基于机器学习算法构建老年人呼吸机相关肺炎早期预警模型

Application machine learning in constructing an early warning model of ventilator associated pneumonia in the elderly

目的开发并验证有创机械通气24h内预警老年人呼吸机相关肺炎(VAP)的机器学习(ML)模型,为老年人VAP的临床管理提供更多证据与思路。方法基于MIMIC Ⅳ 2.2数据库提取重症监护室中急性呼吸衰竭且进行有创机械通气老年患者的临床数据,以VAP为结局指标,按7:3的比例将患者分为训练集与测试集,在训练集中使用4种ML算法建模,用测试集验证模型性能,并将模型在相同的数据集中与序贯器官衰竭评分(SOFA)、全身炎症反应综合征评分(SIRS)、风险评分包括急性生理评分(APSⅢ)评分做比较。结果共纳入1859例老年患者,336例患者诊断为VAP。ML模型的受试者工作曲线的曲线下面积(AUC)均高于临床风险评分(SOFA评分0.44、SIRS评分0.49、APSⅢ评分0.46),其中LightGBM模型和XGBoost模型的预测性能最佳,AUC分别为0.85(95%CI:0.82~0.88)和0.84(95%CI:0.81~0.87)。运用SHAP进一步解释模型,结果显示,SOFA神经系统评分、白细胞计数最大值、呼吸频率最大值、碱剩余最大值以及年龄变量是模型早期预测老年人VAP的重要因素。结论运用机器学习算法构建老年人VAP的早期预警模型,对临床及时启动和调整治疗方案具有重要指导意义,未来应进一步开展模型的外部验证工作。

Objective To develop and verify machine learning(ML)models for the early warning of ventilator-associated pneumonia(VAP)within 24 hours after invasive mechanical ventilation,so as to provide more evidence and ideas for the clinical management of VAP in elderly patients.Methods In this study,clinical data of elderly patients with acute respiratory failure and invasive mechanical ventilation in intensive care unit were extracted from MIMIC Ⅳ 2.2 database.Using VAP as the outcome index,patients were divided into training set and testing set in a ratio of 7:3.Four ML algorithms were used to build a model in the training set,and the performance of the model was verified by the test set.The model was compared with SOFA,systemic inflammatory response syndrome(SIRS)and acute physiology score(APS)Ⅲ scores in the same dataset.Results A total of 1859 elderly patients were included,336 of whom were diagnosed with VAP.The area under the curve(AUC)of the receiver operator characteristic curve of ML models were higher than the clinical risk scores(SOFA score:0.44,SIRS score:0.49,APS Ⅲ score:0.46),and the LightGBM model and XGBoost model had better predictive performance,with AUC of 0.85(95%CI:0.82,0.88)and 0.84(95%CI:0.81,0.87).SHAP was used to further explain the model.The results showed that SOFA neurological score,maximum white blood cell count,maximum respiratory rate,maximum alkali residual and age were important factors for early prediction of elderly VAP.Conclusions In this study,ML algorithms were used to build an early warning model of VAP in elderly patients,which has important guiding significance for clinical timely initiation and adjustment of treatment plan.In the future,external verification of the model should be further carried out.