Han Haolong, Xu Hang, Zhang Jifan, Zhang Weihui, Yang Yi, Wang Xia, Wang Li, Wang Dongjin, Ge Weihong
School of Pharmacy, Faculty of Medicine, Macau University of Science and Technology, Macau, China.
Department of Pharmacy, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, China.
Front Pharmacol. 2025 Jan 7;15:1528390. doi: 10.3389/fphar.2024.1528390. eCollection 2024.
Patients with comorbid coronary artery disease and valvular heart disease usually undergo coronary artery bypass grafting alongside valve replacement or ring repair surgeries. Following these procedures, they typically receive a combination of anticoagulation and antiplatelet therapy, which notably heightens their bleeding risk. However, Current scoring systems provide limited predictive capability.
A total of 500 adult patients treated with anticoagulation plus antiplatelet therapy after cardiac surgery were randomly divided into the training set and the validation set at a ratio of 7:3. Predictive factors were identified using univariate logistic regression, LASSO regression and multivariable analysis. Various models were developed, validated and evaluated by using methods including ROC curves, calibration curves, the Hosmer-Lemeshow test, net reclassification improvement (NRI), integrated discrimination improvement (IDI) index, decision curve analysis (DCA) and clinical impact curves (CIC).
Mod2 showed the best performance (AUC of validation set = 0.863) which consists of 8 independent predictive factors (gender, age > 65 years, diabetes, anemia, atrial fibrillation, cardiopulmonary bypass time, intraoperative bleeding and postoperative drainage), with a significantly higher AUC compared to Mod1 (only preoperative factors) and Mod3 (the HAS-BLED scoring model). NRI and IDI analyses further confirmed the superior predictive ability of Mod2 (NRI < 0.05, IDI < 0.05). Both DCA and CIC indicated that Mod2 exhibited good clinical applicability.
This research established and validated a nomogram model incorporating eight predictive factors to evaluate the bleeding risk in patients who receive anticoagulation combined with antiplatelet therapy following cardiac surgery. The model holds significant potential for clinical applications in bleeding risk assessment, decision-making and personalized treatment strategies.
合并冠状动脉疾病和心脏瓣膜病的患者通常在进行瓣膜置换或环修复手术的同时接受冠状动脉旁路移植术。在这些手术后，他们通常会接受抗凝和抗血小板治疗的联合应用，这显著增加了他们的出血风险。然而，目前的评分系统预测能力有限。
共有500例心脏手术后接受抗凝加抗血小板治疗的成年患者按7:3的比例随机分为训练集和验证集。使用单变量逻辑回归、LASSO回归和多变量分析确定预测因素。通过ROC曲线、校准曲线、Hosmer-Lemeshow检验、净重新分类改善（NRI）、综合判别改善（IDI）指数、决策曲线分析（DCA）和临床影响曲线（CIC）等方法开发、验证和评估各种模型。
Mod2表现最佳（验证集AUC = 0.863），由8个独立预测因素组成（性别、年龄>65岁、糖尿病、贫血、心房颤动、体外循环时间、术中出血和术后引流），与Mod1（仅术前因素）和Mod3（HAS-BLED评分模型）相比，AUC显著更高。NRI和IDI分析进一步证实了Mod2的优越预测能力（NRI < ０.05，IDI < 0.05）。DCA和CIC均表明Mod2具有良好的临床适用性。
本研究建立并验证了一个包含八个预测因素的列线图模型，以评估心脏手术后接受抗凝联合抗血小板治疗患者的出血风险。该模型在出血风险评估、决策制定和个性化治疗策略的临床应用中具有巨大潜力。
