Machine learning for multidimensional response and survival after cardiac resynchronization therapy using features from cardiac magnetic resonance

Figures

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  Figure 1. Gaussian mixture model (GMM) selection. A GMM with each covariance type and number of clusters ranging from 2 to 6 was generated. The Bayesian information criterion (BIC) score of each model was calculated and is shown above. For any number of clusters, the GMM with a diagonal covariance structure exhibited favorable BIC scores. The model with 3 clusters and a diagonal covariance structure had the lowest (most favorable) BIC score.

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  Figure 2. Gaussian mixture model (GMM) cluster analysis. The GMM stratified the patients (based on their cardiac resynchronization therapy [CRT] response measures) into 3 distinct groups. The clusters are plotted as 2-dimensional sets of the 3 response measures. Each point represents 1 patient with the respective left ventricular end-systolic volume index fractional change (LVESVI-FC), post-CRT B-type natriuretic peptide (BNP), and Δ peak VO2 values and is colored based on the assigned cluster.

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  Figure 3. Kaplan-Meier analysis for each cluster. Kaplan-Meier curves demonstrating the probability of survival following cardiac resynchronization therapy implantation are presented for each of the 3 cluster groups. Patients in group 1 have a greater survival probability than patients in group 2 (P < .001) and in group 3 (P < .001). Patients in group 2 have a greater survival probability than patients in group 3 (P < .05).

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  Figure 4. Receiver operating characteristic (ROC) curves for 4-year survival prediction. ROC curves from the logistic regression models are shown. A: Three best pre–cardiac resynchronization therapy (CRT) predictors. The CURE-SVD, pre-CRT B-type natriuretic peptide, and pre-CRT peak VO2 are used as inputs. B: Three best pre-CRT predictors plus response cluster. The response clusters were added to the model in panel A. The addition of the short-term response clusters to these baseline features improved prediction of long-term response (area under the curve [AUC] 0.86 vs 0.78, P = .02).