Improving 10-year cardiovascular risk prediction in apparently healthy people: flexible addition of risk modifiers on top of SCORE2

Steven Hj Hageman, Carmen Petitjean, Lisa Pennells, Stephen Kaptoge, Romin Pajouheshnia, Taavi Tillmann, Michael J Blaha, Robyn L McClelland, Kunihiro Matsushita, Vijay Nambi, Olaf H Klungel, Patrick C Souverein, Yvonne T van der Schouw, Wm Monique Verschuren, Nils Lehmann, Raimund Erbel, Karl-Heinz Jöckel, Emanuele Di Angelantonio, Frank Lj Visseren, Jannick An Dorresteijn*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

AIMS: In clinical practice, factors associated with cardiovascular disease (CVD) like albuminuria, education level, or coronary artery calcium (CAC) are often known, but not incorporated in cardiovascular risk prediction models. The aims of the current study were to evaluate a methodology for the flexible addition of risk modifying characteristics on top of SCORE2 and to quantify the added value of several clinically relevant risk modifying characteristics.

METHODS AND RESULTS: Individuals without previous CVD or DM were included from the UK Biobank; Atherosclerosis Risk in Communities (ARIC); Multi-Ethnic Study of Atherosclerosis (MESA); European Prospective Investigation into Cancer, The Netherlands (EPIC-NL); and Heinz Nixdorf Recall (HNR) studies (n = 409 757) in whom 16 166 CVD events and 19 149 non-cardiovascular deaths were observed over exactly 10.0 years of follow-up. The effect of each possible risk modifying characteristic was derived using competing risk-adjusted Fine and Gray models. The risk modifying characteristics were applied to individual predictions with a flexible method using the population prevalence and the subdistribution hazard ratio (SHR) of the relevant predictor. Risk modifying characteristics that increased discrimination most were CAC percentile with 0.0198 [95% confidence interval (CI) 0.0115; 0.0281] and hs-Troponin-T with 0.0100 (95% CI 0.0063; 0.0137). External validation was performed in the Clinical Practice Research Datalink (CPRD) cohort (UK, n = 518 015, 12 675 CVD events). Adjustment of SCORE2-predicted risks with both single and multiple risk modifiers did not negatively affect calibration and led to a modest increase in discrimination [0.740 (95% CI 0.736-0.745) vs. unimproved SCORE2 risk C-index 0.737 (95% CI 0.732-0.741)].

CONCLUSION: The current paper presents a method on how to integrate possible risk modifying characteristics that are not included in existing CVD risk models for the prediction of CVD event risk in apparently healthy people. This flexible methodology improves the accuracy of predicted risks and increases applicability of prediction models for individuals with additional risk known modifiers.

Original languageEnglish
Pages (from-to)1705-1714
Number of pages10
JournalEuropean Journal of Preventive Cardiology
Volume30
Issue number15
Early online date2 Jun 2023
DOIs
Publication statusPublished - 26 Oct 2023

Bibliographical note

Publisher Copyright:
© 2023 The Author(s). Published by Oxford University Press on behalf of the European Society of Cardiology.

Funding

Conflict of interest: Dr. Matsushita received funding from the National Institutes of Health during the study and personal fees from Fukuda Denshi, Kowa Company, and the American Medical Group Association outside of the submitted work. L.P. is funded by a BHF Programme Grant (RG/18/13/33946) S.K. is funded by a BHF Chair award (CH/12/2/29428). The MESA study was supported by contracts 75N92020D00001, HHSN268201500003I, N01-HC-95159, 75N92020D00005, N01-HC-95160, 75N92020D00002, N01-HC-95161, 75N92020D00003, N01-HC-95162, 75N92020D00006, N01-HC-95163, 75N92020D00004, N01-HC-95164, 75N92020D00007, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168, and N01-HC-95169 from the National Heart, Lung, and Blood Institute and by grants UL1-TR-000040, UL1-TR-001079, and UL1-TR-001420 from the National Center for Advancing Translational Sciences (NCATS). The Atherosclerosis Risk in Communities study has been funded in whole or in part with Federal funds from the National Heart, Lung, and Blood Institute, National Institutes of Health, Department of Health and Human Services, under Contract nos. 75N92022D00001, 75N92022D00002, 75N92022D00003, 75N92022D00004, and 75N92022D00005. This work was supported by core funding from the: British Heart Foundation (RG/18/13/33946), the BHF Chair Award (CH/12/2/29428), the Cambridge British Health Foundation Centre of Research Excellence (RE/18/1/34212), and the National Institute for Health and Care Research Cambridge Biomedical Research Centre (BRC-1215-20014; NIHR203312) [*].

FundersFunder number
Cambridge British Health Foundation Centre of Research ExcellenceRE/18/1/34212
National Institutes of Health
U.S. Department of Health and Human Services75N92022D00003, 75N92022D00004, 75N92022D00001, 75N92022D00002, 75N92022D00005
National Heart, Lung, and Blood InstituteUL1-TR-001079, UL1-TR-001420, UL1-TR-000040
National Center for Advancing Translational Sciences
American Medical Association
British Heart FoundationCH/12/2/29428, RG/18/13/33946
NIHR Cambridge Biomedical Research CentreNIHR203312, BRC-1215-20014

    Keywords

    • Biomarkers
    • Cardiovascular
    • Coronary calcium score
    • Risk prediction
    • Risk stratification
    • SCORE2

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