Predictive modeling of rapid glaucoma progression based on systemic data from electronic medical records

Abstract

This study investigated the baseline systemic features that predict rapid thinning of the retinal nerve fiber layer (RNFL) in patients with primary open-angle glaucoma (POAG). A database drawn from electronic medical records (EMRs) was searched for patients diagnosed with POAG between 2009 and 2016 who had been followed up for > 5 years with the annual evaluation of global RNFL thickness using spectral-domain optical coherence tomography. The rate of change in global RNFL thickness for each eye was determined by linear regression analysis over time. Systemic data obtained within 6 months from the time of glaucoma diagnosis were extracted from the EMRs and incorporated into a model to predict the rate of progressive RNFL thinning. The predictive model was trained and tested using a random forest (RF) method and interpreted using Shapley additive explanation plots (SHAP). The features able to explain the rate of progressive RNFL thinning were identified and interpreted. Data from 1256 eyes of 696 patients and 1107 eyes of 607 patients were included in the training and test sets, respectively. The R2 value for the RF model was 0.88 and mean absolute error of the model was 0.205 μm/year. The prediction model identified higher serum levels of aspartate aminotransferase, lower blood glucose, lower systolic blood pressure, and higher high-density lipoprotein as the four most important systemic features predicting rapid RNFL thinning over 5 years. Among the ophthalmic features, a higher global RNFL thickness and a higher intraocular pressure were the most important factors predicting rapid RNFL thinning. The study revealed baseline systemic features from the EMRs that were of predictive value for progression rate of POAG patients.