肝移植早期移植物功能不全的影响因素及预测模型构建

摘要/Abstract

摘要： 目的探讨肝移植早期移植物功能不全(early allograft dysfunction,EAD)的影响因素并构建列线图预测模型。方法纳入东部战区总医院2018年11月1日至2023年10月31日接受经典原位异体肝移植术的患者266例。比较肝移植术后发生EAD与未发生EAD患者的临床资料。多因素logistic回归分析术后发生EAD的影响因素。将独立危险因素纳入列线图模型,使用受试者工作特征曲线、校正曲线及拟合优度检验等方法评估预测效果。结果 266例受者患者中,发生EAD 74例(27.8%)。EAD组与非EAD组相比:供肝冷缺血时间(451.4±129.9)min比(408.8±127.2)min、手术结束时乳酸浓度(4.8±2.6)mmol/L比(3.9±2.2)mmol/L、MELD评分(22.1±5.4)分比(20.1±6.5)分、术前合并糖尿病为23例(31.1%) 比36例(18.8%)、术前血液中的尿酸浓度为299 μmol/L比260 μmol/L、术中出血量为2000 mL比1500 mL,差异有统计学意义(P<0.05)。多因素logistic回归分析发现,供肝冷缺血时间(OR=1.003,95% CI:1.001~1.005,P=0.009)、手术结束时动脉乳酸浓度(OR=1.167,95% CI:1.030~1.322,P=0.015)、MELD评分(OR=1.060,95% CI:1.011~1.112,P=0.016)、糖尿病(OR=2.186,95% CI:1.109~4.240,P=0.024)、术中出血量(OR=1.026,95% CI:1.002~1.049,P=0.030)是肝移植术后发生EAD的独立危险因素。将以上5个危险因素纳入列线图模型,模型的受试者工作曲线下面积为0.712(95% CI:0.642~0.781),较正曲线验证模型的一致性较好,临床决策曲线显示模型具有一定的临床实用性。结论根据供肝冷缺血时间、手术结束时动脉乳酸浓度、MELD评分、糖尿病、术中出血量等构建的列线图具有一定的临床实用价值,可为诊断EAD提供参考。

关键词: 早期移植物功能不全, 肝移植, 预测模型

Abstract: Objective To identify key factors influencing early allograft dysfunction (EAD) in liver transplantation and to develop a nomogram model for its early detection. Methods This retrospective study included patients who underwent classic in situ allograft liver transplantation at Eastern Theater General Hospital from November 1, 2018, to October 31, 2023. Univariate analysis was performed, followed by multivariable logistic regression to identify significant variables. Independent risk factors were then incorporated into a nomogram models. The model’s predictive accuracy was evaluated using receiver operating characteristic(ROC) curves, calibration curves, and goodness-of-fit tests. Results A total of 266 liver transplant recipients were included, of whom 74 (27.8%) developed EAD. Univariate analysis indicated that, compared to the non-EAD group (n=192), the EAD group (n=74) had significantly higher values in the following variables: donor liver cold ischemia time (451.4±129.9min vs. 408.8±127.2min), lactate concentration at procedure end (4.8±2.6mmol/L vs. 3.9±2.2mmol/L), MELD score (22.1±5.4 vs. 20.1±6.5), preoperative diabetes [31.1% (23 cases) vs. 18.8% (36cases)], preoperative blood uric acid [299μmol/L, 95% CI (133~423) vs. 260μmol/L, 95% CI (204~384)], and intraoperative blood loss [2000ml, 95% CI (1500~2800) vs. 1500ml, 95%CI (1050~2550)] (P<0.05). Multivariablel logistic regression analysis identified five independent risk factors for EAD: donor liver cold ischemia time [OR=1.003, 95% CI (1.001~1.005), P=0.009], arterial lactate concentration at procedure end [OR=1.167, 95% CI (1.030~1.322), P=0.015], MELD score [OR=1.060. 95% CI (1.011~1.112), P=0.016], diabetes mellitus [OR=2.186, 95% CI (1.109~4.240), P=0.024], and intraoperative blood loss [OR=1.026, 95% CI (1.002~1.049), P=0.030]. These factors were incorporated into a nomogram model, achieving an area under the ROC curve of 0.712 (95% CI: 0.642~0.781), demonstrating good predictive performance. The clinical decision curve further confirmed the model’s clinical utility. Conclusion The nomogram model, incorporating donor liver cold ischemia time, arterial lactate concentration at surgery end, MELD score, diabetes mellitus, and intraoperative bleeding volume, demonstrates clinical utility and may serve as a useful tool for the early diagnosis of EAD.

Key words: Early allograft dysfunction, liver transplantation, predictive modeling

刘盼, 刘娓, 张雪, 张韬, 苗素琴. 肝移植早期移植物功能不全的影响因素及预测模型构建[J]. 肝脏, 2024, 29(11): 1413-1417.

LIU Pan, LIU Wei, ZHANG Xue, ZHANG Tao, MIAO Su-qin. Influencing factors and predictive model development for early allograft dysfunction in liver transplantation[J]. Chinese Hepatolgy, 2024, 29(11): 1413-1417.

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参考文献

[1] Agopian V G, Harlander-Locke M P, Markovic D, et al. Evaluation of early allograft function using the liver graft assessment following transplantation risk score model[J]. JAMA Surg, 2018, 153(5): 436-444.
[2] Barrueco-Francioni J E, Seller-Pérez G, Lozano-Saéz R, et al. Early graft dysfunction after liver transplant: comparison of different diagnostic criteria in a single-center prospective cohort[J]. Med Intensiva (Engl Ed), 2020, 44(3): 150-159.
[3] Moosburner S, Sauer I M, Förster F, et al. Early allograft dysfunction increases hospital associated costs after liver transplantation-A propensity score-matched analysis[J]. Hepatol Commun, 2021, 5(3): 526-537.
[4] Wadei H M, Lee D D, Croome K P, et al. Early allograft dysfunction after liver transplantation is associated with short- and long-term kidney function impairment[J]. Am J Transplant, 2016, 16(3): 850-859.
[5] Diaz-Nieto R, Lykoudis P, Robertson F, et al. A simple scoring model for predicting early graft failure and postoperative mortality after liver transplantation[J]. Ann Hepatol, 2019, 18(6): 902-912.
[6] 张鹏飞, 陈雅洁, 张远, 等. 肝移植术后早期移植物功能不全的研究进展[J]. 实用器官移植电子杂志, 2023, 11(04): 379-384.
[7] Olthoff K M, Kulik L, Samstein B, et al. Validation of a current definition of early allograft dysfunction in liver transplant recipients and analysis of risk factors[J]. Liver Transpl, 2010, 16(8): 943-949.
[8] 何泽平, 宋方彬, 徐军明. 肝移植的新进展与挑战[J]. 肝脏, 2023, 28(09): 1015-1018.
[9] Bastos-Neves D, Salvalaggio P R O, Almeida M D. Risk factors, surgical complications and graft survival in liver transplant recipients with early allograft dysfunction[J]. Hepatobiliary Pancreat Dis Int, 2019, 18(5): 423-429.
[10] Xie H, Zhang L, Guo D, et al. Protein profiles of pretransplant grafts predict early allograft dysfunction after liver transplantation from donation after circulatory death[J]. Transplantation, 2020, 104(1): 79-89.
[11] Ito T, Naini B V, Markovic D, et al. Ischemia-reperfusion injury and its relationship with early allograft dysfunction in liver transplant patients[J]. Am J Transplant, 2021, 21(2): 614-625.
[12] 白杨, 史冀华, 张水军. 程序性细胞死亡在肝脏缺血-再灌注损伤中的作用研究进展[J]. 器官移植, 2022, 13(05): 647-652.
[13] 刘浩, 董家勇, 傅志仁, 等. 冷缺血时间对肝移植术后早期移植物功能及排斥反应的影响[J]. 器官移植, 2020, 11(05): 578-583.
[14] Ni C, Masters J, Zhu L, et al. Study design of the DAS-OLT trial: a randomized controlled trial to evaluate the impact of dexmedetomidine on early allograft dysfunction following liver transplantation[J]. Trials, 2020, 21(1): 582.
[15] Fayed N A, Sayed E I, Saleh S M, et al. Effect of dexmedetomidine on hepatic ischemia-reperfusion injury in the setting of adult living donor liver transplantation[J]. Clin Transplant, 2016, 30(4): 470-482.
[16] Sáez de la Fuente I, Sáez de la Fuente J, Molina Collado Z, et al. Combination of arterial lactate levels and Cv-aCO2/Da-vO2 ratio to predict early allograft dysfunction after liver transplantation[J]. Clin Transplant, 2021, 35(12): e14482.
[17] Liu J, Martins P N, Bhat M, et al. Biomarkers and predictive models of early allograft dysfunction in liver transplantation-a systematic review of the literature, meta-analysis, and expert panel recommendations[J]. Clin Transplant, 2022, 36(10): e14635.
[18] 刘晶雪, 李树臣. 不同评分模型对肝衰竭患者预后的预测能力及其研究进展[J]. 肝脏, 2023, 28(07): 857-861.
[19] Yang M, Khan A R, Lu D, et al. Development of a novel prognostic nomogram for high model for end-stage liver disease score recipients following deceased donor liver transplantation[J]. Front Med (Lausanne), 2022, 9: 772048.
[20] Croome K P, Marotta P, Wall W J, et al. Should a lower quality organ go to the least sick patient? Model for end-stage liver disease score and donor risk index as predictors of early allograft dysfunction[J]. Transplant Proc, 2012, 44(5): 1303-1306.
[21] Ling Q, Xu X, Xie H, et al. New-onset diabetes after liver transplantation: a national report from China Liver Transplant Registry[J]. Liver Int, 2016, 36(5): 705-712.
[22] 孟园园, 段怡, 高志峰, 等. 肝移植患者术中血糖变异系数对术后早期移植物功能障碍的影响[J]. 临床麻醉学杂志, 2022, 38(12): 1254-1258.
[23] Barb D, Repetto E M, Stokes M E, et al. Type 2 diabetes mellitus increases the risk of hepatic fibrosis in individuals with obesity and nonalcoholic fatty liver disease[J]. Obesity (Silver Spring), 2021, 29(11): 1950-1960.
[24] Tomescu D, Popescu M, Dima S O. Rotational thromboelastometry (ROTEM) 24 hours post liver transplantation predicts early allograft dysfunction[J]. Rom J Anaesth Intensive Care, 2018, 25(2): 117-122.