SynthesisFrontiers in cardiovascular medicine2022
Machine learning in predicting cardiac surgery-associated acute kidney injury: A systemic review and meta-analysis.
Synthesis in Frontiers in cardiovascular medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 4 of them syntheses 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
19 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Machine learning for the prediction of acute kidney injury post cardiac surgery: a systematic review and meta-analysis.BMC medical informatics and decision making · 2026Pooled it
- Comparing machine learning models and traditional approaches for predicting obstructive coronary artery disease: a systematic review and meta-analysis.Frontiers in cardiovascular medicine · 2026Pooled it
- Machine learning models for predicting postoperative acute kidney injury in pediatric cardiac surgery: a systematic review and meta-analysis.Frontiers in cardiovascular medicine · 2026Pooled it
- TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods: a Korean translation.Ewha medical journal · 2025Guideline
- Intraoperative Arterial Waveform Morphology Trajectories and Prediction of Postoperative Acute Kidney Injury: A Latent-Class Analysis of the VitalDB Registry.Journal of clinical medicine · 2026Article
- Machine learning for predicting acute lung injury after trauma: a systematic review and meta-analysis.Frontiers in medicine · 2026Article
- Machine learning early risk assessment model for acute kidney injury in critically ill children: a retrospective cohort study.Frontiers in pediatrics · 2026Article
- Machine learning prediction of moderate-to-severe acute kidney injury after ICU admission and cardiac surgery with urine trace elements.European journal of clinical investigation · 2026Article
- Predicting kidney injury after cardiac surgery with cardio-pulmonary bypass using machine learning.Frontiers in digital health · 2026Article
- Artificial intelligence optimizes immune rejection prediction and management in heart transplantation: a structured narrative review.Frontiers in cardiovascular medicine · 2026Review
- Influence of qNOX-guided remifentanil titration on intraoperative dosing and postoperative pain in total knee arthroplasty: a pilot randomized trial.Journal of anesthesia, analgesia and critical care · 2025Article
- Review
- Secondary Prevention of AFAIS: Deploying Traditional Regression, Machine Learning, and Deep Learning Models to Validate and Update CHA2DS2-VASc for 90-Day Recurrence.Journal of clinical medicine · 2025Article
- PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods.BMJ (Clinical research ed.) · 2025Article
- Impact of prognostic scores on acute kidney injury assessment in the postoperative period of myocardial revascularization.Revista da Escola de Enfermagem da U S P · 2025Article
- Predictive Factors of Length of Stay in Intensive Care Unit after Coronary Artery Bypass Graft Surgery based on Machine Learning Methods.Archives of academic emergency medicine · 2025Article
- Predictive models of sepsis-associated acute kidney injury based on machine learning: a scoping review.Renal failure · 2024Article
- TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods.BMJ (Clinical research ed.) · 2024Article
- Machine-learning predictions for acute kidney injuries after coronary artery bypass grafting: a real-life muticenter retrospective cohort study.BMC medical informatics and decision making · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Cardiac surgery-associated acute kidney injury (CSA-AKI) is a common complication following cardiac surgery. Early prediction of CSA-AKI is of great significance for improving patients' prognoses. The aim of this study is to systematically evaluate the predictive performance of machine learning models for CSA-AKI. Methods: Cochrane Library, PubMed, EMBASE, and Web of Science were searched from inception to 18 March 2022. Risk of bias assessment was performed using PROBAST. Rsoftware (version 4.1.1) was used to calculate the accuracy and C-index of CSA-AKI prediction. The importance of CSA-AKI prediction was defined according to the frequency of related factors in the models. Results: There were 38 eligible studies included, with a total of 255,943 patients and 60 machine learning models. The models mainly included Logistic Regression ( Conclusion: The machine learning-based model is effective for the early prediction of CSA-AKI. More machine learning methods based on noninvasive or minimally invasive predictive indicators are needed to improve the predictive performance and make accurate predictions of CSA-AKI. Logistic regression remains currently the most commonly applied model in CSA-AKI prediction, although it is not the one with the best performance. There are other models that would be more effective, such as NNET and XGBoost. Systematic review registration: https://www.crd.york.ac.uk/; review registration ID: CRD42022345259.
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