Ho, P. Michael

Abstract Objectives Embedded pragmatic clinical trials (ePCTs) are conducted as part of routine clinical care and therefore use data collected from real-world data sources, such as electronic health record systems and administrative claims. A common approach for using these types of data across all phases of trial conduct is to create a computable phenotype (an explicitly defined data query data, including specified data types, data codes, and logical parameters) to capture patients with a clinical condition, exposure, symptom, characteristic, treatment, or outcome of interest. Materials and Methods The Electronic Health Record (EHR) Core Working Group of the NIH Pragmatic Trials Collaboratory captured the experiences of investigators in developing, adapting, and applying computable phenotype definitions in their studies. Results Four case studies describe different approaches to developing and using computable phenotypes in pragmatic trials. Discussion We recommend: (1) developing computable phenotypes as part of a team with multiple areas of expertise; (2) appropriate validation; (3) dissemination of salient details regarding phenotype creation; and (4) capturing and reporting any modifications made to phenotypes during the conduct of the trial. Conclusion A range of issues and decisions influence how computable phenotypes are developed and used in pragmatic clinical trials. Multidisciplinary teams that understand the context of the data, including the reason the data are collected and potential sources of bias, are best suited for the development and validation of phenotypes for ePCTs.

Abstract Embedded pragmatic clinical trials (ePCTs) play a vital role in addressing current population health problems, and their use of electronic health record (EHR) systems promises efficiencies that will increase the speed and volume of relevant and generalizable research. However, as the number of ePCTs using EHR-derived data grows, so does the risk that research will become more vulnerable to biases due to differences in data capture and access to care for different subsets of the population, thereby propagating inequities in health and the healthcare system. We identify 3 challenges—incomplete and variable capture of data on social determinants of health, lack of representation of vulnerable populations that do not access or receive treatment, and data loss due to variable use of technology—that exacerbate bias when working with EHR data and offer recommendations and examples of ways to actively mitigate bias.

Background: Cardiac rehabilitation (CR) is a class Ia recommendation and endorsed performance measure for the quality of care in acute myocardial infarction (AMI) survivors. While participation in CR after AMI is associated with reduced mortality, conflicting data exists on its association with health status outcomes. Methods: Using data from 2 prospective AMI registries (TRIUMPH and PREMIER), we identified patients for whom baseline and follow-up health status scores and documentation of CR participation (attendance of 1 or more sessions within 6 months post-AMI) were available. Health status was assessed by four Seattle Angina Questionnaire (SAQ) domain scores (quality of life [QoL], angina frequency [AF], treatment satisfaction [TS], and physical limitation [PL]), as well as SF-12 physical and mental health composite scores ( PCS & MCS ). We created propensity matched cohorts of CR participants and non-participants to examine the association between CR participation with health status (6 and 12 months) and all-cause mortality (up to 7 years), using conditional repeated measures and proportional hazards models, respectively. Results: Among 3,957 AMI patients from 31 sites, 2,015 patients (51%) participated in CR after discharge. Compared to non-participants, CR participants were more often Caucasian (83.6% vs. 65.4%), had higher rates of health insurance (90.6% vs. 79.3%), but clinically similar baseline SAQ and SF-12 scores in all domains. After propensity matching, all covariates were well-balanced (Standardized Difference <10 for all patient characteristics) between CR participants and non-participants. In the repeated measures analysis, the mean SAQ and SF-12 domain scores were clinically similar for both groups at 6 and 12 months after hospital discharge (Table). Using a conditional proportional hazards model, a decrease in all-cause mortality was noted in the CR participant group (HR 0.59, 95% CI [0.46, 0.75]). Conclusion: In a large, multi-center AMI cohort, we found that although CR participation seemed to improve survival, CR participants had similar health status improvements after AMI as non-participants. Further investigation is required to identify how CR programs can further maximize the health status benefits to post-AMI participants.