Preferences for Monitoring Comprehensive Heart Failure Care: A Latent Class Analysis

Axel C. Mühlbacher (), Andrew Sadler and Christin Juhnke
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Axel C. Mühlbacher: Hochschule Neubrandenburg
Andrew Sadler: Hochschule Neubrandenburg
Christin Juhnke: Hochschule Neubrandenburg

The Patient: Patient-Centered Outcomes Research, 2024, vol. 17, issue 1, No 8, 83-95

Abstract: Abstract Objective To measure preference heterogeneity for monitoring systems among patients with a chronic heart failure. Methods A best–worst scaling experiment (BWS case 3) was conducted among patients with chronic heart failure to assess preferences for hypothetical monitoring care scenarios. These were characterized by the attributes mobility, risk of death, risk of hospitalization, type and frequency of monitoring, risk of medical device, and system-relevant complications. A latent class analysis (LCA) was used to analyze and interpret the data. In addition, a market simulator was used to examine which treatment configurations participants in the latent classes preferred. Results Data from 278 respondents were analyzed. The LCA identified four heterogeneous classes. For class 1, the most decisive factor was mobility with a longer distance covered being most important. Class 2 respondents directed their attention toward attribute “monitoring,” with a preferred monitoring frequency of nine times per year. The attribute risk of hospitalization was most important for respondents of class 3, closely followed by risk of death. For class 4, however, risk of death was most important. A market simulation showed that, even with high frequency of monitoring, most classes preferred therapy with high improvement in mobility, mortality, and hospitalization. Conclusion Using LCA, variations in preferences among different groups of patients with chronic heart failure were examined. This allows treatment alternatives to be adapted to individual needs of patients and patient groups. The findings of the study enhance clinical and allocative decision-making while elevating the quality of clinical data interpretation.

Date: 2024
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DOI: 10.1007/s40271-023-00656-5

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