TY - JOUR
T1 - Equalization of four cardiovascular risk algorithms after systematic recalibration
T2 - individual-participant meta-analysis of 86 prospective studies
AU - Pennells, Lisa
AU - Kaptoge, Stephen
AU - Wood, Angela
AU - Sweeting, Mike
AU - Zhao, Xiaohui
AU - White, Ian
AU - Burgess, Stephen
AU - Willeit, Peter
AU - Bolton, Thomas
AU - Moons, Karel G.M.
AU - van der Schouw, Yvonne T.
AU - Selmer, Randi
AU - Khaw, Kay Tee
AU - Gudnason, Vilmundur
AU - Assmann, Gerd
AU - Amouyel, Philippe
AU - Salomaa, Veikko
AU - Kivimaki, Mika
AU - Nordestgaard, Børge G.
AU - Blaha, Michael J.
AU - Kuller, Lewis H.
AU - Brenner, Hermann
AU - Gillum, Richard F.
AU - Meisinger, Christa
AU - Ford, Ian
AU - Knuiman, Matthew W.
AU - Rosengren, Annika
AU - Lawlor, Debbie A.
AU - Völzke, Henry
AU - Cooper, Cyrus
AU - Marín Ibañez, Alejandro
AU - Casiglia, Edoardo
AU - Kauhanen, Jussi
AU - Cooper, Jackie A.
AU - Rodriguez, Beatriz
AU - Sundström, Johan
AU - Barrett-Connor, Elizabeth
AU - Dankner, Rachel
AU - Nietert, Paul J.
AU - Davidson, Karina W.
AU - Wallace, Robert B.
AU - Blazer, Dan G.
AU - Björkelund, Cecilia
AU - Donfrancesco, Chiara
AU - Krumholz, Harlan M.
AU - Nissinen, Aulikki
AU - Davis, Barry R.
AU - Coady, Sean
AU - Visser, Marjolein
AU - Monique Verschuren, W. M.
N1 - Publisher Copyright:
© 2018 The Author(s). Published by Oxford University Press on behalf of the European Society of Cardiology.
PY - 2019/2/14
Y1 - 2019/2/14
N2 - 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.
AB - 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.
KW - Calibration
KW - Cardiovascular disease
KW - Discrimination
KW - Risk algorithms
KW - Risk prediction
UR - http://www.scopus.com/inward/record.url?scp=85061592905&partnerID=8YFLogxK
U2 - 10.1093/eurheartj/ehy653
DO - 10.1093/eurheartj/ehy653
M3 - Article
C2 - 30476079
AN - SCOPUS:85061592905
SN - 0195-668X
VL - 40
SP - 621
EP - 631
JO - European Heart Journal
JF - European Heart Journal
IS - 7
ER -