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Survival prediction modeling for 1-year mortality in patients with ST elevation myocardial infarction (STEMI)
Seyed Reza Razavi1, Ashish H Shah2, Zahra Moussavi3
1Biomedical Engineering Program, University of Manitoba, Winnipeg, MB R3T 5V6, Canada.
Insights
Predicting 1-year mortality in ST-elevation myocardial infarction (STEMI) patients after primary percutaneous coronary intervention (PPCI) is crucial. Aortic pressure (AP) signals analyzed with survival models, particularly DeepSurv, accurately identified high-risk patients.
Area of Science:
- Cardiology
- Medical Informatics
- Data Science
Background:
- ST-elevation myocardial infarction (STEMI) carries significant mortality risk, especially with cardiogenic shock.
- Accurate risk stratification post-revascularization is vital for improving STEMI patient outcomes.
- Aortic pressure (AP) signal analysis shows promise for predicting adverse events.
Purpose of the Study:
- To predict 1-year all-cause mortality in STEMI patients undergoing primary percutaneous coronary intervention (PPCI).
- To leverage survival models and features extracted from AP signals for enhanced prognostic accuracy.
Main Methods:
- Retrospective analysis of 600 STEMI patients treated with PPCI.
- Application of Cox proportional hazards, DeepSurv, and random survival forest models using AP-derived features.
- Utilized Shapley Additive Explanations (SHAP) for model interpretability and feature importance identification.
Main Results:
- DeepSurv model achieved superior performance with a C-index of 0.877, IBS of 0.047, and AUC of 0.907.
- Key predictive features from AP signals included systolic area, ejection systolic time, kurtosis, diastolic blood pressure, and age-modified shock index.
- SHAP analysis confirmed the interpretability and identified critical AP-derived predictors.
Conclusions:
- Integrating AP signal data with survival models significantly enhances 1-year mortality prediction in STEMI patients.
- This approach offers a valuable tool for early risk identification and improved patient management post-PPCI.
Background:
ST elevation myocardial infarction (STEMI) is a life-threatening condition, and is associated with significant mortality, especially in patients encountering cardiogenic shock. Accurate risk assessment in this population is essential for improving prognosis through the early identification of patients at increased risk of adverse outcomes following successful revascularization. Our previous work has demonstrated outcome predictive value of the aortic pressure (AP) signal.
Objectives:
The present study uses survival models and features primarily extracted from the AP signal, recorded during primary percutaneous coronary intervention (PPCI), to predict 1-year all-cause mortality.
Methods:
A total of 600 STEMI patients treated with PPCI (64.2 ± 13.2 years; 171 [28.5%] females) were included in this single-center, retrospective study. Three survival models (Cox proportional hazards, DeepSurv, and random survival forest) were applied to predict all-cause mortality using AP-derived features. Then, using Shapley Additive Explanations (SHAP), we enhanced model interpretability and identified the most important features contributing to the best-performing model.
Results:
DeepSurv outperformed the other two survival models, achieving an average concordance index (C-index) of 0.877, an integrated Brier score (IBS) of 0.047, and a mean time-dependent area under the curve (AUC) of 0.907. The SHAP method identified key features extracted from the AP signal, including systolic area, ejection systolic time, kurtosis, diastolic blood pressure, and the age-modified shock index.
Conclusions:
Combining AP signals recorded during PPCI with survival models significantly improved the prediction of 1-year mortality in the STEMI cohort.
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