Predictors of the quality of life of older people with heart failure recruited from primary care

Merryn Gott, Sarah Barnes, Chris Parker, Sheila Payne, David Seamark, Salah Gariballa, Neil Small
Age and Ageing 2006, 35 (2): 172-7

BACKGROUND: Current understanding of quality of life in heart failure is largely derived from clinical trials. Older people, women and those with co-morbidities are underrepresented in these. Little is known about factors predictive of quality of life amongst older people with heart failure recruited from community settings.

OBJECTIVE: To identify factors predictive of quality of life amongst older people recruited from community settings.

DESIGN: prospective questionnaire survey.

SETTING: General practice surgeries located in four areas of the UK: Bradford, Barnsley, East Devon and West Hampshire.

SUBJECTS: A total of 542 people aged >60 years with heart failure.

METHODS: Participants completed a postal questionnaire, which included a disease-specific measure (Kansas City Cardiomyopathy Questionnaire), a generic quality-of-life measure (SF-36) and sociodemographic information.

RESULTS: A multiple linear regression analysis identified the following factors as predictive of decreased quality of life: being female, being in New York Heart Association (NYHA) functional class III or IV, showing evidence of depression, being in socioeconomic groups III-V and experiencing two or more co-morbidities. Older age was associated with decreased quality of life, as measured by a generic health-related quality-of-life tool (the SF-36 mental and physical health functioning scales) but not by a disease-specific tool (the Kansas City Cardiomyopathy Questionnaire).

CONCLUSION: Findings from the study suggest that quality of life for older people with heart failure can be described as challenging and difficult, particularly for women, those in a high NYHA class, patients showing evidence of depression, patients in socioeconomic groups III-V, those experiencing two or more co-morbidities and the 'oldest old'. Such information can help clinicians working with older people identify those at risk of reduced quality of life and target interventions appropriately.

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