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.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 χ2 tests or Fisher exact tests, while Student's t-test or Mann-Whitney U-test was used to compare continuous variables. Subsequently, independent risk factors associated with reduced LVEF one week after TTPCI were identified through comprehensive analysis by combining All-Subsets Regression with Logistic Regression. Based on these indicators, a nomogram model was developed, and validated using the area under the receiver operating characteristic (ROC) curve and the Bootstrap method.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.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.© 2023 Liu, Jiang, Zhang, Pang, Hou, Liu, huang, Peng and Tang.