Abstract: Objective: To develop and validate a prediction model for in-hospital Major Adverse Cardiovascular Events (MACE) in patients with Acute Coronary Syndrome (ACS), providing clinicians with a tool to identify the of MACE in ACS patients during hospitalization.?Methods: ACS patients admitted to the emergency department of a tertiary hospital in Hunan province from September 2021 to January 2023 were retrospectively enrolled via convenience sampling. General clinical data, symptoms, and laboratory parameters were collected. Univariate and multivariate Logistic regression analyses were performed to establish a in-hospital MACE prediction model. A nomogram was constructed and its prediction effect was evaluated. Results: Among 920 ACS patients, 190 occurred in-hospital MACE, with an incidence rate of 20.65%. Logistic regression analysis revealed that female gender and chest pain duration <20 min were protective factors (all P<0.05), while body temperature ≥38.5 °C, coronary heart disease risk factors, ECG abnormalities, and raised troponin levels were risk factors (all P<0.05). The model exhibited an area under the curve of 0.939, specificity of 0.807, sensitivity of 0.921, and Youden index of 0.728. The Hosmer-Lemeshow test showed χ2=10.156 (P=0.254), and decision curve analysis demonstrated favorable clinical utility. Conclusion: The prediction model developed in this study demonstrates good predictive performance and clinical applicability, providing nurses with a reference for early identification of ACS patients at high risk for in-hospital MACE and implementation of targeted interventions.

Key words: Acute Coronary Syndrome; Major Adverse Cardiovascular Event; prediction model; nomogram; nursing care