Objective

2

To construct and validate a prognostic assessment model of acupuncture intervention for lumbar disc herniation (LDH)

and analyze the key factors of acupuncture efficacy

so as to provide the decision support for clinical individualized treatment.

Methods

2

Clinical data of 478 LDH patients were retrospectively analyzed. The least absolute shrinkage and selection operator (LASSO) regression was used to select predictive variables

and 8 machine learning prediction models were constructed

including decision tree

random forest

extreme gradient boosting

support vector machine

multilayer perceptron

logistic regression

light gradient boosting machine

and K-nearest neighbor. The performance of each model was evaluated through five-fold cross-validation. SHapley additive exPlanations (SHAP) method was employed to analyze model interpretability

and an online interactive application based on Shiny was developed.

Results

2

LASSO regression selected 15 predictive variables; the support vector machine performed the best in five-fold cross-validation

with an average area under the receiver operating characteristic curve (AUC) of 0.862 and an average Brier score of 0.157. Decision curve analysis indicated a good clinical application value for this model. SHAP analysis showed that the combined therapies of acupuncture with

Fu

ʹs acupuncture

warm needling

electroacupuncture and acupuncture delivered once daily were associated with favorable prognosis; and the higher body mass indexes (BMI)

engagement in heavy physical labor

and the use of glucocorticoids and hyperosmotic dehydration agents

as well as the disc herniation of different segments and spinal canal stenosis were associated with unfavorable prognosis.

Conclusion

2

The interpretable machine learning-based prognostic assessment model of acupuncture intervention for LDH demonstrates a good predictive performance

providing evidence for clinical individualized treatment. However

more external validations are required for its optimization.