Characterizing Performance Gaps of a Code-Based Dementia Algorithm in a Population-Based Cohort of Cognitive Aging

Abstract

Background: Multiple algorithms with variable performance have been developed to identify dementia using combinations of billing codes and medication data that are widely available from electronic health records (EHR). If the characteristics of misclassified patients are clearly identified, modifying existing algorithms to improve performance may be possible. Objective: To examine the performance of a code-based algorithm to identify dementia cases in the population-based Mayo Clinic Study of Aging (MCSA) where dementia diagnosis (i.e., reference standard) is actively assessed through routine follow-up and describe the characteristics of persons incorrectly categorized. Methods: There were 5,316 participants (age at baseline (mean (SD)): 73.3 (9.68) years; 50.7% male) without dementia at baseline and available EHR data. ICD-9/10 codes and prescription medications for dementia were extracted between baseline and one year after an MCSA dementia diagnosis or last follow-up. Fisher’s exact or Kruskal-Wallis tests were used to compare characteristics between groups. Results: Algorithm sensitivity and specificity were 0.70 (95% CI: 0.67, 0.74) and 0.95 (95% CI: 0.95, 0.96). False positives (i.e., participants falsely diagnosed with dementia by the algorithm) were older, with higher Charlson comorbidity index, more likely to have mild cognitive impairment (MCI), and longer follow-up (versus true negatives). False negatives (versus true positives) were older, more likely to have MCI, or have more functional limitations. Conclusions: We observed a moderate-high performance of the code-based diagnosis method against the population-based MCSA reference standard dementia diagnosis. Older participants and those with MCI at baseline were more likely to be misclassified.