Patient Preferences in Pulmonary Arterial Hypertension, a Latent Class Analysis to Identify Preference Heterogeneity

Abstract

Objectives

Pulmonary arterial hypertension (PAH) is a chronic, progressive disease of the pulmonary circulation characterized by vascular remodeling that, if untreated, can lead to right heart dysfunction and death. This analysis measured heterogeneity in patient preferences for PAH-specific treatment regimens.

Method

Adult patients with PAH with slight to marked limitations during physical activity were recruited through a patient organization in Germany. Participants completed an online best-worst scaling case 3 survey. Patients chose among 3 hypothetical treatment profiles defined by 6 benefits and risks at varying levels. Participants completed 12 choice tasks. Preference heterogeneity was assessed using latent class analysis.

Results

A total of 83 participants (76% female) completed the survey. Best-fit model revealed 4 classes. Class 1 (19% of participants) assigned importance to multiple attributes particularly side effects, class 2 (34%) to physical activity limitations, class 3 (30%) to survival and physical activity limitations, and class 4 (17%) to survival. No differences in sociodemographic characteristics were observed across classes. Compared with other classes, class 4 was most likely to report having marked physical activity limitations (79%) and needing daily help (100%), while considering higher daily activity levels to be ordinary (walking >1 km [71%] or climbing several flights of stairs [50%]).

Conclusion

This first patient preference study in a PAH population suggests that physical activity limitations in addition to survival matter most to patients; however, preference heterogeneity between groups of patients was observed. Patient preferences should be considered in treatment decision making to better balance patient’s expectations regarding the known risk-benefit ratio of treatment.

Authors

Axel Mühlbacher Amélie Beaudet Monika Brand Ellen M. Janssen Holger Gunz Wenjing Li Michael Preiss Andrew Sadler Rachael L. DiSantostefano

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