Assessing Women’s Preferences and Preference Modeling for Breast Reconstruction Decision Making

• Published in: Plastic and reconstructive surgery. Global open Vol. 2; no. 3; p. e125
• Main Authors: Sun, Clement S., Cantor, Scott B., Reece, Gregory P., Crosby, Melissa A., Fingeret, Michelle C., Markey, Mia K.
• Format: Journal Article
• Language: English
• Published: United States American Society of Plastic Surgeons 01.03.2014; Wolters Kluwer Health; Wolters Kluwer
• Subjects: Original; BREAST-Q; Multiattribute utility theory; decision-making; breast reconstruction; decision analysis; patient reported outcome measures; utility; risk attitude; consistency; multiple objectives
• ISSN: 2169-7574; 2169-7574
• DOI: 10.1097/GOX.0000000000000062

BACKGROUND:Women considering breast reconstruction must make challenging trade-offs among issues that often conflict. It may be useful to quantify possible outcomes using a single summary measure to aid a breast cancer patient in choosing a form of breast reconstruction. METHODS:In this study, we used multiattribute utility theory to combine multiple objectives to yield a summary value using 9 different preference models. We elicited the preferences of 36 women, aged 32 or older with no history of breast cancer, for the patient-reported outcome measures of breast satisfaction, psychosocial well-being, chest well-being, abdominal well-being, and sexual well-being as measured by the BREAST-Q in addition to time lost to reconstruction and out-of-pocket cost. Participants ranked hypothetical breast reconstruction outcomes. We examined each multiattribute utility preference model and assessed how often each model agreed with participants’ rankings. RESULTS:The median amount of time required to assess preferences was 34 minutes. Agreement among the 9 preference models with the participants ranged from 75.9% to 78.9%. None of the preference models performed significantly worse than the best-performing risk-averse multiplicative model. We hypothesize an average theoretical agreement of 94.6% for this model if participant error is included. There was a statistically significant positive correlation with more unequal distribution of weight given to the 7 attributes. CONCLUSIONS:We recommend the risk-averse multiplicative model for modeling the preferences of patients considering different forms of breast reconstruction because it agreed most often with the participants in this study.