Variables predicting clinical decision-making to drive – a retrospective analysis

Research Objectives: To identify the pre-driving assessment variables that weighted more on the clinical decision-making to drive outcome.

Design: Retrospective analysis of driving service dataset from 2017 - 2019.

Setting: The driving dataset is from a University driving research laboratory.

Participants: We analyzed our driving service dataset gathered between 2017 and 2019. Records of 99 participants (65 males, and 34 females) who underwent our full battery of assessments were analyzed.

Interventions: This is a retrospective analysis. We did not perform any intervention.

Main Outcome Measures: The full battery included demographic variables such as age, medical condition, and driving history as well as assessments of physical functions, visual functions, and cognitive functions. Physical function assessments included balance, coordination, range of motion, transfer, and history of falls. Visual functions assessment included visual acuity, color vision, and glare recovery. Cognitive tests were MoCA, TMT-A and B, Rey–Osterrieth complex figure (ROCF) test, and the Stroke Drivers Screening Assessment (SDSA) battery. The clinical decision-making to drive was a three-point categorical outcome – permitted to drive, driving with restriction, and no driving.

Results: Our analysis showed that the SDSA battery had a moderate correlation [SDSA Roadside Recognition Test -0.592, p-value < 0.001, SDSA Matrix -0.470, p-value < 0.001] with clinical decision-making. They explained 34.8% and 22.09% of the variance in the model respectively. The Rey–Osterrieth complex figure (ROCF) test had a weak correlation of -0.384, p-value < 0.0001.

Conclusions: Our preliminary results indicate that SDSA battery correlates better with clinical decision-making in driving.

Author(s) Disclosures: I confirm that neither I nor the authors have any financial or non-financial conflict of interest related to this research activity.