Usability of machine learning algorithms based on electronic health records for the prediction of acute kidney injury and transition to acute kidney disease: A proof of concept study

• Published in: PloS one Vol. 20; no. 7; p. e0326124
• Main Authors: Ruinelli, Lorenzo, Cippà, Pietro, Sieber, Chantal, Di Serio, Clelia, Ferrari, Paolo, Bellasi, Antonio
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.07.2025; Public Library of Science (PLoS)
• Subjects: Acute Kidney Injury - diagnosis; Acute renal failure; Adult; Aged; Algorithms; Analysis; Artificial intelligence; Biology and Life Sciences; Computer and Information Sciences; Development and progression; Diagnosis; Electronic Health Records; Electronic medical records; Electronic records; Female; Health aspects; Hospitalization; Hospitals; Humans; Kidney diseases; Kidneys; Laboratories; Learning algorithms; Machine Learning; Male; Medical records; Medicine and Health Sciences; Middle Aged; Mortality; Patients; Proof of Concept Study; Renal failure; Renal function; Retrospective Studies; Risk factors; Switzerland
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0326124

Acute kidney injury (AKI) and acute kidney disease (AKD) are frequent complications of hospitalization, resulting in reduced outcomes and increased cost burden. However, these conditions are only sometimes recognized and promptly treated. Leveraging electronic health records (EHR), we explored the potential of artificial intelligence (AI) in diagnosing AKI and AKD during hospitalization. We retrospectively analyzed EHRs collected from all patients admitted in 2022 to our public hospital network. AKI and AKD were defined according to international guidelines. The database was divided into training and validation sets. Machine Learning (ML) algorithms were developed with 10-fold cross-validation, and diagnostic accuracy was evaluated. We analyzed 34,579 hospitalizations (mean age of 60 years, 50% females). Baseline renal function was available in ~50% of cases. AKI and AKD complicated 10% and 1.5% of hospitalizations, respectively. The majority of AKI episodes (77%) occurred within the first three days of hospitalization, and >50% of subjects with AKI were discharged before complete renal function recovery. ML accurately predicted AKI (AUC-ROC 79%) during hospitalization, based on data available before and at hospital admission. Among subjects with AKI on the first day and longer in-hospital observation, the ML accuracy in predicting AKD transition increased (AUC-ROC from 76% to 88%) by integrating EHR accumulated during the hospitalization. The negative predictive value (NPV) progressively increased from 94% to 98% consistently. Shapely additive explanations documented that age, urgent hospital admission, AKI severity, and baseline renal function were associated with AKI. Renal function trajectory during the first days of hospitalization was the most relevant predictor of AKD. ML, relying on EHR before and during hospitalization, may accurately predict AKI and AKD. The high NPV also suggests its implementation as a tool to rule out the risk of renal failure, aid in individualizing patient care, and allocate healthcare resources.