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Nomogram for Predicting Risk of Digestive Carcinoma Among Patients with Type 2 Diabetes

Authors Feng LH, Bu KP, Ren S, Yang Z, Li BX, Deng CE

Received 4 March 2020

Accepted for publication 29 April 2020

Published 21 May 2020 Volume 2020:13 Pages 1763—1770

DOI https://doi.org/10.2147/DMSO.S251063

Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 2

Editor who approved publication: Prof. Dr. Juei-Tang Cheng


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 China

Correspondence: Cheng-En Deng
Department of Urology, The Affiliated Tumor Hospital of Guangxi Medical University, Nanning 530021, Guangxi Zhuang Autonomous Region, People’s Republic of China
Tel +86 18775391817
Fax +86 771-5719573
Email dengchengen66@126.com
Bi-Xun Li
Department of Comprehensive Internal Medicine, The Affiliated Tumor Hospital of Guangxi Medical University, Nanning 530021, Guangxi Zhuang Autonomous Region, People’s Republic of China
Tel +86 18977100069
Fax +86 771-5719573
Email libixun@126.com

Purpose: 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

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