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Breast Cancer Research and Treatment

, Volume 163, Issue 2, pp 391–398 | Cite as

Using ePrognosis to estimate 2-year all-cause mortality in older women with breast cancer: Cancer and Leukemia Group B (CALGB) 49907 and 369901 (Alliance A151503)

  • Gretchen G. Kimmick
  • Brittny Major
  • Jonathan Clapp
  • Jeff Sloan
  • Brandelyn Pitcher
  • Karla Ballman
  • Myra Barginear
  • Rachel A. Freedman
  • Andrew Artz
  • Heidi D. Klepin
  • Jacqueline M. Lafky
  • Judith Hopkins
  • Eric Winer
  • Clifford Hudis
  • Hyman Muss
  • Harvey Cohen
  • Aminah Jatoi
  • Arti Hurria
  • Jeanne Mandelblatt
Brief Report

Abstract

Purpose

Tools to estimate survival, such as ePrognosis (http://eprognosis.ucsf.edu/carey2.php), were developed for general, not cancer, populations. In older patients with breast cancer, accurate overall survival estimates would facilitate discussions about adjuvant therapies.

Methods

Secondary analyses were performed of data from two parallel breast cancer studies (CALGB/Alliance 49907/NCT000224102 and CALGB/Alliance 369901/NCT00068328). We included patients (n = 971) who were age 70 years and older with complete baseline quality of life data (194 from 49907; 777 from 369901). Estimated versus observed all-cause two-year mortality rates were compared. ePrognosis score was calculated based on age, sex, and daily function (derived from EORTC QLQ-C30). ePrognosis scores range from 0 to 10, with higher scores indicating worse prognosis based on mortality of community-dwelling elders and were categorized into three groups (0–2, 3–6, 7–10). Observed mortality rates were estimated using Kaplan–Meier methods.

Results

Patient mean age was 75.8 years (range 70–91) and 73% had stage I–IIA disease. Most patients were classified by ePrognosis as good prognosis (n = 562, 58% 0–2) and few (n = 18, 2% 7–10) poor prognosis. Two-year observed mortality rates were significantly lower than ePrognosis estimates for patients scoring 0–2 (2% vs 5%, p = 0.001) and 3–6 (8% vs 12%, p = 0.01). The same trend was seen with scores of 7–10 (23% vs 36%, p = 0.25).

Conclusions

ePrognosis tool only modestly overestimates mortality rate in older breast cancer patients enrolled in two cooperative group studies. This tool, which estimates non-cancer mortality risk based on readily available clinical information may inform adjuvant therapy decisions but should be validated in non-clinical trial populations.

Keywords

Breast cancer Survival estimates ePrognosis Elderly 

Notes

Acknowledgements

Support: Research reported in this publication was supported by the National Cancer Institute of the National Institutes of Health under the Award Number UG1CA189823 (Alliance for Clinical Trials in Oncology NCORP Grant), U10CA003927, U10CA007968, U10CA032291, U10CA035279, U10CA041287, U10CA047559, U10CA047577, U10CA077597, U10CA180790, U10CA180836, U10CA180838, U10CA180857, and U10CA180867. The research was also supported by National Cancer Institute at the National Institutes of Health Grants U10CA084131 and R01CA127617 to JSM. The research was also supported, in part, by National Cancer Institute at the National Institutes of Health Grant R35CA197289, R01CA129769, R01CA124924, and K05CA096940 to JSM. Alliance protocol 369901 was further supported, in part, by the National Cancer Institute at the National Institutes of Health legacy Grants #U10CA031946 to the Cancer and Leukemia Group B (now Alliance for Clinical Trials in Oncology) and #U10CA033601 to the CALGB Statistical Center (now Alliance Statistics and Data Center). Earlier portions of the Alliance 369901 trial were also funded in part by a Grant to support patient accrual from Amgen Pharmaceuticals to the CALGB Foundation. The content of this manuscript is solely the responsibility of the authors and does not necessarily represent the official views of the National Cancer Institute at the National Institutes of Health.

Compliance with ethical standards

Conflicts of interest

None of the authors has potential conflicts of interest or specific financial interests relevant to the subject of their manuscript.

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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Gretchen G. Kimmick
    • 1
  • Brittny Major
    • 2
  • Jonathan Clapp
    • 3
    • 4
  • Jeff Sloan
    • 2
  • Brandelyn Pitcher
    • 1
    • 5
  • Karla Ballman
    • 6
  • Myra Barginear
    • 7
  • Rachel A. Freedman
    • 8
  • Andrew Artz
    • 9
  • Heidi D. Klepin
    • 10
  • Jacqueline M. Lafky
    • 2
  • Judith Hopkins
    • 11
  • Eric Winer
    • 8
  • Clifford Hudis
    • 12
  • Hyman Muss
    • 13
  • Harvey Cohen
    • 1
  • Aminah Jatoi
    • 14
  • Arti Hurria
    • 15
  • Jeanne Mandelblatt
    • 16
  1. 1.Duke Cancer InstituteDuke University Medical CenterDurhamUSA
  2. 2.Alliance Statistics and Data CenterMayo ClinicRochesterUSA
  3. 3.Department of OncologyMedStar Georgetown University Medical CenterWashingtonUSA
  4. 4.Department of Biostatistics, Biomathematics and BioinformaticsGeorgetown UniversityWashingtonUSA
  5. 5.Alliance Statistics and Data CenterDuke UniversityDurhamUSA
  6. 6.Division of Biostatistics and EpidemiologyWeill Medical College of Cornell UniversityNew YorkUSA
  7. 7.Hofstra-North Shore LIJ School of MedicineNorthwell Health Cancer InstituteLake SuccessUSA
  8. 8.Dana-Farber/Partners CancerCareBostonUSA
  9. 9.University of Chicago Comprehensive Cancer CenterChicagoUSA
  10. 10.Wake Forest University School of MedicineWinston-SalemUSA
  11. 11.Forsyth Regional Cancer CenterWinston-SalemUSA
  12. 12.American Society of Clinical OncologyAlexandriaUSA
  13. 13.UNC Lineberger Cancer CenterUniversity of North Carolina at Chapel HillChapel HillUSA
  14. 14.Mayo ClinicRochesterUSA
  15. 15.City of HopeDuarteUSA
  16. 16.Department of Oncology and Cancer Prevention and Control Program, Lombardi Comprehensive Cancer Center, MedStar Georgetown University Medical CenterGeorgetown UniversityWashingtonUSA

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