ArticlePloS one2017
A Comparison of a Machine Learning Model with EuroSCORE II in Predicting Mortality after Elective Cardiac Surgery: A Decision Curve Analysis.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 83 papers, 1 of them a synthesis that pooled it.
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Who cites it
83 citing papers in PubMed, 1 synthesis or guideline pooled 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 Predicting Postoperative Complications After Hypospadias Surgery: A 10-Year Single-Center Retrospective Cohort Study.Children (Basel, Switzerland) · 2026Article
- Artificial Intelligence for Perioperative Risk Prediction and Prevention in Cardiac Surgery: A Narrative Review and Proposed Conceptual Framework.Journal of clinical medicine · 2026Review
- Development of machine learning models for preoperative prediction of insufficient clinical improvement after anterior cruciate ligament reconstruction.BMC musculoskeletal disorders · 2026Article
- Development and external validation of a machine learning model for predicting severe immediate postoperative complications in adults undergoing cardiac surgery.BMC anesthesiology · 2026Article
- From innovation to implementation: Addressing the AI adoption gap in cardiac surgery.American heart journal plus : cardiology research and practice · 2026Review
- EuroSCORE II: Current limitations and physiological gaps in risk stratification.Experimental physiology · 2026Review
- Risk assessment in cardiac surgery: Exploring machine learning and laboratory indices as adjunctive tools.PloS one · 2026Article
- Predicting in-hospital mortality after cardiac surgery using machine learning: temporal validation in the MIMIC-IV database.Frontiers in cardiovascular medicine · 2026Article
- Artificial intelligence in cardiovascular medicine: prevention, diagnosis, and intervention.Frontiers in artificial intelligence · 2026Review
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- Shaping the future of cardiac interventions and cardiac surgeries: The impact of virtual reality and artificial intelligence.Global cardiology science & practice · 2025Review
- Machine Learning-Based Prediction of Short-Term Mortality After Coronary Artery Bypass Grafting: A Retrospective Cohort Study.Biomedicines · 2025Article
- Modern statistical techniques for cardiothoracic surgeons: Part 8-Bayesian analysis and beyond.Indian journal of thoracic and cardiovascular surgery · 2025Article
- State-of-the-art artificial intelligence methods for pre-operative planning of cardiothoracic surgery and interventions: a narrative review.Journal of thoracic disease · 2025Review
- Machine learning-based hybrid risk estimation system (ERES) in cardiac surgery: Supplementary insights from the ASA score analysis.PLOS digital health · 2025Article
- Artificial intelligence in cardiovascular procedures: a bibliometric and visual analysis study.Annals of medicine and surgery (2012) · 2025Review
- Machine learning prediction of premature death from multimorbidity among people with inflammatory bowel disease: a population-based retrospective cohort study.CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne · 2025Article
- The ethical considerations of integrating artificial intelligence into surgery: a review.Interdisciplinary cardiovascular and thoracic surgery · 2025Review
23 more citing papers are in PubMed but not listed here.
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Authors and funding
9 authors.
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Abstract
backgroundThe benefits of cardiac surgery are sometimes difficult to predict and the decision to operate on a given individual is complex. Machine Learning and Decision Curve Analysis (DCA) are recent methods developed to create and evaluate prediction models. METHODS AND FINDING: We conducted a retrospective cohort study using a prospective collected database from December 2005 to December 2012, from a cardiac surgical center at University Hospital. The different models of prediction of mortality in-hospital after elective cardiac surgery, including EuroSCORE II, a logistic regression model and a machine learning model, were compared by ROC and DCA. Of the 6,520 patients having elective cardiac surgery with cardiopulmonary bypass, 6.3% died. Mean age was 63.4 years old (standard deviation 14.4), and mean EuroSCORE II was 3.7 (4.8) %. The area under ROC curve (IC95%) for the machine learning model (0.795 (0.755-0.834)) was significantly higher than EuroSCORE II or the logistic regression model (respectively, 0.737 (0.691-0.783) and 0.742 (0.698-0.785), p < 0.0001). Decision Curve Analysis showed that the machine learning model, in this monocentric study, has a greater benefit whatever the probability threshold.
conclusionsAccording to ROC and DCA, machine learning model is more accurate in predicting mortality after elective cardiac surgery than EuroSCORE II. These results confirm the use of machine learning methods in the field of medical prediction.
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