Acute Kidney Injury Prognosis Prediction Using Machine Learning Methods: A Systematic Review.

Rationale & Objective: Accurate estimation of in-hospital outcomes for patients with acute kidney injury (AKI) is crucial for aiding physicians in making optimal clinical decisions. We aimed to review prediction models constructed by machine learning methods for predicting AKI prognosis using administrative databases. Study Design: A systematic review following PRISMA guidelines. Setting & Study Populations: Adult patients diagnosed with AKI who are admitted to either hospitals or intensive care units. Search Strategy & Sources: We searched PubMed, Embase, Web of Science, Scopus, and Cumulative Index to Nursing and Allied Health for studies published between January 1, 2014 and February 29, 2024. Eligible studies employed machine learning models to predict in-hospital outcomes of AKI based on administrative databases. Data Extraction: Extracted data included prediction outcomes and population, prediction models with performance, feature selection methods, and predictive features. Analytical Approach: The included studies were qualitatively synthesized with assessments of quality and bias. We calculated the pooled model discrimination of different AKI prognoses using random-effects models. Results: Of 3,029 studies, 27 studies were eligible for qualitative review. In-hospital outcomes for patients with AKI included acute kidney disease, chronic kidney disease, renal function recovery or kidney failure, and mortality. Compared with models predicting the mortality of patients with AKI during hospitalization, the prediction performance of models on kidney function recovery was less accurate. Meta-analysis showed that machine learning methods outperformed traditional approaches in mortality prediction (area under the receiver operating characteristic curve, 0.831; 95% CI, 0.799-0.859 vs 0.772; 95% CI, 0.744-0.797). The overlapping predictive features for in-hospital mortality identified from ≥6 studies were age, serum creatinine level, serum urea nitrogen level, anion gap, and white blood cell count. Similarly, age, serum creatinine level, AKI stage, estimated glomerular filtration rate, and comorbid conditions were the common predictive features for kidney function recovery. Limitations: Many studies developed prediction models within specific hospital settings without broad validation, restricting their generalizability and clinical application. Conclusions: Machine learning models outperformed traditional approaches in predicting mortality for patients with AKI, although they are less accurate in predicting kidney function recovery. Overall, these models demonstrate significant potential to help physicians improve clinical decision making and patient outcomes. Registration: CRD42024535965.