Parreco Joshua, Hidalgo Antonio, Parks Jonathan J, Kozol Robert, Rattan Rishi
DeWitt Daughtry Family Department of Surgery, University of Miami, Miller School of Medicine, Miami, Florida.
Division of Trauma Surgery and Surgical Critical Care, DeWitt Daughtry Family Department of Surgery, University of Miami, Miller School of Medicine, Miami, Florida.
J Surg Res. 2018 Aug;228:179-187. doi: 10.1016/j.jss.2018.03.028. Epub 2018 Apr 11.
Early identification of critically ill patients who will require prolonged mechanical ventilation (PMV) has proven to be difficult. The purpose of this study was to use machine learning to identify patients at risk for PMV and tracheostomy placement.
The Multiparameter Intelligent Monitoring in Intensive Care III database was queried for all intensive care unit (ICU) stays with mechanical ventilation. PMV was defined as ventilation >7 d. Classifiers with a gradient-boosted decision trees algorithm were created for the outcomes of PMV and tracheostomy placement. The variables used were six different severity-of-illness scores calculated on the first day of ICU admission including their components and 30 comorbidities. Mean receiver operating characteristic curves were calculated for the outcomes, and variable importance was quantified.
There were 20,262 ICU stays identified. PMV was required in 13.6%, and tracheostomy was performed in 6.6% of patients. The classifier for predicting PMV was able to achieve a mean area under the curve (AUC) of 0.820 ± 0.016, and tracheostomy was predicted with an AUC of 0.830 ± 0.011. There were 60.7% patients admitted to a surgical ICU, and the classifiers for these patients predicted PMV with an AUC of 0.852 ± 0.017 and tracheostomy with an AUC of 0.869 ± 0.015. The variable with the highest importance for predicting PMV was the logistic organ dysfunction score pulmonary component (13%), and the most important comorbidity in predicting tracheostomy was cardiac arrhythmia (12%).
This study demonstrates the use of artificial intelligence through machine-learning classifiers for the early identification of patients at risk for PMV and tracheostomy. Application of these identification techniques could lead to improved outcomes by allowing for early intervention.
事实证明，早期识别出需要长期机械通气（PMV）的重症患者很困难。本研究的目的是利用机器学习来识别有PMV和气管造口术风险的患者。
查询重症监护病房（ICU）III数据库中所有接受机械通气的ICU住院病例。PMV定义为通气时间>7天。采用梯度提升决策树算法创建分类器，用于预测PMV和气管造口术的结果。使用的变量包括在ICU入院第一天计算的六种不同的疾病严重程度评分及其组成部分，以及30种合并症。计算各结果的平均受试者工作特征曲线，并对变量重要性进行量化。
共识别出20262例ICU住院病例。13.6%的患者需要PMV，6.6%的患者接受了气管造口术。预测PMV的分类器平均曲线下面积（AUC）为0.820±0.016，预测气管造口术的AUC为0.830±0.011。60.7%的患者入住外科ICU，这些患者的分类器预测PMV的AUC为0.
