ArticleBMC anesthesiology2023
A random forest algorithm-based prediction model for moderate to severe acute postoperative pain after orthopedic surgery under general anesthesia.
Article in BMC anesthesiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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26 citing papers in PubMed.
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- Dynamic prediction model for early postoperative pain based on generalized estimating equations: a multi-timepoint longitudinal study.American journal of translational research · 2026Article
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- Prevalence and factors associated with severe pain in the postoperative period of orthopedic surgery.Revista brasileira de enfermagem · 2026Article
- A machine learning-based risk prediction model for Hospitalized patients with deep vein thrombosis.PeerJ · 2026Article
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- Risk Prediction of Unplanned Extubation in Inpatients Using Random Forest and Logistic Regression Models.Journal of patient safety · 2025Article
- Integrating Artificial Intelligence in Orthopedic Care: Advancements in Bone Care and Future Directions.Bioengineering (Basel, Switzerland) · 2025Review
- Global trends in artificial intelligence research in anesthesia from 2000 to 2023: a bibliometric analysis.Perioperative medicine (London, England) · 2025Review
- Using machine learning methods to investigate the impact of age on the causes of death in patients with early intrahepatic cholangiocarcinoma who underwent surgery.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025Article
- Significant adverse prognostic events in patients with urosepsis: a machine learning based model development and validation study.Frontiers in cellular and infection microbiology · 2025Article
- Prediction Models of Microinvasive Cervical Cancer in High-Grade Squamous Intraepithelial Lesion Treatment by Loop Electrosurgical Excision Procedure.Risk management and healthcare policy · 2025Article
- Observational
- Assessing the impact of waterfront trail aesthetics on psychological restoration in urban environments: a deep learning and random forest approach.Frontiers in public health · 2025Article
- A risk prediction model for neovascular glaucoma secondary to proliferative diabetic retinopathy based on Boruta feature selection and random forest.Frontiers in cell and developmental biology · 2025Article
- Risk of intraoperative hemorrhage during cesarean scar ectopic pregnancy surgery: development and validation of an interpretable machine learning prediction model.EClinicalMedicine · 2024Article
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8 authors.
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Abstract
backgroundPostoperative pain is one of the most common complications after surgery. In order to detect early and intervene in time for moderate to severe postoperative pain, it is necessary to identify risk factors and construct clinical prediction models. This study aimed to identify significant risk factors and establish a better-performing model to predict moderate to severe acute postoperative pain after orthopedic surgery under general anesthesia.
methodsPatients who underwent orthopedic surgery under general anesthesia were divided into patients with moderate to severe pain group (group P) and patients without moderate to severe pain group (group N) based on VAS scores. The features selected by Lasso regression were processed by the random forest and multivariate logistic regression models to predict pain outcomes. The classification performance of the two models was evaluated through the testing set. The area under the curves (AUC), the accuracy of the classifiers, and the classification error rate for both classifiers were calculated, the better-performing model was used to predict moderate to severe acute postoperative pain after orthopedic surgery under general anesthesia.
resultsA total of 327 patients were enrolled in this study (228 in the training set and 99 in the testing set). The incidence of moderate to severe postoperative pain was 41.3%. The random forest model revealed a classification error rate of 25.2% and an AUC of 0.810 in the testing set. The multivariate logistic regression model revealed a classification error rate of 31.3% and an AUC of 0.764 in the testing set. The random forest model was chosen for predicting clinical outcomes in this study. The risk factors with the greatest and second contribution were immobilization and duration of surgery, respectively.
conclusionsThe random forest model can be used to predict moderate to severe acute postoperative pain after orthopedic surgery under general anesthesia, which is of potential clinical application value.
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