A Systematic Review and Meta-Analysis of Bevacizumab in First-Line Metastatic Breast Cancer: Lessons for Research and Regulatory Enterprises.

BACKGROUND: The US Food and Drug Administration's accelerated approval and later withdrawal of bevacizumab in patients with metastatic breast cancer (mBC) is a seminal case for ongoing debates about the validity of using progression-free survival (PFS) as a surrogate measure for overall survival (OS) in cancer drug approvals. We systematically reviewed and meta-analyzed the evidence around bevacizumab's regulatory approval and withdrawal in mBC. METHODS: We searched for all published phase II or III clinical trials testing bevacizumab as a first-line therapy for patients with mBC. Data were extracted on trial demographics, interventions, and outcomes. Descriptive analysis was stratified by whether the trial was initiated before, during, or after the accelerated approval. We used a cumulative random-effects meta-analysis to assess the evolution of evidence of the effect of bevacizumab on PFS and OS. We estimated the association between the trial-level PFS and OS effect using a nonlinear mixed-regression model. RESULTS: Fifty-two studies were included. Trial activity dramatically dropped after the accelerated approval was withdrawn. Eight clinical trials reported hazard ratios (hazard ratios) and were meta-analyzed. The cumulative hazard ratio for PFS was 0.72 (95% CI = 0.65 to 0.79), and the cumulative hazard ratio for OS was 0.90 (95% CI = 0.80 to 1.01). The regression model showed a statistically nonsignificant association between PFS benefit and OS benefit (ß = 0.43, SE = 0.81). CONCLUSION: The US Food and Drug Administration's decision-making in this case was consistent with the evolving state of evidence. However, the fact that seven clinical trials are insufficient to conclude validity (or lack thereof) for a trial-level surrogate suggests that it would be more efficient to conduct trials using the more clinically meaningful endpoints.

Triangulating Methodologies from Software, Medicine and Human Factors Industries to Measure Usability and Clinical Efficacy of Medication Data Visualization in an Electronic Health Record System.

Within the last decade, use of Electronic Health Record (EHR) systems has become intimately integrated into healthcare practice in the United States. However, large gaps remain in the study of clinical usability and require rigorous and innovative approaches for testing usability principles. In this study, validated tools from the core functions that EHRs serve-software, medicine and human factors-were combined to holistically understand and objectively measure usability of medication data displays. The first phase of this study included 132 medical trainee participants who were randomized to one of two simulated EHR environments with either a medication list or a medication timeline visualization. Within these environments human-computer interaction metrics, clinical reasoning and situation awareness tests, and usability surveys captured their multi-faceted interactions. Results showed no statistically significant differences in the two displays from software and situation awareness perspectives, though there were higher statistically significant usability scores of the medication timeline (intervention) as compared to the medication list (control). This first phase of a novel design in triangulating methodologies revealed several limitations from which future experiments will be adjusted with hopes of yielding further insight and a generalizable testing platform for evolving EHR interfaces.

Distribution and clinical impact of functional variants in 50,726 whole-exome sequences from the DiscovEHR study.

The DiscovEHR collaboration between the Regeneron Genetics Center and Geisinger Health System couples high-throughput sequencing to an integrated health care system using longitudinal electronic health records (EHRs). We sequenced the exomes of 50,726 adult participants in the DiscovEHR study to identify ~4.2 million rare single-nucleotide variants and insertion/deletion events, of which ~176,000 are predicted to result in a loss of gene function. Linking these data to EHR-derived clinical phenotypes, we find clinical associations supporting therapeutic targets, including genes encoding drug targets for lipid lowering, and identify previously unidentified rare alleles associated with lipid levels and other blood level traits. About 3.5% of individuals harbor deleterious variants in 76 clinically actionable genes. The DiscovEHR data set provides a blueprint for large-scale precision medicine initiatives and genomics-guided therapeutic discovery.