Kaltenmeier Christof, Ashwat Eishan, Liu Hao, Elias Charbel, Rahman Amaan, Mail-Anthony Jason, Neckermann Isabel, Dharmayan Stalin, Crane Andrew, Packiaraj Godwin, Ayloo Subhashini, Ganoza Armando, Gunabushanam Vikraman, Molinari Michele
Department of Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA.
Department of Surgery, Leicester General Hospital, Leicester, United Kingdom.
Transplant Direct. 2024 Nov 15;10(12):e1724. doi: 10.1097/TXD.0000000000001724. eCollection 2024 Dec.
We compared the performance of the Liver Transplant Risk Score (LTRS) with the survival outcomes following liver transplantation (SOFT), pretransplant SOFT (P-SOFT), Balance of Risk Score (BAR), donor-age and model for end-stage liver disease (D-MELD), and Organ Procurement and Transplantation Network Risk Prediction Score (ORPS) for the prediction of 90-d mortality, 1-y mortality, and 5-y survival after first-time liver transplantation (LT).
A retrospective analysis of the Scientific Registry of Transplant Recipients was conducted using data collected between 2002 and 2021.
A total of 82 696 adult LT recipients with a median age of 56 y were included. The area under the curve for 90-d mortality were 0.61, 0.66, 0.65, 0.61, 0.58, and 0.56 for the LTRS, SOFT, P-SOFT, BAR, D-MELD, and ORPS, respectively (all pairwise comparisons: < 0.05). The area under the curve for 1-y mortality were 0.60, 0.63, 0.62, 0.59, 0.60, 0.57, and 0.59 for the LTRS, SOFT, P-SOFT, BAR, D-MELD, and ORPS, respectively (all pairwise comparisons: < 0.05). The c-statistics for 5-y survival were not statistically significant among the models. For 90-d mortality, 1-y mortality, and 5-y survival, the correlation coefficients between the LTRS and P-SOFT (the 2 models requiring only preoperative parameters) were 0.90. 0.91, and 0.81, respectively ( < 0.01).
None of the predictive models demonstrated sufficient precision to reliably identify LT recipients who died within 90 d and 1 y after LT. However, all models exhibited strong capabilities in perioperative risk stratification. Notably, the P-SOFT and LTRS models, the 2 models that can be calculated using only preoperative data, proved to be valuable tools for identifying candidates at a significant risk of poor outcomes.
我们比较了肝移植风险评分（LTRS）与肝移植后生存结果（SOFT）、移植前SOFT（P-SOFT）、风险平衡评分（BAR）、供体年龄和终末期肝病模型（D-MELD）以及器官获取与移植网络风险预测评分（ORPS）在预测首次肝移植（LT）后90天死亡率、1年死亡率和5年生存率方面的表现。
利用2002年至2021年期间收集的数据对移植受者科学登记处进行了回顾性分析。
共纳入82696例成年LT受者，中位年龄为56岁。LTRS、SOFT、P-SOFT、BAR、D-MELD和ORPS预测90天死亡率的曲线下面积分别为0.61、0.66、0.65、0.61、0.58和0.56（所有两两比较：<0.05）。LTRS、SOFT、P-SOFT、BAR、D-MELD和ORPS预测1年死亡率的曲线下面积分别为0.60、0.63、0.62、0.59、0.60、0.57和0.59（所有两两比较：<0.05）。各模型间5年生存率的c统计量无统计学意义。对于90天死亡率、1年死亡率和5年生存率，LTRS与P-SOFT（仅需术前参数的2个模型）之间的相关系数分别为0.90、0.91和0.81（<0.01）。
没有一个预测模型显示出足够的精度来可靠地识别LT术后90天和1年内死亡的LT受者。然而，所有模型在围手术期风险分层方面都表现出强大的能力。值得注意的是，P-SOFT和LTRS模型这两个仅使用术前数据即可计算的模型，被证明是识别预后不良高风险候选者的有价值工具。
