A random forest algorithm-based prediction model for moderate to severe acute postoperative pain after orthopedic surgery under general anesthesia.
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...
| Publicado en: | BMC Anesthesiology Vol. 23; no. 1; pp. 1 - 14 |
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| Autores principales: | , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
BioMed Central
11/6/2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=173457813&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 173457813 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14712253 1CH3 jtl: BMC Anesthesiology issn: 14712253 maglogo: N pubinfo: dt: 11/6/2023 vid: 23 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 173457813 173457813 173457813 10.1186/s12871-023-02328-1 173457813 ppf: 1 ppct: 13 formats: tig: atl: A random forest algorithm-based prediction model for moderate to severe acute postoperative pain after orthopedic surgery under general anesthesia. aug: au: Shi, Gaoxiang Liu, Geliang Gao, Qichao Zhang, Shengxiao Wang, Qi Wu, Li He, Peifeng Yu, Qi affil: https://ror.org/0265d1010 School of Basic Medical Sciences, Shanxi Medical University, Taiyuan, China sug: subj: Random Forest Algorithms Prediction Models Postoperative Pain Diagnosis Pain Measurement Orthopedic Surgery Anesthesia, General Risk Assessment Postoperative Pain Risk Factors Human Surgical Patients Logistic Regression Multivariate Analysis Machine Learning Descriptive Statistics ab: 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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