ArticlePloS one2020
Machine learning approach to predict postoperative opioid requirements in ambulatory surgery patients.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses that pooled it.
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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.
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Who cites it
22 citing papers in PubMed, 2 syntheses or guidelines pooled it, 45 citations in OpenAlex.
- Moving towards the use of artificial intelligence in pain management.European journal of pain (London, England) · 2025Pooled it
- Systematic Review of Methods for Individual Prediction of Postoperative Pain.Pain research & management · 2025Pooled it
- A machine learning approach to predict treatment response in myofascial pain patients receiving masseter trigger point injections.BMC oral health · 2026Article
- Transforming perioperative care: The current landscape and future trajectory of artificial intelligence in anesthesia-A narrative review.The Journal of international medical research · 2026Review
- Development of prediction models for perioperative opioid needs in laparoscopic cholecystectomy patients: A machine-learning approach.Surgery open science · 2026Article
- Machine Learning Approach to Predict Postoperative Pain and Opioid Usage in Elective Primary Spine Surgery: A Retrospective Study.Journal of pain research · 2026Article
- Development and validation of a clinically available risk model for medium-to-high opioid consumption after total knee arthroplasty.American journal of translational research · 2026Article
- Artificial intelligence in breast surgery: Current applications, challenges, and future perspectives.The Journal of international medical research · 2026Review
- Artificial Intelligence in Ambulatory Surgery: Current Applications, Challenges, and Future Directions.Journal of medical systems · 2025Review
- External Validation of an Algorithm to Guide Opioid Administration at the End of Surgery-Protocol for an Observational Cohort Study of the OPIAID Algorithm.Acta anaesthesiologica Scandinavica · 2025Article
- A DEEP NEURAL NETWORK TWO-PART MODEL AND FEATURE IMPORTANCE TEST FOR SEMI-CONTINUOUS DATA.The annals of applied statistics · 2025Article
- Global trends in artificial intelligence research in anesthesia from 2000 to 2023: a bibliometric analysis.Perioperative medicine (London, England) · 2025Review
- Machine learning approach to predict postoperative pain after spinal morphine administration during caesarean delivery.Heliyon · 2024Article
- Postoperative Pain at Discharge From the Post-anesthesia Care Unit: A Case-Control Study.Cureus · 2024Article
- Predicting Postoperative Pain and Opioid Use with Machine Learning Applied to Longitudinal Electronic Health Record and Wearable Data.Applied clinical informatics · 2024Article
- The association of intraoperative opioid dose with postanesthesia care unit outcomes in children: a retrospective study.Canadian journal of anaesthesia = Journal canadien d'anesthesie · 2024Article
- Use of Artificial Intelligence in the Advancement of Breast Surgery and Implications for Breast Reconstruction: A Narrative Review.Journal of clinical medicine · 2023Review
- Patient-Reported Data Augment Prediction Models of Persistent Opioid Use after Elective Upper Extremity Surgery.Plastic and reconstructive surgery · 2023Article
- A Neural Network Model Using Pain Score Patterns to Predict the Need for Outpatient Opioid Refills Following Ambulatory Surgery: Algorithm Development and Validation.JMIR perioperative medicine · 2023Article
- Preventing pediatric chronic postsurgical pain: Time for increased rigor.Canadian journal of pain = Revue canadienne de la douleur · 2022Review
Corrections and comments
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Authors and funding
10 authors at 5 institutions in 1 country.
Funding
Abstract
Opioids play a critical role in acute postoperative pain management. Our objective was to develop machine learning models to predict postoperative opioid requirements in patients undergoing ambulatory surgery. To develop the models, we used a perioperative dataset of 13,700 patients (≥ 18 years) undergoing ambulatory surgery between the years 2016-2018. The data, comprising of patient, procedure and provider factors that could influence postoperative pain and opioid requirements, was randomly split into training (80%) and validation (20%) datasets. Machine learning models of different classes were developed to predict categorized levels of postoperative opioid requirements using the training dataset and then evaluated on the validation dataset. Prediction accuracy was used to differentiate model performances. The five types of models that were developed returned the following accuracies at two different stages of surgery: 1) Prior to surgery-Multinomial Logistic Regression: 71%, Naïve Bayes: 67%, Neural Network: 30%, Random Forest: 72%, Extreme Gradient Boost: 71% and 2) End of surgery-Multinomial Logistic Regression: 71%, Naïve Bayes: 63%, Neural Network: 32%, Random Forest: 72%, Extreme Gradient Boost: 70%. Analyzing the sensitivities of the best performing Random Forest model showed that the lower opioid requirements are predicted with better accuracy (89%) as compared with higher opioid requirements (43%). Feature importance (% relative importance) of model predictions showed that the type of procedure (15.4%), medical history (12.9%) and procedure duration (12.0%) were the top three features contributing to model predictions. Overall, the contribution of patient and procedure features towards model predictions were 65% and 35% respectively. Machine learning models could be used to predict postoperative opioid requirements in ambulatory surgery patients and could potentially assist in better management of their postoperative acute pain.
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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.