Evaluation of a Computable Phenotype for Successful Cognitive Aging

Glenn Smith; Amber Miller; David E Marra; Yonghui Wu; Jiang Bian; Demetrius M Maraganore; Stephen Anton

doi:10.1016/j.mayocpiqo.2023.04.006

Evaluation of a Computable Phenotype for Successful Cognitive Aging

Mayo Clin Proc Innov Qual Outcomes. 2023 Jun 2;7(4):212-221. doi: 10.1016/j.mayocpiqo.2023.04.006. eCollection 2023 Aug.

Authors

Glenn Smith¹, Amber Miller², David E Marra³, Yonghui Wu⁴, Jiang Bian⁴, Demetrius M Maraganore⁵, Stephen Anton^{1

6}

Affiliations

¹ Department of Clinical and Health Psychology, University of Florida, Gainesville.
² Department of Neurology, College of Medicine, University of Florida, Gainesville.
³ Department of Psychology, VA Boston Healthcare System, Boston, MA.
⁴ Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville.
⁵ Department of Neurology, School of Medicine, Tulane University, New Orleans, LA.
⁶ Department of Physiology and Aging, University of Florida, Gainesville.

Abstract

Objective: To establish, apply, and evaluate a computable phenotype for the recruitment of individuals with successful cognitive aging.

Participants and methods: Interviews with 10 aging experts identified electronic health record (EHR)-available variables representing successful aging among individuals aged 85 years and older. On the basis of the identified variables, we developed a rule-based computable phenotype algorithm composed of 17 eligibility criteria. Starting September 1, 2019, we applied the computable phenotype algorithm to all living persons aged 85 years and older at the University of Florida Health, which identified 24,024 individuals. This sample was comprised of 13,841 (58%) women, 13,906 (58%) Whites, and 16,557 (69%) non-Hispanics. A priori permission to be contacted for research had been obtained for 11,898 individuals, of whom 470 responded to study announcements and 333 consented to evaluation. Then, we contacted those who consented to evaluate whether their cognitive and functional status clinically met out successful cognitive aging criteria of a modified Telephone Interview for Cognitive Status score of more than 27 and Geriatric Depression Scale of less than 6. The study was completed on December 31, 2022.

Results: Of the 45% of living persons aged 85 years and older included in the University of Florida Health EHR database identified by the computable phenotype as successfully aged, approximately 4% of these responded to study announcements and 333 consented, of which 218 (65%) met successful cognitive aging criteria through direct evaluation.

Conclusion: The study evaluated a computable phenotype algorithm for the 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 the recruitment of individuals for prospective cohort studies.