Predicting Risk of Motor Vehicle Collisions in XXXX with @entity726 : A Longitudinal Study.

@entity1 @entity1957 @entity2194 @entity726
PURPOSE: To evaluate the ability of longitudinal Useful Field of View (UFOV) and simulated driving measurements to predict future occurrence of @entity1957 ( @entity1957 ) in drivers with @entity726 . DESIGN: Prospective observational cohort study. @entity1 : 117 drivers with @entity726 followed for an average of 2.1    0.5 years. METHODS: 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 @entity1957 during follow-up were identified from Department of Motor Vehicle records. MAIN OUTCOME MEASURES: Survival models were used to evaluate the ability of driving simulator and UFOV to predict @entity1957 over time, adjusting for potential confounding factors. RESULTS: Mean age at baseline was 64.5    12.6 years. 11 of 117 (9.4%) drivers had a @entity1957 during follow-up. In the multivariable models, low contrast reaction time was significantly predictive of @entity1957 , 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 @entity1957 with a HR of 1.98 per 1 SD worse (95% CI, 1.10 to 3.57; P = 0.022). @entity2194 were not predictive of @entity1957 . The longitudinal model including driving simulator performance was a better predictor of @entity1957 compared to UFOV (R2 = 0.41 vs R2 = 0.18). CONCLUSIONS: Longitudinal divided attention metrics on the UFOV test and during simulated driving were significantly predictive of risk of @entity1957 in @entity726 @entity1 . These findings may help improve the understanding of factors associated with driving impairment related to @entity726 .
