Administrative data algorithms to identify second breast cancer events following early-stage invasive breast cancer.
Background: Studies of breast cancer outcomes rely on the identification of second breast cancer events (recurrences and second breast primary tumors). Cancer registries often do not capture recurrences, and chart abstraction can be infeasible or expensive. An alternative is using administrative hea...
| Publicado en: | JNCI: Journal of the National Cancer Institute Vol. 104; no. 12; pp. 931 - 941 |
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| Autores principales: | , , , , , , , , , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Oxford University Press / USA
Jun2012
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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=104466190&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104466190 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00278874 JNC jtl: JNCI: Journal of the National Cancer Institute issn: 00278874 maglogo: N pubinfo: dt: Jun2012 vid: 104 iid: 12 pid: 622 pub: Oxford University Press / USA artinfo: ui: 104466190 NLM22547340 2011592363 10.1093/jnci/djs233 NLM22547340 PMC3732250 104466190 ppf: 931 ppct: 10 formats: tig: atl: Administrative data algorithms to identify second breast cancer events following early-stage invasive breast cancer. aug: au: Chubak J Yu O Pocobelli G Lamerato L Webster J Prout MN Ulcickas Yood M Barlow WE Buist DS Chubak, Jessica Yu, Onchee Pocobelli, Gaia Lamerato, Lois Webster, Joe Prout, Marianne N Ulcickas Yood, Marianne Barlow, William E Buist, Diana S M affil: Group Health Research Institute, 1730 Minor Ave, Ste. 1600, Seattle, WA 98101, USA sug: subj: Algorithms Breast Neoplasms Pathology Carcinoma, Ductal, Breast Pathology Neoplasms, Second Primary Diagnosis Adult Aged Breast Neoplasms Epidemiology Carcinoma, Ductal, Breast Epidemiology Health Care Delivery, Integrated Female Human Middle Age Neoplasm Staging Neoplasms, Second Primary Epidemiology Predictive Value of Tests Registries, Disease Sensitivity and Specificity Washington Adult: 19-44 years Aged: 65+ years Middle Aged: 45-64 years Female ab: Background: Studies of breast cancer outcomes rely on the identification of second breast cancer events (recurrences and second breast primary tumors). Cancer registries often do not capture recurrences, and chart abstraction can be infeasible or expensive. An alternative is using administrative health-care data to identify second breast cancer events; however, these algorithms must be validated against a gold standard.Methods: We developed algorithms using data from 3152 women in an integrated health-care system who were diagnosed with stage I or II breast cancer in 1993-2006. Medical record review served as the gold standard for second breast cancer events. Administrative data used in algorithm development included procedures, diagnoses, prescription fills, and cancer registry records. We randomly divided the cohort into training and testing samples and used a classification and regression tree analysis to build algorithms for classifying women as having or not having a second breast cancer event. We created several algorithms for researchers to use based on the relative importance of sensitivity, specificity, and positive predictive value (PPV) in future studies.Results: The algorithm with high specificity and PPV had 89% sensitivity (95% confidence interval [CI] = 84% to 92%), 99% specificity (95% CI = 98% to 99%), and 90% PPV (95% CI = 86% to 94%); the high-sensitivity algorithm had 96% sensitivity (95% CI = 93% to 98%), 95% specificity (95% CI = 94% to 96%), and 74% PPV (95% CI = 68% to 78%).Conclusions: Algorithms based on administrative data can identify second breast cancer events with high sensitivity, specificity, and PPV. The algorithms presented here promote efficient outcomes research, allowing researchers to prioritize sensitivity, specificity, or PPV in identifying second breast cancer events. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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