ArticleSurgery2021
Application of machine learning to the prediction of postoperative sepsis after appendectomy.
Article in Surgery, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 4 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed, 4 syntheses or guidelines pooled it, 48 citations in OpenAlex.
- Missing data handling in pediatric appendectomy research: current practices and National Surgical Quality Improvement Program (NSQIP) analysis.Surgical endoscopy · 2026Pooled it
- Artificial Intelligence and Acute Appendicitis: A Systematic Review of Diagnostic and Prognostic Models.World journal of emergency surgery : WJES · 2023Pooled it
- Machine Learning Algorithms for Predicting Surgical Outcomes after Colorectal Surgery: A Systematic Review.World journal of surgery · 2022Pooled it
- Artificial Intelligence in Bariatric Surgery: Current Status and Future Perspectives.Obesity surgery · 2022Pooled it
- A machine learning model for post-operative sepsis prediction in acute surgical patients: a multi-centre, prospective study.Scientific reports · 2026Article
- LASSO-based machine learning algorithm for prediction of dysphagia in patients suffering anterior cervical discectomy and fusion.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Article
- Patient Benefits in the Context of Sepsis-Related AI-Based Clinical Decision Support Systems: Scoping Review.Journal of medical Internet research · 2026Article
- Predictors of Anemia Intolerance for Real-Time Transfusion Decision-Making During Resuscitation of Trauma Subjects: A Machine Learning Approach Using Heart Rate Variability.Critical care explorations · 2025Article
- Progress in the application of machine learning in CT diagnosis of acute appendicitis.Abdominal radiology (New York) · 2025Review
- Machine Learning and Feature Selection in Pediatric Appendicitis.Tomography (Ann Arbor, Mich.) · 2025Article
- A Systematic Integration of Artificial Intelligence Models in Appendicitis Management: A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2025Review
- Exploring the Immunological Role of the Microbial Composition of the Appendix and the Associated Risks of Appendectomies.Journal of personalized medicine · 2025Review
- Development and validation of an ensemble learning risk model for sepsis after abdominal surgery.Archives of medical science : AMS · 2025Article
- Risk assessment and prediction of nosocomial infections based on surveillance data using machine learning methods.BMC public health · 2024Article
- Artificial intelligence in the diagnosis and treatment of acute appendicitis: a narrative review.Updates in surgery · 2024Review
- Clinical applications of machine learning in the survival prediction and classification of sepsis: coagulation and heparin usage matter.Journal of translational medicine · 2022Article
- Machine Learning for the Prediction of Red Blood Cell Transfusion in Patients During or After Liver Transplantation Surgery.Frontiers in medicine · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 3 institutions in 2 countries.
Funding
Abstract
backgroundWe applied various machine learning algorithms to a large national dataset to model the risk of postoperative sepsis after appendectomy to evaluate utility of such methods and identify factors associated with postoperative sepsis in these patients.
methodsThe National Surgery Quality Improvement Program database was used to identify patients undergoing appendectomy between 2005 and 2017. Logistic regression, support vector machines, random forest decision trees, and extreme gradient boosting machines were used to model the occurrence of postoperative sepsis.
resultsIn the study, 223,214 appendectomies were identified; 2,143 (0.96%) were indicated as having postoperative sepsis. Logistic regression (area under the curve 0.70; 95% confidence interval, 0.68-0.73), random forest decision trees (area under the curve 0.70; 95% confidence interval, 0.68-0.73), and extreme gradient boosting (area under the curve 0.70; 95% confidence interval, 0.68-0.73) afforded similar performance, while support vector machines (area under the curve 0.51; 95% confidence interval, 0.50-0.52) had worse performance. Variable importance analyses identified preoperative congestive heart failure, transfusion, and acute renal failure as predictors of postoperative sepsis.
conclusionMachine learning methods can be used to predict the development of sepsis after appendectomy with moderate accuracy. Such predictive modeling has potential to ultimately allow for preoperative recognition of patients at risk for developing postoperative sepsis after appendectomy thus facilitating early intervention and reducing morbidity.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.