AimsKnowledge of patient preferences is vital for delivering optimal healthcare. This study uses utility measurement to assess the preferences of heart failure (HF) patients regarding quality of life or longevity. The utility approach represents the perspective of a patient; facilitates the combination of mortality, morbidity, and treatment regimen into a single score; and makes it possible to compare the effects of different interventions in healthcare.Methods and resultsPatient preferences of 100 patients with HF were assessed in interviews using the time trade-off (TTO) approach. Health-related quality of life (HR-QoL) was assessed with the EQ-5D and the Minnesota Living with Heart Failure Questionnaire (MLHFQ). Patients' own estimation of life expectancy was assessed with a visual analogue scale (VAS). Of the 100 patients (mean age 70 ± 9 years; 71% male), 61% attach more weight to quality of life over longevity; while 9% and 14% were willing to trade 6 and 12 months, respectively, for perfect health and attach more weight to quality of life. Patients willing to trade time had a significantly higher level of NT-proBNP and reported significantly more dyspnoea during exertion. Predictors of willingness to trade time were higher NT-proBNP and lower EQ VAS.ConclusionThe majority of HF patients attach more weight to quality of life over longevity. There was no difference between both groups with respect to life expectancy described by the patients. These insights enable open and personalized discussions of patients' preferences in treatment and care decisions, and could guide the future development of more patient-centred care. © 2013 Published on behalf of the European Society of Cardiology. All rights reserved. © The Author 2013. For permissions please email: journals.permissions@oup.com.
Original languageEnglish
Pages (from-to)1113-1121
Number of pages9
JournalEuropean Journal of Heart Failure
Issue number10
Publication statusPublished - 1 Oct 2013


  • elderly patients
  • depressions
  • dyspnea
  • fatigue
  • health status
  • heart failure
  • logistic models
  • longevity
  • multivariate analysis
  • quality of life
  • social support


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