ArticleJournal of the American College of Surgeons2021
Validation of the Artificial Intelligence-Based Predictive Optimal Trees in Emergency Surgery Risk (POTTER) Calculator in Emergency General Surgery and Emergency Laparotomy Patients.
Article in Journal of the American College of Surgeons, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 2 of them syntheses that pooled it.
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Who cites it
34 citing papers in PubMed, 2 syntheses or guidelines pooled it, 63 citations in OpenAlex.
- Systematic evaluation of machine learning models for postoperative surgical site infection prediction.PloS one · 2024Pooled it
- Prediction of Complications and Prognostication in Perioperative Medicine: A Systematic Review and PROBAST Assessment of Machine Learning Tools.Anesthesiology · 2024Pooled it
- Physiological Emergency Surgery Acuity Score Predicts Mortality and Anastomotic Leak in Emergency Laparotomy: An External Validation Study.World journal of surgery · 2026Article
- Artificial intelligence for predicting 30-day mortality after emergency laparotomy.European journal of trauma and emergency surgery : official publication of the European Trauma Society · 2026Article
- A machine learning model for post-operative sepsis prediction in acute surgical patients: a multi-centre, prospective study.Scientific reports · 2026Article
- Artificial intelligence in emergency surgery: a scoping review within the artificial intelligence in emergency and trauma surgery (ARIES) project.World journal of emergency surgery : WJES · 2026Article
- Artificial intelligence and machine learning approaches for patient safety in complex surgery: a review.Patient safety in surgery · 2025Review
- The complex task of modelling artificial intelligence workflows for forecasting postoperative risk.Journal of anesthesia, analgesia and critical care · 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
- Predictive Models for Necrotizing Soft Tissue Infections: Are the Available Scores Trustable?Journal of clinical medicine · 2025Review
- The application of machine learning models in a resource-constrained environment.Irish journal of medical science · 2025Article
- Review
- Ethical considerations on the role of artificial intelligence in defining the futility in emergency surgery.International journal of surgery (London, England) · 2025Article
- Review
- Emergency laparotomy preoperative risk assessment tool performance: A systematic review.Surgery in practice and science · 2024Review
- Artificial Intelligence in Surgery: A Systematic Review of Use and Validation.Journal of clinical medicine · 2024Review
- Artificial intelligence in surgery.Nature medicine · 2024Review
- Artificial Intelligence in Perioperative Planning and Management of Liver Resection.Indian journal of surgical oncology · 2024Review
- Cardiac risk stratification and adverse outcomes in surgically managed patients with isolated traumatic spine injuries.European journal of trauma and emergency surgery : official publication of the European Trauma Society · 2024Article
- Current and future applications of artificial intelligence in surgery: implications for clinical practice and research.Frontiers in surgery · 2024Review
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe Predictive Optimal Trees in Emergency Surgery Risk (POTTER) tool is an artificial intelligence-based calculator for the prediction of 30-day outcomes in patients undergoing emergency operations. In this study, we sought to assess the performance of POTTER in the emergency general surgery (EGS) population in particular.
methodsAll patients who underwent EGS in the 2017 American College of Surgeons NSQIP database were included. The performance of POTTER in predicting 30-day postoperative mortality, morbidity, and 18 specific complications was assessed using the c-statistic metric. As a subgroup analysis, the performance of POTTER in predicting the outcomes of patients undergoing emergency laparotomy was assessed.
resultsA total of 59,955 patients were included. Median age was 50 years and 51.3% were women. POTTER predicted mortality (c-statistic = 0.93) and morbidity (c-statistic = 0.83) extremely well. Among individual complications, POTTER had the highest performance in predicting septic shock (c-statistic = 0.93), respiratory failure requiring mechanical ventilation for 48 hours or longer (c-statistic = 0.92), and acute renal failure (c-statistic = 0.92). Among patients undergoing emergency laparotomy, the c-statistic performances of POTTER in predicting mortality and morbidity were 0.86 and 0.77, respectively.
conclusionsPOTTER is an interpretable, accurate, and user-friendly predictor of 30-day outcomes in patients undergoing EGS. POTTER could prove useful for bedside counseling of patients and their families and for benchmarking of EGS care.
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