Key result
Random forest model outperforms logistic regression predicting moderate-to-severe acute postoperative pain with AUC ~0.81.
Why the study?
To detect and intervene early for moderate to severe postoperative pain, identifying risk factors and establishing an accurate prediction model after orthopedic surgery under general anesthesia was needed.
Does a random forest algorithm-based prediction model improve the prediction of moderate to severe acute postoperative pain in patients undergoing orthopedic surgery under general anesthesia compared to a multivariate logistic regression model?
Cohort (n=327)
No
Does a random forest algorithm-based prediction model improve the prediction of moderate to severe acute postoperative pain in patients undergoing orthopedic surgery under general anesthesia compared to a multivariate logistic regression model?
A random forest machine learning model outperformed traditional multivariate logistic regression in predicting moderate to severe acute postoperative pain after orthopedic surgery, highlighting its potential utility for guiding early analgesic interventions.
Answers are grounded in this paper and cite their sources.
Supports ML models for postoperative pain prediction in orthopedic cohorts; leaves open clinical utility pending prospective validation.
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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Shi et al. (2023) conducted a cohort in Moderate to severe acute postoperative pain after orthopedic surgery (n=327). Random forest prediction model vs. Multivariate logistic regression model was evaluated on Prediction of moderate to severe acute postoperative pain (VAS score ≥ 4) on postoperative day one (AUC 0.810). A random forest algorithm-based prediction model successfully predicted moderate to severe acute postoperative pain after orthopedic surgery under general anesthesia with an AUC of 0.810 in the testing set, outperforming a multivariate logistic regression model.
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