A machine learning model for predicting outcomes of MitraClip therapy

Background: Severe mitral regurgitation (MR) is a life-threatening mitral valve disease. The MitraClip intervention offers a percutaneous solution for patients who are unsuitable for surgery. However, limited information is available on outcomes post-MitraClip intervention. This study aims to develop an approach for predicting MR outcomes after MitraClip intervention using machine learning-enhanced echocardiography. Methods: We enrolled 164 patients with MR ≥ 3 + degree who underwent MitraClip intervention at our institution between 2021 and 2024. Patients were monitored for approximately three years. The analysis included clinical data and echocardiographic parameters. Study endpoints were the recurrence of MR (2 + or above) and major adverse events during follow-up. A total of 147 patients were randomly divided into training (80%) and testing (90%) sets. An additional 17 patients comprised the validation cohort. Results: The best-performing model for predicting clinical outcomes utilized 81 features in a logistic regression classifier. Using all 81 features in the logistic regression model, specificity increased to approximately 0.797 (95% confidence interval: 0.739 ~ 0.854) and sensitivity to about 0.459 (0.370 ~ 0.549), resulting in an overall accuracy of 0.688 (0.632 ~ 0.745) for the validation dataset. The best-performing model achieved a receiver operating characteristic area under the curve value of 0.773 in both the test and validation groups. Conclusions: Our machine learning model, leveraging echocardiographic characteristics, demonstrated superior predictive performance. This model effectively forecasts patient outcomes following MitraClip intervention, proving beneficial within a clinical setting.