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. Keywords: machine learning, spine, pain, opioid
Saksenberg et al. (Sun,) studied this question.