetic variation within and covariation between disorders. SNPs explained 17-29% of the variance in liability. The genetic correlation calculated using common SNPs was high between schizophrenia and bipolar disorder (0.68 ± 0.04 s.e.), moderate between schizophrenia and major depressive disorder (0.43 ± 0.06 s.e.), bipolar disorder and major depressive disorder (0.47 ± 0.06 s.e.), and ADHD and major depressive disorder (0.32 ± 0.07 s.e.), low between schizophrenia and ASD (0.16 ± 0.06 s.e.) and non-significant for other pairs of disorders as well as between psychiatric disorders and the negative control of Crohn's disease. This empirical evidence of shared genetic etiology for psychiatric disorders can inform nosology and encourages the investigation of common pathophysiologies for related disorders.

Enhanced Recovery After Surgery: A Review

Abstract: Importance Enhanced Recovery After Surgery (ERAS) is a paradigm shift in perioperative care, resulting in substantial improvements in clinical outcomes and cost savings. Observations Enhanced Recovery After Surgery is a multimodal, multidisciplinary approach to the care of the surgical patient. Enhanced Recovery After Surgery process implementation involves a team consisting of surgeons, anesthetists, an ERAS coordinator (often a nurse or a physician assistant), and staff from units that care for the surgical patient. The care protocol is based on published evidence. The ERAS Society, an international nonprofit professional society that promotes, develops, and implements ERAS programs, publishes updated guidelines for many operations, such as evidence-based modern care changes from overnight fasting to carbohydrate drinks 2 hours before surgery, minimally invasive approaches instead of large incisions, management of fluids to seek balance rather than large volumes of intravenous fluids, avoidance of or early removal of drains and tubes, early mobilization, and serving of drinks and food the day of the operation. Enhanced Recovery After Surgery protocols have resulted in shorter length of hospital stay by 30% to 50% and similar reductions in complications, while readmissions and costs are reduced. The elements of the protocol reduce the stress of the operation to retain anabolic homeostasis. The ERAS Society conducts structured implementation programs that are currently in use in more than 20 countries. Local ERAS teams from hospitals are trained to implement ERAS processes. Audit of process compliance and patient outcomes are important features. Enhanced Recovery After Surgery started mainly with colorectal surgery but has been shown to improve outcomes in almost all major surgical specialties. Conclusions and Relevance Enhanced Recovery After Surgery is an evidence-based care improvement process for surgical patients. Implementation of ERAS programs results in major improvements in clinical outcomes and cost, making ERAS an important example of value-based care applied to surgery.

Mechanisms of NAFLD development and therapeutic strategies

Abstract: There has been a rise in the prevalence of nonalcoholic fatty liver disease (NAFLD), paralleling a worldwide increase in diabetes and metabolic syndrome. NAFLD, a continuum of liver abnormalities from nonalcoholic fatty liver (NAFL) to nonalcoholic steatohepatitis (NASH), has a variable course but can lead to cirrhosis and liver cancer. Here we review the pathogenic and clinical features of NAFLD, its major comorbidities, clinical progression and risk of complications and in vitro and animal models of NAFLD enabling refinement of therapeutic targets that can accelerate drug development. We also discuss evolving principles of clinical trial design to evaluate drug efficacy and the emerging targets for drug development that involve either single agents or combination therapies intended to arrest or reverse disease progression.

Statistical Methods in Diagnostic Medicine

Abstract: Preface. Acknowledgments. 1. Introduction. 1.1 Why This Book? 1.2 What Is Diagnostic Accuracy? 1.3 Landmarks in Statistical Methods for Diagnostic Medicine. 1.4 Software. 1.5 Topics not Covered in This Book. 1.6 Summary. I BASIC CONCEPTS AND METHODS. 2. Measures of Diagnostic Accuracy. 2.1 Sensitivity and Specificity. 2.2 The Combined Measures of Sensitivity and Specificity. 2.3 The ROC Curve. 2.4 The Area Under the ROC Curve. 2.5 The Sensitivity at a Fixed FPR. 2.6 The Partial Area Under the ROC Curve. 2.7 Likelihood Ratios. 2.8 Other ROC Curve Indices. 2.9 The Localization and Detection of Multiple Abnormalities. 2.10 Interpretation of Diagnostic Tests. 2.11 Optimal Decision Threshold on the ROC Curve. 2.12 Multiple Tests. 3. The Design of Diagnostic Accuracy Studies. 3.1 Determining the Objective of the Study. 3.2 Identifying the Target Patient Population. 3.3 Selecting a Sampling Plan for Patients. 3.3.1 Phase I: Exploratory Studies. 3.3.2 Phase II: Challenge Studies. 3.3.3 Phase III: Clinical Studies. 3.4 Selecting the Gold Standard. 3.5 Choosing a Measure of Accuracy. 3.6 Identifying the Target Reader Population. 3.7 Selecting a Sampling Plan for Readers. 3.8 Planning the Data Collection. 3.8.1 Format for the Test Results. 3.8.2 Data Collection for the Reader Studies. 3.8.3 Reader Training. 3.9 Planning the Data Analyses. 3.9.1 Statistical Hypotheses. 3.9.2 Reporting the Test Results. 3.10 Determining the Sample Size. 4. Estimation and Hypothesis Testing in a Single Sample. 4.1 Binary Scale Data. 4.1.1 Sensitivity and Specificity. 4.1.2 The Sensitivity and Specificity of Clustered Binary Data. 4.1.3 The Likelihood Ratio (LR). 4.1.4 The Odds Ratio. 4.2 Ordinal Scale Data. 4.2.1 The Empirical ROC Curve. 4.2.2 Fitting a Smooth Curve (Parametric Model). 4.2.3 Estimation of Sensitivity at a Particular FPR. 4.2.4 The Area and Partial Area Under the ROC Curve (Parametric Model). 4.2.5 The Area Under the Curve (Nonparametric Method). 4.2.6 Nonparametric Analysis of Clustered Data. 4.2.7 The Degenerate Data. 4.2.8 Choosing Between Parametric and Nonparametric Methods. 4.3 Continuous Scale Data. 4.3.1 The Empirical ROC Curve. 4.3.2 Fitting a Smooth ROC Curve (Parametric and Nonparametric Methods). 4.3.3 Area Under the ROC Curve (Parametric and Nonparametric). 4.3.4 Fixed FPR The Sensitivity and Decision Threshold. 4.3.5 Choosing the Optimal Operating Point. 4.3.6 Choosing Between Parametric and Nonparametric Techniques. 4.4 Hypothesis Testing About the ROC Area. 5. Comparing the Accuracy of Two Diagnostic Tests. 5.1 Binary Scale Data. 5.1.1 Sensitivity and Specificity. 5.1.2 Sensitivity and Specificity of Clustered Binary Data. 5.2 Ordinal and Continuous Scale Data. 5.2.1 Determining the Equality of Two ROC Curves. 5.2.2 Comparing ROC Curves at a Particular Point. 5.2.3 Determining the Range of FPR for Which TPR Differ. 5.2.4 A Comparison of the Area or Partial Area. 5.3 Tests of Equivalence. 6. Sample Size Calculation. 6.1 The Sample Size for Accuracy Studies of a Single Test. 6.1.1 Sensitivity and Specificity. 6.1.2 The Area Under the ROC Curve. 6.1.3 The Sensitivity at a Fixed FPR. 6.1.4 The Partial Area Under the ROC Curve. 6.2 The Sample Size for the Accuracy of Two Tests. 6.2.1 Sensitivity and Specificity. 6.2.2 The Area Under the ROC Curve. 6.2.3 The Sensitivity at a Fixed FPR. 6.2.4 The Partial Area Under the ROC Curve. 6.3 The Sample Size for Equivalent Studies of Two Tests. 6.4 The Sample Size for Determining a Suitable Cutoff Value. 7. Issues in Meta Analysis for Diagnostic Tests. 7.1 Objectives. 7.2 Retrieval of the Literature. 7.3 Inclusion Exclusion Criteria. 7.4 Extracting Information From the Literature. 7.5 Statistical Analysis. 7.6 Public Presentation. II ADVANCED METHODS. 8. Regression Analysis for Independent ROC Data. 8.1 Four Clinical Studies. 8.1.1 Surgical Lesion in a Carotid Vessel Example. 8.1.2 Pancreatic Cancer Exampl. 8.1.3 Adult Obesity Example. 8.1.4 Staging of Prostate Cancer Example. 8.2 Regression Models for Continuous Scale Tests. 8.2.1 Indirect Regression Models for Smooth ROC Curves. 8.2.2 Direct Regression Models for Smooth ROC Curves. 8.2.3 MRA Use for Surgical Lesion Detection in the Carotid Vessel. 8.2.4 Biomarkers for the Detection of Pancreatic Cancer. 8.2.5 Prediction of Adult Obesity by Using Childhood BMI Measurements. 8.3 Regression Models for Ordinal Scale Tests. 8.3.1 Indirect Regression Models for Latent Smooth ROC Curves. 8.3.2 Direct Regression Model for Latent Smooth ROC Curves. 8.3.3 Detection of Periprostatic Invasion With US. 9. Analysis of Correlated ROC Data. 9.1 Studies With Multiple Test Measurements of the Same Patient. 9.1.1 Indirect Regression Models for Ordinal Scale Tests. 9.1.2 Neonatal Examination Example. 9.1.3 Direct Regression Models for Continuous Scale Tests. 9.2 Studies With Multiple Readers and Tests. 9.2.1 A Mixed Effects ANOVA Model for Summary Measures of Diagnostic Accuracy. 9.2.2 Detection of TAD Example. 9.2.3 The Mixed Effects ANOVA Model for Jackknife Pseudovalues. 9.2.4 Neonatal Examination Example. 9.2.5 A Bootstrap Method. 9.3 Sample Size Calculation for Multireader Studies. 10. Methods for Correcting Verification Bias. 10.1 A Single Binary Scale Test. 10.1.1 Correction Methods With the MAR Assumption. 10.1.2 Correction Methods Without the MAR Assumption. 10.1.3 Hepatic Scintigraph Example. 10.2 Correlated Binary Scale Tests. 10.2.1 An ML Approach Without Covariates. 10.2.2 An ML Approach With Covariates. 10.2.3 Screening Tests for Dementia Disorder Example. 10.3 A Single Ordinal Scale Test. 10.3.1 An ML Approach Without Covariates. 10.3.2 Fever of Uncertain Origin Example. 10.3.3 An ML Approach With Covariates. 10.3.4 Screening Test for Dementia Disorder Example. 10.4 Correlated Ordinal Scale Tests. 10.4.1 The Weighted GEE Approach for Latent Smooth ROC Curves. 10.4.2 A Likelihood Based Approach for ROC Areas. 10.4.3 Use of CT and MRI for Staging Pancreatic Cancer Example. 11. Methods for Correcting Imperfect Standard Bias. 11.1 One Single Test in a Single Population. 11.1.1 Hypothetical and Strongyloides Infection Examples. 11.2 One Single Test in G Populations. 11.2.1 Tuberculosis Example. 11.3 Multiple Tests in One Single Population. 11.3.1 MLEs Under the CIA. 11.3.2 Assessment of Pleural Thickening Example. 11.3.3 ML Approaches Without the CIA. 11.3.4 Bioassays for HIV Example. 11.4 Multiple Binary Tests in G Populations. 11.4.1 ML Approaches Under the CIA. 11.4.2 ML Approaches Without the CIA. 12. Statistical Methods for Meta Analysis. 12.1 Sensitivity and Specificity Pairs. 12.1.1 One Common SROC Curve. 12.1.2 Study Specific SROC Curve. 12.1.3 Evaluation of Duplex Ultrasonography, With and Without Color Guidance. 12.2 ROC Curve Areas. 12.2.1 Fixed Effects Models. 12.2.2 Random Effects Models. 12.2.3 Evaluation of the Dexamethasone Suppression.Test. Index.