Abstract
Pancreatic cancer has very low survival due to late diagnosis. Symptoms are often non-specific, complicating early detection in primary care. The Enriching New-Onset Diabetes for Pancreatic Cancer (ENDPAC) algorithm uses weight change, glycaemic control, and age at diabetes onset to identify new-onset diabetes (NOD) patients at increased pancreatic cancer risk. It was developed in the USA and has not been validated in the UK. Aim To validate ENDPAC in a UK primary care population and assess its predictive utility. Design and setting Retrospective cohort study using ORCHID, a national primary care sentinel network. Method Adults aged ≥50 with NOD and requisite glycated haemoglobin (HbA1c) and weight data were included. ENDPAC scores were calculated. Model performance was evaluated via discrimination, calibration, sensitivity, specificity, PPV and NPV. The Youden index identified optimal cutoffs. Sensitivity analyses assessed measurement timing, repeat HbA1c testing and multiple values. Results Among 70,050 individuals, 185 (0.26%) developed pancreatic cancer. Cases were older with higher HbA1c and greater weight loss at diagnosis. ENDPAC achieved an area under the curve (AUC) of 0.733. An optimal cutoff of ≥3 classified 27.6% of individuals as high-risk, with 62.6% sensitivity, 72.3% specificity, 0.6% PPV and 99.9% NPV. Sensitivity analyses showed similar performance across measurement windows and handling of multiple values. ENDPAC shows moderate discrimination in UK primary care. Although it has a relatively low PPV (0.6%), integration into routine systems could provide scalable, low-cost automated risk stratification, identifying people with NOD at higher pancreatic cancer risk as part of a sequential diagnostic pathway.