Related Experiment Video
Updated: Apr 3, 2026
An Experimental Paradigm for the Prediction of Post-Operative Pain PPOP
Published on: January 27, 2010
Machine Learning Approach to Predict Postoperative Pain and Opioid Usage in Elective Primary Spine Surgery: A
Danny L Saksenberg1,2, Ryan Y Lee1, Swaroopa Vaidya3
1Department of Anesthesiology, Yale University School of Medicine, New Haven, CT, USA.
Background:
Machine learning (ML) was used to predict pain scores and opioid consumption after elective spine surgery in the presence and absence of erector spinae plane block (ESP).
Methods:
A single-center retrospective chart review of 2796 cases was conducted. These cases were divided into the control group (N=1255) consisting of patients who did not receive the ESP blocks and the treatment group consisting of patients who received the blocks (N=1541). The gradient boosting ensemble tree methodology was employed to develop the AI predictive models. Feature importance for each optimized gradient boosting model was quantified using impurity-based importance scores, as implemented in the scikit-learn library. Partial dependence analysis was conducted to characterize the direction, magnitude, and non-linear nature of predictor-outcome relationships across clinically relevant ranges.
Results:
On unadjusted univariate analysis, the ESP block was associated with a statistically significant (p=0.01) yet clinically irrelevant 1% increase in average postsurgical pain scores. Conversely, ESP block was associated with a statistically non-significant (p=0.13) but clinically relevant 6.7% reduction in opioid consumption (MME/kg/day). These associations are exploratory and should not be interpreted as causal. Three AI models were developed to predict postsurgical pain and opioid consumption. The best-performing model, which predicts average postsurgical pain, achieved a mean absolute error of 1.24 on a 10-point scale (approximately 12.4%). High-importance predictors across the models included preoperative pain scores, serum glucose, and white blood cell count, as well as age.
Conclusion:
It is feasible to use machine-learning approaches to identify risk factors for postoperative pain and predict population-level pain scores and opioid consumption in spine surgery using large datasets; these models are not intended for individual-level prediction. The role of ESP in spine surgery, however, remains uncertain, and ESP block findings should be interpreted as exploratory associations only.
