Key result
Novel model adding shock index and culprit vessel improves ischemia-reperfusion injury prediction over TIMI score alone.
Why the study?
Pre-PCI prediction of myocardial ischemia reperfusion injury in elderly STEMI patients undergoing primary PCI remains challenging and requires improved risk models.
Does a TIMI score-based model accurately predict myocardial ischemia reperfusion injury in elderly STEMI patients undergoing primary PCI?
Cohort (n=417)
Single-blind (Adjudicators blinded to patient info)
Yes
Does a TIMI score-based model accurately predict myocardial ischemia reperfusion injury in elderly STEMI patients undergoing primary PCI?
Effect estimate: AUC 0.815 vs 0.718 (95% CI 0.735-0.895)
Absolute Event Rate: 0.815% vs 0.718%
p-value: p=0.049
A TIMI score-based model integrating shock index, TIMI score, and culprit vessel can accurately estimate the risk of myocardial ischemia reperfusion injury in elderly STEMI patients undergoing primary PCI.
May aid MIRI risk stratification in elderly STEMI; leaves open prospective validation before clinical adoption.
Background Pre-PCI prediction of myocardial ischemia reperfusion injury (MIRI) in elderly patients with ST-segment elevation myocardial infarction (STEMI) who are undergoing primary PCI remains challenging. This study aimed to develop and validate a thrombolysis in myocardial infarction (TIMI) score-based model for MIRI prediction. Methods and results This multicenter retrospective study included patients aged ≥ 60 years with STEMI who underwent primary PCI. The cohorts included a derivation cohort (August 2022–February 2025) and an external validation cohort (February 2024–February 2026). In the derivation cohort (n = 117), the incidence of MIRI was 75.2%. Using LASSO and logistic regression, a final model incorporating the shock index, TIMI score, and culprit vessel was developed. The model demonstrated acceptable discriminatory ability for MIRI (AUC 0.815, 95% CI 0.735–0.895), calibration (Hosmer–Lemeshow, P = 0.801), and net benefit on decision curve analysis. The MACE analysis was an exploratory post-hoc analysis; model risk stratification was significantly associated with MACE events (log-rank P < 0.001), suggesting potential prognostic stratification value that warrants further validation in independent external cohorts. The new model showed comparable discriminatory ability to the TIMI score for MIRI (AUC 0.815 vs. 0.718, P = 0.049) and demonstrated improved discrimination for all-cause death (AUC 0.87 vs. 0.769, P = 0.017). In the external validation cohort (n = 300), the model maintained good discrimination (AUC 0.811), calibration (P = 0.406), and accuracy (Brier score = 0.144). The TRIPOD checklist is provided in the supplementary material. Conclusion This TIMI score-based model may help predict MIRI risk in elderly patients with STEMI after primary PCI in rural northern Xuzhou. An online calculator helps clinicians plan and initiate the most appropriate disease management for patients in a timely manner.
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Yang et al. (2026) conducted a cohort in ST-segment elevation myocardial infarction (STEMI) undergoing primary PCI (n=417). TIMI score-based clinical prediction model (incorporating shock index, TIMI score, and culprit vessel) vs. TIMI score alone was evaluated on Discriminatory ability (AUC) for myocardial ischemia reperfusion injury (MIRI) (AUC 0.815 vs 0.718, 95% CI 0.735-0.895, p=0.049). A novel prediction model incorporating shock index, TIMI score, and culprit vessel significantly improved discrimination for myocardial ischemia reperfusion injury compared to the TIMI score alone (AUC 0.815 vs. 0.718).
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