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Peter Vedsted

A validated algorithm for register-based identification of patients with recurrence of breast cancer—Based on Danish Breast Cancer Group (DBCG) data

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A validated algorithm for register-based identification of patients with recurrence of breast cancer—Based on Danish Breast Cancer Group (DBCG) data. / Aagaard Rasmussen, Linda; Jensen, Henry; Flytkjær Virgilsen, Line; Jellesmark Thorsen, Lise Bech; Vrou Offersen, Birgitte; Vedsted, Peter.

I: Cancer epidemiology, Bind 59, 01.04.2019, s. 129-134.

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

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@article{bc83a60b12ac407583445e9a4b671f53,
title = "A validated algorithm for register-based identification of patients with recurrence of breast cancer—Based on Danish Breast Cancer Group (DBCG) data",
abstract = "Background: Cancer recurrence is not routinely and completely registered in Danish national health registers, which challenges register-based research. The aim of this study was to develop and validate a register-based algorithm to identify patients with recurrence of breast cancer (BC). Methods: We conducted a cohort study based on data from Danish national health registers and used the Danish National Patient Register and the Danish National Pathology Register as sources to identify BC recurrence. We used data from the Danish Breast Cancer Group (DBCG) validated against medical records on recurrence status and recurrence date for 471 women with early stage unilateral BC as the gold standard of BC recurrence to assess the accuracy of the algorithm to identify BC recurrence. Results: The algorithm displayed a sensitivity of 97.3% (95% confidence interval (CI): 93.2–99.3), a specificity of 97.2% (95% CI: 94.8–98.7) and a positive predictive value of 94.4% (95% CI: 89.2–97.3). The concordance correlation coefficient for the agreement between recurrence dates generated by the algorithm and the gold standard was 0.97 (95% CI: 0.96-0.98), and the date was estimated within +/−30 days of the gold standard in 66% of the patients and within +/−60 days in 76% of the patients. Conclusion: The developed algorithm almost perfectly identified BC recurrence and with reasonable timing compared to the gold standard.",
keywords = "Algorithms, Breast neoplasms, Denmark, Recurrence, Registries, Validation studies",
author = "{Aagaard Rasmussen}, Linda and Henry Jensen and {Flytkj{\ae}r Virgilsen}, Line and {Jellesmark Thorsen}, {Lise Bech} and {Vrou Offersen}, Birgitte and Peter Vedsted",
year = "2019",
month = apr,
day = "1",
doi = "10.1016/j.canep.2019.01.016",
language = "English",
volume = "59",
pages = "129--134",
journal = "Cancer Epidemiology",
issn = "1877-7821",
publisher = "Elsevier Inc.",

}

RIS

TY - JOUR

T1 - A validated algorithm for register-based identification of patients with recurrence of breast cancer—Based on Danish Breast Cancer Group (DBCG) data

AU - Aagaard Rasmussen, Linda

AU - Jensen, Henry

AU - Flytkjær Virgilsen, Line

AU - Jellesmark Thorsen, Lise Bech

AU - Vrou Offersen, Birgitte

AU - Vedsted, Peter

PY - 2019/4/1

Y1 - 2019/4/1

N2 - Background: Cancer recurrence is not routinely and completely registered in Danish national health registers, which challenges register-based research. The aim of this study was to develop and validate a register-based algorithm to identify patients with recurrence of breast cancer (BC). Methods: We conducted a cohort study based on data from Danish national health registers and used the Danish National Patient Register and the Danish National Pathology Register as sources to identify BC recurrence. We used data from the Danish Breast Cancer Group (DBCG) validated against medical records on recurrence status and recurrence date for 471 women with early stage unilateral BC as the gold standard of BC recurrence to assess the accuracy of the algorithm to identify BC recurrence. Results: The algorithm displayed a sensitivity of 97.3% (95% confidence interval (CI): 93.2–99.3), a specificity of 97.2% (95% CI: 94.8–98.7) and a positive predictive value of 94.4% (95% CI: 89.2–97.3). The concordance correlation coefficient for the agreement between recurrence dates generated by the algorithm and the gold standard was 0.97 (95% CI: 0.96-0.98), and the date was estimated within +/−30 days of the gold standard in 66% of the patients and within +/−60 days in 76% of the patients. Conclusion: The developed algorithm almost perfectly identified BC recurrence and with reasonable timing compared to the gold standard.

AB - Background: Cancer recurrence is not routinely and completely registered in Danish national health registers, which challenges register-based research. The aim of this study was to develop and validate a register-based algorithm to identify patients with recurrence of breast cancer (BC). Methods: We conducted a cohort study based on data from Danish national health registers and used the Danish National Patient Register and the Danish National Pathology Register as sources to identify BC recurrence. We used data from the Danish Breast Cancer Group (DBCG) validated against medical records on recurrence status and recurrence date for 471 women with early stage unilateral BC as the gold standard of BC recurrence to assess the accuracy of the algorithm to identify BC recurrence. Results: The algorithm displayed a sensitivity of 97.3% (95% confidence interval (CI): 93.2–99.3), a specificity of 97.2% (95% CI: 94.8–98.7) and a positive predictive value of 94.4% (95% CI: 89.2–97.3). The concordance correlation coefficient for the agreement between recurrence dates generated by the algorithm and the gold standard was 0.97 (95% CI: 0.96-0.98), and the date was estimated within +/−30 days of the gold standard in 66% of the patients and within +/−60 days in 76% of the patients. Conclusion: The developed algorithm almost perfectly identified BC recurrence and with reasonable timing compared to the gold standard.

KW - Algorithms

KW - Breast neoplasms

KW - Denmark

KW - Recurrence

KW - Registries

KW - Validation studies

UR - http://www.scopus.com/inward/record.url?scp=85061118428&partnerID=8YFLogxK

U2 - 10.1016/j.canep.2019.01.016

DO - 10.1016/j.canep.2019.01.016

M3 - Journal article

C2 - 30743224

AN - SCOPUS:85061118428

VL - 59

SP - 129

EP - 134

JO - Cancer Epidemiology

JF - Cancer Epidemiology

SN - 1877-7821

ER -