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Identifying Breast Cancer Recurrence in Administrative Data: Algorithm Development and Validation
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”...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9406366/ https://www.ncbi.nlm.nih.gov/pubmed/36005162 http://dx.doi.org/10.3390/curroncol29080424 |
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author | Holloway, Claire M. B. Shabestari, Omid Eberg, Maria Forster, Katharina Murray, Paula Green, Bo Esensoy, Ali Vahit Eisen, Andrea Sussman, Jonathan |
author_facet | Holloway, Claire M. B. Shabestari, Omid Eberg, Maria Forster, Katharina Murray, Paula Green, Bo Esensoy, Ali Vahit Eisen, Andrea Sussman, Jonathan |
author_sort | Holloway, Claire M. B. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9406366 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-94063662022-08-26 Identifying Breast Cancer Recurrence in Administrative Data: Algorithm Development and Validation Holloway, Claire M. B. Shabestari, Omid Eberg, Maria Forster, Katharina Murray, Paula Green, Bo Esensoy, Ali Vahit Eisen, Andrea Sussman, Jonathan Curr Oncol Article 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. MDPI 2022-07-28 /pmc/articles/PMC9406366/ /pubmed/36005162 http://dx.doi.org/10.3390/curroncol29080424 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Holloway, Claire M. B. Shabestari, Omid Eberg, Maria Forster, Katharina Murray, Paula Green, Bo Esensoy, Ali Vahit Eisen, Andrea Sussman, Jonathan Identifying Breast Cancer Recurrence in Administrative Data: Algorithm Development and Validation |
title | Identifying Breast Cancer Recurrence in Administrative Data: Algorithm Development and Validation |
title_full | Identifying Breast Cancer Recurrence in Administrative Data: Algorithm Development and Validation |
title_fullStr | Identifying Breast Cancer Recurrence in Administrative Data: Algorithm Development and Validation |
title_full_unstemmed | Identifying Breast Cancer Recurrence in Administrative Data: Algorithm Development and Validation |
title_short | Identifying Breast Cancer Recurrence in Administrative Data: Algorithm Development and Validation |
title_sort | identifying breast cancer recurrence in administrative data: algorithm development and validation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9406366/ https://www.ncbi.nlm.nih.gov/pubmed/36005162 http://dx.doi.org/10.3390/curroncol29080424 |
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