Leveraging the Electronic Health Record for Early Detection of Pancreatic Cancer Among 9.4 Million US Veterans

INTRODUCTION: Early detection of pancreatic ductal adenocarcinoma (PDAC) improves survival. However, screening recommendations are limited to individuals with hereditary risk, accounting for only 10% of PDAC. We explore the feasibility of developing and validating an electronic health record-based model to identify high-risk individuals for PDAC screening within the asymptomatic general population. METHODS: Using multivariable Cox regression, we developed a diagnostic model to predict time to PDAC within 3 years in the Veterans Health Administration. We evaluated the final model using internal and temporally separate data sets using Akaike Information Criterion, Harrell c statistic, calibration curves, and sensitivity/specificity corresponding to a 3-year risk screening threshold of 1%. RESULTS: Among 9,351,261 individuals, 26,119 (0.3%) developed PDAC (107.6 cases per 100,000 person-years) within 3 years. The final model included age, pancreatic cyst, pancreatitis, smoking status, history of a localized solid tumor, race/ethnicity, and body mass index. Glucose and albumin values were highly important, in addition to other metabolic, inflammatory, and liver-related laboratory values. The c statistic (95% CI) was 0.75 (0.75-0.76) in development, 0.75 (0.75-0.76) in internal validation, and 0.74 (0.73-0.75) in temporal validation. At a 3-year risk threshold of 1.0%, 11% of the population would undergo screening, capturing 30% of the PDAC cases. DISCUSSION: We demonstrate good model discrimination in independent data. Compared with current screening practices targeting only genetically predisposed individuals, its implementation could identify 3 times as many PDAC cases. However, predictors beyond the electronic health record (EHR) may be needed to further improve the feasibility of generalized screening.