UCSF

BackgroundApproximately half of people living with Alzheimer's disease and related dementias are undiagnosed.ObjectiveTo develop and validate algorithms that predict risk of undiagnosed dementia using electronic health record (EHR) and/or healthcare claims data.MethodsStudy participants were adult patients aged 65 years or older without evidence of dementia (diagnosis/medication) at baseline in two U.S. data sources: 1) Medicare claims (2010 to 2021); 2) EHR and claims from a primary care network (2016 to 2023). We applied coefficients from an existing, validated EHR-based algorithm to predictors defined using Medicare claims and used machine learning to develop new EHR- and claims-based predictive models. We assessed model discrimination using c-statistics.ResultsStudy participants included 8,374,400 Medicare beneficiaries (mean [SD] age, 76 [7] years; 57% female) and 29,983 primary care patients (age: 75 [6] years; 56% female). Model discrimination was good when applying EHR-based coefficients to Medicare claims-based predictors (c-statistic [95% confidence interval]: 0.770 [0.767, 0.773]) and was improved by refitting the model (0.795 [0.792, 0.798]) with a small added benefit from incorporating new claims-based predictors (0.801 [0.799; 0.804]). Similarly, when both EHR and claims data were available, discrimination was improved by refitting the model with a small additional benefit from including new predictors, regardless of the data source (EHR, claims, or either).ConclusionsA validated EHR-based algorithm predicted risk of undiagnosed dementia in Medicare claims with good discrimination. Model accuracy was improved by refitting and, to a lesser extent, by including novel predictors.