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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., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

Background & Aims: Approximately 50% of all patients with pancreatic ductal adenocarcinoma (PDA) develop diabetes mellitus before their cancer diagnosis. Screening individuals with new-onset diabetes might allow earlier diagnosis of PDA. We sought to develop and validate a PDA risk prediction model to identify high-risk individuals among those with new-onset diabetes., Methods: We conducted a retrospective cohort study in a population representative database from the United Kingdom. Individuals with incident diabetes after the age of 35 years and 3 or more years of follow-up after diagnosis of diabetes were eligible for inclusion. Candidate predictors consisted of epidemiologic and clinical characteristics available at the time of diabetes diagnosis. Variables with P values <.25 in the univariable analyses were evaluated using backward stepwise approach. Model discrimination was assessed using receiver operating characteristic curve analysis. Calibration was evaluated using the Hosmer-Lemeshow test. Results were internally validated using a bootstrapping procedure., Results: We analyzed data from 109,385 patients with new-onset diabetes. Among them, 390 (0.4%) were diagnosed with PDA within 3 years. The final model (area under the curve, 0.82; 95% confidence interval, 0.75-0.89) included age, body mass index, change in body mass index, smoking, use of proton pump inhibitors, and anti-diabetic medications, as well as levels of hemoglobin A1C, cholesterol, hemoglobin, creatinine, and alkaline phosphatase. Bootstrapping validation showed negligible optimism. If the predicted risk threshold for definitive PDA screening was set at 1% over 3 years, only 6.19% of the new-onset diabetes population would undergo definitive screening, which would identify patients with PDA with 44.7% sensitivity, 94.0% specificity, and a positive predictive value of 2.6%., Conclusions: We developed a risk model based on widely available clinical parameters to help identify patients with new-onset diabetes who might benefit from PDA screening., (Copyright © 2017 AGA Institute. Published by Elsevier Inc. All rights reserved.)