Predictive Analytics for Glaucoma Using Data From the All of Us Research Program

PURPOSE: To (1) use All of Us (AoU) data to validate a previously published single-center model predicting need for surgery among individuals with glaucoma, (2) train new models using AoU data, and (3) share insights regarding this novel data source for ophthalmic research. DESIGN: Development and evaluation of machine learning models. METHODS: Electronic health record data were extracted from AoU for 1231 adults diagnosed with primary open-angle glaucoma. The single-center model was applied to AoU data for external validation. AoU data were then used to train new models for predicting need for glaucoma surgery using multivariable logistic regression, artificial neural networks, and random forests. Five-fold cross-validation was performed. Model performance was evaluated based on area under the receiver operating characteristic curve (AUC), accuracy, precision and recall. RESULTS: The mean (standard deviation) age of the All of Us cohort was 69.1 (10.5) years, with 57.3% women and 33.5% Black, significantly exceeding representation in the single-center cohort (p=0.04 and p