Rogers Michael P, Janjua Haroon M, Read Meagan, Cios Konrad, Kundu Madan G, Pietrobon Ricardo, Kuo Paul C
From the OnetoMAP Analytics, Department of Surgery, University of South Florida Morsani College of Medicine, Tampa, FL (Rogers, Janjua, Read, Cios, Kuo).
the Daiichi Sankyo, Inc, Basking Ridge, NJ (Kundu).
J Am Coll Surg. 2023 Apr 1;236(4):563-572. doi: 10.1097/XCS.0000000000000545. Epub 2023 Jan 5.
Elucidating contributors affecting liver transplant survival is paramount. Current methods offer crude global group outcomes. To refine patient-specific mortality probability estimation and to determine covariate interaction using recipient and donor data, we generated a survival tree algorithm, Recipient Survival After Orthotopic Liver Transplantation (ReSOLT), using United Network Organ Sharing (UNOS) transplant data.
The UNOS database was queried for liver transplants in patients ≥18 years old between 2000 and 2021. Preoperative factors were evaluated with stepwise logistic regression; 43 significant factors were used in survival tree modeling. Graft survival of <7 days was excluded. The data were split into training and testing sets and further validated with 10-fold cross-validation. Survival tree pruning and model selection was achieved based on Akaike information criterion and log-likelihood values. Log-rank pairwise comparisons between subgroups and estimated survival probabilities were calculated.
A total of 122,134 liver transplant patients were included for modeling. Multivariable logistic regression (area under the curve = 0.742, F1 = 0.822) and survival tree modeling returned 8 significant recipient survival factors: recipient age, donor age, recipient primary payment, recipient hepatitis C status, recipient diabetes, recipient functional status at registration and at transplantation, and deceased donor pulmonary infection. Twenty subgroups consisting of combinations of these factors were identified with distinct Kaplan-Meier survival curves (p < 0.001 among all by log rank test) with 5- and 10-year survival probabilities.
Survival trees are a flexible and effective approach to understand the effects and interactions of covariates on survival. Individualized survival probability following liver transplant is possible with ReSOLT, allowing for more coherent patient and family counseling and prediction of patient outcome using both recipient and donor factors.
阐明影响肝移植存活的因素至关重要。当前的方法只能提供粗略的总体群体结果。为了优化患者特异性死亡概率估计，并利用受者和供者数据确定协变量相互作用，我们使用器官共享联合网络（UNOS）移植数据生成了一种生存树算法，即原位肝移植后受者生存（ReSOLT）。
查询UNOS数据库，获取2000年至2021年间18岁及以上患者的肝移植数据。术前因素通过逐步逻辑回归进行评估；43个显著因素用于生存树建模。排除移植后7天内的移植物存活情况。数据被分为训练集和测试集，并通过10倍交叉验证进一步验证。基于赤池信息准则和对数似然值进行生存树剪枝和模型选择。计算亚组之间的对数秩成对比较和估计的生存概率。
共有122134例肝移植患者纳入建模。多变量逻辑回归（曲线下面积=0.742，F1=0.822）和生存树建模得出8个影响受者生存的显著因素：受者年龄、供者年龄、受者主要支付方式、受者丙型肝炎状态、受者糖尿病、登记时和移植时受者功能状态以及已故供者肺部感染。由这些因素组合而成的20个亚组被识别出来，其具有不同的Kaplan-Meier生存曲线（通过对数秩检验，所有亚组之间p<0.001）以及5年和10年生存概率。
生存树是一种灵活有效的方法，可用于理解协变量对生存的影响和相互作用。使用ReSOLT可以实现肝移植后的个体化生存概率，从而为患者和家属提供更连贯的咨询，并利用受者和供者因素预测患者预后。
