Prediction of Fitness to Drive in Patients with Alzheimer's Dementia

Piersma, Dafne; Fuermaier, Anselm B. M.; de Waard, Dick; Davidse, R J; De Groot, Jolieke; Doumen, Michelle J.A.; Bredewoud, Ruud A.; Claesen, René; Lemstra, Afina W.; Vermeeren, Annemiek; Ponds, Rudolf; Verhey, Frans R.J.; Brouwer, Wiebo; Tucha, Oliver · 2016 · OpenAlex-citations

DOI: 10.1371/journal.pone.0149566

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Summary

This study addresses the challenge of assessing fitness to drive (FTDr) in patients with Alzheimer’s disease (AD), a population whose driving capabilities decline progressively but variably. While on-road assessments are the gold standard, they are resource-intensive and difficult to scale. The authors aimed to develop a clinically feasible method to predict on-road driving performance using three alternative assessment types: clinical interviews, neuropsychological testing, and driving simulator rides. The research sought to determine which combination of these measures best predicts FTDr, thereby enabling clinicians to advise patients and families on driving safety while balancing mobility needs against public safety risks. The study involved 81 patients with AD and 45 healthy controls, all of whom held valid driver’s licenses. Participants underwent a comprehensive evaluation protocol including clinical interviews (using the Clinical Dementia Rating and a driving questionnaire), a neuropsychological test battery (assessing attention, executive function, visuospatial abilities, and traffic knowledge), and a driving simulator ride. The criterion for fitness to drive was established through an on-road assessment conducted by experts from the CBR Dutch driving test organization. The researchers employed logistic regression, discriminant function analyses, and receiver operating characteristic (ROC) analyses to evaluate the predictive validity of the three assessment types individually and in combination. The results demonstrated that all three assessment types were predictive of on-road driving performance. However, their individual predictive accuracies varied: neuropsychological assessments yielded the highest classification accuracy, followed by driving simulator rides, with clinical interviews providing the lowest standalone predictive value. Crucially, the study found that combining all three assessment types produced the most robust prediction of fitness to drive. This integrated approach achieved an overall accuracy of 92.7%, significantly outperforming any single method. This high level of accuracy suggests that the combined method is highly valid for distinguishing between safe and unsafe drivers among patients with AD. The significance of this research lies in its provision of a standardized, multi-modal assessment tool for clinical settings. By validating a combination of interviews, neuropsychological tests, and simulator rides, the study offers a practical alternative to costly and logistically complex on-road evaluations. This method allows for earlier and more frequent monitoring of driving fitness, helping to maintain patient autonomy for as long as safely possible while mitigating risks to other road users. The findings support the integration of these diverse assessment modalities to achieve high sensitivity and specificity in diagnosing driving impairment in dementia patients.

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Topics

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• fitness to drive assessment
• mci dementia driving
• cognitive impairment

Information type

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• Methodological Resource: validation psychometrics
• Theoretical Contribution: computational model