Decision support tools for pancreatic cancer detection: external validation in Australian primary care - a retrospective cohort study

胰腺癌检测决策支持工具:澳大利亚基层医疗机构的外部验证——一项回顾性队列研究

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Abstract

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.

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