Using Serial Trichotomization to Determine Fitness to Drive in Medically At-Risk Drivers.

Importance: Clinical tests that identify fit and unfit drivers with 100% sensitivity and specificity would reduce uncertainty and improve efficiency of occupational therapists performing comprehensive driving evaluations (CDEs).

Objective: To examine whether serial trichotomization of clinical tests predicts pass-fail outcomes with 100% sensitivity and specificity in a sample of medically at-risk drivers and in drivers with and without cognitive impairment (CI) referred for a CDE.

Design: Retrospective data collection and analysis of scores on the Montreal Cognitive Assessment; Trail Making Test, Part A and Part B; and the Useful Field of View® Subtests 1 to 3 and outcomes on the CDE (pass-fail or indeterminate requiring lessons and retesting). Receiver operating characteristic curves of clinical tests were performed to determine 100% sensitivity and specificity cut points in predicting CDE outcomes. Clinical tests were arranged in order from most to least predictive to identify pass-fail and indeterminate outcomes.

Setting: A driving assessment clinic.

Participants: Among 142 medically at-risk drivers (M age = 69.2 yr, SD = 14.1), 66 with CI, 46 passed and 39 failed the CDE; 57 were indeterminate.

Outcomes and measures: On-road pass-fail outcomes.

Results: Together, the six clinical tests predicted 62 pass and 49 fail outcomes in the total sample; 21 pass and 34 fail outcomes in participants with CI; and 58 pass and 14 fail outcomes in participants without CI.

Conclusions and relevance: Serial trichotomization of clinical tests increases the accuracy of making informed decisions and reduces the number of drivers undergoing unnecessary on-road assessments. Plain-Language Summary: Clinical tests and their cut points that identify fit and unfit drivers vary substantially across settings and research studies. Serial trichotomization is one method that could help control for this variation by combining clinical test scores showing 100% sensitivity and specificity to identify pass (fit drivers) and fail outcomes (unfit drivers) and to reduce the number of drivers undergoing unnecessary on-road assessments.