ArticleJournal of clinical medicine2018
Derivation and Validation of Machine Learning Approaches to Predict Acute Kidney Injury after Cardiac Surgery.
Article in Journal of clinical medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 90 papers, 10 of them syntheses that pooled it.
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Who cites it
90 citing papers in PubMed, 10 syntheses or guidelines pooled it.
- Artificial intelligence in contrast-induced nephropathy after coronary interventions: A meta-analysis.Medicine · 2026Pooled it
- The Role of the Cardiothoracic Surgeon in the Age of AI-Are the Robots Going to Take Our Jobs?Medical sciences (Basel, Switzerland) · 2026Pooled 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
- Pediatric cranial stereotactic radiosurgery: Meta-analysis and international stereotactic radiosurgery society practice guidelines.Neuro-oncology · 2025Guideline
- 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
- Prediction of Complications and Prognostication in Perioperative Medicine: A Systematic Review and PROBAST Assessment of Machine Learning Tools.Anesthesiology · 2024Pooled it
- Characterization of Risk Prediction Models for Acute Kidney Injury: A Systematic Review and Meta-analysis.JAMA network open · 2023Pooled it
- Artificial intelligence for the prediction of acute kidney injury during the perioperative period: systematic review and Meta-analysis of diagnostic test accuracy.BMC nephrology · 2022Pooled it
- Big Data in cardiac surgery: real world and perspectives.Journal of cardiothoracic surgery · 2022Pooled it
- Machine learning in predicting cardiac surgery-associated acute kidney injury: A systemic review and meta-analysis.Frontiers in cardiovascular medicine · 2022Pooled it
- Hierarchical Poincaré analysis for anaesthesia monitoring.Journal of clinical monitoring and computing · 2020Trial
- Risk prediction of postoperative acute kidney injury in patients undergoing off-pump coronary artery bypass grafting based on machine learning.The Journal of international medical research · 2026Article
- Dynamic machine learning prediction of persistent AKI after cardiac surgery with modifiable perioperative risk factors.iScience · 2026Article
- Responsible Use of Artificial Intelligence to Improve Kidney Care: A Statement from the American Society of Nephrology.Journal of the American Society of Nephrology : JASN · 2026Review
- An interpretable machine-learning model for early prediction of acute kidney injury in polytrauma patients.Frontiers in medicine · 2026Article
- Development and Validation of a Machine Learning Model for Predicting Serum Creatinine-Defined Acute Kidney Injury in Older Adults After Cardiac Surgery.Clinical interventions in aging · 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
- A machine learning-based model for predicting the postoperative risk of acute kidney injury in neonates.Translational pediatrics · 2025Article
- Application of artificial intelligence in predicting the results of open-heart surgery: a scoping review.BMC medical informatics and decision making · 2025Article
- Artificial intelligence for electrocardiographic diagnosis of perioperative myocardial ischaemia: a scoping review.British journal of anaesthesia · 2025Article
30 more citing papers are in PubMed but not listed here.
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning approaches were introduced for better or comparable predictive ability than statistical analysis to predict postoperative outcomes. We sought to compare the performance of machine learning approaches with that of logistic regression analysis to predict acute kidney injury after cardiac surgery. We retrospectively reviewed 2010 patients who underwent open heart surgery and thoracic aortic surgery. Baseline medical condition, intraoperative anesthesia, and surgery-related data were obtained. The primary outcome was postoperative acute kidney injury (AKI) defined according to the Kidney Disease Improving Global Outcomes criteria. The following machine learning techniques were used: decision tree, random forest, extreme gradient boosting, support vector machine, neural network classifier, and deep learning. The performance of these techniques was compared with that of logistic regression analysis regarding the area under the receiver-operating characteristic curve (AUC). During the first postoperative week, AKI occurred in 770 patients (38.3%). The best performance regarding AUC was achieved by the gradient boosting machine to predict the AKI of all stages (0.78, 95% confidence interval (CI) 0.75⁻0.80) or stage 2 or 3 AKI. The AUC of logistic regression analysis was 0.69 (95% CI 0.66⁻0.72). Decision tree, random forest, and support vector machine showed similar performance to logistic regression. In our comprehensive comparison of machine learning approaches with logistic regression analysis, gradient boosting technique showed the best performance with the highest AUC and lower error rate. We developed an Internet⁻based risk estimator which could be used for real-time processing of patient data to estimate the risk of AKI at the end of surgery.
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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.