Development and validation of a prognostic model for early mortality risk in patients with fulminant myocarditis.

BACKGROUND

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.

METHODS

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).

RESULTS

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.

CONCLUSIONS

A predictive model has been developed and validated to identify patients who are at high-risk with FM, based on seven key predictive factors.