M. A. Corriere1, R. Barnard1, S. Saldana1, R. J. Guzman3, D. Easterling1, D. Boone2, A. Hyde2, G. L. Burke1, E. Ip1 1Wake Forest University School Of Medicine,Winston-Salem, NC, USA 2Wake Forest University Schools Of Business,Winston-Salem, NC, USA 3Beth Israel Deaconess Medical Center,Vascular Surgery,Boston, MA, USA
Introduction: Multiple treatments are available for peripheral arterial disease (PAD), but an evidence-based ‘best’ choice is often unclear. Providers lack efficient methods for identifying patients’ goals and values, and treatment selection is often based on ambiguous criteria with limited patient input. We developed and pilot tested an app to help providers understand patients’ treatment preferences, facilitating shared decision making by identifying compatibile treatment choices.
Methods: Focus groups were used to explore themes, terminology, and treatment attributes patients with severe PAD (rest pain and/or tissue loss) consider important. Transcripts were analyzed using thematic content analysis. An iPad app was developed to characterize how individuals prioritize key treatment attributes, including: treatment type (i.e., medication, percutaneous procedure, or surgery), anticipated level of symptomatic improvement, odds of success versus failure, risk, and durability. These attributes were presented using 14 randomly ordered pair-wise choice tasks with varying levels for each attribute based on an orthogonal array design. Responses were used to determine part-worth utilities and generate patient-specific scores indicating the relative importance of each attribute.
Results: 26 patients with severe PAD participated in focus groups, and a separate group of 34 participants completed the discrete choice survey in clinic. Mean choice task completion time was 12.8±6.0 minutes. Aggregate importance scores indicated that treatment type was most influential (41%), followed by chance of successful treatment outcome (24%), risk (18%), durability (10%), and level of symptomatic response (7%). Analysis on a per-patient level, however, demonstrated that attributes are prioritized differently between individuals (Figure), often supporting individualized choices based on the patient’s personal goals and values.
Conclusion: Patient treatment preferences are based on complex values systems that differ between individuals with similar PAD severity. Choice-based survey tools can be used to evaluate patient preferences in an objective and quantitative fashion, allowing providers to access information that is otherwise difficult and time-intensive to obtain during a standard clinic visit. Understanding patient’s goals and values has potential to facilitate individualized treatment choices based on the patient’s unique priorities, goals, and values.
