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

Lisa Pennells, Emerging Risk Factors Collaboration, Stephen Kaptoge, Angela Wood, Mike Sweeting, Xiaohui Zhao, Ian White, Stephen Burgess, Peter Willeit, Thomas Bolton, Karel G M Moons, Yvonne T van der Schouw, Randi Selmer, Kay-Tee Khaw, Vilmundur Gudnason, Gerd Assmann, Philippe Amouyel, Veikko Salomaa, Mika Kivimaki, Børge G NordestgaardMichael J Blaha, Lewis H Kuller, Hermann Brenner, Richard F Gillum, Christa Meisinger, Ian Ford, Matthew W Knuiman, Annika Rosengren, Debbie A Lawlor, Henry Völzke, Cyrus Cooper, Alejandro Marín Ibañez, Edoardo Casiglia, Jussi Kauhanen, Jackie A Cooper, Beatriz Rodriguez, Johan Sundström, Elizabeth Barrett-Connor, Rachel Dankner, Paul J Nietert, Karina W Davidson, Robert B Wallace, Dan G Blazer, Cecilia Björkelund, Chiara Donfrancesco, Harlan M Krumholz, Aulikki Nissinen, Barry R Davis, Yoav Ben-Shlomo, Mark Woodward, Simon G Thompson, Emanuele Di Angelantonio

Research output: Contribution to journalArticle (Academic Journal)peer-review

29 Citations (Scopus)
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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.

Original languageEnglish
Article numberehy653
JournalEuropean Heart Journal
Early online date22 Nov 2018
DOIs
Publication statusE-pub ahead of print - 22 Nov 2018

Keywords

  • Cardiovascular disease
  • Risk prediction
  • Risk algorithms
  • Calibration
  • Discrimination

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