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. 2015 Nov 18;108(3):djv336.
doi: 10.1093/jnci/djv336. Print 2016 Mar.

Enhancing Breast Cancer Recurrence Algorithms Through Selective Use of Medical Record Data

Candyce H Kroenke  1 , Jessica Chubak  2 , Lisa Johnson  2 , Adrienne Castillo  2 , Erin Weltzien  2 , Bette J Caan  2
Affiliations

Enhancing Breast Cancer Recurrence Algorithms Through Selective Use of Medical Record Data

Candyce H Kroenke et al. J Natl Cancer Inst. .

Erratum in

  • Erratum.
    [No authors listed] [No authors listed] J Natl Cancer Inst. 2016 Feb 10;108(3):djw025. doi: 10.1093/jnci/djw025. Print 2016 Mar. J Natl Cancer Inst. 2016. PMID: 26865040 Free PMC article. No abstract available.
  • Erratum.
    [No authors listed] [No authors listed] J Natl Cancer Inst. 2016 Feb 10;108(3):djw017. doi: 10.1093/jnci/djw017. Print 2016 Mar. J Natl Cancer Inst. 2016. PMID: 26868856 Free PMC article. No abstract available.

Abstract

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.

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References

    1. U.S. Cancer Statistics Working Group. United States Cancer Statistics: 1999–2011 Incidence and Mortality Web-based Report . Atlanta, GA: : Department of Health and Human Services, Centers for Disease Control and Prevention, and National Cancer Institute; ; 2014. .
    1. Lash TL, Riis AH, Ostenfeld EB, Erichsen R, Vyberg M, Thorlacius-Ussing O . A validated algorithm to ascertain colorectal cancer recurrence using registry resources in Denmark . Int J Cancer . 2015. ; 136(9) : 2210 – 2215 . - PubMed
    1. Deshpande AD, Schootman M, Mayer A . Development of a claims-based algorithm to identify colorectal cancer recurrence . Ann Epidemiol . 2015. ; 25(4) : 297 – 300 . - PMC - PubMed
    1. Hassett MJ, Ritzwoller DP, Taback N, et al. Validating billing/encounter codes as indicators of lung, colorectal, breast, and prostate cancer recurrence using 2 large contemporary cohorts . Med Care . 2014. ; 52(10) : e65 - e73 . - PMC - PubMed
    1. Chubak J, Yu O, Pocobelli G, et al. Administrative data algorithms to identify second breast cancer events following early-stage invasive breast cancer . J Natl Cancer Inst . 2012. ; 104(12) : 931 – 940 . - PMC - PubMed

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