Department of Cardiovascular Surgery, Fujian Medical University Union Hospital, No. 29 Xinquan Road, Fuzhou, 350001, Fujian, China.
Key Laboratory of Cardio-Thoracic Surgery (Fujian Medical University), Fujian Province University, Fuzhou, Fujian, China.
Sci Rep. 2024 Jul 29;14(1):17376. doi: 10.1038/s41598-024-65877-6.
This study aimed to establish a predictive model for the risk of post-thoracic endovascular aortic repair (TEVAR) post-implantation syndrome (PIS) in type B aortic dissection (TBAD) patients, assisting clinical physicians in early risk stratification and decision management for high-risk PIS patients. This study retrospectively analyzed the clinical data of 547 consecutive TBAD patients who underwent TEVAR treatment at our hospital. Feature variables were selected through LASSO regression and logistic regression analysis to construct a nomogram predictive model, and the model's performance was evaluated. The optimal cutoff value for the PIS risk nomogram score was calculated through receiver operating characteristic (ROC) curve analysis, further dividing patients into high-risk group (HRG) and low-risk group (LRG), and comparing the short to midterm postoperative outcomes between the two groups. In the end, a total of 158 cases (28.9%) experienced PIS. Through LASSO regression analysis and multivariable logistic regression analysis, variables including age, emergency surgery, operative time, contrast medium volume, and number of main prosthesis stents were selected to construct the nomogram predictive model. The model achieved an area under the curve (AUC) of 0.86 in the training set and 0.82 in the test set. Results from calibration curve, decision curve analysis (DCA) and clinical impact curve (CIC) demonstrated that the predictive model exhibited good performance and clinical utility. Furthermore, after comparing the postoperative outcomes of HRG and LRG patients, we found that the incidence of postoperative PIS significantly increased in HRG patients. The duration of ICU stay and mechanical assistance time was prolonged, and the incidence of postoperative type II entry flow and acute kidney injury (AKI) was higher. The risk of aortic-related adverse events (ARAEs) and major adverse events (MAEs) at the first and twelfth months of follow-up also significantly increased. However, there was no significant difference in the mortality rate during hospitalization. This study established a nomogram model for predicting the risk of PIS in patients with TBAD undergoing TEVAR. It serves as a practical tool to assist clinicians in early risk stratification and decision-making management for patients.
本研究旨在为 B 型主动脉夹层(TBAD)患者的胸主动脉腔内修复术(TEVAR)后植入综合征(PIS)风险建立预测模型,帮助临床医生对高危 PIS 患者进行早期风险分层和决策管理。本研究回顾性分析了我院 547 例连续接受 TEVAR 治疗的 TBAD 患者的临床资料。通过 LASSO 回归和逻辑回归分析选择特征变量,构建列线图预测模型,并对模型性能进行评估。通过受试者工作特征(ROC)曲线分析计算 PIS 风险列线图评分的最佳截断值,进一步将患者分为高危组(HRG)和低危组(LRG),比较两组患者的短期至中期术后结局。最终,共有 158 例(28.9%)患者发生 PIS。通过 LASSO 回归分析和多变量逻辑回归分析,选择年龄、急诊手术、手术时间、造影剂用量和主假体支架数量等变量构建列线图预测模型。模型在训练集和验证集的曲线下面积(AUC)分别为 0.86 和 0.82。校准曲线、决策曲线分析(DCA)和临床影响曲线(CIC)的结果表明,预测模型具有良好的性能和临床实用性。此外,比较 HRG 和 LRG 患者的术后结局发现,HRG 患者术后 PIS 的发生率显著增加。ICU 停留时间和机械辅助时间延长,术后 II 型入口血流和急性肾损伤(AKI)的发生率较高。随访第 1 个月和第 12 个月主动脉相关不良事件(ARAEs)和主要不良事件(MAEs)的风险也显著增加。然而,住院期间死亡率无显著差异。本研究建立了 TBAD 患者 TEVAR 后 PIS 风险的列线图模型。它是一种实用的工具,可以帮助临床医生对患者进行早期风险分层和决策管理。