Development and Validation of a Predictive Model for Atrial Functional Mitral Regurgitation

Abstract

Background

Identifying atrial etiology in patients with mitral regurgitation remains challenging because the diagnosis is often established by exclusion. The use of a multivariable model may enhance diagnostic accuracy in the context of atrial functional mitral regurgitation.

Objective

To develop and validate a multivariable logistic regression model based on clinical and echocardiographic characteristics to predict atrial functional mitral regurgitation.

Methods

This cross-sectional study included patients with significant mitral regurgitation diagnosed by transesophageal echocardiography. The dataset was randomly divided into a training set (70%) and a validation set (30%). Statistical analyses were performed using a significance level of 5%.

Results

A total of 203 patients were included. The median age was 79 years in the atrial group and 72 years in the non-atrial group (p = 0.0022). Receiver operating characteristic curve analysis demonstrated good discriminative performance, with an area under the curve of 0.896 (95% CI, 0.845-0.947) in the training set. In the validation set, the model achieved an area under the curve of 0.946 (95% CI, 0.89-1.00), which indicates high predictive accuracy. Model calibration assessed by the Hosmer-Lemeshow test (chi-square test = 5.197; df = 8; p = 0.736) demonstrated good agreement between predicted and observed outcomes.

Conclusion

A multivariable model was derived and validated as a useful tool for predicting atrial etiology in patients with mitral regurgitation, potentially reducing diagnostic variability in clinical practice.