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Abstract：
BACKGROUND: Donor-recipient size mismatch (DRSM) is considered a crucial factor for poor outcomes in liver transplantation (LT) because of complications, such as massive intraoperative blood loss (IBL) and early allograft dysfunction (EAD). Liver volumetry is performed routinely in living donor LT, but rarely in deceased donor LT (DDLT), which amplifies the adverse effects of DRSM in DDLT. Due to the various shortcomings of traditional manual liver volumetry and formula methods, a feasible model based on intelligent/interactive qualitative and quantitative analysis-three-dimensional (IQQA-3D) for estimating the degree of DRSM is needed.
OBJECTIVE: To identify benefits of IQQA-3D liver volumetry in DDLT and establish an estimation model to guide perioperative management.
METHODS: We retrospectively determined the accuracy of IQQA-3D liver volumetry for standard total liver volume (TLV) (sTLV) and established an estimation TLV (eTLV) index (eTLVi) model. Receiver operating characteristic (ROC) curves were drawn to detect the optimal cut-off values for predicting massive IBL and EAD in DDLT using donor sTLV to recipient sTLV (called sTLVi). The factors influencing the occurrence of massive IBL and EAD were explored through logistic regression analysis. Finally, the eTLVi model was compared with the sTLVi model through the ROC curve for verification.
RESULTS: A total of 133 patients were included in the analysis. The Changzheng formula was accurate for calculating donor sTLV (P = 0.083) but not for recipient sTLV (P = 0.036). Recipient eTLV calculated using IQQA-3D highly matched with recipient sTLV (P = 0.221). Alcoholic liver disease, gastrointestinal bleeding, and sTLVi > 1.24 were independent risk factors for massive IBL, and drug-induced liver failure was an independent protective factor for massive IBL. Male donor-female recipient combination, model for end-stage liver disease score, sTLVi ≤ 0.85, and sTLVi ≥ 1.32 were independent risk factors for EAD, and viral hepatitis was an independent protective factor for EAD. The overall survival of patients in the 0.85 < sTLVi < 1.32 group was better compared to the sTLVi ≤ 0.85 group and sTLVi ≥ 1.32 group (P < 0.001). There was no statistically significant difference in the area under the curve of the sTLVi model and IQQA-3D eTLVi model in the detection of massive IBL and EAD (all P > 0.05).
CONCLUSIONS: IQQA-3D eTLVi model has high accuracy in predicting massive IBL and EAD in DDLT. We should follow the guidance of the IQQA-3D eTLVi model in perioperative management.
OBJECTIVE: To identify benefits of IQQA-3D liver volumetry in DDLT and establish an estimation model to guide perioperative management.
METHODS: We retrospectively determined the accuracy of IQQA-3D liver volumetry for standard total liver volume (TLV) (sTLV) and established an estimation TLV (eTLV) index (eTLVi) model. Receiver operating characteristic (ROC) curves were drawn to detect the optimal cut-off values for predicting massive IBL and EAD in DDLT using donor sTLV to recipient sTLV (called sTLVi). The factors influencing the occurrence of massive IBL and EAD were explored through logistic regression analysis. Finally, the eTLVi model was compared with the sTLVi model through the ROC curve for verification.
RESULTS: A total of 133 patients were included in the analysis. The Changzheng formula was accurate for calculating donor sTLV (P = 0.083) but not for recipient sTLV (P = 0.036). Recipient eTLV calculated using IQQA-3D highly matched with recipient sTLV (P = 0.221). Alcoholic liver disease, gastrointestinal bleeding, and sTLVi > 1.24 were independent risk factors for massive IBL, and drug-induced liver failure was an independent protective factor for massive IBL. Male donor-female recipient combination, model for end-stage liver disease score, sTLVi ≤ 0.85, and sTLVi ≥ 1.32 were independent risk factors for EAD, and viral hepatitis was an independent protective factor for EAD. The overall survival of patients in the 0.85 < sTLVi < 1.32 group was better compared to the sTLVi ≤ 0.85 group and sTLVi ≥ 1.32 group (P < 0.001). There was no statistically significant difference in the area under the curve of the sTLVi model and IQQA-3D eTLVi model in the detection of massive IBL and EAD (all P > 0.05).
CONCLUSIONS: IQQA-3D eTLVi model has high accuracy in predicting massive IBL and EAD in DDLT. We should follow the guidance of the IQQA-3D eTLVi model in perioperative management.
摘要：
背景：由于并发症，供体-受体大小不匹配（DRSM）被认为是肝移植（LT）预后不良的关键因素，如术中大量失血（IBL）和早期同种异体移植功能障碍（EAD）。在活体供者LT中常规进行肝脏容积测定，但在已故捐赠者LT（DDLT）中很少，这放大了DRSM在DDLT中的不利影响。由于传统人工肝脏容积法和公式法的各种缺点,需要一种基于智能/交互式定性和定量分析的可行模型-三维（IQQA-3D）来估计DRSM的程度。
目的：确定IQQA-3D肝脏容积测量在DDLT中的益处，并建立评估模型以指导围手术期管理。
方法：我们回顾性地确定了IQQA-3D肝脏容积测量标准肝脏总体积（TLV）（sTLV）的准确性，并建立了估算TLV（eTLV）指数（eTLVi）模型。绘制接受者工作特征（ROC）曲线以检测最佳临界值，以使用供体sTLV到受体sTLV（称为sTLVi）预测DDLT中的大量IBL和EAD。通过logistic回归分析探讨了影响大量IBL和EAD发生的因素。最后,通过ROC曲线对eTLVi模型与sTLVi模型进行比较验证。
结果：共133例患者纳入分析。长征公式用于计算供体sTLV（P=0.083），但不用于受体sTLV（P=0.036）。使用IQQA-3D计算的收件人eTLV与收件人sTLV高度匹配(P=0.221)。酒精性肝病，消化道出血,sTLVi>1.24是大规模IBL的独立危险因素，药物性肝衰竭是大量IBL的独立保护因素。男性捐赠者-女性接受者组合，终末期肝病评分模型，sTLVi≤0.85,sTLVi≥1.32是EAD的独立危险因素,病毒性肝炎是EAD的独立保护因素。0.850.05)。
结论：IQQA-3DeTLVi模型对DDLT中大量IBL和EAD的预测具有较高的准确性。围手术期管理应遵循IQQA-3DeTLVi模型的指导。
目的：确定IQQA-3D肝脏容积测量在DDLT中的益处，并建立评估模型以指导围手术期管理。
方法：我们回顾性地确定了IQQA-3D肝脏容积测量标准肝脏总体积（TLV）（sTLV）的准确性，并建立了估算TLV（eTLV）指数（eTLVi）模型。绘制接受者工作特征（ROC）曲线以检测最佳临界值，以使用供体sTLV到受体sTLV（称为sTLVi）预测DDLT中的大量IBL和EAD。通过logistic回归分析探讨了影响大量IBL和EAD发生的因素。最后,通过ROC曲线对eTLVi模型与sTLVi模型进行比较验证。
结果：共133例患者纳入分析。长征公式用于计算供体sTLV（P=0.083），但不用于受体sTLV（P=0.036）。使用IQQA-3D计算的收件人eTLV与收件人sTLV高度匹配(P=0.221)。酒精性肝病，消化道出血,sTLVi>1.24是大规模IBL的独立危险因素，药物性肝衰竭是大量IBL的独立保护因素。男性捐赠者-女性接受者组合，终末期肝病评分模型，sTLVi≤0.85,sTLVi≥1.32是EAD的独立危险因素,病毒性肝炎是EAD的独立保护因素。0.85
结论：IQQA-3DeTLVi模型对DDLT中大量IBL和EAD的预测具有较高的准确性。围手术期管理应遵循IQQA-3DeTLVi模型的指导。
