ArticleFrontiers in cardiovascular medicine2026
A nomogram for predicting intraoperative risk during primary percutaneous coronary intervention based on rapidly obtained data from ST-segment elevation myocardial infarction patients.
Article in Frontiers in cardiovascular medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Primary percutaneous coronary intervention (PCI) in patients with ST-segment elevation myocardial infarction (STEMI) can carry high stakes, as major adverse cardiac events (MACE) like no-reflow, malignant arrhythmias, and cardiogenic shock can disrupt the procedure and worsen outcomes. In light of these challenges, this study aimed to develop a nomogram prediction model to rapidly assess MACE risk during PCI in STEMI patients, aiding in timely risk stratification prior to surgery. Methods: This study included 1050 STEMI patients who underwent primary PCI between December 30, 2016, and May 13, 2023. Clinical data were collected from emergency admissions. Multiple logistic regression models were used to analyze the independent risk factors for intraoperative MACE. A nomogram was then constructed and validated via bootstrap resampling. Model performance was assessed using an ROC curve for discrimination and a calibration curve for accuracy. Results: The incidence of intraoperative MACE in STEMI patients was 38.3%. Independent risk factors for intraoperative MACE included Killip classification, ST-segment elevation in ≥3 leads, white blood cell count, lymphocyte count, and heart rate. A simple and rapidly obtainable nomogram, developed to predict MACE during PCI, showed good differentiation, with an area under the ROC curve of 0.785 (95% CI: 0.755-0.814). Decision curve analysis indicated good fit, calibration, and positive net benefits. Conclusions: A nomogram was developed to rapidly predict intraoperative MACE risk during PCI in STEMI patients before surgery. By enabling early identification of high-risk individuals, this model enhances clinical decision-making, potentially improving patient outcomes and procedural efficiency.
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