Predicting progressive vision loss in glaucoma patients using functional principal component analysis and electronic...

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 for predicting patient visual field (VF) progression by combining Functional Principal Component Analysis (FPCA) with electronic health record (EHR) data.

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.

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).

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.