Objective To explore the value of combined ultrasound parameters, including the hepatorenal index (HRI) and renal resistance index (RRI), with cystatin C (CysC) in monitoring early acute kidney injury (AKI) after liver transplantation. Methods Perioperative data from 121 liver transplant recipients who received organs from donation after brain death were collected. The HRI and RRI of the recipients were measured on postoperative days 1-7 and at 1 month, and the CysC levels were measured on postoperative day 1. The recipients were divided into the AKI group (n=53) and the non-AKI group (n=68) based on whether AKI occurred within 7 days after operation. The data of the two groups were compared, and the ultrasound parameters before and after recovery in the AKI group were analyzed. The value of combined HRI, RRI and CysC in monitoring AKI was also analyzed. Results AKI occurred in 53 recipients, with an incidence rate of 43.8%, including 30 cases of stage 1, 18 cases of stage 2, and 5 cases of stage 3. Among them, 49 cases occurred on postoperative day 1, and 4 cases occurred on postoperative day 2. Of these, 43 cases recovered within 7 days after surgery, 8 cases recovered within 2 months after surgery, 1 case was lost to follow-up, and 1 case received renal replacement therapy. The body mass index and preoperative CysC levels were higher in the AKI group than in the non-AKI group, and the operative time was longer in the AKI group than in the non-AKI group (all P < 0.05). The HRI on postoperative day 1 was lower in the AKI group than in the non-AKI group, while the RRI and CysC levels were higher (all P < 0.05). When AKI occurred, the HRI was lower than the baseline level, and the RRI was higher than the baseline level. As AKI recovered, the HRI gradually increased, and the RRI gradually decreased. The receiver operating characteristic curve analysis showed that the sensitivity and specificity of HRI ≤ 1.12 for predicting AKI were 0.623 and 0.878, respectively, with an area under the curve (AUC) of 0.801. The sensitivity and specificity of RRI ≥ 0.65 for predicting AKI were 0.878 and 0.676, respectively, with an AUC of 0.825. The sensitivity and specificity of CysC ≥ 1.38 mg/L for predicting AKI were 0.736 and 0.882, respectively, with an AUC of 0.851 (all P<0.01). The combination of HRI and CysC (AUC=0.897, P<0.01), RRI and CysC (AUC=0.910, P<0.01), and all three parameters combined (AUC=0.934, P<0.01) were more effective than using each parameter alone. Conclusions HRI and RRI may be used to monitor the occurrence and recovery of early AKI after liver transplantation. The combination of these two parameters with CysC has a high application value in monitoring early AKI after liver transplantation.

Objective:To evaluate the treatment outcome of liver transplantation for patients with polycystic liver disease (PLD).Methods:Clinical data of 28 PLD patients admitted to the Affiliated Hospital of Qingdao University from May 2014 to November 2023 were retrospectively analyzed, including 10 males and 18 females, aged (50.4±6.6) years. Patients were divided into liver transplantation group ( n=15) and non-liver transplantation group ( n=13). In the liver transplantation group, we analyzed seve-ral critical parameters including methods of liver transplantation, intra-abdominal fluid volume, intraoperative blood loss, intraoperative red blood cell transfusion requirements, and postoperative complications. The prognosis of the two groups were also compared. Results:Among the 28 patients with PLD, 15 underwent liver transplantation, including 11 classic in situ liver transplantations, one modified back-to-back liver transplantation, and three liver-kidney combined transplantations. The 15 patients had 2 000 (300, 4 000) ml of abdominal fluid, 1 000 (600, 2 000) ml of intraoperative blood loss, and 8.0 (6.0, 17.0) U of red blood cells transfused during the operation. Postoperative complications occurred in eight cases, with four of which were managemed successfully, and the other four died. The 1-, 5-, and 10-year survival rates of after liver transplantation were 80.0%, 80.0%, and 73.3%, respectively. The 1-, 5-, and 10-year survival rates of patients with PLD without liver transplantation were 69.2%, 46.2%, and 38.5%, respectively. The difference between the two groups was statistically significant ( χ2=3.91, P=0.048). Conclusion:Liver transplantation is a treatment option for patients with PLD, with a better long-term survival compared to patients without liver transplantation.

Objective:To investigate the impact of donor age on early postoperative liver function recovery in liver transplant recipients, as well as the incidence and risk factors for arterial complications following liver transplantation.Methods:A total of 518 patients who underwent liver transplantation at the Organ Transplantation Center of the Affiliated Hospital of Qingdao University between January 2021 and January 2024 were included in the study. Based on donor age, patients were classified into the elderly donor group (≥70 years, n=28) and the non-elderly donor group (<70 years, n=490). Liver function indicators—including aspartate aminotransferase (AST), alanine aminotransferase (ALT), total bilirubin (TBIL), and direct bilirubin (DBIL)—were measured on postoperative days 1, 3, 7, and 14. The incidence of arterial complications, including hepatic artery thrombosis and hepatic artery stenosis, was recorded. Recipients were further categorized into the arterial complication group (n=26) and the non-arterial complication group (n=492) based on postoperative outcomes, and clinical characteristics of donors and recipients were compared. Binary logistic regression analysis was conducted to identify risk factors for arterial complications.Rusults:No significant differences were observed in baseline characteristics between the elderly and non-elderly donor groups ( P>0.05). However, the elderly donor group exhibited significantly higher AST, ALT, TBIL, and DBIL levels at all postoperative time points compared to the non-elderly donor group (all P<0.05). Specifically, on postoperative day 1, AST and ALT levels were (1,024.57±256.49) U/L and (756.24±145.89) U/L in the elderly donor group, compared to (895.23±225.19) U/L and (614.85±126.51) U/L in the non-elderly donor group. On day 3, AST and ALT levels were (402.46±71.61) U/L and (423.31±87.44) U/L versus (226.37±66.54) U/L and (256.79±70.25) U/L, respectively. On day 7, AST and ALT levels were (91.78±21.84) U/L and (92.36±21.62) U/L versus (68.41±18.38) U/L and (77.47±18.16) U/L. By day 14, AST and ALT levels were (67.52±10.35) U/L and (72.17±16.28) U/L versus (35.32±9.27) U/L and (48.56±14.10) U/L, respectively ( P<0.05 for all comparisons). For bilirubin indicators, TBIL and DBIL levels in the elderly donor group were also consistently higher than in the non-elderly donor group. On day 1, TBIL and DBIL were (95.76±21.93) μmol/L and (64.22±15.07) μmol/L, compared to (77.59±20.48) μmol/L and (51.18±12.96) μmol/L. By day 14, TBIL and DBIL levels had decreased to (41.26±8.30) μmol/L and (32.45±6.21) μmol/L, compared to (28.39±7.15) μmol/L and (20.58±5.04) μmol/L in the non-elderly donor group ( P<0.05 for all comparisons). The incidence of hepatic artery complications was 10.71% (3/28) in the elderly donor group and 4.69% (23/490) in the non-elderly donor group, with no statistically significant difference between the two groups ( P>0.05). Statistical analysis employing independent t-tests and χ2 tests demonstrated significant differences between the arterial complication group and non-arterial complication group in donor quality ratio ( P<0.05) and incidence of hepatic arterial hypoperfusion ( P<0.05). Multivariate binary logistic regression analysis, after adjusting for confounding factors (e.g., recipient gender, age, body mass index [BMI], primary disease, and donor-recipient blood type compatibility), identified recipient-to-donor mass ratio ( OR=1.352, P<0.05) and insufficient hepatic arterial blood flow ( OR=1.497, P<0.05) as independent risk factors for arterial complications following liver transplantation. Conclusion:Elderly liver donors can have a certain impact on early postoperative liver function recovery in liver transplant recipients, but have no significant impact on the occurrence of arterial complications after liver transplantation. The mass ratio of recipients to donors and insufficient hepatic arterial blood flow are independent risk factors for arterial complications after liver transplantation.

This study reports the diagnosis and treatment of six cases of post-transplant lymphoproliferative disorder (PTLD) in liver transplant recipients, confirmed at the Affiliated Hospital of Qingdao University between August 2017 and May 2023. The report includes details on anti-rejection therapy, Epstein-Barr virus (EBV) and cytomegalovirus (CMV) infections, imaging findings, histopathological results, treatment courses, and prognoses. By summarizing the clinical experience in the diagnosis and management of PTLD following liver transplantation, this study aims to provide valuable insights and references for the clinical diagnosis and treatment of this condition.

This study retrospectively reviewed the clinical data of 9 recipients with post-transplant lymphoproliferative disorder (PTLD) after liver transplantation admitted to the Organ Transplantation Center of the Affiliated Hospital of Qingdao University from January 2020 to June 2024, and summarized their clinical manifestations, pathological features, treatment regimens, and prognostic conditions, so as to provide a reference for clinical diagnosis and treatment.

Primary hepatic neuroendocrine tumor (PHNET) is an extremely rare subtype of neuroendocrine tumor (NET), accounting for approximately 0.3% - 4.0% of all NETs. This study reports a case of PHNET treated with ABO-incompatible liver transplantation. Intraoperatively, double filtration plasmapheresis was performed to remove antibodies. Postoperatively, the patient's blood concentrations of immunosuppressive drugs and liver function were closely monitored. The recipient maintained stable drug levels, with a gradual recovery of liver function. No acute rejection occurred, and the patient was successfully discharged.

Objective:To investigate the application value of risk prediction model for acute kidney injury (AKI) after donation of cardiac death (DCD) liver transplantation based on machine learning algorithm.Methods:The retrospective cohort study was conducted. The clinicopathological data of 1 001 pairs of DCD liver transplant donors and recipients at five hospitals, including The First Affiliated Hospital of Zhejiang University School of Medicine et al, in the Chinese Liver Transplan-tation Registry from January 2015 to December 2023 were collected. Of the donors, there were 825 males and 176 females. Of the recipients, there were 806 males and 195 females, aged 52 (range, 18-75)years. There were 281 recipients included using oversampling technique, and all 1 282 recipients were divided to the training set of 897 recipients and the validation set of 385 recipients by a ratio of 7∶3 using computer-generated random numbers. Seven prediction models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), and Categorical Boosting (CatBoost), were constructed for AKI after liver transplantation based on machine learning algorithm. Observation indicators: (1) comparison of clinicopathological characteristics between recipients with and without AKI and donors; (2) follow-up and survival of recipients with and without AKI; (3) construction and validation of nomogram prediction model of AKI after liver transplantation; (4) construction and validation of machine learning prediction model of AKI after liver transplantation. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test, and comparison among groups was conducted using the Kruskal-Wallis H test. Comparison of count data between groups was conducted using the chi-square test or corrected chi-square test. Kaplan-Meier method was used to calculate survival rates and plot survival curves. Logistic regression model was performed for univariate and multivariate analyses. The receiver operating characteristic (ROC) curve was plotted to calculate area under curve (AUC) and 95% confidence interval ( CI). The performance of prediction model was evaluated using DeLong test, accuracy, sensitivity, specificity. The calibration curve was plotted to evaluate the performance of predicted probability and actual probability. The interpretability analysis of machine learning algorithm and SHapley Additive exPlanations was used to explain the model decision separately. Results:(1) Comparison of clinicopathological characteristics between recipients with and without AKI and donors. Of 1 001 recipients, there were 360 cases with AKI and 641 cases without AKI after liver transplantation. There were significant differences in body mass index (BMI), hepatic encepha-lopathy, hepatitis B surfact antigen (HBsAg), hepatorenal syndrome (HRS) and donor diabetes, donor blood urea nitrogen, donor alanine aminotransferase, donor aspartate aminotransferase, mass of graft, volume of blood loss during liver transplantation, warm ischema time of donor liver, and operation time between recipients with and without AKI ( Z=-4.337, χ2=9.751, 9.088, H=11.142, χ2=5.286, Z=-3.360, -2.539, -3.084, -1.730, -3.497, -1.996, -2.644, P<0.05). (2) Follow-up and survival of recipients with and without AKI. All the 1 001 recipients received follow-up. The recipients with AKI after liver transplantation were followed up for 18.6(range, 0-102.3)months, and recipients without AKI after liver transplantation were followed up for 31.9(range, 0.1-105.5)months. The 1-, 3-, and 5-year overall survival rates were 72.1%, 63.5%, and 59.3% of recipients with AKI, versus 86.7%, 76.7%, and 72.5% of recipients without AKI, respectively, showing a significant difference in overall survival between them ( χ2=26.028, P<0.05). (3) Construction and validation of nomogram predic-tion model of AKI after liver transplantation. Results of multivariate analysis showed that recipient BMI, recipient creatinine, recipient HBsAg, recipient HRS, donor blood urea nitrogen, donor crea-tinine, anhepatic phase and volume of blood loss during liver transplantation were independent risk factors for AKI of recipients after liver transplantation ( odds ratio=1.113, 0.998, 0.605, 1.580, 1.047, 0.998, 1.006, 1.157, 95% CI as 1.070-1.157, 0.996-1.000, 0.450-0.812, 1.021-2.070, 1.021-1.074, 0.996-0.999, 1.000-1.012, 1.045-1.281, P<0.05). The nomogram prediction model of AKI after liver transplantation was constructed based on the results of multivariate analysis. Results of ROC curve showed that the AUC of 0.666 (95% CI as 0.637-0.696). (4) Construction and validation of machine learning prediction model of AKI after liver transplantation. Based on the Lasso regression analysis, seven machine learning algorithm prediction models, including RF, XGBoost, SVM, LR, DT, KNN, and CatBoost, were constructed, with ROC curves of the validation set plotted. The AUC of above models were 0.863, 0.841, 0.721, 0.637, 0.620, 0.708, 0.731, accuracies were 0.764, 0.782, 0.701, 0.592, 0.605, 0.605, 0.681, sensitivities were 0.764, 0.789, 0.719, 0.588, 0.694, 0.694, 0.704, specificities were 0.763, 0.774, 0.683, 0.597, 0.511, 0.511, 0.656, respectively. Delong test showed that the RF model with the highest AUC of 0.863(95% CI as 0.828-0.899). Calibration curve analysis showed the predicted probability closest to the actual probability of RF model, indicating the model with a good validation value. Further sorting of SHAP of different clinical factors based on RF model showed that recipient BMI, donor blood urea nitrogen, volume of blood loss during liver transplantation, donor age had large effects on the output outcomes. Conclusion:The nomogram prediction model and seven machine learning algorithm prediction models for AKI after DCD liver transplantation are constructed, and the RF model based on machine learning has a better predictive performance.

Objective:To investigate the application value of risk prediction model for acute kidney injury (AKI) after donation of cardiac death (DCD) liver transplantation based on machine learning algorithm.Methods:The retrospective cohort study was conducted. The clinicopathological data of 1 001 pairs of DCD liver transplant donors and recipients at five hospitals, including The First Affiliated Hospital of Zhejiang University School of Medicine et al, in the Chinese Liver Transplan-tation Registry from January 2015 to December 2023 were collected. Of the donors, there were 825 males and 176 females. Of the recipients, there were 806 males and 195 females, aged 52 (range, 18-75)years. There were 281 recipients included using oversampling technique, and all 1 282 recipients were divided to the training set of 897 recipients and the validation set of 385 recipients by a ratio of 7∶3 using computer-generated random numbers. Seven prediction models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), and Categorical Boosting (CatBoost), were constructed for AKI after liver transplantation based on machine learning algorithm. Observation indicators: (1) comparison of clinicopathological characteristics between recipients with and without AKI and donors; (2) follow-up and survival of recipients with and without AKI; (3) construction and validation of nomogram prediction model of AKI after liver transplantation; (4) construction and validation of machine learning prediction model of AKI after liver transplantation. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test, and comparison among groups was conducted using the Kruskal-Wallis H test. Comparison of count data between groups was conducted using the chi-square test or corrected chi-square test. Kaplan-Meier method was used to calculate survival rates and plot survival curves. Logistic regression model was performed for univariate and multivariate analyses. The receiver operating characteristic (ROC) curve was plotted to calculate area under curve (AUC) and 95% confidence interval ( CI). The performance of prediction model was evaluated using DeLong test, accuracy, sensitivity, specificity. The calibration curve was plotted to evaluate the performance of predicted probability and actual probability. The interpretability analysis of machine learning algorithm and SHapley Additive exPlanations was used to explain the model decision separately. Results:(1) Comparison of clinicopathological characteristics between recipients with and without AKI and donors. Of 1 001 recipients, there were 360 cases with AKI and 641 cases without AKI after liver transplantation. There were significant differences in body mass index (BMI), hepatic encepha-lopathy, hepatitis B surfact antigen (HBsAg), hepatorenal syndrome (HRS) and donor diabetes, donor blood urea nitrogen, donor alanine aminotransferase, donor aspartate aminotransferase, mass of graft, volume of blood loss during liver transplantation, warm ischema time of donor liver, and operation time between recipients with and without AKI ( Z=-4.337, χ2=9.751, 9.088, H=11.142, χ2=5.286, Z=-3.360, -2.539, -3.084, -1.730, -3.497, -1.996, -2.644, P<0.05). (2) Follow-up and survival of recipients with and without AKI. All the 1 001 recipients received follow-up. The recipients with AKI after liver transplantation were followed up for 18.6(range, 0-102.3)months, and recipients without AKI after liver transplantation were followed up for 31.9(range, 0.1-105.5)months. The 1-, 3-, and 5-year overall survival rates were 72.1%, 63.5%, and 59.3% of recipients with AKI, versus 86.7%, 76.7%, and 72.5% of recipients without AKI, respectively, showing a significant difference in overall survival between them ( χ2=26.028, P<0.05). (3) Construction and validation of nomogram predic-tion model of AKI after liver transplantation. Results of multivariate analysis showed that recipient BMI, recipient creatinine, recipient HBsAg, recipient HRS, donor blood urea nitrogen, donor crea-tinine, anhepatic phase and volume of blood loss during liver transplantation were independent risk factors for AKI of recipients after liver transplantation ( odds ratio=1.113, 0.998, 0.605, 1.580, 1.047, 0.998, 1.006, 1.157, 95% CI as 1.070-1.157, 0.996-1.000, 0.450-0.812, 1.021-2.070, 1.021-1.074, 0.996-0.999, 1.000-1.012, 1.045-1.281, P<0.05). The nomogram prediction model of AKI after liver transplantation was constructed based on the results of multivariate analysis. Results of ROC curve showed that the AUC of 0.666 (95% CI as 0.637-0.696). (4) Construction and validation of machine learning prediction model of AKI after liver transplantation. Based on the Lasso regression analysis, seven machine learning algorithm prediction models, including RF, XGBoost, SVM, LR, DT, KNN, and CatBoost, were constructed, with ROC curves of the validation set plotted. The AUC of above models were 0.863, 0.841, 0.721, 0.637, 0.620, 0.708, 0.731, accuracies were 0.764, 0.782, 0.701, 0.592, 0.605, 0.605, 0.681, sensitivities were 0.764, 0.789, 0.719, 0.588, 0.694, 0.694, 0.704, specificities were 0.763, 0.774, 0.683, 0.597, 0.511, 0.511, 0.656, respectively. Delong test showed that the RF model with the highest AUC of 0.863(95% CI as 0.828-0.899). Calibration curve analysis showed the predicted probability closest to the actual probability of RF model, indicating the model with a good validation value. Further sorting of SHAP of different clinical factors based on RF model showed that recipient BMI, donor blood urea nitrogen, volume of blood loss during liver transplantation, donor age had large effects on the output outcomes. Conclusion:The nomogram prediction model and seven machine learning algorithm prediction models for AKI after DCD liver transplantation are constructed, and the RF model based on machine learning has a better predictive performance.

Objective:To evaluate the treatment outcome of liver transplantation for patients with polycystic liver disease (PLD).Methods:Clinical data of 28 PLD patients admitted to the Affiliated Hospital of Qingdao University from May 2014 to November 2023 were retrospectively analyzed, including 10 males and 18 females, aged (50.4±6.6) years. Patients were divided into liver transplantation group ( n=15) and non-liver transplantation group ( n=13). In the liver transplantation group, we analyzed seve-ral critical parameters including methods of liver transplantation, intra-abdominal fluid volume, intraoperative blood loss, intraoperative red blood cell transfusion requirements, and postoperative complications. The prognosis of the two groups were also compared. Results:Among the 28 patients with PLD, 15 underwent liver transplantation, including 11 classic in situ liver transplantations, one modified back-to-back liver transplantation, and three liver-kidney combined transplantations. The 15 patients had 2 000 (300, 4 000) ml of abdominal fluid, 1 000 (600, 2 000) ml of intraoperative blood loss, and 8.0 (6.0, 17.0) U of red blood cells transfused during the operation. Postoperative complications occurred in eight cases, with four of which were managemed successfully, and the other four died. The 1-, 5-, and 10-year survival rates of after liver transplantation were 80.0%, 80.0%, and 73.3%, respectively. The 1-, 5-, and 10-year survival rates of patients with PLD without liver transplantation were 69.2%, 46.2%, and 38.5%, respectively. The difference between the two groups was statistically significant ( χ2=3.91, P=0.048). Conclusion:Liver transplantation is a treatment option for patients with PLD, with a better long-term survival compared to patients without liver transplantation.

Objective:To investigate the impact of donor age on early postoperative liver function recovery in liver transplant recipients, as well as the incidence and risk factors for arterial complications following liver transplantation.Methods:A total of 518 patients who underwent liver transplantation at the Organ Transplantation Center of the Affiliated Hospital of Qingdao University between January 2021 and January 2024 were included in the study. Based on donor age, patients were classified into the elderly donor group (≥70 years, n=28) and the non-elderly donor group (<70 years, n=490). Liver function indicators—including aspartate aminotransferase (AST), alanine aminotransferase (ALT), total bilirubin (TBIL), and direct bilirubin (DBIL)—were measured on postoperative days 1, 3, 7, and 14. The incidence of arterial complications, including hepatic artery thrombosis and hepatic artery stenosis, was recorded. Recipients were further categorized into the arterial complication group (n=26) and the non-arterial complication group (n=492) based on postoperative outcomes, and clinical characteristics of donors and recipients were compared. Binary logistic regression analysis was conducted to identify risk factors for arterial complications.Rusults:No significant differences were observed in baseline characteristics between the elderly and non-elderly donor groups ( P>0.05). However, the elderly donor group exhibited significantly higher AST, ALT, TBIL, and DBIL levels at all postoperative time points compared to the non-elderly donor group (all P<0.05). Specifically, on postoperative day 1, AST and ALT levels were (1,024.57±256.49) U/L and (756.24±145.89) U/L in the elderly donor group, compared to (895.23±225.19) U/L and (614.85±126.51) U/L in the non-elderly donor group. On day 3, AST and ALT levels were (402.46±71.61) U/L and (423.31±87.44) U/L versus (226.37±66.54) U/L and (256.79±70.25) U/L, respectively. On day 7, AST and ALT levels were (91.78±21.84) U/L and (92.36±21.62) U/L versus (68.41±18.38) U/L and (77.47±18.16) U/L. By day 14, AST and ALT levels were (67.52±10.35) U/L and (72.17±16.28) U/L versus (35.32±9.27) U/L and (48.56±14.10) U/L, respectively ( P<0.05 for all comparisons). For bilirubin indicators, TBIL and DBIL levels in the elderly donor group were also consistently higher than in the non-elderly donor group. On day 1, TBIL and DBIL were (95.76±21.93) μmol/L and (64.22±15.07) μmol/L, compared to (77.59±20.48) μmol/L and (51.18±12.96) μmol/L. By day 14, TBIL and DBIL levels had decreased to (41.26±8.30) μmol/L and (32.45±6.21) μmol/L, compared to (28.39±7.15) μmol/L and (20.58±5.04) μmol/L in the non-elderly donor group ( P<0.05 for all comparisons). The incidence of hepatic artery complications was 10.71% (3/28) in the elderly donor group and 4.69% (23/490) in the non-elderly donor group, with no statistically significant difference between the two groups ( P>0.05). Statistical analysis employing independent t-tests and χ2 tests demonstrated significant differences between the arterial complication group and non-arterial complication group in donor quality ratio ( P<0.05) and incidence of hepatic arterial hypoperfusion ( P<0.05). Multivariate binary logistic regression analysis, after adjusting for confounding factors (e.g., recipient gender, age, body mass index [BMI], primary disease, and donor-recipient blood type compatibility), identified recipient-to-donor mass ratio ( OR=1.352, P<0.05) and insufficient hepatic arterial blood flow ( OR=1.497, P<0.05) as independent risk factors for arterial complications following liver transplantation. Conclusion:Elderly liver donors can have a certain impact on early postoperative liver function recovery in liver transplant recipients, but have no significant impact on the occurrence of arterial complications after liver transplantation. The mass ratio of recipients to donors and insufficient hepatic arterial blood flow are independent risk factors for arterial complications after liver transplantation.