Key result
Aortic pressure-based DeepSurv model predicts 1-year all-cause mortality in NSTEMI with a ~0.94 C-index.
Why the study?
Despite successful revascularization, NSTEMI patients remain at risk of mortality and morbidity, and accurate mortality prediction could improve outcomes through timely interventions.
Do survival models using aortic pressure signals recorded during cardiac catheterization accurately predict 1-year all-cause mortality in patients with NSTEMI?
Cohort (n=497)
Do survival models using aortic pressure signals recorded during cardiac catheterization accurately predict 1-year all-cause mortality in patients with NSTEMI?
Effect estimate: C-index 0.935
Machine learning survival models utilizing aortic pressure signal features obtained during cardiac catheterization can highly accurately predict 1-year all-cause mortality in NSTEMI patients.
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May aid risk stratification in NSTEMI during catheterization; leaves open prospective validation before clinical use.
Despite successful revascularization, patients with non-ST elevation myocardial infarction (NSTEMI) remain at higher risk of mortality and morbidity. Accurately predicting mortality risk in this cohort can improve outcomes through timely interventions. This study for the first time predicts 1-year all-cause mortality in an NSTEMI cohort using features extracted primarily from the aortic pressure (AP) signal recorded during cardiac catheterization. We analyzed data from 497 NSTEMI patients (66.3 ± 12.9 years, 187 (37.6%) females) retrospectively. We developed three survival models, the multivariate Cox proportional hazards, DeepSurv, and random survival forest, to predict mortality. Then, used Shapley additive explanations (SHAP) to interpret the decision-making process of the best survival model. Using 5-fold stratified cross-validation, DeepSurv achieved an average C-index of 0.935, an IBS of 0.028, and a mean time-dependent AUC of 0.939, outperforming the other models. Ejection systolic time, ejection systolic period, the difference between systolic blood pressure and dicrotic notch pressure (DesP), skewness, the age-modified shock index, and myocardial oxygen supply/demand ratio were identified by SHAP as the most characteristic AP features. In conclusion, AP signal features offer valuable prognostic insight for predicting 1-year all-cause mortality in the NSTEMI population, leading to enhanced risk stratification and clinical decision-making.
Razavi et al. (2026) conducted a cohort in Non-ST Elevation Myocardial Infarction (NSTEMI) (n=497). DeepSurv survival model using aortic pressure signal features vs. Multivariate Cox proportional hazards and random survival forest was evaluated on 1-year all-cause mortality (C-index 0.935). A DeepSurv model using aortic pressure signal features accurately predicted 1-year all-cause mortality in NSTEMI patients, achieving an average C-index of 0.935 and time-dependent AUC of 0.939.
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