Development and evaluation of a virtual reality driving test for patients with cirrhosis

BACKGROUND: Patients with cirrhosis and especially those with HE may have impaired driving skills and may be prone to car accidents. This proof-of-concept study aimed to develop and evaluate the applicability of a virtual reality (VR)–based driving test in patients with cirrhosis. Additionally, the association between the results in the VR test and car accidents was investigated. METHODS: A short driving test using a VR head–mounted display (HTC Vive Pro Eye) was developed to simulate five hazardous situations. The patient has to pull the brake when the hazardous situations occur. The time from triggering the event to pressing the brake is recorded as reaction time. Total reaction time (TRT) is defined as the combined reaction time to all 5 events. Car accidents were assessed retrospectively (previous 12 months) and patients were followed prospectively for 6 months. Minimal HE (MHE) was diagnosed using Psychometric Hepatic Encephalopathy Score. RESULTS: In total, 112 outpatients with cirrhosis and 52 controls without cirrhosis were recruited. MHE was detected in 14% (n = 15). Patients with cirrhosis and MHE (5.67 s) had higher TRTs compared to patients without MHE (5.02 s) and controls without cirrhosis above the age of 50 (4.98 s) (MHE vs. no MHE or controls p<0.01, no MHE vs. controls: not significant). Seven patients reported car accidents during the twelve months prior to study inclusion. TRTs were numerically higher in patients with reported car accidents (p = 0.099). When patients who stopped driving for HE-related/cirrhosis-related reasons (n = 14) were added to the group of patients with accidents, then a longer TRT was significantly associated with the modeled outcome in univariable (p<0.01) and multivariable analyses (OR 2.83, p<0.01). Two car accidents occurred during follow-up. Both patients had TRTs above the 90th percentile. CONCLUSIONS: The VR driving test is easy and rapid to perform in patients with cirrhosis and could be helpful as a point-of-care tool for predicting car accidents.