INTRODUCTION: Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal cancers. Earlier identification through improved risk prediction modelling represents a promising strategy for improving PDAC outcomes. We developed a PDAC risk prediction model using routinely available clinical risk factors, including modifiable factors, to enable targeted screening. METHODS: Data from the UK Biobank, a prospective cohort of adults aged 40-69 years recruited between 2006 and 2010, were used to develop a PDAC risk prediction model. Incident PDAC was identified through linkage to cancer registries, hospital admissions, and death records. Candidate predictors included demographic, lifestyle, clinical, and laboratory variables routinely available in primary care. Time-to-PDAC was analyzed using Cox proportional hazards models with backward selection. Model performance at 5 years was evaluated calibration and discrimination, with internal validation via 5-fold cross-validation. RESULTS: Among 365,326 participants, 314 incident PDAC cases occurred within 5 years. Predictors included age, cigarette smoking, alcohol intake, pancreatitis, blood test alterations (alanine/aspartate aminotransferase, high-density cholesterol, glycated hemoglobin A1c). The model showed good calibration (observed-to-expected ratio was 0.994 95% CI 0.890-1.111; integrated calibration index = 2.402 × 10⁻⁵; E50 = 1.928 × 10⁻⁵; E90 = 3.283 × 10⁻⁵) and discrimination (time‑dependent area under the receiver operating characteristic curve 0.741). Internal 5-fold cross-validation confirmed stable performance. CONCLUSION: We developed a well-calibrated and discriminative PDAC risk prediction model using routinely available clinical and laboratory data. This models offers an actionable framework for risk stratification in primary care settings, though external validation in independent cohorts is warranted before clinical implementation.
Rassy et al. (Tue,) studied this question.