A random forest algorithm-based prediction model for moderate to severe acute postoperative pain after orthopedic surgery under general anesthesia
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Abstract
Background: Postoperative 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. Methods: Patients 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. Results: A 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. Conclusions: The 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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Yang, M. M. H., Hartley, R. L., Leung, A. A., Ronksley, P. E., Jetté, N., Casha, S., & Riva-Cambrin, J. (2019) · BMJ Open · doi:10.1136/bmjopen-2018-025091 · Cited by 414 · cited 1× · see citing works
“In previous studies, it has been reported that preoperative pain can increase the incidence of acute post-operative pain [37].”
BackgroundPredictors for moderate to severe acute postoperative pain after total hip and knee replacement
Liu, S. S., Buvanendran, A., Rathmell, J. P., Sawhney, M., Bae, J. J., Moric, M., Perros, S., Pope, A. J., Poultsides, L., Valle, C. J. D., et al. (2012) · International Orthopaedics · doi:10.1007/s00264-012-1623-5 · Cited by 167 · see citing works
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- Authors
- Shi, G., Liu, G., Gao, Q., Zhang, S., Wang, Q., Wu, L., He, P., & Yu, Q.
- Affiliations
- Shanxi Medical University (CN)
- Corresponding author
- He, Peifeng
- Yu, Qi
- Venue
- BMC Anesthesiology
- Published
- 2023-11-06
- Volume
- 23
- Issue
- 1
- Pages
- 361-361
- ISSN
- 1471-2253 (ISSN-L)
- PMC
- PMC10626723
- OpenAlex
- W4388420151
- Cited by
- 35
- References
- 2
- Citations by year
- 2026: 10 · 2025: 19 · 2024: 6
- FWCI
- 9.38
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- BMC Anesthesiology (publishedVersion, cc-by)
- Research Square (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- the Department of Education of Shanxi Province (2021Y364)
- the Key R&D Projects of Shanxi Province (202102130501003)
- Topics
- Health Sciences › Medicine › Surgery › Anesthesia and Pain Management
- Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Pediatric Pain Management Techniques
- Health Sciences › Medicine › Anesthesiology and Pain Medicine › Pain Management and Opioid Use
- Keywords
- postoperative pain, acute postoperative pain, orthopedic surgery, general anesthesia, random forest, risk factors, immobilization, surgery duration, Lasso regression feature selection, multivariate logistic regression
- MeSH terms
- Random Forest, Humans, Pain, Postoperative, Postoperative Pain, Risk Factors, Orthopedic Procedures
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