Development of an algorithm to identify urgent referrals for suspected cancer from the Danish Primary Care Referral Database
Authors Toftegaard BS, Guldbrandt LM, Flarup KR, Beyer H, Bro F, Vedsted P
Received 10 June 2016
Accepted for publication 16 September 2016
Published 26 October 2016 Volume 2016:8 Pages 751—759
Checked for plagiarism Yes
Review by Single-blind
Peer reviewer comments 3
Editor who approved publication: Professor Henrik Toft Sørensen
Berit Skjødeberg Toftegaard,1,2,3 Louise Mahncke Guldbrandt,1,2,3 Kaare Rud Flarup,1,2 Hanne Beyer,1 Flemming Bro,1,3 Peter Vedsted,1,2,4
1Department of Public Health, Research Unit for General Practice, 2Department of Public Health, Research Centre for Cancer Diagnosis in Primary Care (CaP), Aarhus University, 3Department of Public Health, Section for General Medical Practice, 4Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
Background: Accurate identification of specific patient populations is a crucial tool in health care. A prerequisite for exploring the actions taken by general practitioners (GPs) on symptoms of cancer is being able to identify patients urgently referred for suspected cancer. Such system is not available in Denmark; however, all referrals are electronically stored. This study aimed to develop and test an algorithm based on referral text to identify urgent cancer referrals from general practice.
Methods: Two urgently referred reference populations were extracted from a research database and linked with the Primary Care Referral (PCR) database through the unique Danish civil registration number to identify the corresponding electronic referrals. The PCR database included GP referrals directed to private specialists and hospital departments, and these referrals were scrutinized. The most frequently used words were integrated in the first version of the algorithm, which was further refined by an iterative process involving two population samples from the PCR database. The performance was finally evaluated for two other PCR population samples against manual assessment as the gold standard for urgent cancer referral.
Results: The final algorithm had a sensitivity of 0.939 (95% confidence intervals [CI]: 0.905–0.963) and a specificity of 0.937 (95% CI: 0.925–0.963) compared to the gold standard. The positive and negative predictive values were 69.8% (95% CI: 65.0–74.3) and 99.0% (95% CI: 98.4–99.4), respectively. When applying the algorithm on referrals for a population without earlier cancer diagnoses, the positive predictive value increased to 83.6% (95% CI: 78.7–87.7) and the specificity to 97.3% (95% CI: 96.4–98.0).
Conclusion: The final algorithm identified 94% of the patients urgently referred for suspected cancer; less than 3% of the patients were incorrectly identified. It is now possible to identify patients urgently referred on cancer suspicion from general practice by applying an algorithm for populations in the PCR database.
Keywords: cancer, referral, algorithm, general practice, early diagnosis, Denmark
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