Predicting Risk of Motor Vehicle Collisions in Patients with Glaucoma: A Longitudinal Study

• Published in: PloS one 2015-10, Vol.10 (10), p.e0138288-e0138288
• Main Authors: Gracitelli, Carolina P B, Tatham, Andrew J, Boer, Erwin R, Abe, Ricardo Y, Diniz-Filho, Alberto, Rosen, Peter N, Medeiros, Felipe A
• Format: Article
• Language: English
• Subjects: Accidents, Traffic - statistics & numerical data; Aged; Attention; Automobile Driving; Care and treatment; Cognitive ability; Computer simulation; Correlation analysis; Development and progression; Drivers; Driving; Driving ability; Family medical history; Female; Field of view; Glaucoma; Glaucoma - physiopathology; Human subjects; Humans; Injuries; Laboratories; Life assessment; Longitudinal Studies; Male; Mathematical models; Middle Aged; Motor vehicles; Ophthalmology; Otolaryngology; Patients; Reaction time; Reaction time task; Risk Assessment; Risk factors; Studies; Survival analysis; Traffic accidents; Vehicles; Visual field; Visual Fields; Visual impairment
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0138288

To evaluate the ability of longitudinal Useful Field of View (UFOV) and simulated driving measurements to predict future occurrence of motor vehicle collision (MVC) in drivers with glaucoma. Prospective observational cohort study. 117 drivers with glaucoma followed for an average of 2.1 ± 0.5 years. All subjects had standard automated perimetry (SAP), UFOV, driving simulator, and cognitive assessment obtained at baseline and every 6 months during follow-up. The driving simulator evaluated reaction times to high and low contrast peripheral divided attention stimuli presented while negotiating a winding country road, with central driving task performance assessed as "curve coherence". Drivers with MVC during follow-up were identified from Department of Motor Vehicle records. Survival models were used to evaluate the ability of driving simulator and UFOV to predict MVC over time, adjusting for potential confounding factors. Mean age at baseline was 64.5 ± 12.6 years. 11 of 117 (9.4%) drivers had a MVC during follow-up. In the multivariable models, low contrast reaction time was significantly predictive of MVC, with a hazard ratio (HR) of 2.19 per 1 SD slower reaction time (95% CI, 1.30 to 3.69; P = 0.003). UFOV divided attention was also significantly predictive of MVC with a HR of 1.98 per 1 SD worse (95% CI, 1.10 to 3.57; P = 0.022). Global SAP visual field indices in the better or worse eye were not predictive of MVC. The longitudinal model including driving simulator performance was a better predictor of MVC compared to UFOV (R2 = 0.41 vs R2 = 0.18). Longitudinal divided attention metrics on the UFOV test and during simulated driving were significantly predictive of risk of MVC in glaucoma patients. These findings may help improve the understanding of factors associated with driving impairment related to glaucoma.