Abstract：
The difficulty of accurately identifying patients who would benefit from promising treatments makes it challenging to prove the efficacy of novel treatments for traumatic brain injury (TBI). Although machine learning is being increasingly applied to this task, existing binary outcome prediction models are insufficient for the effective stratification of TBI patients. The aim of this study was to develop an accurate 3-class outcome prediction model to enable appropriate patient stratification. To this end, retrospective balanced data of 1200 blunt TBI patients admitted to six Japanese hospitals from January 2018 onwards (200 consecutive cases at each institution) were used for model training and validation. We incorporated 21 predictors obtained in the emergency department, including age, sex, six clinical findings, four laboratory parameters, eight computed tomography findings, and an emergency craniotomy. We developed two machine learning models (XGBoost and dense neural network) and logistic regression models to predict 3-class outcomes based on the Glasgow Outcome Scale-Extended (GOSE) at discharge. The prediction models were developed using a training dataset with n = 1000, and their prediction performances were evaluated over two validation rounds on a validation dataset (n = 80) and a test dataset (n = 120) using the bootstrap method. Of the 1200 patients in aggregate, the median patient age was 71 years, 199 (16.7%) exhibited severe TBI, and emergency craniotomy was performed on 104 patients (8.7%). The median length of stay was 13.0 days. The 3-class outcomes were good recovery/moderate disability for 709 patients (59.1%), severe disability/vegetative state in 416 patients (34.7%), and death in 75 patients (6.2%). XGBoost model performed well with 69.5% sensitivity, 82.5% accuracy, and an area under the receiver operating characteristic curve of 0.901 in the final validation. In terms of the receiver operating characteristic curve analysis, the XGBoost outperformed the neural network-based and logistic regression models slightly. In particular, XGBoost outperformed the logistic regression model significantly in predicting severe disability/vegetative state. Although each model predicted favorable outcomes accurately, they tended to miss the mortality prediction. The proposed machine learning model was demonstrated to be capable of accurate prediction of in-hospital outcomes following TBI, even with the three GOSE-based categories. As a result, it is expected to be more impactful in the development of appropriate patient stratification methods in future TBI studies than conventional binary prognostic models. Further, outcomes were predicted based on only clinical data obtained from the emergency department. However, developing a robust model with consistent performance in diverse scenarios remains challenging, and further efforts are needed to improve generalization performance.
摘要：
难以准确识别将从有希望的治疗中受益的患者，这使得证明新型治疗方法对创伤性脑损伤（TBI）的有效性具有挑战性。尽管机器学习越来越多地应用于这项任务，现有的二元结果预测模型不足以对TBI患者进行有效分层。这项研究的目的是开发一个准确的3类结果预测模型，以实现适当的患者分层。为此,自2018年1月起在日本6家医院接受治疗的1200例钝性TBI患者的回顾性平衡数据(各机构连续200例)用于模型训练和验证.我们纳入了急诊科获得的21个预测因子，包括年龄,性别,六个临床发现，四个实验室参数，八个计算机断层扫描结果，和紧急开颅手术.我们开发了两种机器学习模型（XGBoost和密集神经网络）和逻辑回归模型，以根据出院时的格拉斯哥结果扩展量表（GOSE）预测3类结果。使用n=1000的训练数据集开发了预测模型，并使用Bootstrap方法在验证数据集（n=80）和测试数据集（n=120）上的两轮验证中评估了其预测性能。在总共1200名患者中，患者年龄中位数为71岁,199（16.7%）表现出严重的TBI，对104例患者(8.7%)进行了紧急开颅手术。中位住院时间为13.0天。3级结果为709例患者恢复良好/中度残疾（59.1%），严重残疾/植物人状态416例（34.7%），75例患者死亡（6.2%）。XGBoost模型表现良好，灵敏度为69.5%，精度82.5%,在最终验证中，接收器工作特性曲线下的面积为0.901。在接收机工作特性曲线分析方面,XGBoost的性能略高于基于神经网络和逻辑回归模型。特别是,XGBoost在预测严重残疾/植物状态方面明显优于逻辑回归模型。尽管每个模型都准确地预测了有利的结果，他们往往错过了死亡率预测。所提出的机器学习模型被证明能够准确预测TBI后的住院结局。即使有三个基于GOSE的类别。因此,在未来的TBI研究中,与传统的二元预后模型相比,该模型有望对制定适当的患者分层方法产生更大的影响.Further,仅基于从急诊科获得的临床数据预测结局.然而,开发一个在不同场景下性能一致的稳健模型仍然具有挑战性，需要进一步努力来提高泛化性能。
