Construction and validation of a nomogram for predicting diabetes remission at 3 months after bariatric surgery in patients with obesity combined with type 2 diabetes mellitus

IF 5.4 2区医学 Q1 ENDOCRINOLOGY & METABOLISM Diabetes, Obesity & Metabolism Pub Date : 2023-10-09 DOI:10.1111/dom.15303

Kaisheng Yuan MD, Bing Wu MD, Ruiqi Zeng MM, Fuqing Zhou MM, Ruixiang Hu MD, Cunchuan Wang MD

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Abstract

Aim

Bariatric metabolic surgery (BMS) is a proven treatment option for patients with both obesity and type 2 diabetes mellitus (T2DM). However, there is a lack of comprehensive reporting on the short-term remission rates of diabetes, and the existing data are inadequate. Hence, this study aimed to investigate the factors that may contribute to diabetes remission (DR) in patients with obesity and T2DM, 3 months after undergoing BMS. Furthermore, our objective was to develop a risk-predicting model using a nomogram.

Methods

In total, 389 patients with obesity and T2DM, who had complete preoperative information and underwent either laparoscopic sleeve gastrectomy or laparoscopic gastric bypass surgery between January 2014 and May 2023, were screened in the Chinese Obesity and Metabolic Surgery Database. The patients were randomly divided into a training set (n = 272) and a validation set (n = 117) in a 7:3 ratio. Potential factors for DR were analysed through univariate and multivariate logistic regression analyses and then modelled using a nomogram. The model's performance was evaluated using receiver operating characteristic curves and the area under the curve (AUC). Calibration plots were used to assess prediction accuracy and decision curve analyses were conducted to evaluate the clinical usefulness of the model.

Results

Glycated haemoglobin, triglycerides, duration of diabetes, insulin requirement and hypercholesterolaemia were identified as independent factors influencing DR. We have incorporated these five indicators into a nomogram, which has shown good efficacy in both the training cohort (AUC = 0.930) and validation cohort (AUC = 0.838). The calibration plots indicated that the model fits well in both the training and the validation cohorts, and decision curve analyses showed that the model had good clinical applicability.

Conclusion

The prediction model developed in this study holds predictive value for short-term DR following BMS in patients with obesity and T2DM.

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预测3岁糖尿病缓解的列线图的构建和验证肥胖合并2型糖尿病患者的减肥手术后数月。

目的：减肥代谢手术（BMS）是肥胖和2型糖尿病（T2DM）患者的一种行之有效的治疗选择。然而，缺乏关于糖尿病短期缓解率的全面报告，现有数据也不充分。因此，本研究旨在调查可能导致肥胖和T2DM患者糖尿病缓解（DR）的因素，3 在接受BMS后数月。此外，我们的目标是使用列线图开发一个风险预测模型。方法：在中国肥胖与代谢外科数据库中，对2014年1月至2023年5月期间接受腹腔镜袖状胃切除术或腹腔镜胃旁路手术的389名肥胖和T2DM患者进行筛查。将患者随机分为训练组（n = 272）和验证集（n = 117）以7:3的比例。通过单变量和多变量逻辑回归分析分析DR的潜在因素，然后使用列线图建模。使用受试者工作特性曲线和曲线下面积（AUC）评估模型的性能。使用校准图来评估预测准确性，并进行决策曲线分析来评估模型的临床有用性。结果：糖化血红蛋白、甘油三酯、糖尿病持续时间、胰岛素需求和高胆固醇血症被确定为影响DR的独立因素。我们将这五个指标纳入列线图中，这在训练队列中都显示出良好的疗效（AUC = 0.930）和验证队列（AUC = 0.838）。校准图表明，该模型在训练和验证队列中都很好地拟合，决策曲线分析表明该模型具有良好的临床适用性。结论：本研究开发的预测模型对肥胖和T2DM患者BMS后的短期DR具有预测价值。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Diabetes, Obesity & Metabolism 医学-内分泌学与代谢

CiteScore

10.90

自引率

6.90%

发文量

319

审稿时长

3-8 weeks

期刊介绍： Diabetes, Obesity and Metabolism is primarily a journal of clinical and experimental pharmacology and therapeutics covering the interrelated areas of diabetes, obesity and metabolism. The journal prioritises high-quality original research that reports on the effects of new or existing therapies, including dietary, exercise and lifestyle (non-pharmacological) interventions, in any aspect of metabolic and endocrine disease, either in humans or animal and cellular systems. ‘Metabolism’ may relate to lipids, bone and drug metabolism, or broader aspects of endocrine dysfunction. Preclinical pharmacology, pharmacokinetic studies, meta-analyses and those addressing drug safety and tolerability are also highly suitable for publication in this journal. Original research may be published as a main paper or as a research letter.