Automated identification of older adults at risk for cognitive decline - Darlene P. Floden; Olivia Hogue; Saket A. Saxena; Anita D. Misra‐Hebert; Alex Milinovich; Michael B. Rothberg; Elizabeth R. Pfoh; Robyn M. Busch; Kamini Krishnan; Robert J. Fox; Michael W. Kattan (Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring)
Abstract INTRODUCTION Automated models that predict cognitive risk in older adults can aid decisions about which patients to screen in busy primary care settings. METHODS In this retrospective prediction model development study, we conducted formal cognitive testing on 337 older primary care patients to establish cognitive status. We used up to 5 years of prior discrete‐field electronic health record (EHR) data to develop a multivariable prediction model that differentiates patients with impaired versus intact cognition. RESULTS The final model included seven easily extractable variables with known associations to cognitive decline: age, race, pulse, systolic blood pressure, non‐steroidal anti‐inflammatory use, history of mood disorder, and family history of neurological disease. The model demonstrated good discrimination of cognitive status (concordance statistic = 0.72). DISCUSSION The cognitive risk model may be useful clinically to prompt for objective cognitive screening in high‐risk patients. The use of common, discrete variables ensures relative ease of implementation in EHRs. Highlights 337 older primary care patients completed full neuropsychological assessment. Risk modeling used data available in a typical primary care record. The model successfully differentiated patients with/without cognitive impairment. This EHR model offers a passive workflow to identify patients at cognitive risk.