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Peter Vedsted

Development of an algorithm to identify urgent referrals for suspected cancer from the Danish Primary Care Referral Database

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Development of an algorithm to identify urgent referrals for suspected cancer from the Danish Primary Care Referral Database. / Toftegaard, Berit Skjødeberg; Guldbrandt, Louise Mahncke; Flarup, Kaare Rud; Beyer, Hanne; Bro, Flemming; Vedsted, Peter.

I: Journal of Clinical Epidemiology, Bind 8, 2016, s. 751-759.

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisTidsskriftartikelForskningpeer review

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@article{b32cf117113440c79fb4644deb52c6e1,
title = "Development of an algorithm to identify urgent referrals for suspected cancer from the Danish Primary Care Referral Database",
abstract = "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.",
author = "Toftegaard, {Berit Skj{\o}deberg} and Guldbrandt, {Louise Mahncke} and Flarup, {Kaare Rud} and Hanne Beyer and Flemming Bro and Peter Vedsted",
year = "2016",
doi = "10.2147/CLEP.S114721",
language = "English",
volume = "8",
pages = "751--759",
journal = "Journal of Clinical Epidemiology",
issn = "0895-4356",
publisher = "Elsevier Inc.",

}

RIS

TY - JOUR

T1 - Development of an algorithm to identify urgent referrals for suspected cancer from the Danish Primary Care Referral Database

AU - Toftegaard, Berit Skjødeberg

AU - Guldbrandt, Louise Mahncke

AU - Flarup, Kaare Rud

AU - Beyer, Hanne

AU - Bro, Flemming

AU - Vedsted, Peter

PY - 2016

Y1 - 2016

N2 - 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.

AB - 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.

U2 - 10.2147/CLEP.S114721

DO - 10.2147/CLEP.S114721

M3 - Journal article

C2 - 27822123

VL - 8

SP - 751

EP - 759

JO - Journal of Clinical Epidemiology

JF - Journal of Clinical Epidemiology

SN - 0895-4356

ER -