急性ST段抬高型心肌梗死患者院内新发心房颤动的列线图预测模型构建与验证

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

摘要:

目的 探究急性ST段抬高型心肌梗死（ST-segment elevation myocardial infarction，STEMI）患者新发心房颤动（new-onset atrial fibrillation，NOAF）的风险预测模型构建并验证，以便为尽早发现高风险人群、及时采取干预措施提供依据。方法 选取2017年1月至2021年12月在重庆市第四人民医院心血管内科住院的1 080名STEMI患者，其中院内新发房颤患者作为NOAF组（n=87），从未发生NOAF的STEMI患者中随机抽取一部分作为对照组（n=199），比较2组患者人口学特征及入院时临床资料，利用LASSO回归模型分析STEMI患者发生院内新发心房颤动的风险因素。对纳入的患者按7∶3的比例划分为模型组及验证组，建立个性化的院内NOAF预测模型并进行验证。结果 LASSO回归结果提示，年龄、入院心率、Killip分级≥II级、右房内径、二尖瓣中度以上反流、氨基末端脑钠肽前体（N-terminal pro-B-type natriuretic peptide，NT-proBNP）为STEMI患者发生院内NOAF的重要预测因素（P<0.05）。利用上述6个预测指标构建列线图模型。模型组曲线下面积（area under the curve，AUC）为 0.842（95%CI=0.780~0.905），准确度为0.840（95%CI=0.782~0.888）；验证组 AUC 为 0.858（95%CI=0.774~0.943），准确度为0.826（95%CI=0.729~0.899）。同时，校准曲线提示预测模型的校正能力良好。结论 本研究成功构建了STEMI患者院内发生NOAF风险的预测模型，经相关指标证实该预测模型具有较好的预测效率和临床适用性。利用列线图可方便直观地协助临床医护人员筛选高NOAF风险患者，制定针对性的干预措施，为早期防治和改善预后提供依据。

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.