Bakar Khandoker Shuvo, Teixeira-Pinto Armando, Gately Ryan, Boroumand Farzaneh, Lim Wai H, Wong Germaine
Sydney School of Public Health, University of Sydney, Sydney, Australia.
Centre for Kidney Research, Kids Research Institute, The Children's Hospital at Westmead, Sydney, Australia.
Clin Kidney J. 2024 Oct 16;17(11):sfae314. doi: 10.1093/ckj/sfae314. eCollection 2024 Nov.
Allograft loss is the most feared outcome of kidney transplant recipients. We aimed to develop a dynamic Bayesian model using estimated glomerular filtration rate (eGFR) trajectories to predict long-term allograft and patient survivals.
We used data from the Australian and New Zealand Dialysis and Transplant registry and included all adult kidney transplant recipients (1980-2017) in Australia (derivation cohort) and New Zealand (NZ, validation cohort). Using a joint model, the temporal changes of eGFR trajectories were used to predict patient and allograft survivals.
The cohort composed of 14 915 kidney transplant recipients [12 777 (86%) from Australia and 2138 (14%) from NZ] who were followed for a median of 8.9 years. In the derivation cohort, eGFR trajectory was inversely associated with allograft loss [every 10 ml/min/1.73 m reduction in eGFR, adjusted hazard ratio [HR, 95% credible intervals (95%CI) 1.31 (1.23-1.39)] and death [1.12 (1.10-1.14)]. Similar estimates were observed in the validation cohort. The respective dynamic area under curve (AUC) (95%CI) estimates for predicting allograft loss at 5-years post-transplantation were 0.83 (0.75-0.91) and 0.81 (0.68-0.93) for the derivation and validation cohorts.
This straightforward model, using a single metric of eGFR trajectory, shows good model performance, and effectively distinguish transplant recipients who are at risk of death and allograft loss from those who are not. This simple bedside tool may facilitate early identification of individuals at risk of allograft loss and death.
移植肾失功是肾移植受者最令人担忧的结局。我们旨在开发一种动态贝叶斯模型，利用估计肾小球滤过率（eGFR）轨迹来预测移植肾和患者的长期生存率。
我们使用了澳大利亚和新西兰透析与移植登记处的数据，纳入了澳大利亚（推导队列）和新西兰（验证队列）所有成年肾移植受者（1980 - 2017年）。使用联合模型，eGFR轨迹的时间变化用于预测患者和移植肾的生存率。
该队列由14915名肾移植受者组成[12777名（86%）来自澳大利亚，2138名（14%）来自新西兰]，中位随访时间为8.9年。在推导队列中，eGFR轨迹与移植肾失功呈负相关[每降低10 ml/min/1.73 m²的eGFR，校正风险比[HR，95%可信区间（95%CI）为1.31（1.23 - 1.39）]和死亡[1.12（1.10 - 1.14）]。在验证队列中观察到类似的估计值。推导队列和验证队列在移植后5年预测移植肾失功的各自动态曲线下面积（AUC）（95%CI）估计值分别为0.83（0.75 - 0.91）和0.81（0.68 - 0.93）。
这个简单的模型，使用单一的eGFR轨迹指标，显示出良好的模型性能，并能有效区分有死亡和移植肾失功风险的移植受者与无风险者。这个简单的床边工具可能有助于早期识别有移植肾失功和死亡风险的个体。
