ArticlePloS one2022
Machine learning for prediction of postoperative nausea and vomiting in patients with intravenous patient-controlled analgesia.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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, 18 citations in OpenAlex.
- Review
- Prediction of Postoperative Vomiting Within 24 Hours Using Machine Learning With Large Language Model-Enhanced Interpretability: Development and Validation Study.JMIR medical informatics · 2026Article
- Machine Learning-Based Prediction of Early Patient-Controlled Analgesia Discontinuation After Total Knee Arthroplasty: A Retrospective Cohort Study.Journal of clinical medicine · 2026Article
- Application of machine learning for the prediction of post-operative nausea and vomiting in adult surgical patients - A systematic review.Indian journal of anaesthesia · 2026Article
- Explainable artificial intelligence for postoperative analgesia risk stratification and clinical decision support in abdominal surgery.Frontiers in digital health · 2026Article
- Leveraging transformer-based artificial intelligence for enhanced anesthetic decision-making in orthopedic surgery.Frontiers in medicine · 2026Article
- Postoperative nausea and vomiting: current concepts, management strategies, and future perspectives.Frontiers in pharmacology · 2026Review
- Development and prospective evaluation of a machine learning model to predict vomiting among pediatric cancer and hematopoietic cell transplant patients.BMC cancer · 2025Article
- Preventing postoperative nausea and vomiting after laparoscopic cholecystectomy: toward personalization.Journal of minimally invasive surgery · 2025Article
- Application of Machine Learning to Predict Postoperative Nausea and Vomiting in Laparoscopic Sleeve Gastrectomy.Obesity surgery · 2025Article
- Narrative Review of Electronic Health Record Systems in Anesthesia: Benefits, Risks, and Medico-Legal Considerations in the United States of America.Journal of medical systems · 2025Review
- Global trends in artificial intelligence research in anesthesia from 2000 to 2023: a bibliometric analysis.Perioperative medicine (London, England) · 2025Review
- Factors associated with acute clinically important postoperative nausea and vomiting in high-risk patients undergoing laparoscopic gastrointestinal surgery: a secondary analysis of the FDP-PONV trial.Frontiers in medicine · 2025Article
- Application of Machine Learning in Predicting Perioperative Outcomes in Patients with Cancer: A Narrative Review for Clinicians.Current oncology (Toronto, Ont.) · 2024Review
- Postoperative Nausea and Vomiting in Female Patients Undergoing Laparoscopic Gastrointestinal Surgery with Double Prophylactic Therapy.Surgery journal (New York, N.Y.) · 2024Article
- Postoperative Nausea and Vomiting Prediction: Machine Learning Insights from a Comprehensive Analysis of Perioperative Data.Bioengineering (Basel, Switzerland) · 2023Article
- Development and assessment of novel machine learning models to predict the probability of postoperative nausea and vomiting for patient-controlled analgesia.Scientific reports · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPostoperative nausea and vomiting (PONV) is a still highly relevant problem and is known to be a distressing side effect in patients. The aim of this study was to develop a machine learning model to predict PONV up to 24 h with fentanyl-based intravenous patient-controlled analgesia (IV-PCA).
methodsFrom July 2019 and July 2020, data from 2,149 patients who received fentanyl-based IV-PCA for analgesia after non-cardiac surgery under general anesthesia were applied to develop predictive models. The rates of PONV at 1 day after surgery were measured according to patient characteristics as well as anesthetic, surgical, or PCA-related factors. All statistical analyses and computations were performed using the R software.
resultsA total of 2,149 patients were enrolled in this study, 337 of whom (15.7%) experienced PONV. After applying the machine-learning algorithm and Apfel model to the test dataset to predict PONV, we found that the area under the receiver operating characteristic curve using logistic regression was 0.576 (95% confidence interval [CI], 0.520-0.633), k-nearest neighbor was 0.597 (95% CI, 0.537-0.656), decision tree was 0.561 (95% CI, 0.498-0.625), random forest was 0.610 (95% CI, 0.552-0.668), gradient boosting machine was 0.580 (95% CI, 0.520-0.639), support vector machine was 0.649 (95% CI, 0.592-0.707), artificial neural network was 0.686 (95% CI, 0.630-0.742), and Apfel model was 0.643 (95% CI, 0.596-0.690).
conclusionsWe developed and validated machine learning models for predicting PONV in the first 24 h. The machine learning model showed better performance than the Apfel model in predicting PONV.
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.