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

Abstract

Background: Acute kidney injury (AKI) increases the risk for chronic kidney disease (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 (ASSESS-AKI) Study. Using machine learning, we investigated 75 candidate predictors including biomarkers measured at three-month post-discharge follow-up to predict major adverse kidney events (MAKE) within three years, defined as a decline in eGFR ≥40%, development of end-stage kidney disease (ESKD), or death.

Results: The mean age of study participants was 64 ± 13 years, 68% were men, and 79% were of White race. Two hundred and four (28%) patients developed MAKE over 3 years of follow-up. Random forest and LASSO penalized regression models using all 75 predictors yielded area under the receiver-operating characteristic curve (AUC) values of 0.80 (95% CI: 0.69-0.91) and 0.79 (95% CI: 0.68-0.90) respectively. The most consistently selected predictors were albuminuria, soluble tumor necrosis factor receptor 1 (sTNFR1), and diuretic use. A parsimonious model using the top eight predictor variables showed similarly strong discrimination for MAKE (AUC = 0.78; 95% CI: 0.66-0.90). 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 event 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: 0.68-0.96).

Conclusion: Combining clinical data and biomarkers can accurately identify high-risk AKI patients, enabling personalized post-AKI care and improved outcomes.