title: A Clinical Prediction Model to Assess Risk for Pancreatic Cancer Among Patients With New-Onset Diabetes.
abstract: 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.
  
  
  
  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.
  
  
  
  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%.
  
  
  
  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.
authors.affiliation: Department of Medicine and Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania; Abramson Cancer Center, University of Pennsylvania, Philadelphia, Pennsylvania; Tel-Aviv University, Tel-Aviv, Israel.
authors.forename: Ben
authors.initials: B
authors.lastname: Boursi
authors.affiliation: Department of Medicine and Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania.
authors.forename: Brian
authors.initials: B
authors.lastname: Finkelman
authors.affiliation: Department of Medicine and Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania; Abramson Cancer Center, University of Pennsylvania, Philadelphia, Pennsylvania.
authors.forename: Bruce J
authors.initials: BJ
authors.lastname: Giantonio
authors.affiliation: Department of Medicine and Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania.
authors.forename: Kevin
authors.initials: K
authors.lastname: Haynes
authors.affiliation: Department of Medicine and Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania.
authors.forename: Anil K
authors.initials: AK
authors.lastname: Rustgi
authors.affiliation: Sheikh Ahmed Bin Zayed Al Nahyan Center for Pancreatic Cancer Research and Department of Gastroenterology, Hepatology and Nutrition, University of Texas, MD Anderson Cancer Center, Houston, Texas.
authors.forename: Andrew D
authors.initials: AD
authors.lastname: Rhim
authors.affiliation: Department of Medicine and Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania; Abramson Cancer Center, University of Pennsylvania, Philadelphia, Pennsylvania.
authors.forename: Ronac
authors.initials: R
authors.lastname: Mamtani
authors.affiliation: Department of Medicine and Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania. Electronic address: yangy@mail.med.upenn.edu.
authors.forename: Yu-Xiao
authors.initials: YX
authors.lastname: Yang
mesh_terms: D000367:Age Factors; D000368:Aged; D000469:Alkaline Phosphatase / Q000097:blood; D019540:Area Under Curve; D015992:Body Mass Index; D021441:Carcinoma, Pancreatic Ductal / Q000175:diagnosis / Q000453:epidemiology*; D002784:Cholesterol / Q000097:blood; D003404:Creatinine / Q000097:blood; D003920:Diabetes Mellitus / Q000097:blood / Q000175:diagnosis* / Q000453:epidemiology*; D055088:Early Detection of Cancer; D005260:Female; D006442:Glycated Hemoglobin / Q000378:metabolism; D006454:Hemoglobins / Q000378:metabolism; D006801:Humans; D007004:Hypoglycemic Agents / Q000627:therapeutic use; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D010190:Pancreatic Neoplasms / Q000175:diagnosis / Q000453:epidemiology*; D011237:Predictive Value of Tests; D054328:Proton Pump Inhibitors / Q000627:therapeutic use; D012372:ROC Curve; D012189:Retrospective Studies; D018570:Risk Assessment; D012907:Smoking / Q000453:epidemiology; D006113:United Kingdom / Q000453:epidemiology
chemical_list: D000469:Alkaline Phosphatase; D002784:Cholesterol; D003404:Creatinine; D006442:Glycated Hemoglobin; D006454:Hemoglobins; D007004:Hypoglycemic Agents; D054328:Proton Pump Inhibitors; C517652:hemoglobin A1c protein, human
keywords: BMI; Glucose; Insulin; Pancreatic Cancer