Department of Surgery, OnetoMap Analytics, University of South Florida Morsani College of Medicine, Tampa, Florida.
Daiichi Sankyo, Inc., Basking Ridge, New Jersey.
J Surg Res. 2024 Jul;299:195-204. doi: 10.1016/j.jss.2024.04.017. Epub 2024 May 17.
Identifying contributors to lung transplant survival is vital in mitigating mortality. To enhance individualized mortality estimation and determine variable interaction, we employed a survival tree algorithm utilizing recipient and donor data.
United Network Organ Sharing data (2000-2021) were queried for single and double lung transplants in adult patients. Graft survival time <7 d was excluded. Sixty preoperative and immediate postoperative factors were evaluated with stepwise logistic regression on mortality; final model variables were included in survival tree modeling. Data were split into training and testing sets and additionally validated with 10-fold cross validation. Survival tree pruning and model selection was based on Akaike information criteria and log-likelihood values. Estimated survival probabilities and log-rank pairwise comparisons between subgroups were calculated.
A total of 27,296 lung transplant patients (8175 single; 19,121 double lung) were included. Stepwise logistic regression yielded 47 significant variables associated with mortality. Survival tree modeling returned six significant factors: recipient age, length of stay from transplant to discharge, recipient ventilator duration post-transplant, double lung transplant, recipient reintubation post-transplant, and donor cytomegalovirus status. Eight subgroups consisting of combinations of these factors were identified with distinct Kaplan-Meier survival curves.
Survival trees provide the ability to understand the effects and interactions of covariates on survival after lung transplantation. Individualized survival probability with this technique found that preoperative and postoperative factors influence survival after lung transplantation. Thus, preoperative patient counseling should acknowledge a degree of uncertainty given the influence of postoperative factors.
确定肺移植存活率的影响因素对于降低死亡率至关重要。为了增强个体死亡率的估计，并确定变量的相互作用，我们使用了一种基于受者和供者数据的生存树算法。
我们从 2000 年至 2021 年的 United Network Organ Sharing 数据中查询了成人患者的单肺和双肺移植数据。排除移植后<7 d 的移植物存活率。我们使用逐步逻辑回归对 60 个术前和术后即刻因素进行了死亡率评估；最终模型变量纳入生存树建模。数据分为训练集和测试集，并进一步进行了 10 倍交叉验证。生存树的修剪和模型选择基于赤池信息量准则和对数似然值。计算了估计的生存概率和亚组间对数秩检验的比较。
共纳入 27296 例肺移植患者（单肺 8175 例，双肺 19121 例）。逐步逻辑回归得出 47 个与死亡率相关的显著变量。生存树模型得出 6 个显著因素：受者年龄、移植到出院的住院时间、移植后受者呼吸机使用时间、双肺移植、移植后受者再插管、供者巨细胞病毒状态。确定了由这些因素组合而成的 8 个亚组，它们具有不同的 Kaplan-Meier 生存曲线。
生存树能够帮助理解术后各种因素对肺移植后生存的影响及其相互作用。使用该技术计算的个体生存概率发现，术前和术后因素均会影响肺移植后的生存。因此，术前患者咨询应考虑到术后因素的影响，承认存在一定程度的不确定性。
