Abstract
IMPORTANCE: Pancreatic ductal adenocarcinoma (PDAC) is a leading cause of cancer deaths in the US. Although early detection improves survival, the rarity of the disease has rendered population screening a difficult approach. OBJECTIVE: To develop and validate a parsimonious, interpretable, and generalizable model predicting incident PDAC-termed PRIME (PDAC Risk Model for Earlier Detection)-using routinely available electronic health record (EHR) data. DESIGN, SETTING, AND PARTICIPANTS: This cohort study used the Optum Labs Data Warehouse, a longitudinal, deidentified US EHR and claims database. Adults 40 years or older with an outpatient clinical encounter between 2016 and 2018 were included. Participants from 23 health systems (n = 4 859 833) comprised the training cohort; 31 additional systems (n = 5 619 091) served as validation. International validation was conducted in the UK Biobank (n = 498 754). Data analysis occurred July 2025 to January 2026. EXPOSURES: Demographics, diagnosis codes, and routinely measured laboratory values were evaluated. Elastic-net regularization with 10-fold cross-validation selected the predictor set. MAIN OUTCOMES AND MEASURES: Incident PDAC was identified by International Classification of Diseases, Ninth and Tenth Revisions (ICD-9/10) codes. Model performance was assessed using time-dependent area under the curve (AUC) and calibration metrics. RESULTS: Overall, the study included more than 11 million adults (2.1% Asian individuals, 8.4% Black individuals, 4.3% Hispanic/Latino individuals, 82.7% White individuals, and 2.4% other race/ethnicity by EHR reporting). In the training cohort (mean [SD] age, 60.4 [11] years), 14 405 individuals were diagnosed with PDAC (incidence 55 per 100 000 person-years) over a mean (SD) of 5.4 (2.5) years; in the validation cohort, 11 693 individuals were diagnosed with PDAC (54 per 100 000 person-years) over a mean (SD) of 3.9 (2.5) years. PRIME retained 19 predictors including history of pancreatitis, gastrointestinal disorders, prior cancers, type 2 diabetes, elevated aspartate aminotransferase levels, smoking, non-type-O blood, and male sex. Discrimination was strong at the 36-month time horizon (AUC = 0.75 in both the training and validation cohorts) with good calibration. In the validation cohort, patients in the top 1% of predicted risk had substantially higher PDAC risk (HR, 7.63; 95% CI, 6.85-8.49) compared with average-risk patients. In the UK Biobank, PRIME achieved a 36-month AUC of 0.71 with good calibration. CONCLUSIONS AND RELEVANCE: In this validation cohort study, PRIME was a transparent EHR-based model that effectively stratified PDAC risk across diverse US health systems and generalized internationally. Prospective studies should evaluate for EHR-guided PDAC case-finding and integration with blood-based early-detection assays.