A prediction model for severe AKI in Critically Ill adults that incorporates clinical and biomarker data

Abstract

Background and objectives Critically illpatientswithworseningAKI are at high risk forpoor outcomes. Predicting whichpatientswill experienceprogressionofAKI remains elusive.We sought to developandvalidate a riskmodel for predicting severe AKI within 72 hours after intensive care unit admission. Design, setting, participants, & measurements 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, enrolledwithin 24-48 hours after hospital admission. The riskmodels were derived andinternally validatedin 1075 patients andexternally validated in 262 patients.Demographics and laboratoryandplasmabiomarkersof inflammationorendothelialdysfunctionwereused inthepredictionmodels. Severe AKI was defined as Kidney Disease Improving Global Outcomes (KDIGO) stage 2 or 3. Results SevereAKI 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, andsolubleTNFreceptor-1 concentrations [ACT])hada c-statistic of 0.95 (95%confidence interval [95%CI], 0.91 to 0.97). TheACT model performedwell 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. TheACT model hadmoderate positive predictive values (0.50-0.95) and high negative predictive values (0.94-0.95) for severe AKI in all three cohorts. ConclusionsACT is a simple, robustmodel that could be applied to improve risk prognostication and better target clinical trial enrollment in critically ill patients with AKI.