A clinical prediction model to assess risk for pancreatic cancer among patients with pre-diabetes

Abstract

e16226 Background: A new diagnosis of diabetes mellitus (DM) has been observed within 2-3 years before the clinical presentation of a substantial proportion of sporadic pancreatic ductal adenocarcinoma (PDA) cases. The objective of the current study was to develop and internally validate a clinical prediction model for 3-year PDA risk among individuals with pre-diabetes defined as newly detected impaired fasting glucose (IFG). Methods: The study was conducted using the Health Improvement Network (THIN), a population-representative general practice medical records database from the UK. Eligible patients were aged ≥35 years, had a newly diagnosed IFG during follow-up and had ≥ 3 years of follow-up following the diagnosis of IFG. The dependent variable was incident PDA diagnosed within 3 years of IFG diagnosis. We evaluated a comprehensive list of PDA risk factors as well as variables related to glucose metabolism and performed multiple imputation for candidate predictors with missing values. We selected predictors using univariable analysis and a backward stepwise approach. A bootstrapping procedure was used for internal validation. Results: We identified 138,232 eligible patients with new-onset IFG. Among this cohort, 245 individuals (0.2%) were diagnosed with PDA within 3 years of IFG diagnosis. The median follow-up from IFG detection to PDA diagnosis was 326 days (IQR 120-588). The full multivariable prediction model included age, BMI, PPIs, total cholesterol, LDL, ALT and alkaline phosphatase. The AUC of the model was 0.71 (95%CI 0.67-0.75), and the p-value for the Hosmer and Lemeshow goodness of fit test was > 0.05. Internal validation of the model using bootstrapping procedure revealed minimal optimism of 0.001 (95%CI -0.009-0.01). Conclusions: We developed and internally validated a PDA prediction model based on clinical information routinely available at the initial appearance of IFG. This model exhibited good discrimination and calibration. The current model may substantially extend our ability to detect subclinical PDAs at an earlier stage when the tumor may potentially be more amenable to curative resection.