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Table 1.  
Responding Physician and Practice Characteristicsa
Responding Physician and Practice Characteristicsa
Table 2.  
Logistic Regression Analyses of Reported Use of Methods of Communication of Risk Information
Logistic Regression Analyses of Reported Use of Methods of Communication of Risk Information
1.
Ravdin  PM, Siminoff  LA, Davis  GJ,  et al.  Computer program to assist in making decisions about adjuvant therapy for women with early breast cancer.  J Clin Oncol. 2001;19(4):980-991.PubMedGoogle Scholar
2.
Siminoff  LA, Gordon  NH, Silverman  P, Budd  T, Ravdin  PM.  A decision aid to assist in adjuvant therapy choices for breast cancer.  Psychooncology. 2006;15(11):1001-1013.PubMedGoogle ScholarCrossref
3.
Peele  PB, Siminoff  LA, Xu  Y, Ravdin  PM.  Decreased use of adjuvant breast cancer therapy in a randomized controlled trial of a decision aid with individualized risk information.  Med Decis Making. 2005;25(3):301-307.PubMedGoogle ScholarCrossref
4.
Belkora  JK, Hutton  DW, Moore  DH, Siminoff  LA.  Does use of the adjuvant model influence use of adjuvant therapy through better risk communication?  J Natl Compr Canc Netw. 2011;9(7):707-712.PubMedGoogle Scholar
5.
Kurian  AW, Lichtensztajn  DY, Keegan  THM, Nelson  DO, Clarke  CA, Gomez  SL.  Use of and mortality after bilateral mastectomy compared with other surgical treatments for breast cancer in California, 1998-2011.  JAMA. 2014;312(9):902-914.PubMedGoogle ScholarCrossref
6.
Elwyn  G, Edwards  A, Kinnersley  P, Grol  R.  Shared decision making and the concept of equipoise: the competences of involving patients in healthcare choices.  Br J Gen Pract. 2000;50(460):892-899.PubMedGoogle Scholar
Research Letter
May 2016

Communication of Recurrence Risk Estimates to Patients Diagnosed With Breast Cancer

Author Affiliations
  • 1Department of Health Behavior & Health Education, School of Public Health, University of Michigan, Ann Arbor
  • 2Division of General Medicine, Department of Internal Medicine, University of Michigan, Arbor
  • 3Center for Bioethics and Social Sciences in Medicine, University of Michigan, Ann Arbor
  • 4Cancer Surveillance & Outcomes Research Team (CanSORT), University of Michigan, North Campus Research Complex, Ann Arbor
  • 5Center for Cancer Biostatistics, Department of Biostatistics, University of Michigan, University of Michigan, Ann Arbor
  • 6Department of Radiation Oncology, University of Michigan, Ann Arbor
 

Copyright 2016 American Medical Association. All Rights Reserved. Applicable FARS/DFARS Restrictions Apply to Government Use.

JAMA Oncol. 2016;2(5):684-686. doi:10.1001/jamaoncol.2015.6416

For patients with breast cancer to play active roles in treatment decisions, they must understand their risk of cancer recurrence and how that risk changes with different treatments. Such information is available to treating clinicians through tools such as AdjuvantOnline,1,2 and the use of such tools may reduce patient selection of treatments with low risk reduction.3,4 Our objective was to identify how often medical oncologists and surgeons use such tools or provide patients diagnosed as having breast cancer with detailed recurrence risk information, as well as to explore what clinician characteristics influence these behaviors.

Methods

After approval by the institutional review board of the University of Michigan, in 2012, we mailed a questionnaire and a $50 cash incentive to 750 medical oncologists and 750 surgeons randomly selected from the American Medical Association Physician Masterfile, a relatively comprehensive list of US physicians. We used a modified Dillman approach to maximize the response rate.

We presented a clinical vignette of a 45-year-old woman with estrogen receptor (ER) and prohesterone receptor (PR) positive, ERBB2-negative (referred to as HER2 or HER2/neu-negative in the vignette) breast cancer who chose to undergo lumpectomy for a pT1c, N0, grade 2 infiltrating ductal carcinoma with negative surgical margins. Participating surgeons then answered 4 questions about communication of risk information: “In a case like this, would you…” (A) “use an online calculator (like AdjuvantOnline) to help estimate the patient’s recurrence risks,” (B) “discuss recurrence risk using specific numerical risk estimates (eg, 10-year risk of recurrence) with the patient,” (C) “discuss recurrence risk using descriptive words such as “high risk” or “low chance” (regardless of whether you provide numerical estimates to patients),” and (D) “give patients a copy of recurrence risk numbers to take home”? Response options were “definitely yes,” “probably yes,” “probably no,” and “definitely no” and were dichotomized for analysis. Participating medical oncologists answered identical items but with the modifier “systemic” for recurrence risk in items B and C.

Respondents also reported their personal and practice characteristics: years since residency, sex, practice setting, whether their primary practice has residents and/or fellows (as a marker of academic contexts), number of new patients with breast cancer seen in the past 12 months, and availability of same-day multispecialty appointments for new patients with breast cancer. Race was self-reported and included in this study as a potentially relevant sociodemographic feature of the respondents.

Using SAS statistical software (version 9.2), we described responses regarding communication of risk information and then used logistic regression models with communication practices designated as the binary dependent variable and specialty and respondent personal and practice characteristics as the independent variables.

Results

In total, 498 of 750 surgeons (66.4%) and 398 of 750 medical oncologists (53.0%) returned surveys. We excluded 95 surgeons and 35 medical oncologists who reported not having seen patients with breast cancer. Table 1 details the personal and practice characteristics of the physicians whose responses were analyzed.

Most medical oncologists (84% [295]) and surgeons (85% [340]) reported discussing recurrence using verbal terms such as high risk. Although 76% (269) of medical oncologists reported using online risk calculators, 88% (311), discussing numerical risk estimates, and 71% (253), giving patients a copy of risk numbers, rates of these behaviors reported among surgeons were dramatically lower (24% [94], 47% [188], and 17% [66], respectively; P < .001 for all comparisons after multivariable adjustment).

Multivariable analyses (Table 2) identified clinical specialty and whether the clinician’s practice offers same-day multispecialty appointments as the 2 primary predictors of recurrence risk communication practices. Clinicians whose practices offer same-day multispecialty appointments were more likely to use risk calculators or discuss numerical risk estimates with patients.

Discussion

After controlling for demographics and other practice characteristics, medical oncologists were found to be far more likely than surgeons to quantify risk estimates for patients. As in any survey, biases due to nonresponse or misclassification are possible but are unlikely to be the sole explanation for the interspecialty differences observed. Patients who do not see a medical oncologist until after surgery, if at all, may make treatment decisions (including the increasingly common decision to remove the contralateral breast)5 without full understanding of relevant risk information. Ensuring that all patients with breast cancer have timely access to such data is essential to supporting shared and value-congruent decision-making.6

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Article Information

Corresponding Author: Reshma Jagsi, MD, DPhil, Department of Radiation Oncology, University of Michigan, UHB2C490, SPC 5010, 1500E Medical Center Dr, Ann Arbor, MI 48109-5010 (rjagsi@med.umich.edu).

Published Online: February 18, 2016. doi:10.1001/jamaoncol.2015.6416.

Author Contributions: Mr Griffith and Dr Jagsi had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Zikmund-Fisher, Hawley, Griffith, Jagsi.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Zikmund-Fisher, Sabolch, Jagsi.

Critical revision of the manuscript for important intellectual content: All authors.

Obtained funding: Jagsi.

Administrative, technical, or material support: Sabolch, Jagsi.

Study supervision: Janz, Jagsi.

Conflict of Interest Disclosures: None reported.

Funding/Support: This work was supported by a Young Investigator Award from the National Comprehensive Cancer Network Foundation to Dr Jagsi.

Role of the Funder/Sponsor: The funder played no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication.

References
1.
Ravdin  PM, Siminoff  LA, Davis  GJ,  et al.  Computer program to assist in making decisions about adjuvant therapy for women with early breast cancer.  J Clin Oncol. 2001;19(4):980-991.PubMedGoogle Scholar
2.
Siminoff  LA, Gordon  NH, Silverman  P, Budd  T, Ravdin  PM.  A decision aid to assist in adjuvant therapy choices for breast cancer.  Psychooncology. 2006;15(11):1001-1013.PubMedGoogle ScholarCrossref
3.
Peele  PB, Siminoff  LA, Xu  Y, Ravdin  PM.  Decreased use of adjuvant breast cancer therapy in a randomized controlled trial of a decision aid with individualized risk information.  Med Decis Making. 2005;25(3):301-307.PubMedGoogle ScholarCrossref
4.
Belkora  JK, Hutton  DW, Moore  DH, Siminoff  LA.  Does use of the adjuvant model influence use of adjuvant therapy through better risk communication?  J Natl Compr Canc Netw. 2011;9(7):707-712.PubMedGoogle Scholar
5.
Kurian  AW, Lichtensztajn  DY, Keegan  THM, Nelson  DO, Clarke  CA, Gomez  SL.  Use of and mortality after bilateral mastectomy compared with other surgical treatments for breast cancer in California, 1998-2011.  JAMA. 2014;312(9):902-914.PubMedGoogle ScholarCrossref
6.
Elwyn  G, Edwards  A, Kinnersley  P, Grol  R.  Shared decision making and the concept of equipoise: the competences of involving patients in healthcare choices.  Br J Gen Pract. 2000;50(460):892-899.PubMedGoogle Scholar
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