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Record W7111761261

World Health Organization cardiovascular disease risk charts: revised models to estimate risk in 21 global regions

2019· article· en· W7111761261 on OpenAlexfundno aff

Bibliographic record

VenueEdinburgh Research Explorer · 2019
Typearticle
Languageen
FieldMedicine
TopicDiabetes, Cardiovascular Risks, and Lipoproteins
Canadian institutionsnot available
FundersCilagNIHR Cambridge Biomedical Research CentreHealth and Social Care Research and Development DivisionEconomic and Social Research CouncilIrving Medical Center, Columbia UniversityInstitut universitaire de cardiologie et de pneumologie de Québec, Université LavalNational Institutes of HealthFONDATION ALZHEIMERHjartaverndTechnische Universität MünchenLeids Universitair Medisch CentrumNovo NordiskScottish GovernmentLunds UniversitetUniversität HeidelbergHealth Research Council of New ZealandCambridge University HospitalsPublic Health AgencyIstituto Superiore di SanitàDiabetes AustraliaUniversity of OxfordSingulexUniversity of CambridgeChief Scientist Office, Scottish Government Health and Social Care DirectorateHáskóli ÍslandsBritish Heart FoundationNational Health and Medical Research CouncilShenzhen Center for Health InformationKowa CompanyUniversity of GlasgowUniversiteit LeidenMedicines CompanyAmgenInternational Society of HypertensionUniversity College LondonWellcome TrustBoston Scientific CorporationMylanRegeneron PharmaceuticalsNational Institute for Health and Care ResearchNHS Blood and TransplantRijksuniversiteit GroningenCapital Medical UniversityHirosaki UniversitySouth African Medical Research CouncilDeutsches KrebsforschungszentrumAetna FoundationResearch Institute for Endocrine Sciences, Shahid Beheshti University of Medical SciencesEli Lilly and CompanyNational Heart, Lung, and Blood InstituteItä-Suomen YliopistoUniversity of MinnesotaBristol-Myers SquibbTeva Pharmaceutical IndustriesUniversité LavalPortland State UniversityMedical Research CouncilServierKidney Health AustraliaDepartment of Health and Social CareDaiichi Sankyo EuropeYale UniversityDeutsches Zentrum für Herz-KreislaufforschungShahid Beheshti University of Medical SciencesGlaxoSmithKlineEngineering and Physical Sciences Research CouncilAstraZenecaSanofiAmerican Heart AssociationPfizerUniversità degli Studi di PadovaUniversity of California, San DiegoJohns Hopkins University
KeywordsDiseaseMyocardial infarctionRisk factorFramingham Risk ScoreRisk assessmentStroke (engine)Baseline (sea)Coronary heart disease
DOInot available

Abstract

fetched live from OpenAlex

Background To help adapt cardiovascular disease risk prediction approaches to low-income and middle-income countries, WHO has convened an effort to develop, evaluate, and illustrate revised risk models. Here, we report the derivation, validation, and illustration of the revised WHO cardiovascular disease risk prediction charts that have been adapted to the circumstances of 21 global regions. Methods In this model revision initiative, we derived 10-year risk prediction models for fatal and non-fatal cardiovascular disease (ie, myocardial infarction and stroke) using individual participant data from the Emerging Risk Factors Collaboration. Models included information on age, smoking status, systolic blood pressure, history of diabetes, and total cholesterol. For derivation, we included participants aged 40–80 years without a known baseline history of cardiovascular disease, who were followed up until the first myocardial infarction, fatal coronary heart disease, or stroke event. We recalibrated models using age-specific and sex-specific incidences and risk factor values available from 21 global regions. For external validation, we analysed individual participant data from studies distinct from those used in model derivation. We illustrated models by analysing data on a further 123 743 individuals from surveys in 79 countries collected with the WHO STEPwise Approach to Surveillance. Findings Our risk model derivation involved 376 177 individuals from 85 cohorts, and 19 333 incident cardiovascular events recorded during 10 years of follow-up. The derived risk prediction models discriminated well in external validation cohorts (19 cohorts, 1 096 061 individuals, 25 950 cardiovascular disease events), with Harrell's C indices ranging from 0·685 (95% CI 0·629–0·741) to 0·833 (0·783–0·882). For a given risk factor profile, we found substantial variation across global regions in the estimated 10-year predicted risk. For example, estimated cardiovascular disease risk for a 60-year-old male smoker without diabetes and with systolic blood pressure of 140 mm Hg and total cholesterol of 5 mmol/L ranged from 11% in Andean Latin America to 30% in central Asia. When applied to data from 79 countries (mostly low-income and middle-income countries), the proportion of individuals aged 40–64 years estimated to be at greater than 20% risk ranged from less than 1% in Uganda to more than 16% in Egypt. Interpretation We have derived, calibrated, and validated new WHO risk prediction models to estimate cardiovascular disease risk in 21 Global Burden of Disease regions. The widespread use of these models could enhance the accuracy, practicability, and sustainability of efforts to reduce the burden of cardiovascular disease worldwide. Funding World Health Organization, British Heart Foundation (BHF), BHF Cambridge Centre for Research Excellence, UK Medical Research Council, and National Institute for Health Research.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.033
metaresearch head score (Gemma)0.085
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.033
Threshold uncertainty score0.175

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0330.085
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0040.003
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0030.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.070
GPT teacher head0.353
Teacher spread0.283 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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