Chen Panpan, Sun Junhua, Chu Yingjie, Zhao Yujie
Department of Cardiovascular Medicine, Zheng Zhou Cardiovascular Hospital, The 7th People's Hospital of Zheng Zhou, No. 17, Jingnan Fifth Road, Huizhuang Development Zone, Zhengzhou, Henan, 450000, China.
Department of Cardiovascular Medicine, Henan Provincial People's Hospital, No. 7, Weiwu Road, Jinshui District, Zhengzhou, Henan, 450000, China.
BMC Med Inform Decis Mak. 2024 Dec 23;24(1):402. doi: 10.1186/s12911-024-02829-0.
Heart failure (HF) and atrial fibrillation (AF) usually coexist and are associated with a poorer prognosis. This study aimed to develop a model to predict in-hospital mortality in patients with HF combined with AF.
Patients with HF and AF were obtained from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database from 2008 to 2019. Feature selection was based on the Mann-Whitney U test and the least absolute shrinkage and selection operator (LASSO) regression model. Random Forest, eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LGBM), K-Nearest Neighbor (KNN) models, and their stacked model (the stacking ensemble model) were established. The area under of the curve (AUC) with 95% confidence interval (CI), sensitivity, specificity, as well as accuracy were applied to assess the performance of the predictive models.
A total of 5,998 patients with HF combined with AF were included, of which 4,198 patients were assigned to the training set and 1,800 to the testing set (7:3). Among these 4,198 patients, 624 (14.86%) died in-hospital and 3,574 (85.14%) survived. Twenty-two features were used to construct the predictive model. Among these four single models, the AUC was 0.747 (95%CI: 0.717-0.777) for the Random Forest model, 0.755 (95%CI: 0.725-0.785) for the XGBoost model, 0.754 (95%CI: 0.724-0.784) for the LGBM model, and 0.746 (95%CI: 0.716-0.776) for the KNN model in the testing set. The stacking ensemble model had the highest AUC compared to the four single models, with AUCs of 0.837 (95%CI: 0.821-0.852) and 0.768 (95%CI: 0.740-0.796) for the training set and testing set, respectively.
The stacking ensemble model showed a good predictive effect in predicting in-hospital mortality in patients with HF combined with AF and may provide clinicians with a reference tool for early identification of mortality risk.
心力衰竭（HF）和心房颤动（AF）通常并存，且与较差的预后相关。本研究旨在建立一个模型来预测合并AF的HF患者的院内死亡率。
从2008年至2019年的重症监护医学信息数据库IV（MIMIC-IV）中获取合并HF和AF的患者。特征选择基于曼-惠特尼U检验和最小绝对收缩与选择算子（LASSO）回归模型。建立了随机森林、极端梯度提升（XGBoost）、轻量级梯度提升机（LGBM）、K近邻（KNN）模型及其堆叠模型（堆叠集成模型）。应用曲线下面积（AUC）及其95%置信区间（CI）、敏感性、特异性以及准确性来评估预测模型的性能。
共纳入5998例合并HF和AF的患者，其中4198例患者被分配到训练集，1800例被分配到测试集（7:3）。在这4198例患者中，624例（14.86%）院内死亡，3574例（85.14%）存活。使用22个特征构建预测模型。在这四个单一模型中，测试集中随机森林模型的AUC为0.747（95%CI：0.717 - 0.777），XGBoost模型为0.755（95%CI：0.725 - 0.785），LGBM模型为0.754（95%CI：0.724 - 0.784），KNN模型为0.746（95%CI：0.716 - 0.776）。与四个单一模型相比，堆叠集成模型的AUC最高，训练集和测试集的AUC分别为0.837（95%CI：0.821 - 0.852）和0.768（95%CI：0.740 - 0.796）。
堆叠集成模型在预测合并AF的HF患者院内死亡率方面显示出良好的预测效果，可为临床医生早期识别死亡风险提供参考工具。
