Construction and validation of a nomogram prediction model for new-onset atrial fibrillation during hospitalization in patients with acute ST-elevation myocardial infarction

Abstract:

Objective To construct and validate a risk predictive model for new-onset atrial fibrillation（NOAF） in patients with acute ST-elevation myocardial infarction（STEMI），and to provide a basis for the early identification of high-risk populations and timely intervention measures.Methods A total of 1 080 STEMI patients who were hospitalized in Department of Cardiology in our hospital from January 2017 to December 2021 were enrolled，among whom 87 patients with NOAF during hospitalization were established as NOAF group，and 199 patients were randomly selected from the STEMI patients without NOAF and were established as control group. The two groups were compared in terms of demographic features and clinical data on admission，and the LASSO regression model was used to analyze the risk factors for NOAF in STEMI patients during hospitalization. The patients enrolled in this study were divided into model group and validation group at a ratio of 7∶3，and a personalized predictive model for NOAF during hospitalization was constructed and validated.Results The LASSO regression analysis showed that age，heart rate on admission，Killip grade≥II，right atrial diameter，mitral regurgitation above the moderate level，and N-terminal pro-brain natriuretic peptide were important predicative factors for NOAF in STEMI patients（P<0.05）. A nomogram model was constructed using the above six predictive factors. In the model group，the model had an area under the ROC curve（AUC） of 0.842（95%CI=0.780-0.905） and an accuracy of 0.840（95%CI=0.782-0.888）； in the validation group，the model had an AUC of 0.858（95%CI=0.774-0.943） and an accuracy of 0.826（95%CI=0.729-0.899）. Meanwhile，the calibration curve showed that the predictive model had a good calibration ability.Conclusion This study successfully constructs a predictive model for the risk of NOAF during hospitalization in STEMI patients，and related indicators show that this predictive model has good predictive efficiency and clinical applicability. The nomogram can help clinicians to identify the patients at a high risk of NOAF and formulate targeted intervention measures，thereby providing a basis for performing early prevention and treatment and improving prognosis.