Li Yuancheng, Liu Xingchao, Zhang Chengcheng, Tao Ran, Pan Bi, Liu Wei, Jiang Di, Hu Feng, Xu Zeliang, Tan Dehong, Ou Yanjiao, Li Xun, You Yuemei, Zhang Leida
Department of Hepatobiliary Surgery, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China.
Department of Hepatobiliary Surgery, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
Clin Transl Gastroenterol. 2025 Jan 1;16(1):e00761. doi: 10.14309/ctg.0000000000000761.
A precise model for predicting outcomes is needed to guide perioperative management. With the development of the liver transplantation (LT) discipline, previous models may become inappropriate or noncomprehensive. Thus, we aimed to develop a novel model integrating variables from donors and recipients for quick assessment of transplant outcomes.
The risk model was based on Cox regression in a randomly selected derivation cohort and verified in a validation cohort. Perioperative data and overall survival were compared between stratifications grouped by X-tile. Receiver-operating characteristic curve and decision curve analysis were used to compare the models. Violin and raincloud plots were generated to present post-LT complications distributed in different stratifications.
Overall, 528 patients receiving LT from 2 centers were included with 2/3 in the derivation cohort and 1/3 in the validation cohort. Cox regression analysis showed that cold ischemia time (CIT) ( P = 0.012) and Model for End-Stage Liver Disease (MELD) ( P = 0.007) score were predictors of survival. After comparison with the logarithmic models, the primitive algorithms of CIT and MELD were defined as the CIT-MELD Index (CMI). CMI was stratified by X-tile (grade 1 ≤1.06, 1.06 < grade 2 ≤ 1.87, grade 3 >1.87). In both cohorts, CMI performed better in calculating transplant outcomes than the balance of risk score, including perioperative incidents and prevalence of complications.
The model integrating variables from graft donors and recipients made the prediction more accurate and available. CMI provided new insight into outcome evaluation and risk factor management of LT.
需要一个精确的模型来预测结果，以指导围手术期管理。随着肝移植（LT）学科的发展，以前的模型可能变得不合适或不全面。因此，我们旨在开发一种整合供体和受体变量的新型模型，以快速评估移植结果。
风险模型基于随机选择的推导队列中的Cox回归，并在验证队列中进行验证。通过X-tile分组的分层之间比较围手术期数据和总生存率。使用受试者操作特征曲线和决策曲线分析来比较模型。生成小提琴图和雨云图以展示LT术后并发症在不同分层中的分布情况。
总体而言，纳入了来自2个中心的528例接受LT的患者，其中2/3在推导队列中，1/3在验证队列中。Cox回归分析表明，冷缺血时间（CIT）（P = 0.012）和终末期肝病模型（MELD）评分（P = 0.007）是生存的预测因素。与对数模型比较后，CIT和MELD的原始算法被定义为CIT-MELD指数（CMI）。CMI通过X-tile进行分层（1级≤1.06，1.06＜2级≤1.87，3级＞1.87）。在两个队列中，CMI在计算移植结果方面比风险评分平衡表现更好，包括围手术期事件和并发症发生率。
整合来自移植物供体和受体变量的模型使预测更加准确和可用。CMI为LT的结果评估和危险因素管理提供了新的见解。
