A clinical prediction model to assess risk for pancreatic cancer among patients with prediabetes. Issue 1 (January 2022)

• Record Type: Journal Article
• Authors: Boursi, Ben; Finkelman, Brian; Giantonio, Bruce J.; Haynes, Kevin; Rustgi, Anil K.; Rhim, Andrew D.; Mamtani, Ronac; Yang, Yu-Xiao
• Is Part Of: European journal of gastroenterology & hepatology. Volume 34:Issue 1(2022)
• Publication Date: 2022-01
• Subjects: clinical prediction model -- diabetes mellitus -- early detection -- pancreatic cancer; Digestive organs -- Diseases -- Periodicals; Liver -- Diseases -- Periodicals; Digestive organs -- Diseases; Liver -- Diseases; Periodicals; 616.33
• DOI: 10.1097/MEG.0000000000002052
• Languages: English
• ISSNs: 0954-691X

Abstract

Abstract : Background: Early detection of pancreatic ductal adenocarcinoma (PDA) may improve survival. We previously developed a clinical prediction model among patients with new-onset diabetes to help identify PDAs 6 months prior to the clinical diagnosis of the cancer. We developed and internally validated a new model to predict PDA risk among those newly diagnosed with impaired fasting glucose (IFG). Methods: We conducted a retrospective cohort study in The Health Improvement Network (THIN) (1995–2013) from the UK. Eligible study patients had newly diagnosed IFG during follow-up in THIN. The outcome was incident PDA diagnosed within 3 years of IFG diagnosis. Candidate predictors were factors associated with PDA, glucose metabolism or both. Results: Among the 138 232 eligible patients with initial IFG diagnosis, 245 (0.2%) were diagnosed with PDA within 3 years. The median time from IFG diagnosis to clinical PDA diagnosis was 326 days (IQR 120–588). The final prediction model included age, BMI, proton pump inhibitor use, total cholesterol, low-density lipoprotein, alanine aminotransferase and alkaline phosphatase. The model achieved good discrimination [area under the curve 0.71 (95% CI, 0.67–0.75)] and calibration (Hosmer and Lemeshow goodness-of-fit test P > 0.05 in 17 of the 20 imputed data sets) with optimism of 0.0012662 (95% CI, −0.00932 to 0.0108771). Conclusions: We developed and internally validated a sequential PDA prediction model based on clinical information routinely available at the initial appearance of IFG. If externally validated, this model could significantly extend our ability to detect PDAs at an earlier stage. Abstract : Supplemental Digital Content is available in the text.