Design and Validation of a Pragmatic, Scalable Prioritization Tool for Cognitive Screening using the Electronic Health Record
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INTRODUCTION
Dementia is a disabling condition that progressively impairs daily function. Timely identification of older adults at high risk for dementia or cognitive impairment (a potential precursor) is critical to maximizing opportunities for intervention.
METHODS
Utilizing structured electronic health record data from 122,633 patients aged 55-80 years, we leveraged demographics, encounter diagnoses, and patient problem lists to develop and prospectively validate a ML model.
RESULTS
The ML model achieved a C-statistic of 0.811 (95% confidence interval: 0.810, 0.812) with adequate calibration overall and in subgroups based on race and sex. Recommending screening for patients with 3-year predicted risk > 5%, the ML model obtained satisfactory fairness across race and sex subgroups, with a net benefit of 18 true positive MCI/dementia diagnoses per 1,000 patients.
DISCUSSION
The ML model developed in this study can effectively identify individuals at high risk for a future diagnosis of MCI/dementia, potentially facilitating earlier screening and intervention to reduce the burden of cognition-related disability.
