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

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 m

SUBMITTER: Donnipadu RK 

PROVIDER: S-EPMC12672302 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature