基于机器学习的 MIMIC-IV 数据库中 ICU 心搏骤停患者院内死亡率预测模型：回顾性分析。

BACKGROUND: Both in-hospital cardiac arrest (IHCA) and out-of-hospital cardiac arrest (OHCA) have higher incidence and lower survival rates. Predictors of in-hospital mortality for intensive care unit (ICU) admitted cardiac arrest (CA) patients remain unclear. METHODS: The Medical Information Mart for Intensive Care IV (MIMIC-IV) database was used to perform a retrospective study. Patients meeting the inclusion criteria were identified from the MIMIC-IV database and randomly divided into training set (n = 1206, 70%) and validation set (n = 516, 30%). Candidate predictors consisted of the demographics, comorbidity, vital signs, laboratory test results, scoring systems, and treatment information on the first day of ICU admission. Independent risk factors for in-hospital mortality were screened using the least absolute shrinkage and selection operator (LASSO) regression model and the extreme gradient boosting (XGBoost) in the training set. Multivariate logistic regression analysis was used to build prediction models in training set, and then validated in validation set. Discrimination, calibration and clinical utility of these models were compared using the area under the curve (AUC) of the receiver operating characteristic (ROC) curves, calibration curves and decision curve analysis (DCA). After pairwise comparison, the best performing model was chosen to build a nomogram. RESULTS: Among the 1722 patients, in-hospital mortality was 53.95%. In both sets, the LASSO, XGBoost,the logistic regression(LR) model and the National Early Warning Score 2 (NEWS 2) models showed acceptable discrimination. In pairwise comparison, the prediction effectiveness was higher with the LASSO,XGBoost and LR models than the NEWS 2 model (p < 0.001). The LASSO,XGBoost and LR models also showed good calibration. The LASSO model was chosen as our final model for its higher net benefit and wider threshold range. And the LASSO model was presented as the nomogram. CONCLUSIONS: The LASSO model enabled good prediction of in-hospital mortality in ICU admission CA patients, which may be widely used in clinical decision-making.

背景：院内心搏骤停（IHCA）和院外心搏骤停（OHCA）的发生率更高，存活率更低。重症监护病房（ICU）收治的心搏骤停（CA）患者院内死亡的预测因素仍不清楚。

方法：使用医疗信息集市 IV（MIMIC-IV）数据库进行回顾性研究。从 MIMIC-IV 数据库中筛选出符合纳入标准的患者，并将其随机分为训练集（n=1206，70%）和验证集（n=516，30%）。候选预测因子包括 ICU 入院第一天的人口统计学、合并症、生命体征、实验室检查结果、评分系统和治疗信息。使用最小绝对收缩和选择算子（LASSO）回归模型和极端梯度增强（XGBoost）在训练集中筛选院内死亡的独立危险因素。使用多变量逻辑回归分析在训练集中构建预测模型，然后在验证集中验证。使用受试者工作特征（ROC）曲线下面积（AUC）、校准曲线和决策曲线分析（DCA）比较这些模型的区分度、校准度和临床实用性。通过两两比较，选择表现最好的模型构建列线图。

结果：在 1722 名患者中，院内死亡率为 53.95%。在两组中，LASSO、XGBoost、逻辑回归（LR）模型和国家早期预警评分 2（NEWS 2）模型均表现出可接受的区分度。两两比较，LASSO、XGBoost 和 LR 模型的预测效果均优于 NEWS 2 模型（p<0.001）。LASSO、XGBoost 和 LR 模型也表现出良好的校准度。LASSO 模型因其更高的净收益和更宽的阈值范围而被选为最终模型。并以 LASSO 模型为代表构建了列线图。

结论：LASSO 模型能够很好地预测 ICU 收治 CA 患者的院内死亡率，可能广泛应用于临床决策。