Development and validation of a prediction model for heart failure in patients with heart valvular regurgitation.

Abstract

Patients with heart valvular regurgitation is increasing; early screening of potential patients developing heart failure (HF) is crucial. From 1 November 2019 to 31 October 2023, a total of 509 patients with heart valvular regurgitation hospitalized in the Department of Cardiovascular Disease of the First Affiliated Hospital of Guangzhou University of Traditional Medicine were enrolled. Three hundred fifty-six cases were selected as the training set for modelling, and 153 cases were selected as the validation set for the internal validation of the model. A predictive model of heart failure with the following nine risk factors was developed: atrial fibrillation (AF), pulmonary infection (PI), coronary artery disease (CAD), creatinine (CREA), low-density lipoprotein cholesterol (LDL-C), d-dimer (DDi), left ventricular end-diastolic diameter (LVEDd), mitral regurgitation (MR) and aortic regurgitation (AR). The model was evaluated by the C-index [the training set: area under curve (AUC) 0.937, 95% confidence interval (CI) 0.911-0.963; the validation set: AUC 0.928, 95% CI 0.890-0.967]. Hosmer-Lemeshow test (the training set: χ2 10.908, P=0.207; the validation set: χ2 4.896, P=0.769) revealed that both the training and validation sets performed well in terms of model differentiation and calibration. Decision curve analysis showed that both the training and validation sets have higher net benefits, indicating that the model has good utility. Ten-fold cross-validation showed that the training set has high similarities with the validation set, which means that the model has good stability. The occurrence of heart failure in patients with valvular regurgitation has a significant correlation with AF, PI, CAD, CREA, LDL-C, DDi, LVEDd, MR and AR. Based on these risk factors, a prediction model for heart failure was developed and validated, which showed good differentiation and utility, high accuracy and stability, providing a method for predicting heart failure.