ArticleJournal of gastrointestinal surgery : official journal of the Society for Surgery of the Alimentary Tract2020
Use of Machine Learning for Prediction of Patient Risk of Postoperative Complications After Liver, Pancreatic, and Colorectal Surgery.
Article in Journal of gastrointestinal surgery : official journal of the Society for Surgery of the Alimentary Tract, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers, 4 of them syntheses that pooled it.
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Who cites it
63 citing papers in PubMed, 4 syntheses or guidelines pooled it, 129 citations in OpenAlex.
- Systematic evaluation of machine learning models for postoperative surgical site infection prediction.PloS one · 2024Pooled it
- Methodological conduct of prognostic prediction models developed using machine learning in oncology: a systematic review.BMC medical research methodology · 2022Pooled it
- The role of artificial intelligence in pancreatic surgery: a systematic review.Updates in surgery · 2022Pooled it
- Machine learning to guide clinical decision-making in abdominal surgery-a systematic literature review.Langenbeck's archives of surgery · 2022Pooled it
- Artificial intelligence in gastrointestinal and hepatopancreatobiliary surgery: innovations, integration, and future directions.Annals of medicine and surgery (2012) · 2026Review
- Machine Learning Model to Predict Postmastectomy Breast Reconstruction Complications.JAMA network open · 2026Article
- Machine learning prediction model for surgical site infections after major abdominal surgery.Patient safety in surgery · 2026Article
- Deep-Learning Virtual Superior Mesenteric Artery Modeling for Risk Stratification in Pancreas Surgery.Annals of surgical oncology · 2026Article
- Article
- Artificial Intelligence in Patient Blood Management: A Systematic Review of Predictive, Diagnostic, and Decision Support Applications.Journal of clinical medicine · 2025Review
- A comprehensive feature importance analysis of surgical site infection following colorectal cancer surgery.Scientific reports · 2025Article
- Review
- Artificial intelligence in hepatopancreatobiliary surgery for clinical outcome prediction: current perspective and future direction.Journal of robotic surgery · 2025Review
- Biorhythms derived from consumer wearables predict postoperative complications in children.Science advances · 2025Article
- 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
- Diagnostic Value of Postsurgical Endoscopy.Visceral medicine · 2025Review
- Impact of Preoperative Glucagon-Like Peptide-1 Receptor Agonist on Outcomes Following Major Surgery.World journal of surgery · 2025Article
- The role of artificial intelligence in pancreatic surgery: Current and future perspectives.Annals of hepato-biliary-pancreatic surgery · 2025Article
- Development and Validation of a Predictive Risk Score for Blood Transfusion in Patients Undergoing Curative-Intent Surgery for Intrahepatic Cholangiocarcinoma.Journal of surgical oncology · 2025Article
- Artificial Intelligence in Minimally Invasive Surgery: Current State and Future Challenges.JMA journal · 2025Review
3 more citing papers are in PubMed but not listed here.
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Authors and funding
12 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSurgical resection is the only potentially curative treatment for patients with colorectal, liver, and pancreatic cancers. Although these procedures are performed with low mortality, rates of complications remain relatively high following hepatopancreatic and colorectal surgery.
methodsThe American College of Surgeons (ACS) National Surgical Quality Improvement Program was utilized to identify patients undergoing liver, pancreatic and colorectal surgery from 2014 to 2016. Decision tree models were utilized to predict the occurrence of any complication, as well as specific complications. To assess the variability of the performance of the classification trees, bootstrapping was performed on 50% of the sample.
resultsAlgorithms were derived from a total of 15,657 patients who met inclusion criteria. The algorithm had a good predictive ability for the occurrence of any complication, with a C-statistic of 0.74, outperforming the ASA (C-statistic 0.58) and ACS-Surgical Risk Calculator (C-statistic 0.71). The algorithm was able to predict with high accuracy thirteen out of the seventeen complications analyzed. The best performance was in the prediction of stroke (C-statistic 0.98), followed by wound dehiscence, cardiac arrest, and progressive renal failure (all C-statistic 0.96). The algorithm had a good predictive ability for superficial SSI (C-statistic 0.76), organ space SSI (C-statistic 0.76), sepsis (C-statistic 0.79), and bleeding requiring transfusion (C-statistic 0.79).
conclusionMachine learning was used to develop an algorithm that accurately predicted patient risk of developing complications following liver, pancreatic, or colorectal surgery. The algorithm had very good predictive ability to predict specific complications and demonstrated superiority over other established methods.
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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.