Massachusetts Institute of Technology, Cambridge, MA, USA.
Beth Israel Deaconess Medical Center, Boston, MA, USA.
Sci Rep. 2021 Oct 1;11(1):19561. doi: 10.1038/s41598-021-97735-0.
Acute kidney injury (AKI) is common in the intensive care unit, where it is associated with increased mortality. AKI is often defined using creatinine and urine output criteria. The creatinine-based definition is more reliable but less expedient, whereas the urine output based definition is rapid but less reliable. Our goal is to examine the urine output criterion and augment it with physiological features for better agreement with creatinine-based definitions of AKI. The objectives are threefold: (1) to characterize the baseline agreement of urine output and creatinine definitions of AKI; (2) to refine the urine output criteria to identify the thresholds that best agree with the creatinine-based definition; and (3) to build generalized estimating equation (GEE) and generalized linear mixed-effects (GLME) models with static and time-varying features to improve the accuracy of a near-real-time marker for AKI. We performed a retrospective observational study using data from two independent critical care databases, MIMIC-III and eICU, for critically ill patients who developed AKI in intensive care units. We found that the conventional urine output criterion (6 hr, 0.5 ml/kg/h) has specificity and sensitivity of 0.49 and 0.54 for MIMIC-III database; and specificity and sensitivity of 0.38 and 0.56 for eICU. Secondly, urine output thresholds of 12 hours and 0.6 ml/kg/h have specificity and sensitivity of 0.58 and 0.48 for MIMIC-III; and urine output thresholds of 10 hours and 0.6 ml/kg/h have specificity and sensitivity of 0.49 and 0.48 for eICU. Thirdly, the GEE model of four hours duration augmented with static and time-varying features can achieve a specificity and sensitivity of 0.66 and 0.61 for MIMIC-III; and specificity and sensitivity of 0.66 and 0.64 for eICU. The GLME model of four hours duration augmented with static and time-varying features can achieve a specificity and sensitivity of 0.71 and 0.55 for MIMIC-III; and specificity and sensitivity of 0.66 and 0.60 for eICU. The GEE model has greater performance than the GLME model, however, the GLME model is more reflective of the variables as fixed effects or random effects. The significant improvement in performance, relative to current definitions, when augmenting with patient features, suggest the need of incorporating these features when detecting disease onset and modeling at window-level rather than patient-level.
急性肾损伤 (AKI) 在重症监护病房很常见，与死亡率增加有关。AKI 通常使用肌酐和尿量标准来定义。基于肌酐的定义更可靠但不太方便，而基于尿量的定义则快速但不太可靠。我们的目标是检查尿量标准，并通过生理特征来增强它，以更好地与基于肌酐的 AKI 定义相吻合。目标有三个方面：(1) 描述 AKI 的基于尿量和肌酐的定义的基线一致性；(2) 改进尿量标准，以确定与基于肌酐的定义最吻合的阈值；(3) 建立广义估计方程 (GEE) 和广义线性混合效应 (GLME) 模型，使用静态和时变特征来提高 AKI 的近实时标志物的准确性。我们使用来自两个独立的重症监护数据库（MIMIC-III 和 eICU）的重症患者数据进行了回顾性观察性研究，这些患者在重症监护病房发生了 AKI。我们发现，传统的尿量标准（6 小时，0.5 ml/kg/h）在 MIMIC-III 数据库中具有 0.49 的特异性和 0.54 的敏感性；在 eICU 中具有 0.38 的特异性和 0.56 的敏感性。其次，12 小时和 0.6 ml/kg/h 的尿量阈值在 MIMIC-III 中具有 0.58 的特异性和 0.48 的敏感性；在 eICU 中，10 小时和 0.6 ml/kg/h 的尿量阈值具有 0.49 的特异性和 0.48 的敏感性。第三，持续 4 小时的 GEE 模型与静态和时变特征相结合，可以实现 MIMIC-III 特异性和敏感性分别为 0.66 和 0.61；在 eICU 中特异性和敏感性分别为 0.66 和 0.64。持续 4 小时的 GLME 模型与静态和时变特征相结合，可以实现 MIMIC-III 特异性和敏感性分别为 0.71 和 0.55；在 eICU 中特异性和敏感性分别为 0.66 和 0.60。与 GLME 模型相比，GEE 模型具有更好的性能，但是，GLME 模型更能反映作为固定效应或随机效应的变量。在使用患者特征增强时，与当前定义相比，性能显著提高，这表明在检测疾病发作和在窗口级而不是患者级建模时，需要纳入这些特征。
