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

Lisa Pennells, University of Cambridge
Stephen Kaptoge, University of Cambridge
Angela Wood, University of Cambridge
Mike Sweeting, University of Cambridge
Xiaohui Zhao, University of Cambridge
Ian White, University College London
Stephen Burgess, University of Cambridge
Peter Willeit, University of Cambridge
Thomas Bolton, University of Cambridge
Karel G.M. Moons, University Medical Center Utrecht
Yvonne T. Van Der Schouw, University Medical Center Utrecht
Randi Selmer, Norwegian Institute of Public Health
Kay Tee Khaw, University of Cambridge
Vilmundur Gudnason, Icelandic Heart Association
Gerd Assmann, Assmann-Foundation for Prevention
Philippe Amouyel, Institut Pasteur Lille
Veikko Salomaa, National Institute for Health and Welfare
Mika Kivimaki, University College London
Børge G. Nordestgaard, Copenhagen University Hospital
Michael J. Blaha, The Johns Hopkins Hospital
Lewis H. Kuller, University of Pittsburgh
Hermann Brenner, German Cancer Research Center
Richard F. Gillum, Howard University College of Medicine
Christa Meisinger, Helmholtz Center Munich German Research Center for Environmental Health
Ian Ford, University of Glasgow
Matthew W. Knuiman, The University of Western Australia
Annika Rosengren, Göteborg University, Sahlgrenska Academy
Debbie A. Lawlor, University of Bristol
Henry Völzke, Universität Greifswald
Cyrus Cooper, MRC Lifecourse Epidemiology Unit
Alejandro Marín Ibañez, San Jose Norte Health Centre
Edoardo Casiglia, Università degli Studi di Padova
Jussi Kauhanen, Itä-Suomen yliopisto
Jackie A. Cooper, University College London

Document Type

Article

Publication Date

2-14-2019

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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