Nomogram for Predicting Risk of Digestive Carcinoma Among Patients with Type 2 Diabetes - Feng LH (Dove Medical Press, 2020)
Lu-Huai Feng,1 Kun-Peng Bu,1 Shuang Ren,1 Zhenhua Yang,2 Bi-Xun Li,1 Cheng-En Deng3 1Department of Comprehensive Internal Medicine, The Affiliated Tumor Hospital of Guangxi Medical University, Nanning, People’s Republic of China; 2Department of Nephrology, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China; 3Department of Urology, The Affiliated Tumor Hospital of Guangxi Medical University, Nanning, People’s Republic of ChinaCorrespondence: Cheng-En DengDepartment of Urology, The Affiliated Tumor Hospital of Guangxi Medical University, Nanning 530021, Guangxi Zhuang Autonomous Region, People’s Republic of ChinaTel +86 18775391817Fax +86 771-5719573Email dengchengen66@126.comBi-Xun LiDepartment of Comprehensive Internal Medicine, The Affiliated Tumor Hospital of Guangxi Medical University, Nanning 530021, Guangxi Zhuang Autonomous Region, People’s Republic of ChinaTel +86 18977100069Fax +86 771-5719573Email libixun@126.comPurpose: Digestive carcinomas remain a major health burden worldwide and are closely related to type 2 diabetes. The aim of this study was to develop and validate a digestive carcinoma risk prediction model to identify high-risk individuals among those with type 2 diabetes.Patients and Methods: The prediction model was developed in a primary cohort that consisted of 655 patients with type 2 diabetes. Data were collected from November 2013 to December 2018. Clinical parameters and demographic characteristics were analyzed by logistic regression to develop a model to predict the risk of digestive carcinomas; then, a nomogram was constructed. The performance of the nomogram was assessed with respect to calibration, discrimination, and clinical usefulness. The results were internally validated by a bootstrapping procedure. The independent validation cohort consisted of 275 patients from January 2019 to December 2019.Results: Predictors in the prediction nomogram included sex, age, insulin use, and body mass index. The model showed good discrimination (C-index 0.747 [95% CI, 0.718– 0.791]) and calibration (Hosmer–Lemeshow test P=0.541). The nomogram showed similar discrimination in the validation cohort (C-index 0.706 [95% CI, 0.682– 0.755]) and good calibration (Hosmer–Lemeshow test P=0.418). Decision curve analysis demonstrated that the nomogram would be clinically useful.Conclusion: We developed a low-cost and low-risk model based on clinical and demographic parameters to help identify patients with type 2 diabetes who might benefit from digestive cancer screening.Keywords: type 2 diabetes, digestive cancer, prediction, demographic