Abstract
OBJECTIVE: To investigate the incidence of new-onset atrial fibrillation (NOAF) and its related risk factors in patients with acute myocardial infarction (AMI), providing a scientific basis for clinical prevention and treatment. METHODS: Clinical data of 240 The First Hospital of Fangshan District between June 2022 and June 2024 were retrospectively analyzed. Patients were divided into a NOAF group (31 patients) and non-NOAF group (209 patients) according to whether NOAF occurred during hospitalization. Baseline data, laboratory test results, and imaging data of patients were collected. Independent risk factors for NOAF were screened by multifactorial logistic regression analysis and their predictive value was assessed by Receiver Operating Characteristic (ROC) curve. Model goodness of fit was evaluated using the calibration curve and Hosmer-Lemeshow test. The clinical efficacy of the model was evaluated by using the decision curve, accuracy, sensitivity, and specificity. RESULTS: The incidence of NOAF in AMI patients was 12.92%. The NOAF group had significantly higher mean age, higher proportion of patients with a history of hypertension, higher Killip classification ≥ 2, larger left atrial diameter (LAD), and elevated troponin I (cTnI) levels compared to the non-NOAF group (P < 0.05). Multifactorial logistic regression analysis identified age (OR = 1.10, 95% CI: 1.03-1.17), history of hypertension (OR = 8.29, 95% CI: 2.81-24.43), cTnI level (OR = 1.35, 95% CI: 1.18-1.54), and LAD (OR = 1.34, 95% CI: 1.15-1.56) as independent risk factors for the development of NOAF in patients with AMI (P < 0.05). ROC curve analysis showed a high predictive efficacy for combining these four indicators, with an AUC of 0.973 (95% CI: 0.951-0.994). In addition, patients in the NOAF group had a longer mean length of stay and a significantly higher rate of adverse events than those in the non-NOAF group (P < 0.05). CONCLUSION: The occurrence of NOAF in AMI patients is associated with a variety of factors. Identifying these high-risk factors may help clinicians to identify patients at high risk of NOAF early, optimize management, and thusreduce the incidence and adverse outcomes of NOAF.