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

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Τίτλος: Equalization of four cardiovascular risk algorithms after systematic recalibration: Individual-participant meta-analysis of 86 prospective studies
Συγγραφείς: Pennells, L, Kaptoge, S, Wood, A, Sweeting, M, Zhao, X, White, I, Burgess, S, Willeit, P, Bolton, T, Moons, KGM, Van Der Schouw, YT, Selmer, R, Khaw, KT, Gudnason, V, Assmann, G, Amouyel, P, Salomaa, V, Kivimaki, M, Nordestgaard, BG, Blaha, MJ, Kuller, LH, Brenner, H, Gillum, RF, Meisinger, C, Ford, I, Knuiman, MW, Rosengren, A, Lawlor, DA, Völzke, H, Cooper, C, Marín Ibañez, A, Casiglia, E, Kauhanen, J, Cooper, JA, Rodriguez, B, Sundström, J, Barrett-Connor, E, Dankner, R, Nietert, PJ, Davidson, KW, Wallace, RB, Blazer, DG, Björkelund, C, Donfrancesco, C, Krumholz, HM, Nissinen, A, Davis, BR, Coady, S, Whincup, PH, Jørgensen, T, Ducimetiere, P, Trevisan, M, Engström, G, Crespo, CJ, Meade, TW, Visser, M, Kromhout, D, Kiechl, S, Daimon, M, Price, JF, Gómez De La Cámara, A, Wouter Jukema, J, Lamarche, B, Onat, A, Simons, LA, Kavousi, M, Ben-Shlomo, Y, Gallacher, J, Dekker, JM, Arima, H, Shara, N, Tipping, RW, Roussel, R, Brunner, EJ, Koenig, W, Sakurai, M, Pavlovic, J, Gansevoort, RT, Nagel, D, Goldbourt, U, Barr, ELM, Palmieri, L, Njølstad, I, Sato, S, Monique Verschuren, WM, Varghese, CV, Graham, I, Onuma, O, Greenland, P, Woodward, M, Ezzati, M, Psaty, BM, Sattar, N, Jackson, R, Ridker, PM, Cook, NR, D'Agostino, RB, Thompson, SG, Danesh, J, Di Angelantonio, E
Πηγή: urn:ISSN:0195-668X ; urn:ISSN:1522-9645 ; European Heart Journal, 40, 7, 621-631
Στοιχεία εκδότη: Oxford University Press
Έτος έκδοσης: 2019
Συλλογή: UNSW Sydney (The University of New South Wales): UNSWorks
Θεματικοί όροι: 32 Biomedical and Clinical Sciences, 3201 Cardiovascular Medicine and Haematology, 3202 Clinical Sciences, Cardiovascular, Heart Disease, Prevention, Aging, Clinical Research, Aged, Algorithms, Calibration, Cardiovascular Diseases, Female, Humans, Male, Middle Aged, Prospective Studies, Risk Assessment, Cardiovascular disease, Discrimination, Risk algorithms, Risk prediction, Emerging Risk Factors Collaboration, anzsrc-for: 32 Biomedical and Clinical Sciences, anzsrc-for: 3201 Cardiovascular Medicine and Haematology, anzsrc-for: 3202 Clinical Sciences, anzsrc-for: 1102 Cardiorespiratory Medicine and Haematology, anzsrc-for: 1103 Clinical Sciences
Περιγραφή: 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.
Τύπος εγγράφου: article in journal/newspaper
Περιγραφή αρχείου: application/pdf
Γλώσσα: unknown
Relation: https://hdl.handle.net/1959.4/unsworks_84413; https://doi.org/10.1093/eurheartj/ehy653
DOI: 10.1093/eurheartj/ehy653
Διαθεσιμότητα: https://hdl.handle.net/1959.4/unsworks_84413
https://unsworks.unsw.edu.au/bitstreams/320a5a2d-df90-4f2a-97ba-e45ca6cf7fd8/download
https://doi.org/10.1093/eurheartj/ehy653
Rights: open access ; https://purl.org/coar/access_right/c_abf2 ; CC BY-NC ; https://creativecommons.org/licenses/by-nc/4.0/ ; free_to_read ; 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/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
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  Data: Equalization of four cardiovascular risk algorithms after systematic recalibration: Individual-participant meta-analysis of 86 prospective studies
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  Data: 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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  Data: open access ; https://purl.org/coar/access_right/c_abf2 ; CC BY-NC ; https://creativecommons.org/licenses/by-nc/4.0/ ; free_to_read ; 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/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
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      – SubjectFull: anzsrc-for: 3202 Clinical Sciences
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      – SubjectFull: anzsrc-for: 1102 Cardiorespiratory Medicine and Haematology
        Type: general
      – SubjectFull: anzsrc-for: 1103 Clinical Sciences
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      – TitleFull: Equalization of four cardiovascular risk algorithms after systematic recalibration: Individual-participant meta-analysis of 86 prospective studies
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              Y: 2019
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            – TitleFull: urn:ISSN:0195-668X ; urn:ISSN:1522-9645 ; European Heart Journal, 40, 7, 621-631
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