Background: 

Traumatic brain injury (TBI) is a major cause of death and disability. Episodes of hypotension are associated with worse TBI outcomes. Our aim was to model the real-time risk of intraoperative hypotension in TBI patients, compare machine learning and traditional modeling techniques, and identify key contributory features from the patient monitor and medical record for the prediction of intraoperative hypotension.

Methods: 

The data included neurosurgical procedures in 1005 TBI patients at an academic level 1 trauma center. The clinical event was intraoperative hypotension, defined as mean arterial pressure <65 mm Hg for 5 or more consecutive minutes. Two types of models were developed: one based on preoperative patient-level predictors and one based on intraoperative predictors measured per minute. For each of these models, we took 2 approaches to predict the occurrence of a hypotensive event: a logistic regression model and a gradient boosting tree model.

Results: 

The area under the receiver operating characteristic curve for the intraoperative logistic regression model was 0.80 (95% confidence interval [CI]: 0.78-0.83), and for the gradient boosting model was 0.83 (95% CI: 0.81-0.85). The area under the precision-recall curve for the intraoperative logistic regression model was 0.16 (95% CI: 0.12-0.20), and for the gradient boosting model was 0.19 (95% CI: 0.14-0.24). Model performance based on preoperative predictors was poor. Features derived from the recent trend of mean arterial pressure emerged as dominantly predictive in both intraoperative models.

Conclusions: 

This study developed a model for real-time prediction of intraoperative hypotension in TBI patients, which can use computationally efficient machine learning techniques and a streamlined feature-set derived from patient monitor data.

中文翻译：

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预测创伤性脑损伤术中低血压实时风险的机器学习方法

背景： 

创伤性脑损伤（TBI）是死亡和残疾的主要原因。低血压发作与更差的 TBI 结局相关。我们的目的是对 TBI 患者术中低血压的实时风险进行建模，比较机器学习和传统建模技术，并从患者监护仪和医疗记录中识别出对术中低血压进行预测的关键贡献特征。

方法： 

这些数据包括在学术 1 级创伤中心对 1005 名 TBI 患者进行的神经外科手术。临床事件是术中低血压，定义为平均动脉压连续 5 分钟或更长时间<65 mm Hg。开发了两种类型的模型：一种基于术前患者水平预测因子，另一种基于每分钟测量的术中预测因子。对于每个模型，我们采用了两种方法来预测低血压事件的发生：逻辑回归模型和梯度提升树模型。

结果： 

术中逻辑回归模型的受试者工作特征曲线下面积为 0.80（95% 置信区间 [CI]：0.78-0.83），梯度提升模型的受试者工作特征曲线下面积为 0.83（95% CI：0.81-0.85）。术中 Logistic 回归模型的精确回忆曲线下面积为 0.16（95% CI：0.12-0.20），梯度提升模型的精确回忆曲线下面积为 0.19（95% CI：0.14-0.24）。基于术前预测的模型性能较差。从平均动脉压的近期趋势得出的特征在两种术中模型中都成为主要预测因素。

结论： 

这项研究开发了一种实时预测 TBI 患者术中低血压的模型，该模型可以使用计算效率高的机器学习技术和从患者监测数据中得出的简化特征集。