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Decision support tools for pancreatic cancer detection: external validation in Australian primary care - a
Silja Schrader1,2, Meena Rafiq3,2,4, Javiera Martinez Gutierrez1,2
1Centre for Cancer Research and Department of General Practice and Primary Care, University of Melbourne, Melbourne, Australia.
Background:
Pancreatic cancer is often diagnosed at an advanced stage with poor survival. Risk assessment tools have been developed to aid early diagnosis of pancreatic cancer in primary care settings (QCancer®, electronic Risk Assessment Tool [eRAT], and the Queensland Institute of Medical Research [QIMR] Berghofer Pancreatic Cancer Decision Support Tool [QPaC Tool]) but have not been validated in the Australian setting.
Aim:
To estimate and compare the performance of these tools for identifying patients with undiagnosed pancreatic cancer using Australian primary care data.
Design And Setting:
A cohort study was conducted using linked primary care and cancer registry data from Victoria, Australia.
Method:
Patients presenting to primary care with signs and/or symptoms included in the tools (recorded in the primary care 'reason for encounter') were included. Diagnostic accuracy statistics for each tool (and their individual signs and symptoms) were compared.
Results:
Patients with pancreatic cancer were more likely (P<0.001) to present with new-onset diabetes, jaundice, and unexpected weight loss pre-diagnosis than patients without pancreatic cancer. The most common pre-diagnostic presentations in patients with pancreatic cancer were jaundice (29.0%), abdominal pain (25.6%), change in bowel habits (17.6%), and new-onset diabetes (14.8%). Jaundice, steatorrhoea, and pancreatitis had the highest positive predictive values (PPV) for pancreatic cancer (1.96%, 1.77%, and 0.89%, respectively). Among the tools, eRAT had the highest PPV of 1.37% (95% confidence interval [CI] = 1.12 to 1.66); the PPV for QPaC was 1.01% (95% CI = 0.82 to 1.22) and QCancer® was 0.8% (95% CI = 0.54 to 1.15).
Conclusion:
When applied to Australian primary care data, none of the tools were strongly predictive of pancreatic cancer. New diagnostic models incorporating additional data could potentially improve their predictive performance.
