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European Heart Journal
Article . 2013 . Peer-reviewed
Data sources: Crossref
European Heart Journal
Other literature type . 2014
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Risks in estimating risk

Authors: M T Cooney; Ian D. Graham;
Abstract

This editorial refers to ‘SCORE performance in Central and Eastern Europe and former Soviet Union: MONICA and HAPIEE results’[†][1], by O. Vikhireva et al. , on page 571 ‘Prediction is very difficult, especially about the future’(attributed to Niels Bohr) ‘I told you I was ill’(Spike Milligan's epitaph) The paper by Vikhireva and colleagues1 is a timely reminder that one size does not fit all when estimating the risk of cardiovascular death. The authors examined the performance of the European Society of Cardiology (ESC) cardiovascular disease (CVD) risk estimation system SCORE in the Czech Republic, Poland, Lithuania, and Russia, using data from the mid 1980s (MONICA) and early 2000s (HAPIEE). Given acknowledged methodological limitations, the high-risk version of the SCORE chart estimated risk fairly well in the older cohorts, apart from underestimating it substantially in Russia. In the more recent cohorts, SCORE overestimated risk apart from in Russia. It was concluded that the low-risk version of SCORE might now be more appropriate for the Czech Republic and Poland. The fact that re-calibrated versions exist for these two countries was not discussed because it was felt that there was insufficient information on the methods used; it is not clear whether this information was sought. The estimation of CVD risk is not an exact science. The frequently used Cox's proportional hazard model uses regression coefficients to estimate risks relative to an absolute baseline risk. The coefficients are assumed to remain constant over time and in the context of different combinations of other risk factors. Explanatory variables are considered to act multiplicatively on the hazard function. At best, these assumptions may be regarded as usable approximations to ‘truth’. For example, different combinations of risk factors may interact in complex ways that are difficult to model. We have recently shown that beta coefficients … [1]: #fn-2

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Keywords

Male, Humans, Female, Atherosclerosis

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
11
Average
Top 10%
Top 10%
bronze