A random forest of regression trees machine learning algorithm predicted pulmonary hypertension with an AUC of 0.87, achieving comparable accuracy to the established Aduen formula but with less reliance on estimated right atrial pressure.
Observational (n=90)
Yes
Do machine learning algorithms improve the echocardiographic prediction of pulmonary hypertension compared to conventional formulas in patients undergoing right heart catheterization?
Machine learning algorithms can predict pulmonary hypertension from echocardiographic data with high accuracy comparable to the best conventional formulas, while reducing reliance on error-prone right atrial pressure estimations.
Effect estimate: AUC 0.87 (95% CI 0.78-0.96)
Absolute Event Rate: 0.87% vs 0.87%
BACKGROUND: Machine learning (ML) is a powerful tool for identifying and structuring several informative variables for predictive tasks. Here, we investigated how ML algorithms may assist in echocardiographic pulmonary hypertension (PH) prediction, where current guidelines recommend integrating several echocardiographic parameters. METHODS: In our database of 90 patients with invasively determined pulmonary artery pressure (PAP) with corresponding echocardiographic estimations of PAP obtained within 24 hours, we trained and applied five ML algorithms (random forest of classification trees, random forest of regression trees, lasso penalized logistic regression, boosted classification trees, support vector machines) using a 10 times 3-fold cross-validation (CV) scheme. RESULTS: ML algorithms achieved high prediction accuracies: support vector machines (AUC 0.83; 95% CI 0.73-0.93), boosted classification trees (AUC 0.80; 95% CI 0.68-0.92), lasso penalized logistic regression (AUC 0.78; 95% CI 0.67-0.89), random forest of classification trees (AUC 0.85; 95% CI 0.75-0.95), random forest of regression trees (AUC 0.87; 95% CI 0.78-0.96). In contrast to the best of several conventional formulae (by Aduen et al.), this ML algorithm is based on several echocardiographic signs and feature selection, with estimated right atrial pressure (RAP) being of minor importance. CONCLUSIONS: Using ML, we were able to predict pulmonary hypertension based on a broader set of echocardiographic data with little reliance on estimated RAP compared to an existing formula with non-inferior performance. With the conceptual advantages of a broader and unbiased selection and weighting of data our ML approach is suited for high level assistance in PH prediction.
Leha et al. (Fri,) conducted a observational in Pulmonary hypertension (n=90). Machine learning algorithms (random forest of regression trees) vs. Established formula by Aduen et al. was evaluated on Prediction accuracy (AUC) for pulmonary hypertension (AUC 0.87, 95% CI 0.78-0.96). A random forest of regression trees machine learning algorithm predicted pulmonary hypertension with an AUC of 0.87, achieving comparable accuracy to the established Aduen formula but with less reliance on estimated right atrial pressure.