SynthesisFrontiers in nephrology2023
Systematic review of externally validated machine learning models for predicting acute kidney injury in general hospital patients.
Synthesis in Frontiers in nephrology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed.
- Evaluation Frameworks for Clinical AI Incorporating Validation Strategies, Real-World Applicability, and Ethical Principles: Scoping Review.Journal of medical Internet research · 2026Article
- Artificial intelligence for predicting paediatric acute kidney injury: a systematic review and meta-analysis.Clinical kidney journal · 2026Article
- Forecasting ICU Acute Kidney Injury with Actionable Lead Time Using Interpretable Machine Learning: Development and Multi-Center Validation.Journal of clinical medicine · 2026Article
- Predicting Acute Kidney Injury with Nephrotoxic Burden in Noncritical Patients: An Internal and External Validation Study.Kidney360 · 2026Article
- Beyond the diagnostic threshold: paradigm shift in laboratory medicine from passive reaction to predictive capability.Frontiers in medicine · 2026Article
- Construction of a Machine Learning-Based Risk Prediction Model for Drug-Induced Acute Kidney Injury in Elderly Patients.International journal of nephrology and renovascular disease · 2026Article
- Review
- Systematic Review and Meta-Analysis of Machine Learning Models for Acute Kidney Injury Risk Classification.Journal of the American Society of Nephrology : JASN · 2025Article
- Extended sample size calculations for evaluation of prediction models using a threshold for classification.BMC medical research methodology · 2025Article
- Applications of Machine Learning on Electronic Health Record Data to Combat Antibiotic Resistance.The Journal of infectious diseases · 2024Review
- Artificial intelligence and machine learning's role in sepsis-associated acute kidney injury.Kidney research and clinical practice · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Acute kidney injury (AKI) is one of the most common and consequential complications among hospitalized patients. Timely AKI risk prediction may allow simple interventions that can minimize or avoid the harm associated with its development. Given the multifactorial and complex etiology of AKI, machine learning (ML) models may be best placed to process the available health data to generate accurate and timely predictions. Accordingly, we searched the literature for externally validated ML models developed from general hospital populations using the current definition of AKI. Of 889 studies screened, only three were retrieved that fit these criteria. While most models performed well and had a sound methodological approach, the main concerns relate to their development and validation in populations with limited diversity, comparable digital ecosystems, use of a vast number of predictor variables and over-reliance on an easily accessible biomarker of kidney injury. These are potentially critical limitations to their applicability in diverse socioeconomic and cultural settings, prompting a need for simpler, more transportable prediction models which can offer a competitive advantage over the current tools used to predict and diagnose AKI.
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Registered trials
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