Plasma parathyroid hormone and risk of congestive heart failure in the community.
ABSTRACT In experimental studies parathyroid hormone (PTH) has been associated with underlying causes of heart failure (HF) such as atherosclerosis, left ventricular hypertrophy, and myocardial fibrosis. Individuals with increased levels of PTH, such as primary or secondary hyperparathyroidism patients, have increased risk of ischaemic heart disease and HF. Moreover, increasing PTH is associated with worse prognosis in patients with overt HF. However, the association between PTH and the development HF in the community has not been reported.
In a prospective, community-based study of 864 elderly men without HF or valvular disease at baseline (mean age 71 years, the ULSAM study) the association between plasma (P)-PTH and HF hospitalization was investigated adjusted for established HF risk factors (myocardial infarction, hypertension, diabetes, electrocardiographic left ventricular hypertrophy, smoking, and hypercholesterolaemia) and variables reflecting mineral metabolism (S-calcium, S-phosphate, P-vitamin D, S-albumin, dietary calcium and vitamin D intake, physical activity, glomerular filtration rate, and blood draw season). During follow-up (median 8 years), 75 individuals were hospitalized due to HF. In multivariable Cox-regression analyses, higher P-PTH was associated with increased HF hospitalization (hazard ratio for 1-SD increase of PTH, 1.41, 95% CI 1.12-1.77, P = 0.003). Parathyroid hormone also predicted hospitalization in participants without apparent ischaemic HF and in participants with normal P-PTH.
In a large community-based sample of elderly men, PTH predicted HF hospitalizations, also after accounting for established risk factors and mineral metabolism variables. Our data suggest a role for PTH in the development of HF even in the absence of overt hyperparathyroidism.
- SourceAvailable from: aphapublications.orgAmerican Journal of Public Health 02/1998; 88(1):15-9. · 3.93 Impact Factor
- [show abstract] [hide abstract]
ABSTRACT: Multivariable regression models are powerful tools that are used frequently in studies of clinical outcomes. These models can use a mixture of categorical and continuous variables and can handle partially observed (censored) responses. However, uncritical application of modelling techniques can result in models that poorly fit the dataset at hand, or, even more likely, inaccurately predict outcomes on new subjects. One must know how to measure qualities of a model's fit in order to avoid poorly fitted or overfitted models. Measurement of predictive accuracy can be difficult for survival time data in the presence of censoring. We discuss an easily interpretable index of predictive discrimination as well as methods for assessing calibration of predicted survival probabilities. Both types of predictive accuracy should be unbiasedly validated using bootstrapping or cross-validation, before using predictions in a new data series. We discuss some of the hazards of poorly fitted and overfitted regression models and present one modelling strategy that avoids many of the problems discussed. The methods described are applicable to all regression models, but are particularly needed for binary, ordinal, and time-to-event outcomes. Methods are illustrated with a survival analysis in prostate cancer using Cox regression.Statistics in Medicine 02/1996; 15(4):361 - 387. · 2.04 Impact Factor
Article: Economics of chronic heart failure.[show abstract] [hide abstract]
ABSTRACT: Chronic heart failure (CHF) is now recognized as a major and escalating public health problem. The costs of this syndrome, both in economic and personal terms, are considerable. The prevalence of CHF is 1-2% and appears to be increasing, in part because of ageing of the population. Economic analyses of CHF should include both direct and indirect costs of care. Healthcare expenditure on CHF in developed countries consumes 1-2% of the total health care budget. The cost of hospitalization represents the greatest proportion of total expenditure. Optimization of drug therapy represents the most effective way of reducing costs. Recent economic analyses in the Netherlands and Sweden suggest the costs of care are rising.European Journal of Heart Failure 07/2001; 3(3):283-91. · 5.25 Impact Factor