Division of Nephrology, St. Michael's Hospital and University of Toronto, Toronto, Canada.
Division of Nephrology, St. Michael's Hospital and University of Toronto, Toronto, Canada; ICES, Ontario, Canada.
J Crit Care. 2020 Apr;56:113-119. doi: 10.1016/j.jcrc.2019.12.015. Epub 2019 Dec 18.
Severe acute kidney injury (AKI) is associated with a significant risk of mortality and persistent renal replacement therapy (RRT) dependence. The objective of this study was to develop prediction models for mortality at 90-day and 1-year following RRT initiation in critically ill patients with AKI.
All patients who commenced RRT in the intensive care unit for AKI at a tertiary care hospital between 2007 and 2014 constituted the development cohort. We evaluated the external validity of our mortality models using data from the multicentre OPTIMAL-AKI study.
The development cohort consisted of 594 patients, of whom 320(54%) died and 40 (15% of surviving patients) remained RRT-dependent at 90-day Eleven variables were included in the model to predict 90-day mortality (AUC:0.79, 95%CI:0.76-0.82). The performance of the 90-day mortality model declined upon validation in the OPTIMAL-AKI cohort (AUC:0.61, 95%CI:0.54-0.69) and showed modest calibration. Similar results were obtained for mortality model at 1-year.
Routinely collected variables at the time of RRT initiation have limited ability to predict mortality in critically ill patients with AKI who commence RRT.
严重急性肾损伤(AKI)与死亡率和持续肾脏替代治疗(RRT)依赖显著相关。本研究的目的是为 RRT 起始后 90 天和 1 年发生 AKI 的危重症患者建立死亡率预测模型。
本研究纳入了 2007 年至 2014 年期间在一家三级护理医院重症监护病房因 AKI 开始 RRT 的所有患者作为开发队列。我们使用来自多中心 OPTIMAL-AKI 研究的数据评估了我们的死亡率模型的外部有效性。
开发队列包括 594 例患者,其中 320 例(54%)死亡,40 例(存活患者的 15%)在 90 天仍依赖 RRT。有 11 个变量被纳入预测 90 天死亡率的模型(AUC:0.79,95%CI:0.76-0.82)。该 90 天死亡率模型在 OPTIMAL-AKI 队列中的验证中表现不佳(AUC:0.61,95%CI:0.54-0.69),且校准效果一般。在 1 年时,死亡率模型也得到了类似的结果。
在开始 RRT 时收集的常规变量对开始 RRT 的 AKI 危重症患者的死亡率预测能力有限。