Machine learning-based mortality prediction of patients undergoing cardiac resynchronization therapy: the SEMMELWEIS-CRT score

Figures

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  Figure 1. Computation of survival probabilities: illustrating the methodology through an example case. (A) The outputs of each model were series of class membership probabilities. (B) Cumulative probabilities were calculated by summing these values until the given year of follow-up. (C) To calibrate the cumulative probabilities, Platt’s scaling was performed. Using these calibrated cumulative probabilities, the survival curve could be plotted for each patient. (D) Then, the expected survival time of each patient was estimated from the annual survival probabilities. Pi, the calibrated cumulative probability of all-cause mortality at year i.

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  Figure 2. Receiver operating characteristic curve analysis of the evaluated risk scores. Calibrated cumulative probabilities were used in the receiver operating characteristic curve analysis.

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  Figure 3. The 12 most important predictors of all-cause mortality as assessed by the SEMMELWEIS-CRT score. The importance of each feature was quantified by calculating the decrease in the model’s performance (area under the receiver operating characteristic curve) after permuting its values (permutation feature importances method). The higher its value, the more important the feature is. As the values of feature importances were spread over a wide range (more orders of magnitude), base-10 logarithmic transformation was performed to facilitate plotting. CRT, cardiac resynchronization therapy; LVEF, left ventricular ejection fraction; NYHA, New York Heart Failure Association functional class.

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  Figure 4. Effect of the nine most important continuous features on the calibrated cumulative probability of mortality in the test cohort. Annual probabilities of each patient are marked with different colours (five dots per patient on each plot): 1-year (blue), 2-year (orange), 3-year (green), 4-year (red), and 5-year (purple) calibrated cumulative probabilities. Second order polynomial trendlines are fitted to each year’s probabilities. EF, ejection fraction; GFR, glomerular filtration rate.

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  Figure 5. Survival analysis of the quartiles. Based on the predicted probability of death, patients were split into four quartiles at each year of follow-up. The survival of the quartiles was visualized on Kaplan–Meier curves and log-rank test was performed for comparison.