ArticleFrontiers in cardiovascular medicine2023
A nomogramic model for predicting the left ventricular ejection fraction of STEMI patients after thrombolysis-transfer PCI.
Article in Frontiers in cardiovascular medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Machine Learning-Based Prediction of Early Left Ventricular Function After STEMI.Journal of clinical medicine · 2025Article
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9 authors.
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Abstract
Background: The prognosis of ST-segment elevation myocardial infarction (STEMI) is closely linked to left ventricular ejection fraction (LVEF). In contrast to primary percutaneous coronary intervention (PPCI), thrombolysis-transfer PCI (TTPCI) is influenced by multiple factors that lead to heterogeneity in cardiac function and prognosis. The aim of this study is to develop a nomogram model for predicting early LVEF in STEMI patients with TTPCI, based on routine indicators at admission. Method: We retrospectively reviewed data from patients diagnosed with STEMI at five network hospitals of our PCI center who performed TTPCI as door-to-balloon time (the interval between arrival at the hospital and intracoronary balloon inflation) over 120 min, from February 2018 to April 2022. Categorical variables were analyzed using Pearson Results: A total of 288 patients were analyzed, including 60 with LVEF < 50% and 228 with LVEF ≥ 50%. The nomogram model based on six independent risk factors including age, heart rate (HR), hypertension, smoking history, Alanine aminotransferase (ALT), and Killip class, demonstrated excellent discrimination with an AUC of 0.84 (95% CI: 0.78-0.89), predicted C-index of 0.84 and curve fit of 0.713. Conclusions: The nomogram model incorporating age, HR, hypertension, smoking history, ALT and Killip class could accurately predict the early LVEF ≥ 50% probability of STEMI patients undergoing TTPCI, and enable clinicians' early evaluation of cardiac function in STEMI patients with TTPCI and early optimization of treatment.
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