IDENTIFYING PRODROMAL FEATURES OF DEMENTIA IN UK PRIMARY CARE: A NESTED CASE-CONTROL STUDY USING ELECTRONIC HEALTH RECORDS FROM 92,623 DEMENTIA PATIENTS

Abstract

The process of diagnosing dementia is often lengthy and based on a narrow range of cognitive presentations. We aimed to identify prodromal features of dementia in UK primary care electronic health records, in order to facilitate more timely diagnosis. A systematic review was conducted to identify prodromal features of dementia. We generated a study population from the Clinical Practice Research Datalink (CPRD). Based on a dementia phenotyping algorithm, patients were matched with disease-free individuals (1:5). We investigated prodromal features’ six-monthly prevalence and applied Least Absolute Shrinkage and Selection Operator (LASSO) regression to fit models and calculate odds ratios (OR) for dementia diagnosis at thirteen time intervals 20 years prior to diagnosis. We identified 92,623 dementia patients (median age: 83, female: 65.1%, Alzheimer's: 31.6%) and 329 prodromal features. From the period prevalence, medications were the earliest discernable features between cases and controls, including Antiplatelet Drugs and Analgesics, 19 years before diagnosis. Feature odds ratios showed that medications had the greatest number of negatively associated features, physiological features were most consistently positively associated with dementia and features of health system utilization had the greatest extremes of association. Model recall (sensitivity) ranged from 0.69 at 20–18 years to diagnosis, to 0.76 at 20–1 years to diagnosis. Overall precision of the models ranged from 0.13 to 0.30 due to the high proportion of false positives.