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

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.

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%.

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.

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.