Yuan Hai-Liang, Zhang Xian, Chu Wei-Wei, Lin Guan-Bin, Xu Chun-Xia
Department of Gastroenterology, Beilun Branch of the First Affiliated Hospital of Zhejiang University, Ningbo, China.
The Precision Medicine Laboratory, Beilun Branch of the First Affiliated Hospital of Zhejiang University, Ningbo, China.
Heliyon. 2024 Feb 14;10(4):e26221. doi: 10.1016/j.heliyon.2024.e26221. eCollection 2024 Feb 29.
The incidence of gastroparesis is higher in individuals diagnosed with type 2 diabetes mellitus (T2DM) compared to the healthy individuals. Our study aimed to explore the risk factors for gastroparesis in T2DM and to establish a clinical prediction model (nomogram).
Our study enlisted 694 patients with T2DM from two medical centers over a period of time. From January 2020 to December 2022, 347 and 149 patients were recruited from the Beilun branch of Zhejiang University's First Affiliated Hospital in the training and internal validation cohorts, respectively. The external validation cohort consisted of 198 patients who were enrolled at Nanchang University's First Affiliated Hospital from October 2020 to September 2021. We conducted univariate and multivariate logistic regression analyses to select the risk factors for gastroparesis in patients with T2DM; subsequently,we developed a nomogram model. The performance of the nomogram was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC), calibration curve, and decision curve analysis(DCA).
Four clinical variables, including age, regular exercise, glycated hemoglobin level(HbA1c), and () infection, were identified and included in the model. The model demonstrated excellent discrimination, with an AUC of 0.951 (95% CI = 0.925-0.978) in the training group, and 0.910 (95% CI = 0.859-0.961) and 0.875 (95% CI = 0.813-0.937) in the internal and external validation groups, respectively. The calibration curve showed good consistency between prediction of the model and observed gastroparesis. The DCA also demonstrated good clinical efficacy.
The nomogram model developed in this study showed good performance in predicting gastroparesis in patients with T2DM.
与健康个体相比，2型糖尿病（T2DM）患者胃轻瘫的发病率更高。我们的研究旨在探讨T2DM患者胃轻瘫的危险因素，并建立临床预测模型（列线图）。
我们的研究在一段时间内从两个医疗中心招募了694例T2DM患者。2020年1月至2022年12月，分别从浙江大学医学院附属第一医院北仑分院招募了347例和149例患者作为训练队列和内部验证队列。外部验证队列由198例于2020年10月至2021年9月在南昌大学第一附属医院入组的患者组成。我们进行了单因素和多因素逻辑回归分析，以选择T2DM患者胃轻瘫的危险因素；随后，我们开发了一个列线图模型。使用受试者操作特征（ROC）曲线下面积（AUC）、校准曲线和决策曲线分析（DCA）对列线图的性能进行评估。
确定了四个临床变量，包括年龄、规律运动、糖化血红蛋白水平（HbA1c）和（）感染，并纳入模型。该模型显示出优异的区分能力，训练组的AUC为0.951（95%CI = 0.925 - 0.978），内部验证组和外部验证组的AUC分别为0.910（95%CI = 0.859 - 0.961）和0.875（95%CI = 0.813 - 0.937）。校准曲线显示模型预测与观察到的胃轻瘫之间具有良好的一致性。DCA也显示出良好的临床疗效。
本研究开发的列线图模型在预测T2DM患者胃轻瘫方面表现良好。
