Development and Validation of a Machine Learning Model for Predicting One-year Anemia After Bariatric Surgery: A Multicenter Study.

INTRODUCTION

Anemia is a common nutritional complication after bariatric surgery, but preoperative tools for individualized risk assessment remain limited. This study aims to develop and externally validate a machine learning model for predicting anemia status one year after bariatric surgery.

METHODS

This multicenter study included patients aged 16-65 years undergoing sleeve gastrectomy or Roux-en-Y gastric bypass. Patients from one hospital formed the derivation cohort, and patients from two independent hospitals formed the external validation cohort. Forty-five preoperative variables were evaluated using logistic regression, random forest, support vector machine, and extreme gradient boosting. Model discrimination, calibration, and clinical utility were assessed and compared with blinded predictions from 10 clinicians with different levels of experience.

RESULTS

The derivation cohort included 511 patients (409 training, 102 internal testing), and the external cohort included 300 patients. One-year anemia occurred in 39.6%, 39.2%, and 19.0% of patients in the training, testing, and external validation cohorts, respectively. Random forest showed the most favorable overall performance profile and was simplified to seven predictors. The final model achieved an area under the curve of 0.827 (95% CI, 0.746-0.896) in the testing set and 0.823 (95% CI, 0.771-0.873) in the external validation cohort, with satisfactory calibration and decision-curve net benefit. In external validation, the model outperformed junior, middle-level, and senior clinicians and remained superior in clinician-defined challenging cases.

CONCLUSIONS

A seven-variable preoperative model showed good discrimination for one-year anemia status after bariatric surgery and may support individualized postoperative monitoring and nutritional care.