Work

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

Year: 2025

Type: article

Abstract: 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 for predicting patient visual field (VF) progression by combining Functional Principal Component Analysis (FPCA) with electronic health record (EHR) data. 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 R 2 of 0.646 and an RMSE of 3.67 for fast-progressors, and an R 2 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 R 2 0.920, RMSE 1.83; year 5 R 2 0.515, RMSE 4.26; slow progressors: year 1 R 2 0.918, RMSE 1.771; year 5 R 2 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.

Source: Frontiers in Ophthalmology

DOI: 10.3389/fopht.2025.1632827

Authors: Rithvik Krishna Donnipadu, Maxim Sivolella, Cody Carroll, Sophia Y. Wang

Institutions: University of San Francisco, Stanford University

Language: English

Topic: Glaucoma and retinal disorders

Subfield: Ophthalmology

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,353