A Prediction Model for Severe AKI in Critically Ill Adults That Incorporates Clinical and Biomarker Data

Critically ill patients with worsening AKI are at high risk for poor outcomes. Predicting which patients will experience progression of AKI remains elusive. We sought to develop and validate a risk model for predicting severe AKI within 72 hours after intensive care unit admission.

We applied least absolute shrinkage and selection operator regression methodology to two prospectively enrolled, critically ill cohorts of patients who met criteria for the systemic inflammatory response syndrome, enrolled within 24-48 hours after hospital admission. The risk models were derived and internally validated in 1075 patients and externally validated in 262 patients. Demographics and laboratory and plasma biomarkers of inflammation or endothelial dysfunction were used in the prediction models. Severe AKI was defined as Kidney Disease Improving Global Outcomes (KDIGO) stage 2 or 3.

Severe AKI developed in 62 (8%) patients in the derivation, 26 (8%) patients in the internal validation, and 15 (6%) patients in the external validation cohorts. In the derivation cohort, a three-variable model (age, cirrhosis, and soluble TNF receptor-1 concentrations [ACT]) had a c-statistic of 0.95 (95% confidence interval [95% CI], 0.91 to 0.97). The ACT model performed well in the internal (c-statistic, 0.90; 95% CI, 0.82 to 0.96) and external (c-statistic, 0.93; 95% CI, 0.89 to 0.97) validation cohorts. The ACT model had moderate positive predictive values (0.50-0.95) and high negative predictive values (0.94-0.95) for severe AKI in all three cohorts.

ACT is a simple, robust model that could be applied to improve risk prognostication and better target clinical trial enrollment in critically ill patients with AKI.