Preliminary Validation of a Questionnaire Covering Risk Factors for Impaired Driving Skills in Elderly Patients
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Updated: Jan 25, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Philipp Schulz1, Stefan Spannhorst2, Thomas Beblo3
1Evangelisches Krankenhaus Bielefeld, Department of Psychiatry and Psychotherapy Bethel, Research Division, Remterweg 69-71, D-33617 Bielefeld, Germany. Philipp.Schulz@evkb.de.
Abstract:
Due to rather unspecific statutory regulations in Germany, particularly for patients with neurodegenerative disorders, many seniors still drive despite severe driving-related cognitive deficits. An accurate assessment of driving fitness requires immense financial, personnel and temporal resources which go beyond daily clinical routines. In cooperation with a working group from Switzerland, we therefore developed the questionnaire Safety Advice For Elderly drivers (SAFE), an economic instrument covering different risk factors for driving safety. The main aim of the current work was a first validation of the SAFE. Twenty-two driving seniors performed the Corporal A, a test battery permitted by law to assess driving-related cognitive functions. Based upon the Corporal results and the percentile rank 16 criterion, participants were divided into cognitively impaired and unimpaired drivers. Moreover, participants were assessed using the SAFE and an extensive neuropsychological test battery. The results revealed high sensitivity and specifity scores for the SAFE suggesting that the SAFE may be a valuable and economical instrument to quantify and document individual risk factors for driving safety and to differentiate between impaired and unimpaired drivers. Notably, the results must be replicated in future studies including a larger sample, different clinical subgroups, and a practical driving lesson.
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