Wang Xiaoke, Fan Xiaojuan, Wu Taibo, Che Shaopeng, Shi Xue, Liu Peining, Liu Junhui, Luo Yongbai, Lan Beidi, Wu Yue
School of Basic Medical Science, Wuhan University, Wuhan, China.
School of Journalism and Communication, Tsinghua University.
Shock. 2025 May 1;63(5):706-713. doi: 10.1097/SHK.0000000000002535. Epub 2024 Dec 10.
Background : While acute myocardial infarction (AMI) is widely recognized as the primary cause of cardiogenic shock (CS), non-AMI-related CS has been excluded from the majority of CS studies. Information on its prognostic factors remains largely understudied, and it is necessary to focus on these patients to identify the specific risk factors. In this study, we aimed to build and validate a predictive nomogram and risk classification system. Methods: 1298 patients and 548 patients with CS from the Medical Information Mart for Intensive Care IV and III databases were included in the study after excluding patients with AMI. Lasso and logistic regression analysis were used to identify statistically significant predictors, which were finally involved in the nomogram. The predictive performance of the nomogram was validated by calibration plots and was compared with other scoring systems by area under curve and decision curve analysis curves. Results: Age, heart rate, white blood cell count, albumin level, lactic acid level, GCS score, 24-h urine output, and vasopressor use were identified as the most critical factors for in-hospital death. Based on these results, a nomogram was established for predicting in-hospital mortality. The area under curve value of the nomogram was 0.806 in the training group and 0.814 and 0.730 in the internal and external validation sets, respectively, which were significantly higher than those of other commonly used intensive care unit scoring systems (Simplified Acute Physiology Score II, Acute Physiology Score III, and Sequential Organ Failure Assessment). In addition, the survival curve showed significant differences in the 30-day survival of the three risk subgroups divided by the nomogram. Conclusion: For non-AMI-associated CS, a predictive nomogram and risk classification system were developed and validated, and the nomogram demonstrated good performance in prognostic prediction and risk stratification.
虽然急性心肌梗死(AMI)被广泛认为是心源性休克(CS)的主要原因,但大多数CS研究都排除了与非AMI相关的CS。关于其预后因素的信息在很大程度上仍未得到充分研究,有必要关注这些患者以确定具体的危险因素。在本研究中,我们旨在构建并验证一个预测列线图和风险分类系统。方法:在排除AMI患者后,将来自重症监护医学信息集市IV和III数据库的1298例患者和548例CS患者纳入研究。使用套索回归和逻辑回归分析来识别具有统计学意义的预测因素,这些因素最终被纳入列线图。通过校准图验证列线图的预测性能,并通过曲线下面积和决策曲线分析曲线与其他评分系统进行比较。结果:年龄、心率、白细胞计数、白蛋白水平、乳酸水平、格拉斯哥昏迷量表(GCS)评分、24小时尿量和血管升压药的使用被确定为院内死亡的最关键因素。基于这些结果,建立了一个预测院内死亡率的列线图。列线图在训练组中的曲线下面积值为0.806,在内部和外部验证集中分别为0.814和0.730,显著高于其他常用的重症监护病房评分系统(简化急性生理学评分II、急性生理学评分III和序贯器官衰竭评估)。此外,生存曲线显示,根据列线图划分的三个风险亚组在30天生存率方面存在显著差异。结论:对于非AMI相关的CS,开发并验证了一个预测列线图和风险分类系统,该列线图在预后预测和风险分层方面表现良好。