Validation of a Computable Phenotype for Successful Cognitive Aging

Abstract

AbstractBackgroundStudying successful cognitive aging presents an opportunity to identify factors that may mitigate risk for Alzheimer’s disease and related disorders to inform public health initiatives, community interventions, and policy. Yet, identifying these cohorts is challenging, especially at advanced ages. Commutable phenotype algorithms using electronic health records may serve as an aid to identify cohorts of advanced age adults with intact cognitive function. The purpose of this study was to 1) establish, 2) apply, and 3) validate a machine learning‐based computing algorithm for recruitment of successful aging individuals for clinical cohort study.MethodWe identified EHR‐available variables representing successful aging among individuals 85 years and older (85+) by interviewing a panel comprised of 10 aging experts. Based on the identified variables, we developed a rule‐based computable phenotype algorithm composed of 17 inclusion or exclusion criteria. We applied the computable phenotype algorithm to screen all living persons age 85+ at UF Health and identified those meeting successful aging criteria and endeavored to directly evaluate them.There were 52,841 living 85+ individuals identified at UF Health. Of these 24,024 were identified as successful aged by the computable phenotype algorithm. This sample frame was 58% female, 59% White, and 69% non‐Hispanic. A priori permission to be contacted for research had been obtained on 11,898 of these persons, of whom 470 responded to study announcements and 333 consented to evaluation.The main outcomes measures were individuals identified by the computational algorithm subsequently being validated by virtue of having a modified Telephone Interview for Cognitive Status score >27 and Geriatric Depression Scale <6.ResultsForty‐five percent of all living persons 85+ in the UF Health EHR database were identified by the computable phenotype as successfully aged. Approximately 4% of these persons responded to study announcements and 333 were consented. Of these 333, 218 (65%) were ‘validated’ as meeting successful cognitive aging criteria via direct evaluation (See Figure).ConclusionsThe study validated a computable phenotype algorithm for recruitment of individuals for a successful aging study using large‐scale EHRs. Our study provides proof‐of‐concept of using big data and informatics as aids for recruitment of individuals for prospective cohort studies.