Establishing a Prediction Model for Weight Loss Outcomes After LSG in Chinese Obese Patients with BMI ≥ 32.5 Kg/m2 Using Body Composition Data

Abstract

Background: Laparoscopic sleeve gastrectomy (LSG) is associated with sustained and substantial weight loss. However, suboptimal results are observed in certain patients.

Objective: Drawing from body composition data at our center, clinically accessible predictive factors for weight loss outcomes were identified, leading to the development and validation of a preoperative predictive model for weight loss following LSG.

Methods and materials: A retrospective analysis was conducted on the general clinical baseline and body composition data of obese patients (body mass index [BMI] ≥ 32.5 kg/m²) who underwent LSG between December 2016 and December 2022. Independent predictors for weight loss outcomes were selected through univariate logistic regression, random forest analysis, and multivariate logistic regression. Subsequently, a nomogram was developed to predict weight loss outcomes and was evaluated for discrimination, accuracy, and clinical utility, with validation performed in a separate cohort.

Results: A total of 473 patients with mean BMI were included. The preoperative resting energy expenditure to body weight ratio (REE/BW), fat-free mass index (FFMI), and waist circumference (WC) emerged as independent predictive factors for weight loss outcomes at one year post-LSG. These body composition parameters were incorporated into the construction of an Inbody predictive nomogram, which yielded area under the curve (AUC) values of 0.868 (95% CI: 0.826-0.902) for the modeling cohort and 0.829 (95% CI: 0.756-0.887) for the validation cohort. Calibration curves, decision curve analysis (DCA), and clinical impact curves (CIC) from both groups demonstrated the model's robust discrimination, accuracy, and clinical utility.

Conclusion: In obese Chinese patients with a BMI ≥ 32.5 kg/m², the Inbody-based nomogram integrating REE/BW, FFMI, and WC offers an effective preoperative tool for predicting weight loss outcomes one year after LSG, facilitating surgical planning and postoperative management.

Keywords: body composition data; laparoscopic sleeve gastrectomy; metabolic bariatric surgery; obesity; prognostic prediction.

Figure 1

Postoperative Changes in BMI and %EWL for Patients in the Modeling Group. (a) BMI Change Trend Chart. (b) %EWL Change Trend Chart.

Figure 2

Relationship between Random Forest Model Error and Number of Decision Trees.

Figure 3

Feature Importance Ranking in the Random Forest Model.

Figure 4

Inbody Nomogram for Predicting Weight Loss Outcomes in LSG Based on Body Composition Indicators.

Figure 5

Comparison of ROC Curves for Internal Validation of the Inbody Predictive Nomogram Model and Individual Predictors.

Figure 6

Internal Validation of the Inbody Predictive Nomogram Model. (a) Calibration Curve: The closer the red curve is to the diagonal line, the better the model’s goodness-of-fit. (b) Decision Curve Analysis (DCA): The horizontal axis represents the probability threshold, and the vertical axis represents the net benefit. The further the red line is from the gray and black lines, the higher the clinical utility of the model. (c) Clinical Impact Curve (CIC): The horizontal axis represents the probability threshold, and the vertical axis represents the number of patients. The red line shows the number of patients predicted by the model to experience the outcome at different probability thresholds, while the purple line indicates the number of patients who are both predicted and actually experience the outcome. The bottom row shows the benefit ratio, reflecting the ratio of benefit to harm at different probability thresholds.

Figure 7

Comparison of ROC Curves for External Validation of the Inbody Predictive Nomogram Model and Individual Predictors.

Figure 8

External Validation of the Inbody Predictive Nomogram Model. (a) Calibration Curve: The closer the blue curve is to the diagonal line, the better the model’s goodness-of-fit. (b) Decision Curve Analysis (DCA): The horizontal axis represents the probability threshold, and the vertical axis represents the net benefit. The further the blue line is from the gray and black lines, the higher the clinical utility of the model. (c) Clinical Impact Curve (CIC): The horizontal axis represents the probability threshold, and the vertical axis represents the number of patients. The blue line shows the number of patients predicted by the model to experience the outcome at different probability thresholds, while the purple line indicates the number of patients who are both predicted and actually experience the outcome. The bottom row shows the benefit ratio, reflecting the ratio of benefit to harm at different probability thresholds.

Figure 9

Web Application of the Inbody Predictive Nomogram Model for LSG Weight Loss Outcomes.