1Department of Neurological Surgery, Rush University Medical Center, Chicago, Illinois.
2Department of Neurological Surgery, University of Miami Miller School of Medicine, Miami, Florida; and.
Neurosurg Focus. 2022 Apr;52(4):E12. doi: 10.3171/2022.1.FOCUS21739.
Motor vehicle collisions (MVCs) account for 1.35 million deaths and cost $518 billion US dollars each year worldwide, disproportionately affecting young patients and low-income nations. The ability to successfully anticipate clinical outcomes will help physicians form effective management strategies and counsel families with greater accuracy. The authors aimed to train several classifiers, including a neural network model, to accurately predict MVC outcomes.
A prospectively maintained database at a single institution's level I trauma center was queried to identify all patients involved in MVCs over a 20-year period, generating a final study sample of 16,287 patients from 1998 to 2017. Patients were categorized by in-hospital mortality (during admission) and length of stay (LOS), if admitted. All models included age (years), Glasgow Coma Scale (GCS) score, and Injury Severity Score (ISS). The in-hospital mortality and hospital LOS models further included time to admission.
After comparing a variety of machine learning classifiers, a neural network most effectively predicted the target features. In isolated testing phases, the neural network models returned reliable, highly accurate predictions: the in-hospital mortality model performed with 92% sensitivity, 90% specificity, and a 0.98 area under the receiver operating characteristic curve (AUROC), and the LOS model performed with 2.23 days mean absolute error after optimization.
The neural network models in this study predicted mortality and hospital LOS with high accuracy from the relatively few clinical variables available in real time. Multicenter prospective validation is ultimately required to assess the generalizability of these findings. These next steps are currently in preparation.
机动车碰撞（MVC）造成全球每年 135 万人死亡，造成 5180 亿美元的损失，对年轻患者和低收入国家的影响不成比例。成功预测临床结果的能力将有助于医生制定更有效的管理策略，并更准确地为家属提供咨询。作者旨在训练几种分类器，包括神经网络模型，以准确预测 MVC 结果。
对一家机构一级创伤中心的前瞻性维护数据库进行查询，以确定 20 年来所有涉及 MVC 的患者，从 1998 年到 2017 年共产生了 16287 名患者的最终研究样本。患者根据住院死亡率（住院期间）和住院时间（LOS）进行分类，如果住院。所有模型均包括年龄（岁）、格拉斯哥昏迷评分（GCS）和损伤严重程度评分（ISS）。住院死亡率和医院 LOS 模型还包括入院时间。
在比较了多种机器学习分类器后，神经网络最有效地预测了目标特征。在单独的测试阶段，神经网络模型返回了可靠的、高度准确的预测结果：住院死亡率模型的灵敏度为 92%，特异性为 90%，受试者工作特征曲线（AUROC）下面积为 0.98，LOS 模型在优化后平均绝对误差为 2.23 天。
本研究中的神经网络模型从实时可用的相对较少的临床变量中高度准确地预测了死亡率和住院时间。最终需要多中心前瞻性验证来评估这些发现的普遍性。这些下一步工作正在准备中。
