A Random Forest machine learning model predicted death or heart transplantation in adult myocarditis patients with 89.2% accuracy (95% CI 86.1-92.3).
Cohort (n=938)
No
Can a machine learning model accurately predict death or heart transplantation in adult patients with myocarditis?
A machine learning approach using Random Forest can accurately predict the risk of death and heart transplantation in adult patients with myocarditis based on clinical and histological features.
Estimación del efecto: Accuracy 89.2% (95% CI 86.1-92.3)
Abstract Background and aims Identifying early risk predictors in myocarditis is clinically relevant, as patients’ outcomes may be very diverse. We aimed to explore predictors of death and heart transplant (HTx) in a large single-center cohort of adult patients with myocarditis using a machine learning (ML) technique. Materials and methods We retrospectively enrolled consecutive adult patients with biopsy-proven or clinically suspected myocarditis, collecting clinical, laboratory, and imaging data, both at diagnosis and during follow-up. A predictive model of death/HTx was developed using Random Forest (RF), ranking covariates according to their predictive accuracy. Results We included 938 patients (median age 36 years, 69% male) with clinically suspected (n=549) or biopsy-proven (n=389) myocarditis. During follow-up, 35 patients died, and 26 underwent HTx. The most important variables in predicting survival were NYHA class (variable importance, VIMP, 10%) LVEF (3.6%) and clinical presentation (2.5%) at diagnosis, histological type of myocarditis on endomyocardial biopsy (EMB)(2.9%), anti-endothelial cell antibodies (0.6%) and anti-nuclear antibodies (0.4%) positivity. Overall, the predictive accuracy of our RF model was good (89.2%, 95% C.I. 86.1-92.3). Conclusions Based on a ML approach, we found, with good predictive accuracy, that advanced NYHA class, reduced LVEF and heart failure at diagnosis, and giant cell myocarditis on EMB are predictors of worse prognosis in adult patients with myocarditis.
Baritussio et al. (Tue,) conducted a cohort in Myocarditis (n=938). Clinical, laboratory, and imaging predictors (e.g., NYHA class, LVEF) was evaluated on Death and heart transplant (HTx) (Accuracy 89.2%, 95% CI 86.1-92.3). A Random Forest machine learning model predicted death or heart transplantation in adult myocarditis patients with 89.2% accuracy (95% CI 86.1-92.3).