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"...
| Publicado en: | Current Oncology Vol. 29; no. 8; pp. 5338 - 5368 |
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| Autores principales: | , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
MDPI
Aug2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=158749422&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 158749422 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 11980052 5EKK jtl: Current Oncology issn: 11980052 maglogo: N pubinfo: dt: Aug2022 vid: 29 iid: 8 pid: 97109 pub: MDPI artinfo: ui: 158749422 10.3390/curroncol29080424 158749422 ppf: 5338 ppct: 30 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Identifying Breast Cancer Recurrence in Administrative Data: Algorithm Development and Validation. aug: au: Holloway, Claire M. B. Shabestari, Omid Eberg, Maria Forster, Katharina Murray, Paula Green, Bo Esensoy, Ali Vahit Eisen, Andrea Sussman, Jonathan affil: Disease Pathway Management, Clinical Institutes and Quality Programs, Ontario Health, 525 University Avenue, Toronto, ON M5G 2L3, Canada sug: ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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