Lin Xuping, Pan Xi, Yang Yanfang, Yang Wencheng, Wang Xiaomeng, Zou Kaiwei, Wang Yizhang, Xiu Jiaming, Yu Pei, Lu Jin, Zhao Yukun, Lu Haichuan
Department of Spinal Surgery, Longyan First Affiliated Hospital of Fujian Medical University, Longyan, China.
Department of Pathology, Zhongshan Hospital Affiliated to Xiamen University, Xiamen, China.
Front Cardiovasc Med. 2024 Sep 20;11:1368022. doi: 10.3389/fcvm.2024.1368022. eCollection 2024.
The identification of efficient predictors for short-term mortality among patients with myocardial infarction (MI) in coronary care units (CCU) remains a challenge. This study seeks to investigate the potential of machine learning (ML) to improve risk prediction and develop a predictive model specifically tailored for 30-day mortality in critical MI patients.
This study focused on MI patients extracted from the Medical Information Mart for Intensive Care-IV database. The patient cohort was randomly stratified into derivation ( = 1,389, 70%) and validation ( = 595, 30%) groups. Independent risk factors were identified through eXtreme Gradient Boosting (XGBoost) and random decision forest (RDF) methodologies. Subsequently, multivariate logistic regression analysis was employed to construct predictive models. The discrimination, calibration and clinical utility were assessed utilizing metrics such as receiver operating characteristic (ROC) curve, calibration plot and decision curve analysis (DCA).
A total of 1,984 patients were identified (mean [SD] age, 69.4 [13.0] years; 659 [33.2%] female). The predictive performance of the XGBoost and RDF-based models demonstrated similar efficacy. Subsequently, a 30-day mortality prediction algorithm was developed using the same selected variables, and a regression model was visually represented through a nomogram. In the validation group, the nomogram (Area Under the Curve [AUC]: 0.835, 95% Confidence Interval [CI]: [0.774-0.897]) exhibited superior discriminative capability for 30-day mortality compared to the Sequential Organ Failure Assessment (SOFA) score [AUC: 0.735, 95% CI: (0.662-0.809)]. The nomogram (Accuracy: 0.914) and the SOFA score (Accuracy: 0.913) demonstrated satisfactory calibration. DCA indicated that the nomogram outperformed the SOFA score, providing a net benefit in predicting mortality.
The ML-based predictive model demonstrated significant efficacy in forecasting 30-day mortality among MI patients admitted to the CCU. The prognostic factors identified were age, blood urea nitrogen, heart rate, pulse oximetry-derived oxygen saturation, bicarbonate, and metoprolol use. This model serves as a valuable decision-making tool for clinicians.
在冠心病监护病房（CCU）中，识别心肌梗死（MI）患者短期死亡率的有效预测指标仍然是一项挑战。本研究旨在探讨机器学习（ML）在改善风险预测方面的潜力，并开发一种专门针对重症MI患者30天死亡率的预测模型。
本研究聚焦于从重症监护医学信息数据库-IV中提取的MI患者。患者队列被随机分层为推导组（n = 1389，70%）和验证组（n = 595，30%）。通过极端梯度提升（XGBoost）和随机决策森林（RDF）方法识别独立危险因素。随后，采用多变量逻辑回归分析构建预测模型。利用受试者操作特征（ROC）曲线、校准图和决策曲线分析（DCA）等指标评估辨别力、校准度和临床实用性。
共识别出1984例患者（平均[标准差]年龄，69.4[13.0]岁；659例[33.2%]为女性）。基于XGBoost和RDF的模型的预测性能显示出相似的效果。随后，使用相同的选定变量开发了一个30天死亡率预测算法，并通过列线图直观地展示了回归模型。在验证组中，列线图（曲线下面积[AUC]：0.835，95%置信区间[CI]：[0.774 - 0.897]）与序贯器官衰竭评估（SOFA）评分[AUC：0.735，CI：(0.662 - 0.809)]相比，对30天死亡率表现出更高的辨别能力。列线图（准确率：0.914）和SOFA评分（准确率：精度：0.913）显示出令人满意的校准度。DCA表明列线图优于SOFA评分，在预测死亡率方面提供了净效益。
基于ML的预测模型在预测CCU收治的MI患者30天死亡率方面显示出显著效果。确定的预后因素为年龄、血尿素氮、心率、脉搏血氧饱和度衍生的氧饱和度、碳酸氢盐和美托洛尔的使用。该模型是临床医生的宝贵决策工具。
