Introduction Overall cancer survival has increased over recent decades, but the very low survival rates of pancreatic cancer have hardly changed in the last 50 years. This is attributed to late diagnosis. Pancreatic cancer symptoms are non-specific which makes early diagnosis challenging. Data-driven approaches, including algorithms using combinations of symptoms to predict cancer risk, can aid clinicians. A simple but effective algorithm called Enriching New-Onset Diabetes for Pancreatic Cancer (ENDPAC) has been developed in the United States (US). ENDPAC has not yet been used in the United Kingdom (UK), our aim is to translate ENDPAC into the UK setting. The objectives are to validate ENDPAC and report its predictive utility within primary care. Methods A retrospective cohort study of people with new-onset diabetes using the nationally representative Oxford-Royal College of General Practitioners Clinical Informatics Digital Hub (ORCHID) database. ORCHID holds over 10 million primary care electronic healthcare records. ENDPAC scores will be calculated for eligible people along with positive predictive value, negative predictive value, sensitivity and specificity of the algorithm. We will evaluate the optimal cut-off for defining people with high-risk of having pancreatic cancer. Discussion Once validated within the UK, ENDPAC could be implemented in practice to improve early pancreatic cancer diagnosis by using routine data. ENDPAC is currently being tested in the US in a clinical trial to evaluate its effectiveness. ENDPAC offers an automatable and inexpensive way to improve early diagnosis as part of a sequential approach to identify individuals at high-risk of having undiagnosed pancreatic cancer.

The authors have declared no competing interest.

This project was funded as part of an EPSRC iCase studentship undertaken by CP. The work of NPL co-authors was funded by the UK Governments Department for Science, Innovation & Technology through the UKs National Measurement System programmes.

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

The Ethics Committee of University of Surrey gave ethical approval for this work (reference number: FHMS 21-22 269 EGA). Access to ORCHID data has been approved by Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) under data request RSC_0420.

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Data will remain under the control of the Oxford-Royal College of General Practitioners Clinical Informatics Digital Hub (ORCHID, orchid.phc.ox.ac.uk) and can be accessed following all necessary approvals.