A Nomogram for Prediction of Weight Loss Outcomes after Bariatric Surgery

This study aims to comprehensively investigate the factors influencing weight reduction outcomes one year after bariatric surgery and construct a Nomogram.

A retrospective study analyzed 546 patients who underwent bariatric surgery at the bariatric center from 2015 to 2021. They were randomly divided into a 7:3 ratio for a training set (382 cases) and a testing set (164 cases). Univariate logistic regression and two machine learning techniques (LASSO, best subset regression) were employed for variable selection. The optimal model was derived via stepwise backward regression, Akaike Information Criterion (AIC), and Area Under the Curve (AUC). Receiver operating characteristic (ROC) curve analysis, calibration curve analysis, and Hosmer-Lemeshow test were employed to graphically evaluate and validate the performance of the model, while decision curve analysis (DCA) was utilized to assess its clinical value.

The predictive factors in the final nomogram included hip circumference, the surgical procedure and T2DM. Utilizing these three independent risk factors, a nomogram prediction model was developed, demonstrating robust discriminative ability with an area under the curve (AUC) of 0.742 (95% CI: 0.672-0.813) for the training set and 0.726 (95% CI: 0.607-0.845) for the test set. Furthermore, the model exhibited high accuracy, as evidenced by the non-significant Hosmer-Lemeshow test (P > 0.05) for both the validation and test sets. The decision curve analysis further confirmed the model's effectiveness in accurately predicting one-year weight loss outcomes following bariatric surgery.

The nomogram prediction model based on hip circumference, surgical procedure, and T2DM reasonably predicts one-year weight loss after bariatric surgery.