Predicting Diagnosis of Alzheimer's Disease and Related Dementias Using Administrative Claims

Abstract

Background: Predictive models for earlier diagnosis of Alzheimer's disease and related dementias (ADRD) that rely on variables requiring assessment during an office visit, such as cognitive function, body mass index, or lifestyle factors, may not be broadly applicable, since that level of data may be inaccessible or inefficient.

Objective: To build a predictive model for earlier diagnosis of ADRD using only administrative claims data to enhance applicability at the health care-system level. Building on the strength of this approach and knowledge that health care utilization (HCU) is increased before dementia diagnosis, it was hypothesized that previous HCU history would improve predictive ability of the model.

Methods: We conducted a case-control study using data from the OptumLabs Data Warehouse. ADRD was defined using ICD-9-CM codes and prescription fills for antidementia medications. We included individuals with mild cognitive impairment. Cases aged ≥ 18 years with a diagnosis between 2011-2014 were matched to controls without ADRD. HCU variables were incorporated into regression models along with comorbidities and symptoms.

Results: The derivation cohort comprised 24,521 cases and 95,464 controls. Final adjusted models were stratified by age. We obtained moderate accuracy (c-statistic = 0.76) for the model among younger (aged < 65 years) adults and poor discriminatory ability (c-statistic = 0.63) for the model among older adults (aged ≥ 65 years). Neurological and psychological disorders had the largest effect estimates.

Conclusions: We created age-stratified predictive models for earlier diagnosis of dementia using information available in administrative claims. These models could be used in decision support systems to promote targeted cognitive screening and earlier dementia recognition for individuals aged < 65 years. These models should be validated in other cohorts.

Disclosures: This research was supported by AstraZeneca, Global CEO Initiative, Janssen, OptumLabs, and Roche. Albrecht was supported by Agency for Healthcare Quality and Research grant K01HS024560. Perfetto is employed by the National Health Council, which accepts membership dues and sponsorships from a variety of organizations and companies. The authors declare no other potential conflicts of interest.

FIGURE 1

Creation of Study Cohort