Wu Wenzhi, Li Zhao, Yuan Chentong, Yang Mingyu, Song Yi, Xu Zhenying, Li Zhaopeng, Lu Yun, Zhou Xiaoming, Wang Dongsheng, Li Yu
The Affiliated Hospital of Qingdao University, Qingdao, China.
Qingdao University, Qingdao, China.
Obes Surg. 2025 May 20. doi: 10.1007/s11695-025-07798-5.
Laparoscopic sleeve gastrectomy (LSG) is an effective treatment for obesity, but early weight loss outcomes vary owing to individual nutritional and metabolic differences. We developed a nomogram model to predict early weight loss after LSG, incorporating computed tomography (CT)-based body composition metrics and preoperative inflammatory-nutritional markers.
We retrospectively analyzed 305 patients with obesity who underwent LSG at the Affiliated Hospital of Qingdao University between January 2016 and June 2023. An external validation cohort of 105 patients from a separate institution was also included. Patients were categorized into optimal remission (%total weight loss [%TWL] ≥ 25%) and suboptimal remission (%TWL < 25%) weight loss groups one year postoperatively. Predictive variables were identified using Least Absolute Shrinkage and Selection Operator (LASSO) regression and multivariate logistic regression. A nomogram was constructed based on the significant predictors. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), calibration curves, decision curve analysis (DCA), and clinical impact curve (CIC).
Independent predictors of suboptimal remission included BMI > 40 kg/m, elevated total cholesterol, high neutrophil-to-lymphocyte ratio, high cortisol, low skeletal muscle index, and elevated visceral-to-subcutaneous adipose tissue area ratio. The constructed nomogram demonstrated strong predictive performance, with AUCs of 0.864 and 0.842 in the training and external validation cohorts, respectively. Calibration curves indicated excellent agreement between predicted and observed outcomes. DCA and CIC confirmed the model's clinical utility in both cohorts.
The developed nomogram effectively predicts early weight loss outcomes after LSG, supporting targeted perioperative management and personalized nutritional interventions.
腹腔镜袖状胃切除术（LSG）是治疗肥胖症的有效方法，但由于个体营养和代谢差异，早期体重减轻结果有所不同。我们开发了一种列线图模型，以预测LSG术后的早期体重减轻情况，该模型纳入了基于计算机断层扫描（CT）的身体成分指标和术前炎症营养标志物。
我们回顾性分析了2016年1月至2023年6月在青岛大学附属医院接受LSG的305例肥胖患者。还纳入了来自另一家机构的105例患者组成的外部验证队列。术后一年，将患者分为最佳缓解组（总体重减轻百分比[%TWL]≥25%）和次优缓解组（%TWL<25%）体重减轻组。使用最小绝对收缩和选择算子（LASSO）回归和多变量逻辑回归确定预测变量。根据显著预测因子构建列线图。使用受试者操作特征曲线（AUC）下的面积、校准曲线、决策曲线分析（DCA）和临床影响曲线（CIC）评估模型性能。
次优缓解的独立预测因子包括BMI>40kg/m²、总胆固醇升高、中性粒细胞与淋巴细胞比值高、皮质醇高、骨骼肌指数低以及内脏与皮下脂肪组织面积比升高。构建的列线图显示出强大的预测性能，训练队列和外部验证队列的AUC分别为0.864和0.842。校准曲线表明预测结果与观察结果之间具有良好的一致性。DCA和CIC证实了该模型在两个队列中的临床实用性。
所开发的列线图有效地预测了LSG术后的早期体重减轻结果，支持有针对性的围手术期管理和个性化营养干预。
