Preferences for Outcomes of Treatment for Rectal Cancer: Patient and Clinician Utilities and Their Application in an Interactive Computer-Based Decision Aid

PURPOSE:To quantify the importance that patients and clinicians assign to specific quality-of-life outcomes associated with the treatment of rectal cancer and to demonstrate a clinical application of these data in a computer-based multidimension decision aid (Annalisa©). METHODS:For patients, a rese...

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Veröffentlicht in:Diseases of the colon & rectum 2009-12, Vol.52 (12), p.1994-2002
Hauptverfasser: Masya, Lindy M, Young, Jane M, Solomon, Michael J, Harrison, James D, Dennis, Rebecca J, Salkeld, Glenn P
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Sprache:eng
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Zusammenfassung:PURPOSE:To quantify the importance that patients and clinicians assign to specific quality-of-life outcomes associated with the treatment of rectal cancer and to demonstrate a clinical application of these data in a computer-based multidimension decision aid (Annalisa©). METHODS:For patients, a researcher-administered questionnaire using the time trade-off method was used to quantify the importance of nine outcomes. Information was ascertained from clinicians by use of a self-administered questionnaire. Responses were ranked and compared between groups. Mean values for each outcome were entered into Annalisa©. RESULTS:Overall, 103 patients, 87 colorectal surgeons, 97 medical oncologists, and 80 radiation oncologists participated. For all groups, local cancer recurrence in the pelvis and fecal incontinence (mean utility scores 0.53 and 0.57, respectively) were the two outcomes to most avoid. In Annalisa©, the “best fit” treatment for patients and surgeons was a low anterior resection with postoperative chemotherapy, whereas for medical and radiation oncologists the best-fit treatment was surgery alone. CONCLUSION:Local recurrence and fecal incontinence are considered the worst outcomes by patients and clinicians alike, but values for other outcomes vary. Decision aids that incorporate patients’ individual values with evidence-based data hold considerable potential to optimize treatment decision-making.
ISSN:0012-3706
1530-0358
DOI:10.1007/DCR.0b013e3181c001b9