Interpretable machine learning predicts cardiac resynchronization therapy responses from personalized biochemical and biomechanical features

Figures

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  Fig. 1. General workflow of algorithm development and testing Patients were previously enrolled in the SMART-AV clinical trial based upon their New York Heart Association (NYHA) heart failure designation, left ventricular ejection fraction (LVEF), and duration of the Q-R-S wave from electrocardiography, and patients were then classified as responders or non-responders based upon their change in left ventricular end-systolic volume (LVESV) after six months of therapy [19]. We first processed the dataset by imputing missing values, numerically encoding categorical variables, and data scaling, and then we separated patients into the training set (for model parameter fitting) and testing set (for model performance testing). Lastly, we used SHapley Additive exPlanations (SHAP) analysis and Local Interpretable Model-agnostic Explanations (LIME) to improve model interpretation through feature explanation

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  Fig. 2. Overall performance of the machine learning model The Receiver-Operating Characteristic curve for the supervised, binary classification ensemble model demonstrates high predictive capability with an area-under-the-curve of 0.784 for the majority voting classifier. (B) Model-predicted responders exhibited a 69% response rate (61/88), while model-predicted non-responders exhibited only a 27% response rate (19/71). Further stratification based on the model-predicted responses probability score demonstrated a greater predictive accuracy

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  Fig. 3. Cardiac remodeling across patient stratifications Model-predicted responders showed statistically significant differences in left ventricle remodeling metrics compared to the model-predicted non-responders. (A) Binary classification identified a responder group with substantially greater improvements in ESV, EDV, and EF from 0–6 months after CRT intervention. (B) More detailed patient stratification further amplified the remodeling differences across groups

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  Fig. 4. Global and local interpretations of model predictions (A) SHAP plot shows the feature importance in our model. 1D stretch, biomarker score, ischemic cardiomyopathy, QOL score, and age were indicated as the top 5 most important features for determining patient response probability. The scatter width and separation indicate the feature importance, and the color indicates which direction of that feature value is predictive of high vs. low patient response. (B) LIME plot shows the most significant contributing features for an example responder wherein a 1D Stretch of ≤ 1.08 along with a lack of RBBB, atrial flutter, ischemic cardiomyopathy, AT_PSVT, PAF, and SA surgery increased the probability of responding favorably to CRT treatment. (C) LIME plot shows the most significant contributing features for an example non-responder wherein a 1D Stretch of > 1.14 along with a lack of VT-SVT, Afib, and nonsustained VT increased the probability of not responding to treatment. On the other hand, a history of ischemic cardiomyopathy also affected the predicted non-response to CRT.