Institute of Sciences in Emergency Medicine, Department of Emergency Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
Medical Research Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
Nephrol Dial Transplant. 2023 Sep 29;38(10):2368-2378. doi: 10.1093/ndt/gfad065.
Due to the convenience of serum creatinine (SCr) monitoring and the relative complexity of urine output (UO) monitoring, most studies have predicted acute kidney injury (AKI) only based on SCr criteria. This study aimed to compare the differences between SCr alone and combined UO criteria in predicting AKI.
We applied machine learning methods to evaluate the performance of 13 prediction models composed of different feature categories on 16 risk assessment tasks (half used only SCr criteria, half used both SCr and UO criteria). The area under receiver operator characteristic curve (AUROC), the area under precision recall curve (AUPRC) and calibration were used to assess the prediction performance.
In the first week after ICU admission, the prevalence of any AKI was 29% under SCr criteria alone and increased to 60% when the UO criteria was combined. Adding UO to SCr criteria can significantly identify more AKI patients. The predictive importance of feature types with and without UO was different. Using only laboratory data maintained similar predictive performance to the full feature model under only SCr criteria [e.g. for AKI within the 48-h time window after 1 day of ICU admission, AUROC (95% confidence interval) 0.83 (0.82, 0.84) vs 0.84 (0.83, 0.85)], but it was not sufficient when the UO was added [corresponding AUROC (95% confidence interval) 0.75 (0.74, 0.76) vs 0.84 (0.83, 0.85)].
This study found that SCr and UO measures should not be regarded as equivalent criteria for AKI staging, and emphasizes the importance and necessity of UO criteria in AKI risk assessment.
由于血清肌酐 (SCr) 监测方便，尿液输出 (UO) 监测相对复杂，大多数研究仅根据 SCr 标准预测急性肾损伤 (AKI)。本研究旨在比较单独使用 SCr 和联合 UO 标准预测 AKI 的差异。
我们应用机器学习方法评估由不同特征类别组成的 13 个预测模型在 16 个风险评估任务上的性能（一半仅使用 SCr 标准，一半同时使用 SCr 和 UO 标准）。使用接收者操作特征曲线下面积 (AUROC)、精度召回曲线下面积 (AUPRC) 和校准来评估预测性能。
在 ICU 入院后的第一周，仅根据 SCr 标准，任何 AKI 的患病率为 29%，当联合使用 UO 标准时，患病率增加到 60%。将 UO 添加到 SCr 标准可以显著识别更多的 AKI 患者。有和没有 UO 的特征类型的预测重要性不同。仅使用实验室数据在仅使用 SCr 标准的情况下保持与全特征模型相似的预测性能[例如，在 ICU 入院后 1 天的 48 小时时间窗口内发生 AKI，AUROC（95%置信区间）为 0.83（0.82，0.84）与 0.84（0.83，0.85）]，但当添加 UO 时则不够[相应的 AUROC（95%置信区间）为 0.75（0.74，0.76）与 0.84（0.83，0.85）]。
本研究发现，SCr 和 UO 测量不应被视为 AKI 分期的等效标准，并强调了 UO 标准在 AKI 风险评估中的重要性和必要性。
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