Enhancing Breast Cancer Recurrence Algorithms Through Selective Use of Medical Record Data.
Background: The utility of data-based algorithms in research has been questioned because of errors in identification of cancer recurrences. We adapted previously published breast cancer recurrence algorithms, selectively using medical record (MR) data to improve classification.Methods: We evaluated...
| Publicado en: | JNCI: Journal of the National Cancer Institute Vol. 108; no. 3; pp. 1 - 8 |
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| Autores principales: | , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Oxford University Press / USA
Mar2016
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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=113660975&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 113660975 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: Mar2016 vid: 108 iid: 3 pid: 622 pub: Oxford University Press / USA artinfo: ui: 113660975 113660975 NLM26582243 113660975 10.1093/jnci/djv336 NLM26582243 113660975 ppf: 1 ppct: 7 formats: tig: atl: Enhancing Breast Cancer Recurrence Algorithms Through Selective Use of Medical Record Data. aug: au: Kroenke, Candyce H. Chubak, Jessica Johnson, Lisa Castillo, Adrienne Weltzien, Erin Caan, Bette J. affil: Kaiser Permanente Northern California, Division of Research, Oakland, CA sug: subj: Neoplasm Recurrence, Local Epidemiology Medical Records Algorithms Neoplasm Recurrence, Local Diagnosis Breast Neoplasms Diagnosis Breast Neoplasms Epidemiology United States Adult Aged, 80 and Over Neoplasm Recurrence, Local Mortality Odds Ratio Middle Age Prognosis Breast Neoplasms Mortality Prospective Studies Female Sensitivity and Specificity Aged Risk Factors Funding Source Human Adult: 19-44 years Aged, 80 & over Middle Aged: 45-64 years Aged: 65+ years Female ab: Background: The utility of data-based algorithms in research has been questioned because of errors in identification of cancer recurrences. We adapted previously published breast cancer recurrence algorithms, selectively using medical record (MR) data to improve classification.Methods: We evaluated second breast cancer event (SBCE) and recurrence-specific algorithms previously published by Chubak and colleagues in 1535 women from the Life After Cancer Epidemiology (LACE) and 225 women from the Women's Health Initiative cohorts and compared classification statistics to published values. We also sought to improve classification with minimal MR examination. We selected pairs of algorithms-one with high sensitivity/high positive predictive value (PPV) and another with high specificity/high PPV-using MR information to resolve discrepancies between algorithms, properly classifying events based on review; we called this "triangulation." Finally, in LACE, we compared associations between breast cancer survival risk factors and recurrence using MR data, single Chubak algorithms, and triangulation.Results: The SBCE algorithms performed well in identifying SBCE and recurrences. Recurrence-specific algorithms performed more poorly than published except for the high-specificity/high-PPV algorithm, which performed well. The triangulation method (sensitivity = 81.3%, specificity = 99.7%, PPV = 98.1%, NPV = 96.5%) improved recurrence classification over two single algorithms (sensitivity = 57.1%, specificity = 95.5%, PPV = 71.3%, NPV = 91.9%; and sensitivity = 74.6%, specificity = 97.3%, PPV = 84.7%, NPV = 95.1%), with 10.6% MR review. Triangulation performed well in survival risk factor analyses vs analyses using MR-identified recurrences.Conclusions: Use of multiple recurrence algorithms in administrative data, in combination with selective examination of MR data, may improve recurrence data quality and reduce research costs. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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