Fig 1.
Study flowchart.
Table 1.
Baseline characteristics of key explanatory variables.
Fig 2.
This figure shows the c-index for both the conventional scoring system and SurvTrace. The upper and lower black lines represent the upper and lower limits of the 95% confidence intervals, respectively. The orange line shows the mean c-index value calculated from five pseudo-complete datasets.
Fig 3.
Learning curve of SurvTrace during the training process.
This figure illustrates the variation in the loss function over the course of the training process. The left panel shows the fluctuations in loss values for the training dataset, while the right panel shows these changes for the validation dataset.
Fig 4.
Kaplan–Meier curves of the models.
This figure shows the Kaplan–Meier curves generated by both the conventional scoring model and SurvTrace. The blue lines represent the Kaplan–Meier curve for the low-risk group as stratified by risk scores from both models. Similarly, the orange and green lines represent the curves for the intermediate- and high-risk groups, respectively. The translucent segments of each line indicate the 95% confidence interval.
Table 2.
Results for both models.
Fig 5.
This figure illustrates the Shapley additive explanations (SHAP) of SurvTrace. The horizontal axis indicates the impact on the model’s prediction, with points situated to the right representing a higher risk of future major adverse cardiovascular events (MACE) compared with points on the left. The vertical axis indicates the importance of the explanatory variables. In this model, a history of hospitalization for heart failure (HF) exerts the greatest impact on predicting the risk of future MACE events. The color of each dot indicates the high or low status within each variable; for example, in the “History of HF Hospitalization” column, red indicates that the patient has a history of HF hospitalization, while blue indicates no such history.