Predicting intraoperative blood loss risk in severe lumbar disc herniation patients undergoing PLIF: a multicenter cohort study using ensemble learning.

BACKGROUND

Patients with severe lumbar disc herniation (LDH), particularly those complicated by spinal stenosis or vertebral instability, frequently require posterior lumbar interbody fusion to alleviate nerve compression and reconstruct spinal biomechanical stability. Aiming to optimize individualized surgical planning, it is necessary to establish accurate predictive models derived from multidimensional clinical data.

METHODS

In this retrospective, multi-center study, the data utilized in this study were sourced from the Degenerative Spine Diseases in China (DSDC2024, NCT05867732). The model was trained on 3055 cases and externally validated across four geographically distinct cohorts ( n = 3186). Leveraging a two-stage ensemble framework, we first applied Lasso regression to select target predictive variables from 38 clinical accessibility features (demographics, comorbidities, surgical parameters, and laboratory indices), then integrated XGBoost, random forest, and logistic regression through stacked generalization. Bayesian optimization with 10-fold cross-validation refined hyperparameters, while decision curve analysis quantified clinical utility against traditional risk assessment methods. Shapley Additive exPlanations analysis quantified feature contributions and interaction effects.

RESULTS

Amongst the 70 algorithmic combinations evaluated, the integration of Lasso with Stack emerged as the most predictive, achieving an impressive average area under the receiver operating characteristic curve of 0.884. The top five significant predictors were the fusion levels, clinical course duration, preoperative hospitalization, preoperative hemoglobin, and preoperative albumin.

CONCLUSION

The IBLED-LDH model provides a valuable tool for preoperative intraoperative blood loss risk stratification, balancing predictive accuracy with interpretability through advanced ensemble learning.