Li Jiang, Xi Fengchan, Yu Wenkui, Sun Chuanrui, Wang Xiling
School of Public Health and Key Laboratory of Public Health Safety, Fudan University, Shanghai, China.
Research Institute of General Surgery, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, China.
JMIR Form Res. 2023 Mar 31;7:e42452. doi: 10.2196/42452.
Sepsis is a leading cause of death in patients with trauma, and the risk of mortality increases significantly for each hour of delay in treatment. A hypermetabolic baseline and explosive inflammatory immune response mask clinical signs and symptoms of sepsis in trauma patients, making early diagnosis of sepsis more challenging. Machine learning-based predictive modeling has shown great promise in evaluating and predicting sepsis risk in the general intensive care unit (ICU) setting, but there has been no sepsis prediction model specifically developed for trauma patients so far.
To develop a machine learning model to predict the risk of sepsis at an hourly scale among ICU-admitted trauma patients.
We extracted data from adult trauma patients admitted to the ICU at Beth Israel Deaconess Medical Center between 2008 and 2019. A total of 42 raw variables were collected, including demographics, vital signs, arterial blood gas, and laboratory tests. We further derived a total of 485 features, including measurement pattern features, scoring features, and time-series variables, from the raw variables by feature engineering. The data set was randomly split into 70% for model development with stratified 5-fold cross-validation, 15% for calibration, and 15% for testing. An Extreme Gradient Boosting (XGBoost) model was developed to predict the hourly risk of sepsis at prediction windows of 4, 6, 8, 12, and 24 hours. We evaluated model performance for discrimination and calibration both at time-step and outcome levels. Clinical applicability of the model was evaluated with varying levels of precision, and the potential clinical net benefit was assessed with decision curve analysis (DCA). A Shapley additive explanation algorithm was applied to show the effect of features on the prediction model. In addition, we trained an L2-regularized logistic regression model to compare its performance with XGBoost.
We included 4603 trauma patients in the study, 1196 (26%) of whom developed sepsis. The XGBoost model achieved an area under the receiver operating characteristics curve (AUROC) ranging from 0.83 to 0.88 at the 4-to-24-hour prediction window in the test set. With a ratio of 9 false alerts for every true alert, it predicted 73% (386/529) of sepsis-positive timesteps and 91% (163/179) of sepsis events in the subsequent 6 hours. The DCA showed our model had a positive net benefit in the threshold probability range of 0 to 0.6. In comparison, the logistic regression model achieved lower performance, with AUROC ranging from 0.76 to 0.84 at the 4-to-24-hour prediction window.
The machine learning-based model had good discrimination and calibration performance for sepsis prediction in critical trauma patients. Using the model in clinical practice might help to identify patients at risk of sepsis in a time window that enables personalized intervention and early treatment.
脓毒症是创伤患者死亡的主要原因，治疗每延迟一小时，死亡风险就会显著增加。高代谢基线和爆发性炎症免疫反应掩盖了创伤患者脓毒症的临床体征和症状，使得脓毒症的早期诊断更具挑战性。基于机器学习的预测模型在评估和预测普通重症监护病房（ICU）环境中的脓毒症风险方面显示出巨大潜力，但到目前为止，尚未有专门为创伤患者开发的脓毒症预测模型。
开发一种机器学习模型，以小时为尺度预测入住ICU的创伤患者发生脓毒症的风险。
我们从2008年至2019年期间入住贝斯以色列女执事医疗中心ICU的成年创伤患者中提取数据。总共收集了42个原始变量，包括人口统计学、生命体征、动脉血气和实验室检查。通过特征工程，我们从原始变量中进一步衍生出总共485个特征，包括测量模式特征、评分特征和时间序列变量。数据集被随机分为70%用于模型开发，并进行分层5折交叉验证，15%用于校准，15%用于测试。开发了一种极端梯度提升（XGBoost）模型，以预测4、6、8、12和24小时预测窗口下脓毒症的每小时风险。我们在时间步长和结果水平上评估了模型的判别和校准性能。使用不同精度水平评估模型的临床适用性，并通过决策曲线分析（DCA）评估潜在的临床净效益。应用Shapley加法解释算法来显示特征对预测模型的影响。此外，我们训练了一个L2正则化逻辑回归模型，以将其性能与XGBoost进行比较。
我们在研究中纳入了4603名创伤患者，其中1196名（26%）发生了脓毒症。在测试集中，XGBoost模型在4至24小时预测窗口下的受试者工作特征曲线下面积（AUROC）范围为0.83至0.88。每出现9次假警报对应1次真警报，它预测了73%（386/529）的脓毒症阳性时间步长以及随后6小时内91%（163/179）的脓毒症事件。DCA显示我们的模型在阈值概率范围为0至0.6时具有正净效益。相比之下，逻辑回归模型的性能较低，在4至24小时预测窗口下的AUROC范围为0.76至0.84。
基于机器学习的模型在预测重症创伤患者的脓毒症方面具有良好的判别和校准性能。在临床实践中使用该模型可能有助于在一个能够实现个性化干预和早期治疗的时间窗口内识别有脓毒症风险的患者。
