Predicting Future Self-Reported Motor Vehicle Collisions in Subjects with Primary Open-Angle Glaucoma Using the Penalized Support Vector Machine Method

Abstract

Purpose: We predict the likelihood of a future motor vehicle collision (MVC) from visual function data, attitudes to driving, and past MVC history using the penalized support vector machine (pSVM) in subjects with primary open-angle glaucoma (POAG). Methods: Patients with POAG were screened prospectively for eligibility and 185 were analyzed in this study. Self-reported MVCs of all participants were recorded for 3 years from the baseline using a survey questionnaire every 12 months. A binocular integrated visual field (IVF) was calculated for each patient by merging a patient's monocular Humphrey Field Analyzer (HFA) visual fields (VFs). The IVF was divided into six regions, based on eccentricity and the right or left hemifield, and the average of the total deviation (TD) values in each of these six areas was calculated. Then, the future MVCs were predicted using various variables, including age, sex, 63 variables of 52 TD values, mean of the TD values, visual acuities (VAs), six sector average TDs with (pred_{penSVM_all}) and without (pred_{penSVM_basic}) the attitudes in driving, and also past MVC history, using the pSVM method, applying the leave-one-out cross validation. Results: The relationship between pred_{penSVM_basic} and the future MVC approached significance (odds ratio = 1.15, [0.99–1.29], P = 0.064, logistic regression). A significant relationship was observed between pred_{penSVM_all} and the future MVC (odds ratio = 1.21, P = 0.0015). Conclusions: It was useful to predict future MVCs in patients with POAG using visual function metrics, patients' attitudes to driving, and past MVC history, using the pSVM. Translational Relevance: Careful consideration is needed when predicting future MVCs in POAG patients using visual function, and without driving attitude and MVC history.