(100) Developing a novel claim-based algorithm to identify incident and prevalent dementia using Medicare claims of the Atherosclerosis Risk in Communities (ARIC) cohort

Background: High quality pharmacoepi studies assessing drug effectiveness for Alzheimer’s disease and related dementias (ADRD) are urgently needed given the critical need for effective therapies for ADRD. However, algorithms for identifying ADRD in claims data are limited.

Objectives: To meet the urgent need for robust dementia ascertainment from real-world data, we aimed to develop a novel algorithm for identifying incident and prevalent (general) dementia in claims.

Methods: We developed algorithm candidates using different timing/frequency of diagnoses/treatments to identify dementia using inpatient/outpatient/prescription claims for 3,318 and 1,828 participants from Visits 5 (2011-2013; mean age 76 (SD 5) years) and 6 (2016-2017; mean age 80 (SD 5) years) of the ARIC cohort with ≥ 1-year continuous Medicare Part A & B enrollment (Part D is required for algorithms involving treatment) prior to each visit. We evaluated algorithm performance (sensitivity/specificity/positive predictive value (PPV)/negative predictive value (NPV)) against the gold standard of the ARIC dementia classification based on an extensive battery of neurocognitive tests administered at each visit (dementia prevalence: 5.7% at V5, 8.7% at V6; incident cases: 5.6% at V6). We selected the algorithm with the highest PPV and compared its performance with that of three existing algorithms (Jain, Bynum, Lee), their relaxed forms (4-year window, dementia diagnosis plus a depression diagnosis or dementia evaluation procedure, ignoring code position), and expanded to include ICD-10 diagnosis codes.

Results: The newly developed primary algorithm requires presence of a dementia diagnosis code in any position followed by another diagnosis code ≥ 1 day apart within 1 year (requiring ≥ 1 code is not in the following non-specific ICD-9 diagnostic codes, 290.40, 290.41, 290.11, 290.3) or a new dementia treatment prescription within 90 days. The PPV of the new algorithm in the identification of incident dementia at V6 was 72.7%, with a specificity of 99.1%, outperforming existing algorithms. Its sensitivity of 38.7% and NPV of 96.2% were comparable to those achieved by existing algorithms. Sensitivity analysis restricting to ICD-10 diagnostic codes increased the PPV (74.1%) and attenuated its sensitivity (32.7%). Similar comparative performance was observed in the identification of prevalent dementia.

Conclusions: A novel algorithm for identifying dementia from Medicare claims, developed using linked ARIC-Medicare data, outperforms previous algorithms and stands as a valuable tool in the assessment of ADRD drugs from real-world data.