Abstract
BACKGROUND: Obstructive sleep apnoea (OSA) has been shown to be associated with an increased risk of road traffic accidents (RTAs). Predicting the driving ability and risk of RTAs in an individual with OSA is difficult. On-road testing is the gold standard, but this is time consuming, expensive, and potentially dangerous. Simple computer based driving simulators have been developed to help determine driving ability. Although patients with OSA have been shown to perform poorly compared with matched controls, it is not known whether these simulators can predict those at most risk of accidents. In this study we evaluated whether data derived from a simple driving simulator provided information over and above that obtained from the history and a sleep study that might be useful for advising patients about driving. METHODS: We examined 150 patients admitted for routine sleep studies for investigation of OSA and snoring. Each patient performed a 20 minute driving simulation and completed a questionnaire regarding their driving history and experience. RESULTS: Logistic regression analysis was used to investigate factors associated with patients' performance on the simulator. It was found that patient characteristics, older age (OR 1.05, 95% CI 1.01 to 1.09, p<0.01), female sex (OR 9.32, 95% CI 1.09 to 79.4, p<0.04), and self-reported alcohol consumption (OR 1.04, 95% CI 1.01 to 1.07, p<0.01) had the greatest influence; however, the number of self-reported near miss accidents was independently associated with a poor performance (OR 2.62, 95% CI 1.00 to 6.88, p<0.05). A further logistic regression was used to investigate whether clinical history, sleep study results, and data from the driving simulator were useful in classifying patients with OSA as having had an RTA. The number of off-road events per hour on the simulator was independently associated with a history of previous RTA (OR 1.004, 95% CI 1.0004 to 1.008, p<0.03). The Epworth score was independently associated with episodes of falling asleep at the wheel (OR 1.21, 95% CI 1.12 to 1.31, p<0.00001) and near miss accidents (OR 1.15, 95% CI 1.07 to 1.23, p<0.0001). Using this model, 100% of patients who did not have an accident could be identified, but only 10% of those who did. CONCLUSIONS: Although factors not directly related to OSA influence performance on a driving simulator, there is an independent relationship between driving ability in patients with OSA and performance on a simple computer based simulator. When combined with clinical history, it is those not reporting hypersomnolence and not having off-road events on the simulator who appear to be at least risk of adverse driving events. Poor performance on the simulator, however, relates poorly to accident history. These data require confirmation in future studies before simple computer simulators can be used in clinical practice to advise whether an individual is safe to drive.