Department of Gastrointestinal Surgery, Department of General Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, People's Republic of China.
Shantou University Medical College, Shantou, People's Republic of China.
Obes Surg. 2023 Sep;33(9):2898-2905. doi: 10.1007/s11695-023-06729-6. Epub 2023 Jul 15.
Suboptimal response is one of the major problems for bariatric surgery, and constructing an individualized model for predicting outcomes of bariatric surgery is essential. Thus, the aim of this study is to develop a nomogram to predict the response to bariatric surgery.
509 patients who underwent bariatric surgery between 2019 to 2020 from 6 centers were retrieved and assessed. Multiple Imputation was used to replace missing data. Patients with %TWL ≥ 20% 1 year after bariatric surgery were classified as patients with optimal response, while the others were patients with suboptimal response. A web-based nomogram was constructed and validated. ROC curve and calibration curve were used to determine the predictive ability of our model.
56 (11.0%) patients were classified as patients with suboptimal response, and they showed advanced age, lower pre-operative BMI, smaller waist circumference, higher fasting glucose, higher HbA1c and lower fasting insulin compared to patients with optimal response. A forward likelihood ratio logistic regression analysis indicated that age (OR = 0.943, 95% CI: 0.915-0.971, p < 0.001), pre-operative BMI (OR = 1.109, 95% CI: 1.002-1.228, p = 0.046) and waist circumference (OR = 1.043, 95% CI: 1.000-1.088, p = 0.048) were essential factors contributing to the response to bariatric surgery. Lastly, a web-based nomogram was constructed to predict the response to bariatric surgery and demonstrated an AUC of 0.829 and 0.798 upon internal and external validation.
Age, BMI and fasting glucose were proved to be essential factors influencing the response to bariatric surgery. The nomogram constructed in this study demonstrated good adaptivity.
减重手术效果不理想是减重手术的主要问题之一，因此构建预测减重手术效果的个体化模型至关重要。本研究旨在开发一种列线图来预测减重手术的效果。
回顾性分析了 2019 年至 2020 年间 6 家中心的 509 例行减重手术的患者。采用多重插补法替换缺失数据。术后 1 年体重减轻率（%TWL）≥20%的患者被归类为手术效果理想，其余患者被归类为手术效果不理想。构建并验证了基于网络的列线图。使用 ROC 曲线和校准曲线来确定模型的预测能力。
56 例（11.0%）患者被归类为手术效果不理想，与手术效果理想的患者相比，他们的年龄更大，术前 BMI 更低，腰围更小，空腹血糖更高，HbA1c 更高，空腹胰岛素水平更低。向前似然比逻辑回归分析表明，年龄（OR=0.943，95%CI：0.915-0.971，p<0.001）、术前 BMI（OR=1.109，95%CI：1.002-1.228，p=0.046）和腰围（OR=1.043，95%CI：1.000-1.088，p=0.048）是影响减重手术效果的重要因素。最后，构建了一个基于网络的列线图来预测减重手术的效果，内部和外部验证的 AUC 分别为 0.829 和 0.798。
年龄、BMI 和空腹血糖是影响减重手术效果的重要因素。本研究构建的列线图具有良好的适应性。
