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Article

Identifying Breast Cancer Recurrence in Administrative Data: Algorithm Development and Validation

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Disease Pathway Management, Clinical Institutes and Quality Programs, Ontario Health, 525 University Avenue, Toronto, ON M5G 2L3, Canada
2
Department of Surgery, University of Toronto, 149 College Street, Toronto, ON M5T 1P5, Canada
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Institute of Health Policy, Management, and Evaluation, University of Toronto, 155 College Street 4th Floor, Toronto, ON M5T 3M6, Canada
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Data and Decision Sciences, Health System Performance and Support, Ontario Health, 525 University Avenue, Toronto, ON M5G 2L3, Canada
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Quality Measurement and Evaluation, Clinical Institutes and Quality Programs, Ontario Health, 525 University Avenue, Toronto, ON M5G 2L3, Canada
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Medical Oncology, Sunnybrook Health Sciences Centre, 2075 Bayview Avenue, Toronto, ON M4N 3M5, Canada
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Department of Oncology, McMaster University, 699 Concession Street Suite 4-204, Hamilton, ON L8V 5C2, Canada
*
Author to whom correspondence should be addressed.
Current Affiliations: Klick Labs, Klick Health, Toronto, ON M4W 3R8, Canada.
These authors contributed equally to this work.
Curr. Oncol. 2022, 29(8), 5338-5367; https://doi.org/10.3390/curroncol29080424
Received: 16 May 2022 / Revised: 9 July 2022 / Accepted: 19 July 2022 / Published: 28 July 2022
Breast cancer recurrence is an important outcome for patients and healthcare systems, but it is not routinely reported in cancer registries. We developed an algorithm to identify patients who experienced recurrence or a second case of primary breast cancer (combined as a “second breast cancer event”) using administrative data from the population of Ontario, Canada. A retrospective cohort study design was used including patients diagnosed with stage 0-III breast cancer in the Ontario Cancer Registry between 1 January 2009 and 31 December 2012 and alive six months post-diagnosis. We applied the algorithm to healthcare utilization data from six months post-diagnosis until death or 31 December 2013, whichever came first. We validated the algorithm’s diagnostic accuracy against a manual patient record review (n = 2245 patients). The algorithm had a sensitivity of 85%, a specificity of 94%, a positive predictive value of 67%, a negative predictive value of 98%, an accuracy of 93%, a kappa value of 71%, and a prevalence-adjusted bias-adjusted kappa value of 85%. The second breast cancer event rate was 16.5% according to the algorithm and 13.0% according to manual review. Our algorithm’s performance was comparable to previously published algorithms and is sufficient for healthcare system monitoring. Administrative data from a population can, therefore, be interpreted using new methods to identify new outcome measures. View Full-Text
Keywords: breast neoplasms; neoplasm recurrence; local; recurrence; algorithms; outcome assessment; healthcare; predictive value of tests; diagnostic techniques and procedures; prevalence; humans; cohort studies breast neoplasms; neoplasm recurrence; local; recurrence; algorithms; outcome assessment; healthcare; predictive value of tests; diagnostic techniques and procedures; prevalence; humans; cohort studies
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MDPI and ACS Style

Holloway, C.M.B.; Shabestari, O.; Eberg, M.; Forster, K.; Murray, P.; Green, B.; Esensoy, A.V.; Eisen, A.; Sussman, J. Identifying Breast Cancer Recurrence in Administrative Data: Algorithm Development and Validation. Curr. Oncol. 2022, 29, 5338-5367. https://doi.org/10.3390/curroncol29080424

AMA Style

Holloway CMB, Shabestari O, Eberg M, Forster K, Murray P, Green B, Esensoy AV, Eisen A, Sussman J. Identifying Breast Cancer Recurrence in Administrative Data: Algorithm Development and Validation. Current Oncology. 2022; 29(8):5338-5367. https://doi.org/10.3390/curroncol29080424

Chicago/Turabian Style

Holloway, Claire M. B., Omid Shabestari, Maria Eberg, Katharina Forster, Paula Murray, Bo Green, Ali Vahit Esensoy, Andrea Eisen, and Jonathan Sussman. 2022. "Identifying Breast Cancer Recurrence in Administrative Data: Algorithm Development and Validation" Current Oncology 29, no. 8: 5338-5367. https://doi.org/10.3390/curroncol29080424

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