Development and Validation of a Prediction Model on Adult Emergency Department Patients for Early Identification of Fulminant Myocarditis

Abstract

Objective

It is difficult to predict fulminant myocarditis at an early stage in the emergency department. The objective of this study was to construct and validate a simple prediction model for the early identification of fulminant myocarditis.

Methods

A total of 61 patients with fulminant myocarditis and 160 patients with acute myocarditis were enrolled in the training and internal validation cohorts. LASSO regression and multivariate logistic regression were selected to develop the prediction model. The selection of the model was based on overall performance and simplicity. A nomogram based on the optimal model was built, and its clinical usefulness was evaluated by decision curve analysis. The predictive model was further validated in an external validation group.

Results

The resulting prediction model was based on 4 factors: systolic blood pressure, troponin I, left ventricular ejection fraction, and ventricular wall motion abnormality. The Brier scores of the final model were 0.078 in the training data set and 0.061 in the internal testing data set, respectively. The C-indexes of the training data set and the testing data set were 0.952 and 0.968, respectively. Decision curve analysis showed that the nomogram model developed based on the 4 predictors above had a positive net benefit for predicting probability thresholds. In the external validation cohort, the model also showed good performance (Brier score=0.007, and C-index=0.989).

Conclusion

We developed and validated an early prediction model consisting of 4 clinical factors (systolic blood pressure, troponin I, left ventricular ejection fraction, and ventricular wall motion abnormality) to identify potential fulminant myocarditis patients in the emergency department.

Keywords

fulminant myocarditis / emergency / risk prediction / nomogram

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