Machine learning models for acute kidney injury prediction and management: a scoping review of externally validated studies

Despite advancements in medical care, acute kidney injury (AKI) remains a major contributor to adverse patient outcomes and presents a significant challenge due to its associated morbidity, mortality, and financial cost. Machine learning (ML) is increasingly being recognized for its potential to transform AKI care by enabling early prediction, detection, and facilitating an individualized approach to patient management. This scoping review aims to provide a comprehensive analysis of externally validated ML models for the prediction, detection, and management of AKI. We systematically searched for relevant literature from inception to 15 February 2024, using four databases—MEDLINE, EMBASE, Web of Science, and Scopus. We focused solely on models that had undergone external validation, employed Kidney Disease Improving Global Outcomes (KDIGO) definitions for AKI, and utilized ML models (excluding logistic regression models). A total of 44 studies encompassing 161 ML models for AKI prediction, severity assessment, and outcomes in both adult and pediatric populations were included in the review. These studies encompassed 4,153,424 patient admissions, with 1,209,659 in the development and internal validation cohorts and 2,943,765 in the external validation cohorts. The ML models demonstrated significant variability in performance owing to differing clinical settings, populations, and predictors used. Most of the included models were developed in specialized patient populations, such as those in intensive care units, post-surgical settings, and specific disease states (e.g. congestive heart failure, traumatic brain injury, etc.). Moreover, only a few models incorporated dynamic predictors of AKI which are crucial for improving clinical utility in rapidly evolving clinical conditions like AKI. The variable performance of these models when applied to external validation cohorts highlights the challenges of reproducibility and generalizability in implementing ML models in AKI care. Despite acceptable performance metrics, none of the models assessed in this review underwent validation or implementation in real-world clinical workflows. These findings underscore the need for standardized performance metrics and validation protocols to enhance the generalizability and clinical applicability of these models. Future efforts should focus on enhancing model adaptability by incorporating dynamic predictors and unstructured data and by ensuring that models are developed in diverse patient populations. Moreover, collaboration between clinicians and data scientists is critical to ensure the development of models that are clinically relevant, fair, and tailored to real-world healthcare environments.