[Risk factor analysis on body mass rebound after laparoscopic sleeve gastrectomy and establishment of a nomogram prediction model].

Objective: To investigate the potential independent risk factors of body mass rebound following laparoscopic sleeve gastrectomy (LSG) and construct a nomogram prediction model based on these factors. Methods: In this retrospective observational study, patients with obesity who had undergone LSG at the Department of Gastrointestinal Surgery of the Affiliated Changzhou No. 2 People's Hospital of Nanjing Medical University between January 2015 and July 2017 were retrospectively enrolled. These patients were divided according to their status of postoperative body mass rebound. The inclusion criteria were patients aged between 16 and 65 years who had undergone LSG bariatric surgery with surgical indications according to the 2014 Chinese Guidelines for the Surgical Management of Obesity and Type 2 Diabetes Mellitus. The exclusion criteria were patients who had undergone other bariatric surgeries, who were taking weight-loss drugs or drugs that affected their body weight, who had severe gastroesophageal reflux and hiatal hernia, who were pregnant, who had incomplete clinical data, and who were lost to follow-up or were followed up for <3 years. In total, 241 patients with obesity (69 males and 172 females) who had undergone LSG surgery were enrolled. The mean age and body mass index (BMI) were (29.9±5.8) years and (40.8±4.8) kg/m2, respectively. The patients were followed up till July 2022, with a focus on their body weight. Postoperative body mass rebound was defined as a percentage increase of ≥10% from the nadir body mass, which was the lowest body mass during the 3-year follow-up period. The body weight rebound following LSG and its influencing factors were observed, based on which a nomogram model was constructed and evaluated. The relationships between the patients' basic data, clinical indicators, preoperative hematological indicators, postoperative indicators, and body weight rebound following LSG were analyzed via univariate analysis. Independent risk factors were further screened by multivariate logistic regression analysis. Factors with a statistically significant difference were included into the nomogram prediction model. Moreover, the model was internally (modeling set) and externally (validation set, 80 baseline data-matched patients with obesity from our center) validated using receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) via R software. ROC curve analysis was used to analyze the predictive and cutoff values of the measurement data for body mass rebound. Results: Overall, 90 patients (37.3%) exhibited postoperative body weight rebound, with the lowest BMI of (29.5±2.6) kg/m2 and time to reach the lowest BMI of (15.4±2.3) months; 151 patients (62.7%) did not exhibit body weight rebound, with the lowest BMI of (29.8±2.3) kg/m2 and time to reach the lowest BMI of (14.7±2.1) months. The results of univariate analysis showed that BMI, depression, anxiety, C-reactive protein (CRP) levels, systemic immune inflammatory index (SII), prognostic nutritional index (PNI), and albumin/fibrinogen ratio (AFR) were associated with body weight rebound following LSG with statistically significant differences (all P<0.05). The results of multivariate regression analyses suggested that depression [odds ration (OR) = 1.31, 95% confidence interval (CI): 1.08-1.62, P=0.010], preoperative CRP levels of ≥8 mg/L (OR = 1.34, 95% CI: 1.09-1.69, P=0.007), SII (OR = 0.58, 95% CI: 0.41-0.86, P=0.013), PNI (OR = 2.06, 95% CI: 1.03-4.21, P=0.007), and AFR (OR: 0.49, 95% CI: 0.33-0.69, P=0.011) were five independent risk factors for body mass rebound. A nomogram prediction model was constructed based on the multivariate analysis results. The scores of PNI, SII, AFR, CRP, and depression were 92.5, 100, 72.5, 25, and 27.5, respectively. The total score was calculated by adding the individual scores of each risk factor, which was used to calculate the probability of body mass rebound following LSG. The evaluation results of the nomogram model showed a C-index of 0.713 and 0.762, sensitivity of 0.656 and 0.594, and specificity of 0.715 and 0.909 in the modeling and validation sets, respectively. The calibration curve analysis and DCA indicated that the nomogram model has a good predictive value for body mass rebound after LSG. Conclusion: Preoperative depression, CRP of ≥8 mg/L, SII, PNI, and AFR were independent risk factors for body mass rebound following LSG. Hence, the nomogram prediction model based on these factors can effectively predict body mass rebound in patients undergoing LSG.目的： 探讨肥胖症患者腹腔镜袖状胃切除术（LSG）后体质量反弹的独立危险因素，并构建列线图预测模型。 方法： 本研究为回顾性观察性研究，回顾性收集南京医科大学附属常州二院胃肠外科2015年1月至2019年7月期间，施行LSG的肥胖症患者的临床资料和随访资料。纳入标准为年龄16~65周岁、根据2014版中国肥胖和2型糖尿病外科治疗相关指南具有明确的手术指征并施行LSG减重手术的患者。排除服用减肥药物或影响体质量的药物者、伴严重胃食管反流和食管裂孔疝者、术后怀孕的女性患者、临床资料不全或失访的患者以及随访不足3年者。共纳入241例行LSG手术的肥胖症患者；男69例，女172例；年龄（29.9±5.8）岁；体质指数（40.8±4.8）kg/m2。随访截至2022年7月，重点了解患者体质量情况。术后体质量反弹的定义为相对于最低点体质量，体质量增加百分比≥10%，而术后最低点体质量定义为术后3年随访期间的最低体质量。本研究观察指标包括LSG术后体质量反弹情况和影响因素，以及列线图模型的构建和评价。采用单因素分析方法分析患者基本资料、临床指标、术前血液学指标以及术后指标与LSG术后体质量反弹的关系。进一步用多因素Logistic回归分析筛选出体质量反弹的独立危险因素，并通过R软件将有差异的多因素进一步纳入列线图预测模型，对该模型进行内部（建模集）及外部（验证集，本中心基线资料与建模集相匹配的80例患者作为外部验证集）评估。采用受试者工作特征（ROC）曲线、校准（calibrate）曲线和决策曲线分析法（DCA）分析计量资料对体质量反弹的预测价值及临界值。 结果： 术后有90例（37.3%）出现体质量反弹，体质指数最低点为（29.5±2.6）kg/m2，体质量达到最低点时间为（15.4±2.3）个月；151例（62.7%）未出现体质量反弹，体质指数最低点为（29.8±2.3）kg/m2，体质量达到最低点时间为（14.7±2.1）个月。单因素分析结果显示，体质指数、抑郁、焦虑、C-反应蛋白、全身免疫炎性指数、预后营养指数和白蛋白/纤维蛋白原比值水平与LSG术后体质量出现反弹有关，差异均有统计学意义（均P<0.05）。多因素Logistic回归分析结果显示，抑郁（OR=1.31，95%CI：1.08~1.62，P=0.010）、C-反应蛋白≥8 mg/L（OR=1.34，95%CI：1.09~1.69，P=0.007）、全身免疫炎性指数（OR=0.58，95%CI：0.41~0.86，P=0.013）、预后营养指数（OR=2.06，95%CI：1.03~4.21，P=0.007）和白蛋白/纤维蛋白原比值（OR=0.49，95%CI：0.33~0.69，P=0.011）为肥胖症患者LSG术后出现体质量反弹的独立危险因素。根据此结果构建列线图预测模型，预后营养指数为92.5分、全身免疫炎性指数为100分、白蛋白/纤维蛋白原比值为72.5分、C-反应蛋白为25分、抑郁为27.5分，通过对每个危险因素单项评分相加得到总分，总分所对应的值即为模型预测LSG术后体质量反弹的发生概率。列线图模型评估结果显示，该模型建模集C指数为0.713，灵敏度为0.656，特异度为0.715；验证集C指数为0.762，灵敏度为0.594，特异度为0.909。经校准拟合及DCA曲线提示，该列线图模型对于LSG术后体质量反弹的预测价值良好。 结论： 术前抑郁、C-反应蛋白≥8 mg/L、全身免疫炎性指数、预后营养指数和白蛋白/纤维蛋白原比值增加为肥胖症患者LSG术后出现体质量反弹的独立危险因素，构建的列线图模型可有效预测减重术后体质量反弹。.