Molnar Amber O, van Walraven Carl, Fergusson Dean, Garg Amit X, Knoll Greg
Division of Nephrology, Department of Medicine, McMaster University, Hamilton, Ontario, Canada; Institute for Clinical Evaluative Sciences, London, Ontario, Canada.
Institute for Clinical Evaluative Sciences, London, Ontario, Canada; Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ontario, Canada; Department of Medicine, University of Ottawa, Ontario, Canada.
Can J Kidney Health Dis. 2017 Jan 30;4:2054358116688228. doi: 10.1177/2054358116688228. eCollection 2017.
Acute kidney injury (AKI) is common in the kidney transplant population.
To derive a multivariable survival model that predicts time to graft loss following AKI.
Retrospective cohort study using health care administrative and laboratory databases.
Southwestern Ontario (1999-2013) and Ottawa, Ontario, Canada (1996-2013).
We included first-time kidney only transplant recipients who had a hospitalization with AKI 6 months or greater following transplant.
AKI was defined using the Acute Kidney Injury Network criteria (stage 1 or greater). The first episode of AKI was included in the analysis. Graft loss was defined by return to dialysis or repeat kidney transplant.
We performed a competing risk survival regression analysis using the Fine and Gray method and modified the model into a simple point system. Graft loss with death as a competing event was the primary outcome of interest.
A total of 315 kidney transplant recipients who had a hospitalization with AKI 6 months or greater following transplant were included. The median (interquartile range) follow-up time was 6.7 (3.3-10.3) years. Graft loss occurred in 27.6% of the cohort. The final model included 6 variables associated with an increased risk of graft loss: younger age, increased severity of AKI, failure to recover from AKI, lower baseline estimated glomerular filtration rate, increased time from kidney transplant to AKI admission, and receipt of a kidney from a deceased donor. The risk score had a concordance probability of 0.75 (95% confidence interval [CI], 0.69-0.82). The predicted 5-year risk of graft loss fell within the 95% CI of the observed risk more than 95% of the time.
The CIs of the estimates were wide, and model overfitting is possible due to the limited sample size; the risk score requires validation to determine its clinical utility.
Our prognostic risk score uses commonly available information to predict the risk of graft loss in kidney transplant patients hospitalized with AKI. If validated, this predictive model will allow clinicians to identify high-risk patients who may benefit from closer follow-up or targeted enrollment in future intervention trials designed to improve outcomes.
急性肾损伤(AKI)在肾移植人群中很常见。
推导一个多变量生存模型,以预测AKI后移植肾失功的时间。
利用医疗保健管理和实验室数据库进行回顾性队列研究。
加拿大安大略省西南部(1999 - 2013年)和渥太华(1996 - 2013年)。
我们纳入了首次仅接受肾移植且在移植后6个月或更长时间因AKI住院的受者。
AKI根据急性肾损伤网络标准(1期或更高分期)定义。分析纳入首次发生的AKI。移植肾失功定义为恢复透析或再次肾移植。
我们使用Fine和Gray方法进行竞争风险生存回归分析,并将模型修改为一个简单的评分系统。以死亡作为竞争事件的移植肾失功是主要关注的结局。
总共纳入了315例在移植后6个月或更长时间因AKI住院的肾移植受者。中位(四分位间距)随访时间为6.7(3.3 - 10.3)年。队列中27.6%发生了移植肾失功。最终模型包括6个与移植肾失功风险增加相关的变量:年龄较小、AKI严重程度增加、AKI未恢复、基线估计肾小球滤过率较低、从肾移植到因AKI入院的时间增加以及接受来自 deceased donor的肾脏。风险评分的一致性概率为0.75(95%置信区间[CI],0.69 - 0.82)。在超过95%的时间里,预测的5年移植肾失功风险落在观察到的风险的95%CI范围内。
估计值的CI较宽,由于样本量有限可能存在模型过度拟合的情况;风险评分需要验证以确定其临床实用性。
我们的预后风险评分利用常见信息来预测因AKI住院的肾移植患者移植肾失功的风险。如果得到验证,这个预测模型将使临床医生能够识别可能从更密切的随访或未来旨在改善结局的干预试验的靶向入组中获益的高危患者。