脑卒中后驾车：一个支持不确定情况下决策的三分逻辑回归模型

IF 1.8 4区医学 Q3 NEUROSCIENCES

Journal of Stroke & Cerebrovascular Diseases Pub Date : 2025-08-26 DOI:10.1016/j.jstrokecerebrovasdis.2025.108439

Gábor Szabó MS , József Pintér MS , Roland Molontay MS, PhD , Gábor Fazekas MD, PhD

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引用次数: 0

摘要

评估中风后驾车的适应性是一项复杂的临床任务，因为即使是轻微的认知缺陷也会影响安全性。由于安全驾驶员和不安全驾驶员的认知测试结果存在较大的重叠，二元分类模型不可避免地存在误分类的风险。本研究旨在开发和验证引入第三种不确定类别的逻辑回归模型，为临床医生在不确定病例中保留判断留下空间，从而支持更谨慎的、基于证据的决策。方法对115例中风幸存者进行了标准化的神经心理学评估，包括注意力、执行功能和视觉空间规划的评估。包括新的动态响应时间测量。通过标准化的道路测试来评估驾驶健康，这是主要的结果。逻辑回归模型与留一交叉验证和三分分类相结合，以尽量减少过拟合和管理诊断不确定性。结果根据被视为该领域金标准的道路评估，70%的参与者被判定为安全驾驶员。我们的模型验证后显示ROC-AUC值为0.95，而15%的病例被归类为不确定。造径测试，Stroop测试，匈牙利版道路法和道路工艺知识测试和星空测试都有助于模型的准确性。结论我们的逻辑回归模型允许临床医生在认知测试结果不确定的情况下避免做出没有根据的决定。在一小部分不确定的情况下，建议进行进一步的评估，最好是进行道路测试。该模型通过识别单独的认知测试结果不足的情况，支持更有针对性地使用道路评估。

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Driving after stroke: A trichotomous logistic regression model to support decision making in uncertain cases

Background

Assessing fitness to drive after stroke is a complex clinical task, as even mild cognitive deficits can undermine safety. Due to the substantial overlap in cognitive test results between safe and unsafe drivers, binary classification models inevitably carry a risk of misclassification. This study aimed to develop and validate a logistic regression model that introduces a third, indeterminate category – leaving room for clinicians to withhold judgment in uncertain cases and thereby support more cautious, evidence-based decisions.

Methods

A total of 115 stroke survivors underwent a standardized neuropsychological evaluation, including assessments of attention, executive function and visuospatial planning. Novel dynamic response time measures were included. Driving fitness was evaluated through a standardized on-road test, which served as the primary outcome. Logistic regression modeling was combined with leave-one-out cross-validation and trichotomous classification to minimize overfitting and manage diagnostic uncertainty.

Results

Based on the on-road evaluation, regarded as the gold standard in the field, 70 % of participants were judged to be safe drivers. Our model demonstrated a ROC-AUC value of 0.95 after validation, while 15 % of the cases were classified as indeterminate. The Trail Making Test, Stroop test, Hungarian version of Road Law and Road Craft Knowledge test and the Starry Night Test all contributed to the model’s accuracy.

Conclusion

Our logistic regression model allows clinicians to refrain from making unfounded decisions in cases where cognitive test results are inconclusive. In a small proportion of uncertain cases, further assessment is recommended, ideally an on-road test. The model supports more targeted use of on-road evaluations by identifying cases where cognitive test results alone are insufficient.

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来源期刊

Journal of Stroke & Cerebrovascular Diseases Medicine-Surgery

CiteScore

5.00

自引率

4.00%

发文量

583

审稿时长

62 days

期刊介绍： The Journal of Stroke & Cerebrovascular Diseases publishes original papers on basic and clinical science related to the fields of stroke and cerebrovascular diseases. The Journal also features review articles, controversies, methods and technical notes, selected case reports and other original articles of special nature. Its editorial mission is to focus on prevention and repair of cerebrovascular disease. Clinical papers emphasize medical and surgical aspects of stroke, clinical trials and design, epidemiology, stroke care delivery systems and outcomes, imaging sciences and rehabilitation of stroke. The Journal will be of special interest to specialists involved in caring for patients with cerebrovascular disease, including neurologists, neurosurgeons and cardiologists.