SynthesisBMC nephrology2022
Artificial intelligence for the prediction of acute kidney injury during the perioperative period: systematic review and Meta-analysis of diagnostic test accuracy.
Synthesis in BMC nephrology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- AI Trustworthiness in the Perioperative Period for Patients with Serious Illness: Scoping Narrative Review.JMIR perioperative medicine · 2026Article
- Navigating the complexities of digital health technology implementation: a scoping review of barriers and facilitators.Implementation science communications · 2026Review
- Machine Learning to Assist in Managing Acute Kidney Injury in General Wards: Multicenter Retrospective Study.Journal of medical Internet research · 2025Article
- New approaches to acute kidney injury.Clinical kidney journal · 2024Article
- Machine learning approaches toward an understanding of acute kidney injury: current trends and future directions.The Korean journal of internal medicine · 2024Review
- Artificial intelligence and predictive models for early detection of acute kidney injury: transforming clinical practice.BMC nephrology · 2024Review
- [Recent developments in acute kidney injury : Definition, biomarkers, subphenotypes, and management].Medizinische Klinik, Intensivmedizin und Notfallmedizin · 2024Review
- Application of Machine Learning in Predicting Perioperative Outcomes in Patients with Cancer: A Narrative Review for Clinicians.Current oncology (Toronto, Ont.) · 2024Review
- [Artificial intelligence and acute kidney injury].Medizinische Klinik, Intensivmedizin und Notfallmedizin · 2024Review
- [Artificial intelligence in intensive care medicine].Medizinische Klinik, Intensivmedizin und Notfallmedizin · 2024Review
- Perioperative use of immersive head-mounted virtual reality display enhances patient satisfaction in great saphenous vein surgery: a single-center clinical observation study.American journal of translational research · 2024Article
- Benefits of Information Technology in Healthcare: Artificial Intelligence, Internet of Things, and Personal Health Records.Healthcare informatics research · 2023Article
- Acute kidney injury.Intensive care medicine · 2023Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAcute kidney injury (AKI) is independently associated with morbidity and mortality in a wide range of surgical settings. Nowadays, with the increasing use of electronic health records (EHR), advances in patient information retrieval, and cost reduction in clinical informatics, artificial intelligence is increasingly being used to improve early recognition and management for perioperative AKI. However, there is no quantitative synthesis of the performance of these methods. We conducted this systematic review and meta-analysis to estimate the sensitivity and specificity of artificial intelligence for the prediction of acute kidney injury during the perioperative period.
methodsPubmed, Embase, and Cochrane Library were searched to 2nd October 2021. Studies presenting diagnostic performance of artificial intelligence in the early detection of perioperative acute kidney injury were included. True positives, false positives, true negatives and false negatives were pooled to collate specificity and sensitivity with 95% CIs and results were portrayed in forest plots. The risk of bias of eligible studies was assessed using the PROBAST tool.
resultsNineteen studies involving 304,076 patients were included. Quantitative random-effects meta-analysis using the Rutter and Gatsonis hierarchical summary receiver operating characteristics (HSROC) model revealed pooled sensitivity, specificity, and diagnostic odds ratio of 0.77 (95% CI: 0.73 to 0.81),0.75 (95% CI: 0.71 to 0.80), and 10.7 (95% CI 8.5 to 13.5), respectively. Threshold effect was found to be the only source of heterogeneity, and there was no evidence of publication bias.
conclusionsOur review demonstrates the promising performance of artificial intelligence for early prediction of perioperative AKI. The limitations of lacking external validation performance and being conducted only at a single center should be overcome.
trial registrationThis study was not registered with PROSPERO.
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