SynthesisBMC nephrology2023
Validated risk prediction models for outcomes of acute kidney injury: a systematic review.
Synthesis in BMC nephrology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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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
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Exploring the role of Artificial Intelligence in Acute Kidney Injury management: a comprehensive review and future research agenda.BMC medical informatics and decision making · 2024Pooled it
- Using health systems evidence to study and predict long-term outcomes of childhood acute kidney injury.Pediatric nephrology (Berlin, Germany) · 2026Article
- Validation of biomarker-based stratification for risk of long-term outcomes after acute kidney injury.Clinical kidney journal · 2026Article
- Research progress of nanoparticles in the diagnosis and treatment of renal ischemia-reperfusion injury.Journal of nanobiotechnology · 2026Review
- Enhancing therapeutic strategies and drug development for patients with kidney disease.Expert opinion on drug safety · 2026Review
- Predictive model for severe intraventricular hemorrhage risk in preterm infants: a systematic review and meta-analysis.Frontiers in pediatrics · 2026Review
- Systematic Review and Meta-Analysis of Machine Learning Models for Acute Kidney Injury Risk Classification.Journal of the American Society of Nephrology : JASN · 2025Article
- Machine Learning to Assist in Managing Acute Kidney Injury in General Wards: Multicenter Retrospective Study.Journal of medical Internet research · 2025Article
- The development and validation of a prediction model for post-AKI outcomes of pediatric inpatients.Clinical kidney journal · 2025Article
- A crucial role of miR-155 in the pathomechanism of acute kidney injury.Frontiers in pharmacology · 2025Review
- Acute Kidney Injury Prognosis Prediction Using Machine Learning Methods: A Systematic Review.Kidney medicine · 2025Review
- Predictive models of sepsis-associated acute kidney injury based on machine learning: a scoping review.Renal failure · 2024Article
- Development and validation of a deep learning algorithm for the prediction of serum creatinine in critically ill patients.JAMIA open · 2024Article
- A machine learning-based approach for predicting renal function recovery in general ward patients with acute kidney injury.Kidney research and clinical practice · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAcute Kidney Injury (AKI) is frequently seen in hospitalized and critically ill patients. Studies have shown that AKI is a risk factor for the development of acute kidney disease (AKD), chronic kidney disease (CKD), and mortality.
methodsA systematic review is performed on validated risk prediction models for developing poor renal outcomes after AKI scenarios. Medline, EMBASE, Cochrane, and Web of Science were searched for articles that developed or validated a prediction model. Moreover, studies that report prediction models for recovery after AKI also have been included. This review was registered with PROSPERO (CRD42022303197).
resultWe screened 25,812 potentially relevant abstracts. Among the 149 remaining articles in the first selection, eight met the inclusion criteria. All of the included models developed more than one prediction model with different variables. The models included between 3 and 28 independent variables and c-statistics ranged from 0.55 to 1.
conclusionFew validated risk prediction models targeting the development of renal insufficiency after experiencing AKI have been developed, most of which are based on simple statistical or machine learning models. While some of these models have been externally validated, none of these models are available in a way that can be used or evaluated in a clinical setting.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.