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- W2981314394 abstract "To date, predictive modeling for Alzheimer's disease (AD) risk has focused on data not routinely collected in clinical care and is limited to short prediction horizons (e.g., 2–4 years). Given the limitations of existing datasets, we sought to leverage electronic health records (EHRs) that contain decades of longitudinal data for thousands of patients, with the goal of developing and validating a predictive model for AD onset with a 10-year prediction horizon. A retrospective study included patients aged 68–72 admitted to Michigan Medicine prior to 2008 with at least 10 years of follow-up. To control for age, we aligned patients between 68–72 years. Given EHR data up to and including the visit of alignment, we sought to predict the primary outcome: probable AD within the next 10 years (labeled by a previously validated cohort discovery tool). Patients were repeatedly randomly divided into a training and test sets. Using EHR-based covariates (e.g., demographics, diagnoses, procedures, vital signs, laboratory results, medications), we learned a linear model to predict AD. Predictive performance was measured on held-out test data using area under the receiver operating characteristics curve (AUROC). Covariates were ranked according to predictive power, using permutation importance. After applying inclusion/exclusion criteria (Figure 1), patients in the final study population (n=8,416) had a median of 11 encounters prior to alignment (interquartile range (IQR)=4-25) and a median of 84 encounters during follow-up (IQR=36-172). The model achieved an AUROC of 0.708 (IQR=0.686-0.733). Important predictive factors included healthcare utilization, testing for secondary causes of AD, and cardiovascular factors (Figure 2). Identifying at-risk patients more than 6 years in advance was possible, though more difficult (Figure 3)." @default.
- W2981314394 created "2019-10-25" @default.
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- W2981314394 date "2019-07-01" @default.
- W2981314394 modified "2023-10-18" @default.
- W2981314394 title "P4-555: EHR-BASED PATIENT RISK STRATIFICATION TOOL FOR PROBABLE AD" @default.
- W2981314394 doi "https://doi.org/10.1016/j.jalz.2019.08.102" @default.
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