Development and Validation of a Prognostic Model to Predict Mortality in Patients With Heart Failure With Mildly Reduced Ejection Fraction After Acute Myocardial Infarction

Figures

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  Figure 1. Flow diagram for participant screening, eligibility, and analysis. HFmrEF: heart failure with mildly reduced ejection fraction; LVEF: left ventricular ejection fraction.

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  Figure 2. LASSO regression coefficient path and CV LASSO regression coefficient path. (a) LASSO regression coefficient path. (b) CV LASSO regression coefficient path. The LASSO regression coefficient path displays how the coefficients of each variable change with increasing values of the regularization parameter λ. The CV LASSO regression coefficient path illustrates the coefficients’ behavior with λ tuned through cross-validation. Both paths provide insights into the impact of regularization on variable selection and coefficient estimation in the LASSO regression model. LASSO: least absolute shrinkage and selection operator.

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  Figure 3. Area under the receiver operating characteristic (ROC) curve and bootstrap validation. (a) ROC curves for the training set at 6 months, 2 years, and 3 years. (b) ROC curves for the validation set at 6 months, 2 years, and 3 years. (c) Comparison of model stability between the original model and 500 rounds of bootstrap validation on the training set. (d) Comparison of model stability between the original model and 500 rounds of bootstrap validation on the validation set. AUC: area under the curve; CI: confidence interval.

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  Figure 4. Calibration curves at different time points. (a) Calibration curves for the training set at 6 months, 2 years, and 3 years. (b) Calibration curves for the validation set at 6 months, 2 years, and 3 years. The calibration curves depict the agreement between the predicted probabilities and the observed outcomes at different time points. The curves represent the performance of the predictive model in terms of calibration, indicating how well the model’s predicted probabilities align with the actual probabilities. OS: overall survival.

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  Figure 5. Decision curve analysis (DCA) with time and DCA nomogram. (a) DCA with time for the training set. (b) DCA with time for the validation set. (c) DCA nomogram for the training set. (d) DCA nomogram for the validation set. The DCA curves in Figure 5a and 5b illustrate the net benefit of the predictive model over a range of threshold probabilities at different time points for the training and validation sets. These curves provide insights into the clinical usefulness and added value of the model compared to alternative decision strategies. Additionally, the DCA nomograms in Figure 5c and 5d provide a graphical representation of the decision curves, allowing for a more intuitive interpretation and application of the model’s results. Hb: hemoglobin; NYHA: New York Heart Association; eGFR: estimated glomerular filtration rate; PCI: percutaneous coronary intervention.

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  Figure 6. Nomogram for all-cause mortality risk prediction. The nomogram presents a visual tool for predicting the risk of all-cause mortality. It combines various predictors or risk factors into a comprehensive model that provides an individualized risk assessment. The nomogram allows for a simple and intuitive estimation of the probability of mortality based on the values assigned to each predictor. Clinicians can use this nomogram as a practical aid in risk assessment and shared decision-making with patients regarding appropriate interventions and management strategies. NYHA: New York Heart Association; eGFR: estimated glomerular filtration rate; PPCI: primary percutaneous coronary intervention.

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  Figure 7. Rationality analysis: Kaplan-Meier survival curves of high-score and low-score groups. (a) Rationality analysis for the training set. (b) Rationality analysis for the validation set. The Kaplan-Meier survival curves depicted in Figure 7a and 7b demonstrate the differences in survival outcomes between the high-score and low-score groups. These curves serve as a rationality analysis to evaluate the predictive performance of the scoring system or model. The separation of the survival curves indicates the ability of the scoring system to stratify patients into distinct risk groups. This analysis provides insights into the reliability and validity of the scoring system in predicting survival outcomes and aids in assessing its clinical utility.