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

• Published in: Diabetes, metabolic syndrome and obesity metabolic syndrome and obesity, 2020-01, Vol.13, p.1763-1770
• Main Authors: Feng, Lu-Huai, Bu, Kun-Peng, Ren, Shuang, Yang, Zhenhua, Li, Bi-Xun, Deng, Cheng-En
• Format: Article
• Language: English
• Subjects: Biomarkers; Body mass index; Calibration; Cancer; Cancer screening; Carcinoma; Cardiovascular disease; Cerebrovascular disease; Coronary vessels; demographic; Diabetes; digestive cancer; Discrimination; Family medical history; Glucose; Insulin; Medical research; Nomograms; Original Research; Patients; prediction; Regression analysis; Risk factors; Software; Type 2 diabetes; Variables
• ISSN: 1178-7007; 1178-7007
• DOI: 10.2147/DMSO.S251063

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. 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. 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. 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.