Zhu Hongmei, Guo Peisen, Zhao Yi, Wu Xiaolin, Wang Bing, Yang Huawu, Yu Jiahui
The Third People's Hospital of Chengdu, Chengdu, China.
Obes Surg. 2025 Jan;35(1):249-256. doi: 10.1007/s11695-024-07634-2. Epub 2024 Dec 14.
Although numerous prediction models are available for diabetes remission following metabolic bariatric surgery, few are based on sleeve gastrectomy (SG). This study aimed to establish a predictive model for type 2 diabetes mellitus (T2DM) remission following SG and evaluate the efficacy of existing predictive models.
Patient data were gathered from a cohort study titled "Longitudinal Study of Bariatric Surgery in Western China." The synthetic minority oversampling technique was implemented, with 70% randomly selected as the training set and the remaining 30% as the testing set. Univariate logistic regression was used to identify factors associated with T2DM remission. These were included in subsequent stepwise multivariate analyses. A nomogram was then constructed. It was evaluated using a receiver operating characteristic (ROC) curve, calibration plot, and decision curve analysis. Finally, eight pre-existing predictive models were validated.
Initially, 166 patients were enrolled with a T2DM remission rate of 89.2%. Univariate logistic regression indicated that male patients, T2DM duration exceeding 1 year, elevated fasting blood glucose levels, and higher HbA1c levels were less likely to achieve remission 1 year following SG. A nomogram was constructed using variables, including sex, T2DM duration, and HbA1c levels. The ROC curve indicated that the nomogram had higher accuracy (AUC = 0.826, 95%CI: 0.768-0.884). Moreover, the AUCs were 0.790 (95%CI: 0.692-0.887), 0.865 (95%CI: 0.774-0.956) and 0.813 (95%CI: 0.733-0.893) for the testing, externally validated, and raw datasets, respectively.
The nomogram exhibited high efficacy in predicting T2DM remission in Chinese patients who underwent SG.
尽管有许多预测模型可用于代谢性减重手术后的糖尿病缓解情况，但基于袖状胃切除术（SG）的模型却很少。本研究旨在建立一个预测SG术后2型糖尿病（T2DM）缓解的模型，并评估现有预测模型的有效性。
从一项名为“中国西部减重手术纵向研究”的队列研究中收集患者数据。采用合成少数过采样技术，随机选取70%作为训练集，其余30%作为测试集。使用单因素逻辑回归来识别与T2DM缓解相关的因素。这些因素被纳入后续的逐步多因素分析中。然后构建了一个列线图。使用受试者工作特征（ROC）曲线、校准图和决策曲线分析对其进行评估。最后，对八个现有的预测模型进行了验证。
最初纳入了166例患者，T2DM缓解率为89.2%。单因素逻辑回归表明，男性患者、T2DM病程超过1年、空腹血糖水平升高和糖化血红蛋白（HbA1c）水平较高的患者在SG术后1年实现缓解的可能性较小。使用性别、T2DM病程和HbA1c水平等变量构建了列线图。ROC曲线表明，列线图具有更高的准确性（AUC = 0.826，95%CI：0.768 - 0.884）。此外，测试集、外部验证集和原始数据集的AUC分别为0.790（95%CI：0.692 - 0.887）、0.865（95%CI：0.774 - 0.956）和0.813（95%CI：0.733 - 0.893）。
该列线图在预测接受SG手术的中国患者的T2DM缓解方面显示出高效性。
