Zhang Jingjing, Gao Chuanyu, Zhang Jing, Ye Famin, Guo Suping
Department of Coronary Care Unit, People's Hospital of Zhengzhou University, Heart Center of Henan Provincial People's Hospital/Central China Fuwai Hospital, Zhengzhou, China.
Department of Coronary Heart Disease, People's Hospital of Zhengzhou University, Heart Center of Henan Provincial People's Hospital/Central China Fuwai Hospital, Zhengzhou, China.
Cardiovasc Diagn Ther. 2025 Apr 30;15(2):318-335. doi: 10.21037/cdt-2024-583. Epub 2025 Apr 23.
Fulminant myocarditis (FM) is a severe, rapidly progressing disease with high mortality, and early identification of high-risk patients is crucial for improving outcomes. This study aims to identify factors associated with early mortality in FM and develop a risk prediction model for the early identification of high-risk patients.
A retrospective analysis was conducted using clinical data from 119 patients with FM who were hospitalized at Central China Fuwai Hospital between 2018 and 2023. The patients were divided into a training set (n=83) and a validation set (n=36). Predictive factors were identified through univariate analysis and least absolute shrinkage and selection operator (LASSO) Cox regression, followed by multivariate Cox regression. A nomogram was constructed, and its accuracy was validated using bootstrap and calibration curves. The discriminative ability and clinical utility of the model were assessed using receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA).
Multivariate analysis identified respiratory symptoms, cardiopulmonary resuscitation (CPR), serum creatinine, direct bilirubin, thyroid-stimulating hormone (TSH), lactate, and left ventricular ejection fraction (LVEF) as independent predictors of early mortality. The area under the curve (AUC) for the training set was 0.907 and 0.880 on days 14 and 28, respectively, while the validation set achieved AUCs of 0.853 and 0.942 for the same time points. The overall concordance index (C-index) was 0.889 for the training set and 0.809 for the validation set. Kaplan-Meier analysis demonstrated lower mortality rates in the low-risk group. DCA demonstrated that the model provides a clinical net benefit across a range of probability thresholds, indicating its potential value in clinical decision-making.
A predictive model has been developed and validated to identify patients who are at high-risk with FM, based on seven key predictive factors.
暴发性心肌炎（FM）是一种严重的、进展迅速且死亡率高的疾病，早期识别高危患者对于改善预后至关重要。本研究旨在确定与FM早期死亡相关的因素，并开发一种风险预测模型以早期识别高危患者。
采用回顾性分析，使用2018年至2023年期间在中国医学科学院阜外医院华中阜外医院住院的119例FM患者的临床数据。将患者分为训练集（n = 83）和验证集（n = 36）。通过单因素分析、最小绝对收缩和选择算子（LASSO）Cox回归确定预测因素，随后进行多因素Cox回归。构建列线图，并使用自助法和校准曲线验证其准确性。使用受试者工作特征（ROC）曲线分析和决策曲线分析（DCA）评估模型的鉴别能力和临床实用性。
多因素分析确定呼吸症状、心肺复苏（CPR）、血清肌酐、直接胆红素、促甲状腺激素（TSH）、乳酸和左心室射血分数（LVEF）为早期死亡的独立预测因素。训练集在第14天和第28天的曲线下面积（AUC）分别为0.907和0.880，而验证集在相同时间点的AUC分别为0.853和0.942。训练集的总体一致性指数（C指数）为0.889，验证集为0.809。Kaplan-Meier分析显示低风险组的死亡率较低。DCA表明该模型在一系列概率阈值范围内提供临床净效益，表明其在临床决策中的潜在价值。
基于七个关键预测因素，已开发并验证了一种预测模型，以识别FM高危患者。
