Machine Learning Identifies Clinically Distinct Phenotypes in Patients With Aortic Regurgitation

Abstract

Background

Aortic regurgitation (AR) is a prevalent valve disease with a long latent period before symptoms appear. Recent data has suggested the role of novel markers of myocardial overload in assessing onset of decompensation.

Methods

The aim of this study was to evaluate the role of unsupervised cluster analyses in identifying different clinical clusters, including clinical status, and a large number of echocardiographic variables including left ventricular volumes, and their associations with mortality. Patients with moderate to severe or greater chronic AR identified using echocardiography at the Mayo Clinic in Rochester, Minnesota, were retrospectively analyzed. The primary outcome was all-cause mortality censored at aortic valve surgery. Uniform manifold approximation and projection with the k-means algorithm was used to cluster patients using clinical and echocardiographic variables at the time of presentation. Missing data were imputed using the multiple imputation by chained equations method. A supervised approach trained on the training set was used to find cluster membership in a hold-out validation set. Log-rank tests were used to assess differences in mortality rates among the clusters in both the training and validation sets.

Results

Three distinct clusters were identified among 1,100 patients (log-rank P for survival < .001). Cluster 1 (n = 337), which included younger males with severe AR but fewer symptoms, showed the best survival at 75.6% (95% CI, 69.5%-82.3%). Cluster 2 (n = 235), including older patients and more females with elevated filling pressures, showed intermediate survival of 64.2% (95% CI, 56.8%-72.5%). Cluster 3 (n = 253), characterized by severe symptomatic AR, demonstrated the lowest survival of 45.3% (95% CI, 34.4%-59.8%) at 5 years. Similar clusters were identified in the internal validation cohort.

Conclusions

Distinct clusters with variable echocardiographic features and mortality differences exist within patients with chronic moderate to severe or greater AR. Recognizing these clusters can refine individual risk stratification and clinical decision-making after verification in future prospective studies.