Background: Sepsis-associated acute kidney injury (AKI) is a complex clinical disorder associated with inflammation, endothelial dysfunction, and dysregulated coagulation. With standard regression methods, collinearity among biomarkers may lead to the exclusion of important biological pathways in a single final model. Best subsets regression is an analytic technique that identifies statistically equivalent models, allowing for more robust evaluation of correlated variables. Our objective was to identify common clinical characteristics and biomarkers associated with sepsis-associated AKI. Methods: We enrolled 453 septic adults within 24 hours of intensive care unit admission. Using best subsets regression, we evaluated for associations using a range of models consisting of 1 to 38 predictors (composed of clinical risk factors, plasma and urine biomarkers) with AKI as the outcome (defined as serum creatinine (Scr) increase ≥0.3mg/dL within 48 hours or ≥1.5x baseline Scr within 7 days). Results: 297 patients had AKI. Five-variable models were found to be of optimal complexity as the best subset of five- and six-variable models were statistically equivalent. Within the subset of five-variable models, 46 permutations of predictors were noted to be statistically equivalent. The most common predictors in this subset include diabetes, baseline Scr, Ang2, IL8, sTNFR1, and urine NGAL. The models had a c-statistic of ~0.70 [95% CI 0.65-0.75]. Conclusions: Using best subsets regression, we identified common clinical characteristics and biomarkers associated with sepsis-associated AKI. These variables may be especially relevant in the pathogenesis of sepsis-associated AKI.

中文翻译：

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使用最佳子集回归来识别与脓毒症相关的急性肾损伤相关的临床特征和生物标志物

背景：脓毒症相关急性肾损伤（AKI）是一种与炎症、内皮功能障碍和凝血失调相关的复杂临床疾病。使用标准回归方法，生物标志物之间的共线性可能导致在单个最终模型中排除重要的生物途径。最佳子集回归是一种分析技术，可识别统计上等效的模型，从而可以对相关变量进行更稳健的评估。我们的目标是确定与脓毒症相关 AKI 相关的常见临床特征和生物标志物。方法：我们招募了 453 名入重症监护病房 24 小时内患有脓毒症的成人。使用最佳子集回归，我们使用由 1 至 38 个预测因子（由临床危险因素、血浆和尿液生物标志物组成）组成的一系列模型来评估关联性，并以 AKI 作为结果（定义为血清肌酐 (Scr) 增加 ≥0.3mg/ 48 小时内 dL 或 7 天内 ≥1.5 倍基线 Scr）。结果：297 名患者患有 AKI。人们发现五变量模型具有最佳复杂性，因为五变量和六变量模型的最佳子集在统计上是等效的。在五变量模型的子集中，有 46 个预测变量的排列在统计上是等效的。该子集中最常见的预测因子包括糖尿病、基线 Scr、Ang2、IL8、sTNFR1 和尿液 NGAL。这些模型的 c 统计量约为 0.70 [95% CI 0.65-0.75]。结论：利用最佳子集回归，我们确定了与脓毒症相关 AKI 相关的常见临床特征和生物标志物。这些变量可能与脓毒症相关 AKI 的发病机制特别相关。