Wang Zhiwen, Bao Wei, Cai Dongdong, Hu Min, Gao Xingchun, Li Chengzong
Department of Cardiology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu 221000, China; Department of Cardiology, The Affiliated Shuyang Hospital of Xuzhou Medical University, Suqian, Jiangsu 223600, China.
Department of Cardiology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu 221000, China.
J Electrocardiol. 2024 Mar-Apr;83:56-63. doi: 10.1016/j.jelectrocard.2024.01.005. Epub 2024 Jan 28.
In this study, we aimed to identify the risk factors for new-onset atrial fibrillation (NOAF) after postcoronary intervention in patients with acute myocardial infarction (AMI) and to establish a nomogram prediction model.
The clinical data of 506 patients hospitalized for AMI from March 2020 to February 2023 were retrospectively collected, and the patients were randomized into a training cohort (70%; n = 354) and a validation cohort (30%; n = 152). Independent risk factors were determined using least absolute shrinkage and selection operator and multivariate logistic regression. Predictive nomogram modeling was performed using R software. Nomograms were evaluated based on discrimination, correction, and clinical efficacy using the C-statistic, calibration plot, and decision curve analysis, respectively.
The multivariate logistic regression analysis showed that P-wave amplitude in lead V1, age, and infarct type were independent risk factors for NOAF, and the area under the receiver operating characteristic curve of the training and validation sets was 0.760 (95% confidence interval [CI] 0.674-0.846) and 0.732 (95% CI 0.580-0.883), respectively. The calibration curves showed good agreement between the predicted and observed values in both the training and validation sets, supporting that the actual predictive power was close to the ideal predictive power.
P-wave amplitude in lead V1, age, and infarct type were independent risk factors for NOAF in patients with AMI after intervention. The nomogram model constructed in this study can be used to assess the risk of NOAF development and has some clinical application value.
在本研究中，我们旨在确定急性心肌梗死（AMI）患者冠状动脉介入治疗后新发房颤（NOAF）的危险因素，并建立列线图预测模型。
回顾性收集2020年3月至2023年2月因AMI住院的506例患者的临床资料，并将患者随机分为训练队列（70%；n = 354）和验证队列（30%；n = 152）。使用最小绝对收缩和选择算子以及多变量逻辑回归确定独立危险因素。使用R软件进行预测列线图建模。分别使用C统计量、校准图和决策曲线分析，基于辨别力、校正和临床疗效对列线图进行评估。
多变量逻辑回归分析显示，V1导联P波振幅、年龄和梗死类型是NOAF的独立危险因素，训练集和验证集的受试者工作特征曲线下面积分别为0.760（95%置信区间[CI] 0.674 - 0.846）和0.732（95% CI 0.580 - 0.883）。校准曲线显示训练集和验证集的预测值与观察值之间具有良好的一致性，支持实际预测能力接近理想预测能力。
V1导联P波振幅、年龄和梗死类型是AMI患者介入治疗后NOAF的独立危险因素。本研究构建的列线图模型可用于评估NOAF发生风险，具有一定的临床应用价值。
