Mathematical Model May Help Assess 'Trade-Offs' in Breast Reconstruction
'Multiattribute Utility Theory' Lends Insights into Women's Values and Preferences, Reports PRS GO

Philadelphia -- A mathematical model may provide a valuable new approach to helping women with breast cancer make complex decisions regarding breast reconstruction, according to a paper in Plastic and Reconstructive Surgery—Global Open®, the official open-access medical journal of the American Society of Plastic Surgeons (ASPS).

A half-hour spent going through patient's personal values and preferences can help to clarify the trade-offs among competing options for breast reconstruction, suggests the new study by Mia K. Markey, PhD, of The University of Texas MD Anderson Cancer Center and colleagues.

Modeling Patient Values and Preferences for Breast Reconstruction

The researchers explored a mathematical modeling approach for use in evaluating risk tolerance and preferences related to breast reconstruction. The modeling approach, called multiattribute utility theory (MAUT), has emerged as a useful tool for making various types of complex decision. In medical decision-making, MAUT "enables the quantification of outcomes given the preferences of the patient," Dr. Markey and coauthors write.

The preliminary study included 36 healthy women with no history of breast cancer, who rated their preferences for hypothetical breast reconstruction in seven areas. Five areas—satisfaction with the breasts, psychological well-being, well-being of the chest and abdomen, and sexual well-being—were drawn from a specific questionnaire (the BREAST-Q) assessing quality of life related to breast reconstruction. In addition, the women ranked their preferences related to out-of-pocket costs and time lost to reconstruction and associated medical procedures.

The median time spent going through preferences in the seven areas was 34 minutes. Out of nine mathematical models evaluated, a "risk averse multiplicative model" was most consistent in ranking values and preferences.

Using this model, the results were consistent with the women's preferences in more than 94 percent of cases. Thus, for all but six percent of women, going through the preference-ranking process would provide useful insights for deciding on the best breast reconstruction option for that individual.

Models Could Be Incorporated into Decision Aids

In some cases, values changed as the women ranked the various areas. Some women started off "risk averse" to high out-of-pocket costs—but came to place a higher value on well-being as they considered the trade-offs they would have to consider if they really did have breast cancer.

Previous studies have used MAUT to explore decisions related to prostate cancer and newborn intensive care, but the new study is the first to apply it to breast cancer. "Making trade-offs is hard," Dr. Markey and coauthors write. They note that "[T]he sheer number of possible outcomes…would overwhelm even the most rational, considerate, and persistent decision-maker."

Their preliminary results suggest that "encoding preferences in the form of probability" through MAUT offers a relatively fast approach to representing women's preferences for breast reconstruction. With further development, mathematical models could be a useful addition to computerized decision aids—tools designed to help patients sort through the issues affecting complex health care decisions.

"The use of MAUT in modeling a patient's preferences is an important step towards a decision support system that may ease making decisions about breast reconstruction," Dr. Markey and colleagues conclude. More research will be needed to evaluate this approach in women facing real-life decisions about reconstruction after breast cancer surgery, including changes in preferences over time.

Plastic and Reconstructive Surgery—Global Open® is published by Lippincott Williams & Wilkins, part of Wolters Kluwer Health.