Biomarker Panels for Predicting Progression of Kidney Disease in Acute Kidney Injury Survivors

Key Points Clinical characteristics and biomarkers after hospital discharge can predict major adverse kidney events among AKI survivors. Clinical impact plots based on parsimonious prediction models illustrate the potential to optimize post-AKI care by identifying high-risk patients. Background AKI increases the risk of CKD. We aimed to identify combinations of clinical variables and biomarkers that predict long-term kidney disease risk after AKI. Methods We analyzed data from a prospective cohort of 723 hospitalized patients with AKI in the Assessment, Serial Evaluation, and Subsequent Sequelae of AKI study. Using machine learning, we investigated 75 candidate predictors including biomarkers measured at 3-month postdischarge follow-up to predict major adverse kidney events (MAKEs) within 3 years, defined as a decline in eGFR ≥40%, development of ESKD, or death. Results The mean age of study participants was 64±13 years, 68% were male, and 79% were of White race. Two hundred four patients (28%) developed MAKEs over 3 years of follow-up. Random forest and least absolute shrinkage and selection operator penalized regression models using all 75 predictors yielded area under the receiver-operating characteristic curve (AUC) values of 0.80 (95% confidence interval [CI], 0.69 to 0.91) and 0.79 (95% CI, 0.68 to 0.90), respectively. The most consistently selected predictors were albuminuria, soluble TNF receptor-1, and diuretic use. A parsimonious model using the top eight predictor variables showed similarly strong discrimination for MAKEs (AUC, 0.78; 95% CI, 0.66 to 0.90). Clinical impact utility analyses demonstrated that the eight-predictor model would have 55% higher efficiency of post-AKI care (number needed to screen/follow-up for a MAKE decreased from 3.55 to 1.97). For a kidney-specific outcome of eGFR decline or ESKD, a four-predictor model showed strong discrimination (AUC, 0.82; 95% CI, 0.68 to 0.96). Conclusions Combining clinical data and biomarkers can accurately identify patients with high-risk AKI, enabling personalized post-AKI care and improved outcomes.