ArticleFrontiers in medicine2021
Machine Learning Prediction Model for Acute Renal Failure After Acute Aortic Syndrome Surgery.
Article in Frontiers in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 4 of them syntheses that pooled it.
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Who cites it
15 citing papers in PubMed, 4 syntheses or guidelines pooled it, 24 citations in OpenAlex.
- 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
- Cardiac surgery-associated acute kidney injury: a decade of research trends and developments.Frontiers in medicine · 2025Pooled 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
- Machine learning in predicting cardiac surgery-associated acute kidney injury: A systemic review and meta-analysis.Frontiers in cardiovascular medicine · 2022Pooled it
- Applications of Artificial Intelligence as a Prognostic Tool in the Management of Acute Aortic Syndrome and Aneurysm: A Comprehensive Review.Journal of clinical medicine · 2025Review
- Application of artificial intelligence in predicting the results of open-heart surgery: a scoping review.BMC medical informatics and decision making · 2025Article
- Systematic Review and Meta-Analysis of Machine Learning Models for Acute Kidney Injury Risk Classification.Journal of the American Society of Nephrology : JASN · 2025Article
- Prediction model of in-hospital mortality risk in intensive care unit patients with cardiac arrest: a multicenter retrospective cohort study based on an ensemble model.Frontiers in cardiovascular medicine · 2025Article
- PiCCO hemodynamic parameters in cardiogenic shock: prediction of LVEF, NT-proBNP and MACE based on XGBoost machine learning model.Frontiers in medicine · 2025Article
- Development and validation of a clinical prediction model for in-hospital mortality of severe pneumonia based on machine learning.Frontiers in pharmacology · 2025Article
- Bradykinin induces acute kidney injury after hypothermic circulatory arrest through the repression of the Nrf2-xCT pathway.iScience · 2024Article
- Application of three-dimensional printing in the planning and execution of aortic aneurysm repair.Frontiers in cardiovascular medicine · 2024Review
- A machine learning model for early candidemia prediction in the intensive care unit: Clinical application.PloS one · 2024Article
- Early identification of STEMI patients with emergency chest pain using lipidomics combined with machine learning.Journal of geriatric cardiology : JGC · 2022Article
- The shared biomarkers and pathways of systemic lupus erythematosus and metabolic syndrome analyzed by bioinformatics combining machine learning algorithm and single-cell sequencing analysis.Frontiers in immunology · 2022Article
Corrections and comments
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Authors and funding
15 authors at 11 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAcute renal failure (ARF) is the most common major complication following cardiac surgery for acute aortic syndrome (AAS) and worsens the postoperative prognosis. Our aim was to establish a machine learning prediction model for ARF occurrence in AAS patients.
methodsWe included AAS patient data from nine medical centers (
resultsThe eXtreme Gradient Boosting (XGBoost) algorithm performed best in the internal validation process (AUC = 0.82), which was better than both the logistic regression (LR) prediction model (AUC = 0.77,
conclusionsWe have developed a machine learning model that has better predictive performance than traditional LR prediction models as well as other existing risk scoring systems for postoperative ARF. This model can be utilized to provide early warnings when high-risk patients are found, enabling clinicians to take prompt measures.
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Registered trials
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.