Li Haochao, Liu Chenyu, Chen Pengfei, Sun Xiaogang, Qian Xiangyang, Wang Shaoye, Feng Wei, Chen Zujun, Wang Liqing
Department of Cardiovascular Surgery, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China.
Medicine (Baltimore). 2025 Sep 19;104(38):e44556. doi: 10.1097/MD.0000000000044556.
Heart valve disease is one of the important factors leading to heart failure and cardiovascular death. Double valve replacement (DVR) and tricuspid valve plasty (TVP) have become important surgical approaches for treating severe valve lesions. However, combining with other surgeries may increase perioperative risk and have an impact on the long-term prognosis of patients. To address the complexity of postoperative outcomes in cardiac surgery, this study employs a combination of traditional statistical methods and machine learning techniques to assess risk factors. The primary aim was to investigate the effects of DVR + TVP and combined surgery on postoperative survival and adverse outcomes. Patients who underwent DVR + TVP surgery, they were divided into 4 groups: DVR + TVP, MAZE + TVP, coronary artery bypass grafting (CABG) + TVP, or ascending aortic surgery (AAS) + TVP. Kaplan-Meier survival analysis was used to evaluate the impact of different surgical approaches on postoperative survival rate, Cox proportional hazards regression model was used to analyze contribution of postoperative complications and reoperation to the mortality risk. A neural network model was used to identify factors affecting postoperative mortality risk of patients, to evaluate role of perioperative biomarkers in predicting postoperative mortality risk. The survival rate of patients in AAS + TVP group was the lowest (2.5%), while that in TVP group was the highest (78.8%). Postoperative complications and reoperation were independent predictors of postoperative death. The mortality risk of patients with complications was 2.164 times that of patients without complications (hazard ratio (HR) = 2.164, 95% confidence interval (CI): 1.275-3.671, P = .004), underwent reoperation had a 2.6-fold increased risk of mortality (HR = 2.599, 95% CI: 1.221-5.532, P = .013). Postoperative biomarkers (lactate dehydrogenase (LDH), D-dimer) were significantly associated with postoperative mortality risk. When using neural network model to evaluate the postoperative mortality risk, age (2.0783) and length of stay in the intensive care unit (ICU) (2.0135) were the most important predictors, the area under the curve value of the model was 0.79. Different surgical approaches have a significant impact on postoperative survival rate and the incidence of complications in patients undergoing DVR + TVP. Complications and reoperation are independent factors for poor prognosis. Perioperative biomarkers (LDH, D-dimer) have important value in predicting postoperative mortality risk. The machine learning model based on neural networks can effectively predict postoperative adverse outcomes.
心脏瓣膜病是导致心力衰竭和心血管死亡的重要因素之一。双瓣膜置换术（DVR）和三尖瓣成形术（TVP）已成为治疗严重瓣膜病变的重要手术方法。然而，与其他手术联合可能会增加围手术期风险，并对患者的长期预后产生影响。为解决心脏手术术后结果的复杂性，本研究采用传统统计方法和机器学习技术相结合的方式来评估风险因素。主要目的是研究DVR+TVP及联合手术对术后生存和不良结局的影响。接受DVR+TVP手术的患者被分为4组：DVR+TVP、迷宫术+TVP、冠状动脉旁路移植术（CABG）+TVP或升主动脉手术（AAS）+TVP。采用Kaplan-Meier生存分析评估不同手术方式对术后生存率的影响，采用Cox比例风险回归模型分析术后并发症和再次手术对死亡风险的贡献。使用神经网络模型识别影响患者术后死亡风险的因素，评估围手术期生物标志物在预测术后死亡风险中的作用。AAS+TVP组患者的生存率最低（2.5%），而TVP组最高（78.8%）。术后并发症和再次手术是术后死亡的独立预测因素。有并发症患者的死亡风险是无并发症患者的2.164倍（风险比（HR）=2.164，95%置信区间（CI）：1.275-3.671，P=.004），接受再次手术的患者死亡风险增加2.6倍（HR=2.599，95%CI：1.221-5.532，P=.013）。术后生物标志物（乳酸脱氢酶（LDH）、D-二聚体）与术后死亡风险显著相关。当使用神经网络模型评估术后死亡风险时，年龄（2.0783）和重症监护病房（ICU）住院时间（2.0135）是最重要的预测因素，模型的曲线下面积值为0.79。不同手术方式对接受DVR+TVP手术患者的术后生存率和并发症发生率有显著影响。并发症和再次手术是预后不良的独立因素。围手术期生物标志物（LDH、D-二聚体）在预测术后死亡风险方面具有重要价值。基于神经网络的机器学习模型可以有效预测术后不良结局。
