Development and validation of algorithms to differentiate ductal carcinoma in situ from invasive breast cancer within administrative claims data

Jacqueline Hirth, Sandra Hatch, Yu Li Lin, Sharon H. Giordano, H. Colleen Silva, Yong Fang Kuo

Research output: Contribution to journalArticle

Abstract

BACKGROUND: Overtreatment is a common concern for patients with ductal carcinoma in situ (DCIS), but this entity is difficult to distinguish from invasive breast cancers in administrative claims data sets because DCIS often is coded as invasive breast cancer. Therefore, the authors developed and validated algorithms to select DCIS cases from administrative claims data to enable outcomes research in this type of data. METHODS: This retrospective cohort using invasive breast cancer and DCIS cases included women aged 66 to 70 years in the 2004 through 2011 Texas Cancer Registry (TCR) data linked to Medicare administrative claims data. TCR records were used as "gold" standards to evaluate the sensitivity, specificity, and positive predictive value (PPV) of 2 algorithms. Women with a biopsy enrolled in Medicare parts A and B at 12 months before and 6 months after their first biopsy without a second incident diagnosis of DCIS or invasive breast cancer within 12 months in the TCR were included. Women in 2010 Medicare data were selected to test the algorithms in a general sample. RESULTS: In the TCR data set, a total of 6907 cases met inclusion criteria, with 1244 DCIS cases. The first algorithm had a sensitivity of 79%, a specificity of 89%, and a PPV of 62%. The second algorithm had a sensitivity of 50%, a specificity of 97%. and a PPV of 77%. Among women in the general sample, the specificity was high and the sensitivity was similar for both algorithms. However, the PPV was approximately 6% to 7% lower. CONCLUSIONS: DCIS frequently is miscoded as invasive breast cancer, and thus the proposed algorithms are useful to examine DCIS outcomes using data sets not linked to cancer registries.

Original languageEnglish (US)
JournalCancer
DOIs
StateAccepted/In press - Jan 1 2018

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Carcinoma, Intraductal, Noninfiltrating
Breast Neoplasms
Registries
Neoplasms
Medicare
Medicare Part A
Medicare Part B
Biopsy
Gold
Outcome Assessment (Health Care)
Sensitivity and Specificity

Keywords

  • Administrative claims data
  • Algorithm performance
  • Breast cancer
  • Ductal carcinoma in situ (DCIS)
  • Validation

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

Cite this

@article{6c161a5d0b6a497db160f216f503544f,
title = "Development and validation of algorithms to differentiate ductal carcinoma in situ from invasive breast cancer within administrative claims data",
abstract = "BACKGROUND: Overtreatment is a common concern for patients with ductal carcinoma in situ (DCIS), but this entity is difficult to distinguish from invasive breast cancers in administrative claims data sets because DCIS often is coded as invasive breast cancer. Therefore, the authors developed and validated algorithms to select DCIS cases from administrative claims data to enable outcomes research in this type of data. METHODS: This retrospective cohort using invasive breast cancer and DCIS cases included women aged 66 to 70 years in the 2004 through 2011 Texas Cancer Registry (TCR) data linked to Medicare administrative claims data. TCR records were used as {"}gold{"} standards to evaluate the sensitivity, specificity, and positive predictive value (PPV) of 2 algorithms. Women with a biopsy enrolled in Medicare parts A and B at 12 months before and 6 months after their first biopsy without a second incident diagnosis of DCIS or invasive breast cancer within 12 months in the TCR were included. Women in 2010 Medicare data were selected to test the algorithms in a general sample. RESULTS: In the TCR data set, a total of 6907 cases met inclusion criteria, with 1244 DCIS cases. The first algorithm had a sensitivity of 79{\%}, a specificity of 89{\%}, and a PPV of 62{\%}. The second algorithm had a sensitivity of 50{\%}, a specificity of 97{\%}. and a PPV of 77{\%}. Among women in the general sample, the specificity was high and the sensitivity was similar for both algorithms. However, the PPV was approximately 6{\%} to 7{\%} lower. CONCLUSIONS: DCIS frequently is miscoded as invasive breast cancer, and thus the proposed algorithms are useful to examine DCIS outcomes using data sets not linked to cancer registries.",
keywords = "Administrative claims data, Algorithm performance, Breast cancer, Ductal carcinoma in situ (DCIS), Validation",
author = "Jacqueline Hirth and Sandra Hatch and Lin, {Yu Li} and Giordano, {Sharon H.} and Silva, {H. Colleen} and Kuo, {Yong Fang}",
year = "2018",
month = "1",
day = "1",
doi = "10.1002/cncr.31393",
language = "English (US)",
journal = "Cancer",
issn = "0008-543X",
publisher = "John Wiley and Sons Inc.",

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T1 - Development and validation of algorithms to differentiate ductal carcinoma in situ from invasive breast cancer within administrative claims data

AU - Hirth, Jacqueline

AU - Hatch, Sandra

AU - Lin, Yu Li

AU - Giordano, Sharon H.

AU - Silva, H. Colleen

AU - Kuo, Yong Fang

PY - 2018/1/1

Y1 - 2018/1/1

N2 - BACKGROUND: Overtreatment is a common concern for patients with ductal carcinoma in situ (DCIS), but this entity is difficult to distinguish from invasive breast cancers in administrative claims data sets because DCIS often is coded as invasive breast cancer. Therefore, the authors developed and validated algorithms to select DCIS cases from administrative claims data to enable outcomes research in this type of data. METHODS: This retrospective cohort using invasive breast cancer and DCIS cases included women aged 66 to 70 years in the 2004 through 2011 Texas Cancer Registry (TCR) data linked to Medicare administrative claims data. TCR records were used as "gold" standards to evaluate the sensitivity, specificity, and positive predictive value (PPV) of 2 algorithms. Women with a biopsy enrolled in Medicare parts A and B at 12 months before and 6 months after their first biopsy without a second incident diagnosis of DCIS or invasive breast cancer within 12 months in the TCR were included. Women in 2010 Medicare data were selected to test the algorithms in a general sample. RESULTS: In the TCR data set, a total of 6907 cases met inclusion criteria, with 1244 DCIS cases. The first algorithm had a sensitivity of 79%, a specificity of 89%, and a PPV of 62%. The second algorithm had a sensitivity of 50%, a specificity of 97%. and a PPV of 77%. Among women in the general sample, the specificity was high and the sensitivity was similar for both algorithms. However, the PPV was approximately 6% to 7% lower. CONCLUSIONS: DCIS frequently is miscoded as invasive breast cancer, and thus the proposed algorithms are useful to examine DCIS outcomes using data sets not linked to cancer registries.

AB - BACKGROUND: Overtreatment is a common concern for patients with ductal carcinoma in situ (DCIS), but this entity is difficult to distinguish from invasive breast cancers in administrative claims data sets because DCIS often is coded as invasive breast cancer. Therefore, the authors developed and validated algorithms to select DCIS cases from administrative claims data to enable outcomes research in this type of data. METHODS: This retrospective cohort using invasive breast cancer and DCIS cases included women aged 66 to 70 years in the 2004 through 2011 Texas Cancer Registry (TCR) data linked to Medicare administrative claims data. TCR records were used as "gold" standards to evaluate the sensitivity, specificity, and positive predictive value (PPV) of 2 algorithms. Women with a biopsy enrolled in Medicare parts A and B at 12 months before and 6 months after their first biopsy without a second incident diagnosis of DCIS or invasive breast cancer within 12 months in the TCR were included. Women in 2010 Medicare data were selected to test the algorithms in a general sample. RESULTS: In the TCR data set, a total of 6907 cases met inclusion criteria, with 1244 DCIS cases. The first algorithm had a sensitivity of 79%, a specificity of 89%, and a PPV of 62%. The second algorithm had a sensitivity of 50%, a specificity of 97%. and a PPV of 77%. Among women in the general sample, the specificity was high and the sensitivity was similar for both algorithms. However, the PPV was approximately 6% to 7% lower. CONCLUSIONS: DCIS frequently is miscoded as invasive breast cancer, and thus the proposed algorithms are useful to examine DCIS outcomes using data sets not linked to cancer registries.

KW - Administrative claims data

KW - Algorithm performance

KW - Breast cancer

KW - Ductal carcinoma in situ (DCIS)

KW - Validation

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