Predicting mortality in patients with heart failure: a pragmatic approach.
OBJECTIVE
To develop a comprehensive and easily applicable prognostic model predicting mortality risk in patients with moderate to severe heart failure.


METHODS
Prospective follow up study.


METHODS
Seven general hospitals in the Netherlands.


METHODS
152 outpatients with heart failure or patients admitted to hospital because of heart failure, who were included in a randomised trial to assess the impact of a pharmacist led intervention to improve drug compliance. Duration of follow up was at least 18 months.


METHODS
Multivariable logistic regression modelling was used to evaluate information from history, physical examination (for example, blood pressure), drug use, and quality of life questionnaires that independently contributed to the prediction of death. The area under receiver operating characteristic curves (AUC) was used to estimate the predictive ability of the prognostic models.


RESULTS
During the 18 months of follow up, 51 patients (34%) died. Independent predictors of mortality were diabetes mellitus, a history of renal dysfunction (or higher creatinine), New York Heart Association (NYHA) functional class III or IV, lower weight or body mass index, lower blood pressure, ankle oedema, and higher scores on a disease specific quality of life questionnaire. The use of beta blockers was predictive of a better prognosis. These factors were used to derive various prediction formulas. A model based on medical history, weight, presence of oedema, and lower blood pressure had an AUC of 0.77. Addition of use of beta blockers to this model improved the AUC to 0.80. Addition of NYHA class increased the AUC to 0.84. Data on quality of life did not improve the AUC further (AUC 0.85).


CONCLUSIONS
A prognostic model produced on the basis of easily obtainable information from medical history and physical examination can adequately stratify heart failure patients according to their short term risk of death.
Bouvy
M L
ML
Department of Pharmacoepidemiology and Pharmacotherapy, Utrecht Institute for Pharmaceutical Sciences, Utrecht, Netherlands. m.bouvy@pharm.uu.nl
Heerdink
E R
ER
Leufkens
H G M
HG
Hoes
A W
AW
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D019540:Area Under Curve; D002303:Cardiac Output, Low / Q000401:mortality*; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D015994:Incidence; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D009426:Netherlands / Q000453:epidemiology; D011446:Prospective Studies; D012044:Regression Analysis; D012307:Risk Factors