Qian Jun, Zan Jiyong, Kuang Lijun, Che Lin, Yu Yunan, Shen Ting, Tang Jiani, Chen Fei, Liu Xuebo
Department of Cardiology, Tongji Hospital, Tongji University School of Medicine, Shanghai, China.
Department of Gastroenterology, Huazhong University of Science and Technology Union Shenzhen Hospital (Nanshan Hospital), Shenzhen, China.
Ann Transl Med. 2021 Feb;9(3):193. doi: 10.21037/atm-20-3971.
The use of anticoagulants and antiplatelet therapies is associated with a higher risk of bleeding in atrial fibrillation (AF) patients after percutaneous coronary intervention, especially after stent implantation. However, no accurate bleeding risk prediction tool has been developed for these patients. The aim of this study was thus to establish a bleeding risk prediction model (predictive nomogram) for patients with AF after stent implantation.
Construction of the predictive nomogram was based on a retrospective study, which enrolled 943 AF patients who underwent drug-eluting stent implantation between May 2012 and September 2016. A range of factors, including demographics, comorbidities, medication strategies, arterial access, and laboratory tests, were collected as baseline data. The least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression analysis were used to identify the key clinical features for construction of the predictive nomogram. The concordance index (C-index) and internal validation were used to evaluate the efficacy of the nomogram.
Of the 943 AF patients that underwent stent implantation, the occurrence of bleeding events was 8.2% (77 out of 943). Key predictors included the number of antiplatelet drugs, peptic ulcer, cerebral infarction, type 2 diabetes, thrombocytopenia, anemia, prior myocardial infarction, sex (male), use of anticoagulant drugs, liver dysfunction, hypertension, and acute myocardial infarction. These predictors were used to construct the nomogram. The C-index for the prediction of bleeding risk by the nomogram was 0.841 (95% CI: 0.79-0.89), which indicated good discrimination and calibration. The C-index of internal validation was 0.795, which demonstrated good efficacy of the model.
This study suggests that our novel nomogram can accurately predict bleeding risk in AF patients after stent implantation during hospitalization, thereby helping to avoid complications. The nomogram may also be helpful for the creation of individualized post-discharge medication strategies.
在经皮冠状动脉介入治疗后，尤其是支架植入后，心房颤动（AF）患者使用抗凝剂和抗血小板治疗与出血风险较高相关。然而，尚未为这些患者开发出准确的出血风险预测工具。因此，本研究的目的是为支架植入后的AF患者建立出血风险预测模型（预测列线图）。
预测列线图的构建基于一项回顾性研究，该研究纳入了2012年5月至2016年9月期间接受药物洗脱支架植入的943例AF患者。收集了一系列因素，包括人口统计学、合并症、用药策略、动脉入路和实验室检查等作为基线数据。使用最小绝对收缩和选择算子（LASSO）及多变量逻辑回归分析来确定构建预测列线图的关键临床特征。一致性指数（C指数）和内部验证用于评估列线图的有效性。
在943例接受支架植入的AF患者中，出血事件的发生率为8.2%（943例中的77例）。关键预测因素包括抗血小板药物数量、消化性溃疡、脑梗死、2型糖尿病、血小板减少症、贫血、既往心肌梗死、性别（男性）、抗凝药物使用、肝功能不全、高血压和急性心肌梗死。这些预测因素用于构建列线图。列线图预测出血风险的C指数为0.841（95%CI：0.79 - 0.89），表明具有良好的区分度和校准度。内部验证的C指数为0.795，证明模型具有良好的有效性。
本研究表明，我们的新型列线图可以准确预测住院期间支架植入后AF患者的出血风险，从而有助于避免并发症。该列线图也可能有助于制定个体化的出院后用药策略。
