Department of Critical Care, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
Division of Intensive Care Medicine, Department of Anesthesiology, Intensive Care and Pain Medicine, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.
BMC Nephrol. 2019 Dec 31;21(1):1. doi: 10.1186/s12882-019-1645-y.
Mortality rates associated with acute kidney injury (AKI) vary among critically ill patients. Outcomes are often not corrected for severity or duration of AKI. Our objective was to analyse whether a new variable, AKI burden, would outperform 1) presence of AKI, 2) highest AKI stage, or 3) AKI duration in predicting 90-day mortality.
Kidney Diseases: Improving Global Outcomes (KDIGO) criteria using creatinine, urine output and renal replacement therapy were used to diagnose AKI. AKI burden was defined as AKI stage multiplied with the number of days that each stage was present (maximum five), divided by the maximum possible score yielding a proportion. The AKI burden as a predictor of 90-day mortality was assessed in two independent cohorts (Finnish Acute Kidney Injury, FINNAKI and Simple Intensive Care Studies I, SICS-I) by comparing four multivariate logistic regression models that respectively incorporated either the presence of AKI, the highest AKI stage, the duration of AKI, or the AKI burden.
In the FINNAKI cohort 1096 of 2809 patients (39%) had AKI and 90-day mortality of the cohort was 23%. Median AKI burden was 0.17 (IQR 0.07-0.50), 1.0 being the maximum. The model including AKI burden (area under the receiver operator curve (AUROC) 0.78, 0.76-0.80) outperformed the models using AKI presence (AUROC 0.77, 0.75-0.79, p = 0.026) or AKI severity (AUROC 0.77, 0.75-0.79, p = 0.012), but not AKI duration (AUROC 0.77, 0.75-0.79, p = 0.06). In the SICS-I, 603 of 1075 patients (56%) had AKI and 90-day mortality was 28%. Median AKI burden was 0.19 (IQR 0.08-0.46). The model using AKI burden performed better (AUROC 0.77, 0.74-0.80) than the models using AKI presence (AUROC 0.75, 0.71-0.78, p = 0.001), AKI severity (AUROC 0.76, 0.72-0.79. p = 0.008) or AKI duration (AUROC 0.76, 0.73-0.79, p = 0.009).
AKI burden, which appreciates both severity and duration of AKI, was superior to using only presence or the highest stage of AKI in predicting 90-day mortality. Using AKI burden or other more granular methods may be helpful in future epidemiological studies of AKI.
急性肾损伤（AKI）相关死亡率在重症患者中存在差异。结果通常未校正 AKI 的严重程度或持续时间。我们的目的是分析新变量 AKI 负担是否优于 1）AKI 的存在，2）最高 AKI 分期，或 3）预测 90 天死亡率的 AKI 持续时间。
采用肾脏疾病：改善全球结局（KDIGO）标准，使用肌酐、尿量和肾脏替代治疗来诊断 AKI。AKI 负担定义为 AKI 分期乘以每个分期存在的天数（最多 5 天），除以最大可能得分，得出一个比例。在两个独立的队列（芬兰急性肾损伤，FINNAKI 和简单重症监护研究 I，SICS-I）中，通过比较四个多变量逻辑回归模型来评估 AKI 负担作为 90 天死亡率的预测因子，这四个模型分别包含 AKI 的存在、最高 AKI 分期、AKI 的持续时间或 AKI 负担。
在 FINNAKI 队列中，2809 例患者中有 1096 例（39%）发生 AKI，该队列的 90 天死亡率为 23%。中位 AKI 负担为 0.17（IQR 0.07-0.50），最大值为 1.0。包含 AKI 负担的模型（接受者操作特征曲线下面积（AUROC）0.78，0.76-0.80）优于使用 AKI 存在的模型（AUROC 0.77，0.75-0.79，p=0.026）或 AKI 严重程度的模型（AUROC 0.77，0.75-0.79，p=0.012），但不优于 AKI 持续时间的模型（AUROC 0.77，0.75-0.79，p=0.06）。在 SICS-I 中，1075 例患者中有 603 例（56%）发生 AKI，90 天死亡率为 28%。中位 AKI 负担为 0.19（IQR 0.08-0.46）。使用 AKI 负担的模型表现优于使用 AKI 存在的模型（AUROC 0.77，0.74-0.80）（p=0.001）、AKI 严重程度的模型（AUROC 0.76，0.72-0.79，p=0.008）或 AKI 持续时间的模型（AUROC 0.76，0.73-0.79，p=0.009）。
AKI 负担既考虑了 AKI 的严重程度，也考虑了其持续时间，在预测 90 天死亡率方面优于仅使用 AKI 的存在或最高分期。在未来的 AKI 流行病学研究中，使用 AKI 负担或其他更精细的方法可能会有所帮助。
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