Influencing factors and predictive model development for early allograft dysfunction in liver transplantation

Abstract

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

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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