Long-term weight loss and cardiorenal outcomes by baseline BMI in the VERTIS CV trial.

Aim: To assess weight loss and cardiorenal outcomes by baseline body mass index (BMI) in VERTIS CV.

Methods: Patients with type 2 diabetes and atherosclerotic cardiovascular (CV) disease were randomized to ertugliflozin or placebo. These post hoc analyses evaluated cardiometabolic and cardiorenal outcomes (a composite of death from CV causes or hospitalization for heart failure [HHF], CV death, HHF and an exploratory composite kidney outcome including ≥40% estimated glomerular filtration rate [eGFR] decrease) by baseline BMI, using conventional clinical categories and Cox proportional hazards models.

Efficacy and Safety of Sotatercept Across Ranges of Cardiac Index in Patients with Pulmonary Arterial Hypertension: A Pooled Analysis of PULSAR and STELLAR.

Background: This analysis compared effects of the activin signaling inhibitor, sotatercept, across pulmonary arterial hypertension (PAH) subgroups stratified by baseline cardiac index (CI).

Methods: Pooled data from PULSAR (N=106; NCT03496207) and STELLAR (N=323; NCT04576988) were analyzed using two different CI thresholds, < and ≥ 2.0 L/min/m or 2.

Prediction of Outcomes After Heart Transplantation in Pediatric Patients Using National Registry Data: Evaluation of Machine Learning Approaches.

Background: The prediction of posttransplant health outcomes for pediatric heart transplantation is critical for risk stratification and high-quality posttransplant care.

Objective: The purpose of this study was to examine the use of machine learning (ML) models to predict rejection and mortality for pediatric heart transplant recipients.

Methods: Various ML models were used to predict rejection and mortality at 1, 3, and 5 years after transplantation in pediatric heart transplant recipients using United Network for Organ Sharing data from 1987 to 2019.

Empirical assessment of fragility index based on a large database of clinical studies in the Cochrane Library.

Rationale Aims And Objectives: The fragility index (FI) and fragility quotient (FQ) are increasingly used measures for assessing the robustness of clinical studies with binary outcomes in terms of statistical significance. The FI is the minimum number of event status modifications that can alter a study result's statistical significance (or nonsignificance), and the FQ is calculated as the FI divided by the study's total sample size. The literature has no widely recognized criteria for interpreting the fragility measures' magnitudes.

Assessing the robustness of results from clinical trials and meta-analyses with the fragility index.

The fragility index has been increasingly used to assess the robustness of the results of clinical trials since 2014. It aims at finding the smallest number of event changes that could alter originally statistically significant results. Despite its popularity, some researchers have expressed several concerns about the validity and usefulness of the fragility index.

Assessing the use of prescription drugs and dietary supplements in obese respondents in the National Health and Nutrition Examination Survey.

Introduction: Obesity is a common disease and a known risk factor for many other conditions such as hypertension, type 2 diabetes, and cancer. Treatment options for obesity include lifestyle changes, pharmacotherapy, and surgical interventions such as bariatric surgery. In this study, we examine the use of prescription drugs and dietary supplements by the individuals with obesity.

Assessing the Impact of Imputation on the Interpretations of Prediction Models: A Case Study on Mortality Prediction for Patients with Acute Myocardial Infarction.

Acute myocardial infarction poses significant health risks and financial burden on healthcare and families. Prediction of mortality risk among AM! patients using rich electronic health record (EHR) data can potentially save lives and healthcare costs. Nevertheless, EHR-based prediction models usually use a missing data imputation method without considering its impact on the performance and interpretability of the model, hampering its real-world applicability in the healthcare setting.

How the clinical research community responded to the COVID-19 pandemic: an analysis of the COVID-19 clinical studies in ClinicalTrials.gov.

Objective: In the past few months, a large number of clinical studies on the novel coronavirus disease (COVID-19) have been initiated worldwide to find effective therapeutics, vaccines, and preventive strategies for COVID-19. In this study, we aim to understand the landscape of COVID-19 clinical research and identify the issues that may cause recruitment difficulty or reduce study generalizability.

Methods: We analyzed 3765 COVID-19 studies registered in the largest public registry-ClinicalTrials.

Prior Choices of Between-Study Heterogeneity in Contemporary Bayesian Network Meta-analyses: an Empirical Study.

Background: Network meta-analysis (NMA) is a popular tool to compare multiple treatments in medical research. It is frequently implemented via Bayesian methods. The prior choice of between-study heterogeneity is critical in Bayesian NMAs.

Patient Challenges and Needs in Comprehending Laboratory Test Results: Mixed Methods Study.

Background: Patients are increasingly able to access their laboratory test results via patient portals. However, merely providing access does not guarantee comprehension. Patients could experience confusion when reviewing their test results.

How the clinical research community responded to the COVID-19 pandemic: An analysis of the COVID-19 clinical studies in ClinicalTrials.gov.

Objective: The novel coronavirus disease (COVID-19), broke out in December 2019, and is now a global pandemic. In the past few months, a large number of clinical studies have been initiated worldwide to find effective therapeutics, vaccines, and preventive strategies for COVID-19. In this study, we aim to understand the landscape of COVID-19 clinical research and identify the gaps such as the lack of population representativeness and issues that may cause recruitment difficulty.

Fragility index of network meta-analysis with application to smoking cessation data.

Background And Objectives: The network meta-analysis (NMA) is frequently used to synthesize evidence for multiple treatment comparisons, but its complexity may affect the robustness (or fragility) of the results. The fragility index (FI) is recently proposed to assess the fragility of the results from clinical studies and from pairwise meta-analyses. We extend the FI to NMAs with binary outcomes.

An Informatics Framework to Assess Consumer Health Language Complexity Differences: Proof-of-Concept Study.

Background: The language gap between health consumers and health professionals has been long recognized as the main hindrance to effective health information comprehension. Although providing health information access in consumer health language (CHL) is widely accepted as the solution to the problem, health consumers are found to have varying health language preferences and proficiencies. To simplify health documents for heterogeneous consumer groups, it is important to quantify how CHLs are different in terms of complexity among various consumer groups.

Borrowing of strength from indirect evidence in 40 network meta-analyses.

Objectives: Network meta-analysis (NMA) is increasingly being used to synthesize direct and indirect evidence and help decision makers simultaneously compare multiple treatments. We empirically evaluate the incremental gain in precision achieved by incorporating indirect evidence in NMAs.

Study Design And Setting: We performed both network and pairwise meta-analyses on 40 published data sets of multiple-treatment comparisons.

Inhibition of Tyrosine 3-Monooxygenase/Tryptophan 5-Monooxygenase Activation Protein Zeta (YWHAZ) Overcomes Drug Resistance and Tumorigenicity in Ovarian Cancer.

Background/aims: Cancer stem-like cells are the main cause of tumor occurrence, progression, and therapeutic resistance. However, the precise signals required for the maintenance of the stem-like traits of these cells in ovarian cancer remain elusive. We have thus worked to elucidate the functional role of Tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein zeta (YWHAZ), a gene encoding the 14-3-3ζ protein, in the regulation of multidrug resistance and stem cell-like traits in ovarian cancer.