ArticleScientific reports2023
Enabling personalized perioperative risk prediction by using a machine-learning model based on preoperative data.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Multioutput Machine Learning Model for Predicting Postoperative Outcomes After Liposuction: Algorithm Development and Validation Study in a Multicenter Cohort.JMIR medical informatics · 2026Article
- Echocardiography-based intelligent diagnosis and risk stratification management for tetralogy of Fallot.EBioMedicine · 2026Article
- Multitask Artificial Intelligence-Based Electrocardiogram Tool for Preoperative Cardiac Testing in Noncardiac Surgery: Retrospective Cohort Study of Health Care Utilization and Costs.Journal of medical Internet research · 2026Article
- Review
- Risk prediction in spine surgery: a scoping review of traditional models, artificial intelligence, and the challenge of clinical translation.Spine deformity · 2026Review
- Artificial intelligence-enhanced ECG score for perioperative risk assessment in non-cardiac surgery.European heart journal. Digital health · 2026Article
- Overcoming the data barrier: transfer learning for 90-day mortality prediction in general surgery - a retrospective multicenter development and comparison study.International journal of surgery (London, England) · 2026Article
- Review
- Artificial intelligence and machine learning approaches for patient safety in complex surgery: a review.Patient safety in surgery · 2025Review
- 90-day mortality prediction in elective visceral surgery using machine learning: a retrospective multicenter development, validation, and comparison study.International journal of surgery (London, England) · 2025Article
- Artificial intelligence for predicting 30-day mortality after surgery for femoral shaft fractures: A retrospective study.Indian journal of anaesthesia · 2025Article
- Machine learning-based prediction of postoperative mortality risk after abdominal surgery.World journal of gastrointestinal surgery · 2025Article
- Article
- Development of machine learning models for predicting postoperative hyperglycemia in non-diabetic gastric cancer patients: a retrospective cohort study analysis.Frontiers in endocrinology · 2025Article
- Challenges and opportunities in enhanced recovery after surgery programs: An overview.Indian journal of anaesthesia · 2024Review
- Large Language Model Capabilities in Perioperative Risk Prediction and Prognostication.JAMA surgery · 2024Article
- Machine learning-based model development for predicting risk factors of prolonged intra-aortic balloon pump therapy in patients with coronary artery bypass grafting.Journal of cardiothoracic surgery · 2024Article
- [Artificial intelligence in intensive care medicine].Medizinische Klinik, Intensivmedizin und Notfallmedizin · 2024Review
- Susceptibility of AutoML mortality prediction algorithms to model drift caused by the COVID pandemic.BMC medical informatics and decision making · 2024Review
- [Big data and artificial intelligence in anesthesia : Reality or fiction?]Die Anaesthesiologie · 2024Review
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Preoperative risk assessment is essential for shared decision-making and adequate perioperative care. Common scores provide limited predictive quality and lack personalized information. The aim of this study was to create an interpretable machine-learning-based model to assess the patient's individual risk of postoperative mortality based on preoperative data to allow analysis of personal risk factors. After ethical approval, a model for prediction of postoperative in-hospital mortality based on preoperative data of 66,846 patients undergoing elective non-cardiac surgery between June 2014 and March 2020 was created with extreme gradient boosting. Model performance and the most relevant parameters were shown using receiver operating characteristic (ROC-) and precision-recall (PR-) curves and importance plots. Individual risks of index patients were presented in waterfall diagrams. The model included 201 features and showed good predictive abilities with an area under receiver operating characteristic (AUROC) curve of 0.95 and an area under precision-recall curve (AUPRC) of 0.109. The feature with the highest information gain was the preoperative order for red packed cell concentrates followed by age and c-reactive protein. Individual risk factors could be identified on patient level. We created a highly accurate and interpretable machine learning model to preoperatively predict the risk of postoperative in-hospital mortality. The algorithm can be used to identify factors susceptible to preoperative optimization measures and to identify risk factors influencing individual patient risk.
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