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<p>The American Heart Association’s PREVENT equations estimate risk of total cardiovascular disease (CVD), atherosclerotic cardiovascular disease (ASCVD) and heart failure (HF) to guide lipid-lowering and blood pressure-lowering therapy in people ages 30−79 years in the United States. The SCORE2 risk algorithm is used to estimate CVD risk for similar purposes in people ages 40 and older in Europe. Neither set of equations has been comprehensively validated in global observational cohorts and randomized trials. In this study, in 44 observational cohorts and 18 randomized trials, we assessed discrimination and calibration of the two risk algorithms across geographical regions (North America, Europe and Asia/Other or multiregional trials). We also created scaling factors for risk prediction over 1–9 years using the PREVENT equations, enabling shorter-term risk prediction for research purposes or to facilitate clinical trial enrollment. Over 5.1 years of mean follow-up, 293,737 PREVENT total CVD events (fatal and non-fatal ASCVD or HF) and 258,086 SCORE2 CVD events (myocardial infarction, stroke or cardiovascular death) were observed among 6,422,714 and 5,437,384 individuals, respectively. Despite differences in CVD outcome definitions, target populations and predictor variables, overall discrimination and calibration were similar for both equations, with generally good performance across regions, including in multiregional randomized trials. These findings lend support for adoption of PREVENT or SCORE2 for cardiovascular risk stratification across diverse settings.</p>

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Multinational validation of the PREVENT and SCORE2 cardiovascular risk equations across 6.4 million individuals

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月5日

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  • Brendon L. Neuen,
  • Rupert W. Major,
  • Morgan E. Grams,
  • Yingying Sang,
  • Josef Coresh,
  • Shoshana H. Ballew,
  • Aditya Surapaneni,
  • Natalia Alencar de Pinho,
  • Johan Ärnlöv,
  • Clare Arnott,
  • Samira Bell,
  • Jarett Berry,
  • Hermann Brenner,
  • Elizabeth Ciemins,
  • Alexander R. Chang,
  • Mary Cushman,
  • James A. de Lemos,
  • Juan Miguel Diaz-Tocados,
  • Alfredo E. Farjat,
  • Robert A. Fletcher,
  • Ron T. Gansevoort,
  • Hiddo J. L. Heerspink,
  • William G. Herrington,
  • Chuan Hong,
  • Edward J. Horwitz,
  • Shih-Jen Hwang,
  • Simerjot K. Jassal,
  • Philip A. Kalra,
  • Ronit Katz,
  • Sadiya S. Khan,
  • Csaba P. Kovesdy,
  • Florian Kronenberg,
  • Jennifer S. Lees,
  • Donald M. Lloyd-Jones,
  • Jose Geraldo Mill,
  • David M. J. Naimark,
  • Chiadi Ndumele,
  • Kevan R. Polkinghorne,
  • Bruce M. Psaty,
  • Patrick Schloemer,
  • Michael G. Shlipak,
  • Prabin Shrestha,
  • Zean Song,
  • Natalie Staplin,
  • Dominik Steubl,
  • Priya Vart,
  • Frank L. J. Visseren,
  • Mark Woodward,
  • Kazumasa Yamagishi,
  • Bessie A. Young,
  • Charumathi Sabanayagam,
  • Jose M. Valdivielso,
  • John Chalmers,
  • Katie Harris,
  • Hiroshi Yatsuya,
  • Yuanying Li,
  • Midori Takada,
  • Steven Chadban,
  • Bruce Neal,
  • Isao Muraki,
  • Hironori Imano,
  • Hiroyasu Iso,
  • Natalia Alencar de Pinho,
  • Ziad Massy,
  • Benedicte Stengel,
  • Marie Metzger,
  • Kenneth W. Mahaffey,
  • Meg Jardine,
  • Vlado Perkovic,
  • Matthew Weir,
  • Jing Chen,
  • James Lash,
  • Panduranga Rao,
  • Colby Ayers,
  • Amit Khera,
  • Parag Joshi,
  • Amil Shah,
  • Michael J. Pencina,
  • Matthew Engelhard,
  • Mu Niu,
  • Haoyuan Wang,
  • Paula Bracco,
  • Maria de Jesus Mendes da Fonseca,
  • Isabela Bensenor,
  • Maria da Conceição Chagas de Almeida,
  • Luana Giatti,
  • Richard Haynes,
  • Parminder Judge,
  • Ulla T. Schultheiss,
  • Wibke Bechtel-Walz,
  • Markus Schneider,
  • Turgay Saritas,
  • Ewan Pearson,
  • Colin Palmer,
  • Shona J. Livingstone,
  • Emilie Lambourg,
  • Serafi Cambray,
  • Marcelino Bermudez-Lopez,
  • Maria Luisa Martin-Conde,
  • Maite Caus,
  • Nigel J. Brunskill,
  • James F. Medcalf,
  • Jaclyn Bergstrom,
  • Joachim Ix,
  • Dena Rifkin,
  • Keiichi Sumida,
  • Orlando Gutierrez,
  • Katharine Cheung,
  • Titi Ilori,
  • Ching Yu Cheng,
  • Cancan Xue,
  • Xiayin Zhang,
  • Crystal Chong,
  • Colin Baigent,
  • Martin Landray,
  • Rajkumar Chinnadurai,
  • Smeeta Sinha,
  • Sharmilee Rengarajan,
  • Ivona Baricevic-Jones,
  • Robin W. M. Vernooij,
  • Simon Benjamin Ascher,
  • Navdeep Tangri,
  • Anders Larsson,
  • Vilmantas Giedraitis

摘要

The American Heart Association’s PREVENT equations estimate risk of total cardiovascular disease (CVD), atherosclerotic cardiovascular disease (ASCVD) and heart failure (HF) to guide lipid-lowering and blood pressure-lowering therapy in people ages 30−79 years in the United States. The SCORE2 risk algorithm is used to estimate CVD risk for similar purposes in people ages 40 and older in Europe. Neither set of equations has been comprehensively validated in global observational cohorts and randomized trials. In this study, in 44 observational cohorts and 18 randomized trials, we assessed discrimination and calibration of the two risk algorithms across geographical regions (North America, Europe and Asia/Other or multiregional trials). We also created scaling factors for risk prediction over 1–9 years using the PREVENT equations, enabling shorter-term risk prediction for research purposes or to facilitate clinical trial enrollment. Over 5.1 years of mean follow-up, 293,737 PREVENT total CVD events (fatal and non-fatal ASCVD or HF) and 258,086 SCORE2 CVD events (myocardial infarction, stroke or cardiovascular death) were observed among 6,422,714 and 5,437,384 individuals, respectively. Despite differences in CVD outcome definitions, target populations and predictor variables, overall discrimination and calibration were similar for both equations, with generally good performance across regions, including in multiregional randomized trials. These findings lend support for adoption of PREVENT or SCORE2 for cardiovascular risk stratification across diverse settings.