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

Authors

• 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, Hjartavernd Holtasmari
• Gerd Assman, Assmann-Foundation for Prevention
• Philippe Amouyel, Institut Pasteur de Lille
• Veikko Salomaa, National Institute for Health and Welfare, Finland
• Mika Kivimaki, University College London
• Borge G. Nordestgaard, Copenhagen University Hospital
• Michael J. Blaha, John Hopkins Hospital
• Lewis H. Kuller, University of Pittsburgh
• Hermann Brenner, German Cancer Research Center
• Richard F. Gillum, Howard University
• Christa Meisinger, German Research Center for Environmental Health
• Ian Ford, University of Glasgow
• Matthew W. Knuiman, University of Western Australia
• Annika Rosengren, University of Gothenberg
• Debbie A. Lawlor, University of Bristol
• Henry Volzke, University of Greifswald
• Cyrus Cooper, University of Southampton
• Alejandro Marin Ibañez, San Jose Norte Health Centre
• Edoardo Casiglia, University of Padova
• Jussi Kauhanen, University of Eastern Finland
• Jackie A. Cooper, University College London
• Beatriz Rodriguez, University of Hawaii, Hilo
• Johan Sundstrom, Uppsala University
• Elizabeth Barrett-Connor, University of California San Diego
• Rachel Dankner, Sheba Medical Center
• Paul J. Nietert, Medical University of South Carolina
• Karina W. Davidson, Columbia University Irving Medical Center
• Robert B. Wallace, University of Iowa
• Dan G. Blazer, Duke University
• Cecilia Bjorkelund, University of Gothenburg
• Chiara Donfrancesco, Istituto Superiore di Sanita
• Harlan M. Krumholz, Yale School of Medicine
• Aulikki Nissinen, National Institute for Health and Welfare, Finland
• Barry R. Davis, University of Texas School of Public Health
• Sean Coady, National Heart, Lung, and Blood Institute
• Peter H. Whincup, St. George's University
• Torben Jorgensen, Research Centre for Prevention and Health, Denmark
• Pierre Ducimetiere, Universite´ Paris Descartes
• Maurizo Trevisan, CUNY City CollegeFollow
• Gunnar Engstrom, Lund University
• Carlos J. Crespo, Portland State University
• Tom W. Meade, London School of Hygiene and Tropical Medicine
• Marjolein Visser, Vrije Universiteit Amsterdam
• Daan Kromhout, University Medical Centre Groningen
• Stefan Kiechl, Medical University of Innsbruck
• Makoto Daimon, Yamagata University
• Jackie F. Price, University of Edinburgh
• Agustin Gomez de la Camara, Hospital 12 de Octubre, Av. Cordoba
• J. Wouter Jukema, Leiden University
• Benoit Lamarche, Universite Laval
• Altan Onat, Istanbul University
• Leon A. Simons, UNSW, Sydney
• Maryam Kavousi, University Medical Center Rotterdam
• Yoav Ben-Shlomo, Bristol University
• John Gallacher, University of Oxford
• Jacqueline M. Dekker, VU University Medical Center
• Hisatomi Arima, Kyushu University
• Nawar Shara, MedStar Health Research Institute
• Robert W. Tipping, Clinical Biostatistics, Merck
• Ronan Roussel, Centre de Recherche des Cordeliers
• Eric J. Brunner, University College London
• Wolfgang Koenig, Technische Universita¨t Mu¨nchen
• Masaru Sakurai, Kanazawa Medical University
• Jelena Pavlovic, University Medical Center Rotterdam
• Ron T. Gansevoort, University of Grogingen
• Dorothea Nagel, Ludwig-Maximilians-Universitat
• Uri Goldbourt, Tel Aviv University
• Elizabeth L. M. Barr, Baker Heart and Diabetes Institute
• Luigi Palmieri, Istituto Superiore di Sanita
• Inger Njolstad, University of Tromsø
• Shinichi Sato, Chiba Prefectural Institute of Public Health
• W. M. Monique Verschuren, National Institute for Public Health and the Environment
• Cherian V. Varghese, World Health Organization
• Ian Graham, University of Dublin, Trinity College
• Oyere Onuma, World Health Organization
• Philip Greenland, Northwestern University
• Mark Woodward, University of New South Wales
• Majid Ezzati, Imperial College London
• Bruce M. Psaty, University of Washington
• Naveed Sattar, University of Glasgow
• Rod Jackson, University of Auckland
• Paul M. Ridker, Harvard Medical School
• Nancy R. Cook, Harvard Medical School
• Ralph B. D'Agostino Sr., Boston University
• Simone G. Thompson, University of Cambridge
• John Danesh, University of Cambridge
• Emanuele Di Angelantonio, University of Cambridge

Document Type

Article

Publication Date

11-22-2018

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 & Results: Using individual-participant data on 360737 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 overpredicted 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 >_40years 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.

Conclusions: 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.

Comments

This article was originally published in the European Heart Journal, available at DOI: 10.1093/eurheartj/ehy653.

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/).