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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.
American Journal of Physiology-Renal Physiology – The American Physiological Society
Published: Dec 1, 2020
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