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Equalization of four cardiovascular risk algorithms after systematic recalibration: individual-participant meta-analysis of 86 prospective studies

Equalization of four cardiovascular risk algorithms after systematic recalibration: individual-participant meta-analysis of 86 prospective studies
Equalization of four cardiovascular risk algorithms after systematic recalibration: individual-participant meta-analysis of 86 prospective studies
Aims

There is debate about the optimum algorithm for cardiovascular disease (CVD) risk estimation. We conducted head-to-head comparisons of four algorithms recommended by primary prevention guidelines, before and after ‘recalibration’, a method that adapts risk algorithms to take account of differences in the risk characteristics of the populations being studied.

Methods and results

Using individual-participant data on 360 737 participants without CVD at baseline in 86 prospective studies from 22 countries, we compared the Framingham risk score (FRS), Systematic COronary Risk Evaluation (SCORE), pooled cohort equations (PCE), and Reynolds risk score (RRS). We calculated measures of risk discrimination and calibration, and modelled clinical implications of initiating statin therapy in people judged to be at ‘high’ 10 year CVD risk. Original risk algorithms were recalibrated using the risk factor profile and CVD incidence of target populations. The four algorithms had similar risk discrimination. Before recalibration, FRS, SCORE, and PCE over-predicted CVD risk on average by 10%, 52%, and 41%, respectively, whereas RRS under-predicted by 10%. Original versions of algorithms classified 29–39% of individuals aged ≥40 years as high risk. By contrast, recalibration reduced this proportion to 22–24% for every algorithm. We estimated that to prevent one CVD event, it would be necessary to initiate statin therapy in 44–51 such individuals using original algorithms, in contrast to 37–39 individuals with recalibrated algorithms.

Conclusion

Before recalibration, the clinical performance of four widely used CVD risk algorithms varied substantially. By contrast, simple recalibration nearly equalized their performance and improved modelled targeting of preventive action to clinical need.
0195-668X
621–631
Pennells, L.
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Kaptoge, S.
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Wood, A
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Zhao, X.
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White, I.
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Burgess, Stephen
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Willeit, P.
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Bolton, T.
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Moons, Karel G.M.
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Selmer, R.
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Khaw, K-T
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Gudnason, V.
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Assman, G.
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Amouyel, P.
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Salomaa, V.
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Nordestgaard, Børge G.
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Blaha, M.J.
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Emerging Risk Factors Collaboration
Pennells, L.
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Kaptoge, S.
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Wood, A
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White, I.
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Selmer, R.
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Gudnason, V.
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Amouyel, P.
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Knuiman, M.W.
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Rosengren, A.
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Cooper, Cyrus
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Emerging Risk Factors Collaboration (2019) Equalization of four cardiovascular risk algorithms after systematic recalibration: individual-participant meta-analysis of 86 prospective studies. European Heart Journal, 40 (7), 621–631, [ehy653]. (doi:10.1093/eurheartj/ehy653).

Record type: Article

Abstract

Aims

There is debate about the optimum algorithm for cardiovascular disease (CVD) risk estimation. We conducted head-to-head comparisons of four algorithms recommended by primary prevention guidelines, before and after ‘recalibration’, a method that adapts risk algorithms to take account of differences in the risk characteristics of the populations being studied.

Methods and results

Using individual-participant data on 360 737 participants without CVD at baseline in 86 prospective studies from 22 countries, we compared the Framingham risk score (FRS), Systematic COronary Risk Evaluation (SCORE), pooled cohort equations (PCE), and Reynolds risk score (RRS). We calculated measures of risk discrimination and calibration, and modelled clinical implications of initiating statin therapy in people judged to be at ‘high’ 10 year CVD risk. Original risk algorithms were recalibrated using the risk factor profile and CVD incidence of target populations. The four algorithms had similar risk discrimination. Before recalibration, FRS, SCORE, and PCE over-predicted CVD risk on average by 10%, 52%, and 41%, respectively, whereas RRS under-predicted by 10%. Original versions of algorithms classified 29–39% of individuals aged ≥40 years as high risk. By contrast, recalibration reduced this proportion to 22–24% for every algorithm. We estimated that to prevent one CVD event, it would be necessary to initiate statin therapy in 44–51 such individuals using original algorithms, in contrast to 37–39 individuals with recalibrated algorithms.

Conclusion

Before recalibration, the clinical performance of four widely used CVD risk algorithms varied substantially. By contrast, simple recalibration nearly equalized their performance and improved modelled targeting of preventive action to clinical need.

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ERFC CVD risk score comparison 05 July 2018 v2 - Accepted Manuscript
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More information

Accepted/In Press date: 4 October 2018
e-pub ahead of print date: 22 November 2018
Published date: 14 February 2019

Identifiers

Local EPrints ID: 426757
URI: http://eprints.soton.ac.uk/id/eprint/426757
ISSN: 0195-668X
PURE UUID: 2934bc18-3b6b-4350-8385-e6f69520e7e4
ORCID for Cyrus Cooper: ORCID iD orcid.org/0000-0003-3510-0709

Catalogue record

Date deposited: 11 Dec 2018 17:31
Last modified: 26 Nov 2021 05:50

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Contributors

Author: L. Pennells
Author: S. Kaptoge
Author: A Wood
Author: M. Sweeting
Author: X. Zhao
Author: I. White
Author: Stephen Burgess
Author: P. Willeit
Author: T. Bolton
Author: Karel G.M. Moons
Author: Y.T. van der Schouw
Author: R. Selmer
Author: K-T Khaw
Author: V. Gudnason
Author: G. Assman
Author: P. Amouyel
Author: V. Salomaa
Author: M. Kivimaki
Author: Børge G. Nordestgaard
Author: M.J. Blaha
Author: L.H. Kuller
Author: H. Brenner
Author: R.F. Gillum
Author: C. Meisinger
Author: I. Ford
Author: M.W. Knuiman
Author: A. Rosengren
Author: Cyrus Cooper ORCID iD
Corporate Author: Emerging Risk Factors Collaboration

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