Using best subset regression to identify clinical characteristics and biomarkers associated with sepsis-associated acute kidney injury

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 subset 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. We enrolled 453 septic adults within 24 h of intensive care unit admission. Using best subset regression, we evaluated for associations using a range of models consisting of 1−38 predictors (composed of clinical risk factors and plasma and urine biomarkers) with AKI as the outcome [defined as a serum creatinine (SCr) increase of ≥0.3 mg/dL within 48 h or ≥1.5× baseline SCr within 7 days]. Two hundred ninety-seven 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 included diabetes, baseline SCr, angiopoetin-2, IL-8, soluble tumor necrosis factor receptor-1, and urine neutrophil gelatinase-associated lipocalin. The models had a c-statistic of ∼0.70 (95% confidence interval: 0.65–0.75). In conclusion, using best subset 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.

Procalcitonin as a Marker of Comorbid Atrial Fibrillation in Chronic Kidney Disease and History of Sepsis

Cardiovascular disease and infection are the leading causes of mortality in patients with stage 5 chronic kidney disease on hemodialysis (CKD5-HD). Inflammation is a large component in the pathogenesis of both atrial fibrillation (AF) and sepsis and may link these conditions in CKD5-HD. Procalcitonin (PCT) is an inflammatory biomarker elevated in systemic infection and CKD5-HD, yet its value with regard to comorbid AF has not been thoroughly investigated. The aim of this study sought to evaluate circulating inflammatory markers, including PCT, Angiopoietin-1, Angiopoetin-2, CD40-L, C-reactive protein, d-dimer, and von Willebrand factor in relation to these conditions. Plasma levels of inflammatory markers were measured by enzyme linked immunosorbent assay method in CKD5-HD (n = 97) patients and controls (n = 50). Procalcitonin levels were significantly elevated ( P = .0270) in CKD5-HD with comorbid AF compared to those without AF. Further analysis of patients with a history of sepsis demonstrated significantly elevated levels of PCT ( P = .0405) in those with comorbid AF (160.7 ± 39.5 pg/mL) compared to those without AF (117.4 ± 25.3 pg/mL). This study demonstrates that the inflammatory biomarker PCT is further elevated in the presence of both AF and a history of sepsis in hemodialysis patients and suggests that underlying chronic inflammation following sepsis resolution may place these patients at greater risk of developing AF.