Reynolds Geary, Carol

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.

Background Reductions in blood pressure (BP) control among patients with hypertension were observed early in the COVID‐19 pandemic. The degree to which BP control may have returned to prepandemic levels is unknown. Methods Individuals aged 18 to 85 years with hypertension from 17 health systems participating in the National Patient‐Centered Clinical Research Network were identified using electronic health record data collected as part of routine care. BP control (percentage of patients whose most recent BP measurement was <140/<90 mm Hg) was estimated in a series of 12‐month rolling measurement periods from 2017 through 2022 (January 1, 2017 through December 31, 2017; April 1, 2017 through March 31, 2018; … January 1, 2022 through December 31, 2022). Differences in average BP control between 2022 (January 1, 2022 through December 31, 2022) and 2019 (January 1, 2019 through December 31, 2019) were estimated overall (adjusted for age, sex, and race and ethnicity) and by race and ethnicity (adjusted for age and sex). Results Our sample included 1 193 314 persons with hypertension in 2019 (48.9% aged 65–85 years, 52.9% men, 66.2% non‐Hispanic White) and 1 499 418 individuals in 2022 (50.6% aged 65–85 years, 47.1% men, 62.7% non‐Hispanic White). The weighted average BP control dropped from 65.3% in 2019 to 61.8% in 2020 and then partially recovered to 62.6% in 2022 (adjusted mean difference, −2.6 percentage points [95% CI, −5.0 to −0.2]). Non‐Hispanic Asian individuals experienced the largest temporal drop in BP control, declining from 68.4% in 2019 to 63.9% in 2022. Conclusions BP control was disrupted during the COVID‐19 pandemic and had not fully rebounded to prepandemic levels by the end of 2022. Continued surveillance is needed to determine whether the decline in BP control will persist and will result in future adverse cardiovascular events.