Predicting Progressive Vision Loss in Glaucoma Patients Using Functional Principal Component Analysis and Electronic Health Records

Rithvik Krishna Donnipadu¹Maxim Sivolella¹Cody Carroll²Sophia Ying Wang^3*

• ¹Data Institute, University of San Francisco, San Francisco, California, United States
• ²Data Institute, Department of Mathematics and Statistics, University of San Francisco, San Francisco, California, United States
• ³Byers Eye Institute, Stanford Healthcare, Palo Alto, California, United States

Background: Glaucoma is a leading cause of irreversible blindness worldwide. Predicting a patient’s future clinical trajectory would help physicians personalize management. We present a novel approach to predicting patient visual field (VF) progression using Functional Principal Component Analysis (FPCA) combined with clinical data from electronic health records (EHR). Methods: We identified glaucoma patients using diagnosis codes who had >=3 VF tests. We developed a 2-stage modeling pipeline: 1) Patients were split 80:10:10 into train, validation, and test sets and classified as fast-progressors or slow-progressors. 2) FPCA was used to predict mean deviation (MD) trajectories over 10 years after the baseline year of VF exams, using the first 2 principal components. To make predictions, the model uses up to one year of baseline VF and EHR data as input, but can flexibly make predictions from as few as a single VF test. Results: 15,764 VF tests belonging to 2,372 patients were included, of which 8.92% of eyes were fast progressors. On the held-out test set, predictions over 10 years of future MD trajectories using baseline VF and EHR clinical data yielded an R2 of 0.646 and an RMSE of 3.67 for fast-progressors, and an R2 of 0.728 and an RMSE of 3.09 for slow-progressors. Performance was higher when predicting over the near term (fast progressors: year 1 R2 0.920, RMSE 1.83; year 5 R2 0.515, RMSE 4.26; slow progressors: year 1 R2 0.918, RMSE 1.771; year 5 R2 0.717, RMSE 3.12). Conclusion: A novel modeling approach combining FPCA with clinical data from EHR was able to model longitudinal clinical trajectories of glaucoma patients. This method is well-suited for modeling longitudinal healthcare data, handling sparse and irregular observation schedules with a varying number of inputs.