OBJECTIVE: This research aims to evaluate the impact of eligibility criteria on recruitment and observable clinical outcomes of COVID-19 clinical trials using electronic health record (EHR) data. MATERIALS AND METHODS: On June 18, 2020, we identified frequently used eligibility criteria from all the interventional COVID-19 trials in ClinicalTrials.gov (n = 288), including age, pregnancy, oxygen saturation, alanine/aspartate aminotransferase, platelets, and estimated glomerular filtration rate. We applied the frequently used criteria to the EHR data of COVID-19 patients in Columbia University Irving Medical Center (CUIMC) (March 2020-June 2020) and evaluated their impact on patient accrual and the occurrence of a composite endpoint of mechanical ventilation, tracheostomy, and in-hospital death. RESULTS: There were 3251 patients diagnosed with COVID-19 from the CUIMC EHR included in the analysis. The median follow-up period was 10 days (interquartile range 4-28 days). The composite events occurred in 18.1% (n = 587) of the COVID-19 cohort during the follow-up. In a hypothetical trial with common eligibility criteria, 33.6% (690/2051) were eligible among patients with evaluable data and 22.2% (153/690) had the composite event. DISCUSSION: By adjusting the thresholds of common eligibility criteria based on the characteristics of COVID-19 patients, we could observe more composite events from fewer patients. CONCLUSIONS: This research demonstrated the potential of using the EHR data of COVID-19 patients to inform the selection of eligibility criteria and their thresholds, supporting data-driven optimization of participant selection towards improved statistical power of COVID-19 trials.

OBJECTIVE: Real-world data (RWD), defined as routinely collected healthcare data, can be a potential catalyst for addressing challenges faced in clinical trials. We performed a scoping review of database-specific RWD applications within clinical trial contexts, synthesizing prominent uses and themes. MATERIALS AND METHODS: Querying 3 biomedical literature databases, research articles using electronic health records, administrative claims databases, or clinical registries either within a clinical trial or in tandem with methodology related to clinical trials were included. Articles were required to use at least 1 US RWD source. All abstract screening, full-text screening, and data extraction was performed by 1 reviewer. Two reviewers independently verified all decisions. RESULTS: Of 2020 screened articles, 89 qualified: 59 articles used electronic health records, 29 used administrative claims, and 26 used registries. Our synthesis was driven by the general life cycle of a clinical trial, culminating into 3 major themes: trial process tasks (51 articles); dissemination strategies (6); and generalizability assessments (34). Despite a diverse set of diseases studied, <10% of trials using RWD for trial process tasks evaluated medications or procedures (5/51). All articles highlighted data-related challenges, such as missing values. DISCUSSION: Database-specific RWD have been occasionally leveraged for various clinical trial tasks. We observed underuse of RWD within conducted medication or procedure trials, though it is subject to the confounder of implicit report of RWD use. CONCLUSION: Enhanced incorporation of RWD should be further explored for medication or procedure trials, including better understanding of how to handle related data quality issues to facilitate RWD use.

Scientific commentaries are expected to play an important role in evidence appraisal, but it is unknown whether this expectation has been fulfilled. This study aims to better understand the role of scientific commentary in evidence appraisal. We queried PubMed for all clinical research articles with accompanying comments and extracted corresponding metadata. Five percent of clinical research studies (N = 130 629) received postpublication comments (N = 171 556), resulting in 178 882 comment-article pairings, with 90% published in the same journal. We obtained 5197 full-text comments for topic modeling and exploratory sentiment analysis. Topics were generally disease specific with only a few topics relevant to the appraisal of studies, which were highly prevalent in letters. Of a random sample of 518 full-text comments, 67% had a supportive tone. Based on our results, published commentary, with the exception of letters, most often highlight or endorse previous publications rather than serve as a prominent mechanism for critical appraisal.

BACKGROUND: Practice guidelines recommend anticoagulation therapy for patients with atrial fibrillation (AF) who have other risk factors putting them at an elevated risk of stroke. These patients remain undertreated, but, with increasing use of electronic healthcare records (EHRs), it may be possible to identify candidates for treatment. OBJECTIVE: To test algorithms for identifying AF patients who also have known risk factors for stroke and major bleeding using EHR data. MATERIALS AND METHODS: We evaluated the performance of algorithms using EHR data from the Partners Healthcare System at identifying AF patients and 16 additional conditions that are risk factors in the CHA 2 DS 2 -VASc and HAS-BLED risk scores for stroke and major bleeding. Algorithms were based on information contained in problem lists, billing codes, laboratory data, prescription data, vital status, and clinical notes. The performance of candidate algorithms in 1000 bootstrap resamples was compared to a gold standard of manual chart review by experienced resident physicians. RESULTS: : Physicians reviewed 480 patient charts. For 11 conditions, the median positive predictive value (PPV) of the EHR-derived algorithms was greater than 0.90. Although the PPV for some risk factors was poor, the median PPV for identifying patients with a CHA 2 DS 2 -VASc score ≥2 or a HAS-BLED score ≥3 was 1.00 and 0.92, respectively. DISCUSSION: We developed and tested a set of algorithms to identify AF patients and known risk factors for stroke and major bleeding using EHR data. Algorithms such as these can be built into EHR systems to facilitate informed decision making and help shift population health management efforts towards patients with the greatest need.