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
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引用次数: 0
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.
期刊介绍：
Alzheimer''s & Dementia: Diagnosis, Assessment & Disease Monitoring (DADM) is an open access, peer-reviewed, journal from the Alzheimer''s Association® that will publish new research that reports the discovery, development and validation of instruments, technologies, algorithms, and innovative processes. Papers will cover a range of topics interested in the early and accurate detection of individuals with memory complaints and/or among asymptomatic individuals at elevated risk for various forms of memory disorders. The expectation for published papers will be to translate fundamental knowledge about the neurobiology of the disease into practical reports that describe both the conceptual and methodological aspects of the submitted scientific inquiry. Published topics will explore the development of biomarkers, surrogate markers, and conceptual/methodological challenges. Publication priority will be given to papers that 1) describe putative surrogate markers that accurately track disease progression, 2) biomarkers that fulfill international regulatory requirements, 3) reports from large, well-characterized population-based cohorts that comprise the heterogeneity and diversity of asymptomatic individuals and 4) algorithmic development that considers multi-marker arrays (e.g., integrated-omics, genetics, biofluids, imaging, etc.) and advanced computational analytics and technologies.
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