Predicting mortality among a general practice-based sample of older people with heart failure.
OBJECTIVE
To identify factors available to general practitioners (GPs) that are predictive of mortality within a general practice-based population of heart failure patients, and to report the sensitivity and specificity of prognostic information from GPs.


METHODS
Five hundred and forty-two heart failure patients aged >60 years were recruited from 16 UK GP surgeries. Patients completed quality-of-life and services use questionnaires every 3 months for 24 months or until death. Factors with independent significant association with survival were identified using Cox proportional hazards regression analysis.


RESULTS
Women had a 58% lower risk of death. Patients self-reporting New York Heart Association Classification III or IV had an 81% higher risk of death. Patients aged 85+ years had over a five-fold risk of death as compared with those aged <65 years. Patients with a co-morbidity of cancer had a 78% higher risk of death. Of the 14 patients who died in a 12-month period, the GPs identified 11 (sensitivity 79%). They identified 133 of the 217 who did not die (specificity 61%).


CONCLUSIONS
Predictors readily available to GPs, such as patient characteristics, are easy to adapt to use in general practice, where most heart failure patients are diagnosed and treated. Identifying factors likely to influence death is useful in primary care, as this can initiate discussion about end-of-life care.
Barnes
Sarah
S
Section of Public Health, School of Health and Related Research, University of Sheffield, Regent Court, 30 Regent Street, Sheffield, S1 4DA, UK. s.barnes@sheffield.ac.uk
Gott
Merryn
M
Payne
Sheila
S
Parker
Chris
C
Seamark
David
D
Gariballa
Salah
S
Small
Neil
N
D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D005194:Family Practice; D005260:Female; D006333:Heart Failure / Q000401:mortality*; D006801:Humans; D008297:Male; D008875:Middle Aged; D009026:Mortality / Q000639:trends; D011379:Prognosis; D016016:Proportional Hazards Models; D012307:Risk Factors; D012680:Sensitivity and Specificity; D017678:Sex Distribution; D018434:Sickness Impact Profile; D006113:United Kingdom / Q000453:epidemiology