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Record W4396943792 · doi:10.1093/eurjpc/zwae174

Prediction of individual lifetime cardiovascular risk and potential treatment benefit: development and recalibration of the LIFE-CVD2 model to four European risk regions

2024· article· en· W4396943792 on OpenAlex
Steven H J Hageman, Stephen Kaptoge, Tamar I. de Vries, Wentian Lu, Janet M Kist, Hendrikus J. A. van Os, Mattijs E. Numans, Kristi Läll, Martin Bobák, Hynek Pikhart, Růžena Kubínová, Sofia Malyutina, Andrzej Pająk, Abdonas Tamošiūnas, Raimund Erbel, Andreas Stang, Börge Schmidt, Sara Schramm, Thomas R Bolton, Sarah Spackman, Stephan J. L. Bakker, Michael J. Blaha, Jolanda M.A. Boer, Amélie Bonnefond, Hermann Brenner, Eric J. Brunner, Nancy R. Cook, Karina W. Davidson, Elaine Dennison, Chiara Donfrancesco, Marcus Dörr, James S. Floyd, Ian Ford, Michael Fu, Ron T. Gansevoort, Simona Giampaoli, Richard F. Gillum, Agustı́n Gómez de la Cámara, Lise Lund Håheim, Per-Olof Hansson, P. D. Harms, Steve E. Humphries, M. Kamran Ikram, J. Wouter Jukema, Maryam Kavousi, Stefan Kiechl, Anna Kucharska‐Newton, Kunihiro Matsushita, Helmut E. Meyer, Karel G.M. Moons, Martin Bødtker Mortensen, Mirthe Muilwijk, Børge G. Nordestgaard, Chris J. Packard, Luigi Pamieri, Demosthenes B. Panagiotakos, Annette Peters, Louis Potier, Rui Providência, Bruce M. Psaty, Paul M. Ridker, Beatriz L. Rodríguez, Annika Rosengren, Naveed Sattar, Ben Schöttker, Joseph E. Schwartz, Steven Shea, Martin J. Shipley, Reecha Sofat, Barbara Thorand, W M Monique Verschuren, Henry Völzke, Nicholas J. Wareham, Leo D. Westbury, Peter Willeit, Bin Zhou, John Danesh, Frank L.J. Visseren, Emanuele Di Angelantonio, Lisa Pennells, Jannick A N Dorresteijn

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEuropean Journal of Preventive Cardiology · 2024
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Health and Risk Factors
Canadian institutionsColumbia College
FundersMailman School of Public Health, Columbia UniversityNIHR Cambridge Biomedical Research CentreSchool of Public Health, Imperial College LondonHelmholtz Zentrum MünchenDet Sundhedsvidenskabelige Fakultet, Københavns UniversitetEngineering and Physical Sciences Research CouncilJohns Hopkins Bloomberg School of Public HealthEuropean Regional Development FundUniversität InnsbruckMasarykova UniverzitaRECETOX Přírodovědecké Fakulty Masarykovy UniverzityHarokopio UniversityFaculty of Health and Medical Sciences, University of Western AustraliaTartu ÜlikoolSahlgrenska UniversitetssjukhusetUniversiteit LeidenAarhus UniversitetshospitalNational Institute for Health and Care ResearchGentofte HospitalUniversity of GlasgowBiotechnology and Biological Sciences Research CouncilUniversity College LondonUniversity of SouthamptonMedical Research CouncilLeids Universitair Medisch CentrumNarodowe Centrum NaukiDepartment of Health and Social CareHorizon 2020 Framework ProgrammeNational Institute on Handicapped ResearchAarhus UniversitetUniversiteit UtrechtMedizinische Universität InnsbruckEuropean CommissionIstituto Superiore di SanitàGlaxoSmithKlineImperial College LondonDeutsches Zentrum für Herz-KreislaufforschungUniversitair Medisch Centrum UtrechtUniversidad Complutense de MadridJohns Hopkins UniversityUniversity of WashingtonNorwegian Institute of Public HealthHarvard UniversityEesti TeadusagentuurBritish Heart FoundationNarodowym Centrum NaukiUniversität Heidelberg
KeywordsMedicineRisk modelRisk assessmentDemographyRisk analysis (engineering)

Abstract

fetched live from OpenAlex

AIMS: The 2021 European Society of Cardiology prevention guidelines recommend the use of (lifetime) risk prediction models to aid decisions regarding initiation of prevention. We aimed to update and systematically recalibrate the LIFEtime-perspective CardioVascular Disease (LIFE-CVD) model to four European risk regions for the estimation of lifetime CVD risk for apparently healthy individuals. METHODS AND RESULTS: The updated LIFE-CVD (i.e. LIFE-CVD2) models were derived using individual participant data from 44 cohorts in 13 countries (687 135 individuals without established CVD, 30 939 CVD events in median 10.7 years of follow-up). LIFE-CVD2 uses sex-specific functions to estimate the lifetime risk of fatal and non-fatal CVD events with adjustment for the competing risk of non-CVD death and is systematically recalibrated to four distinct European risk regions. The updated models showed good discrimination in external validation among 1 657 707 individuals (61 311 CVD events) from eight additional European cohorts in seven countries, with a pooled C-index of 0.795 (95% confidence interval 0.767-0.822). Predicted and observed CVD event risks were well calibrated in population-wide electronic health records data in the UK (Clinical Practice Research Datalink) and the Netherlands (Extramural LUMC Academic Network). When using LIFE-CVD2 to estimate potential gain in CVD-free life expectancy from preventive therapy, projections varied by risk region reflecting important regional differences in absolute lifetime risk. For example, a 50-year-old smoking woman with a systolic blood pressure (SBP) of 140 mmHg was estimated to gain 0.9 years in the low-risk region vs. 1.6 years in the very high-risk region from lifelong 10 mmHg SBP reduction. The benefit of smoking cessation for this individual ranged from 3.6 years in the low-risk region to 4.8 years in the very high-risk region. CONCLUSION: By taking into account geographical differences in CVD incidence using contemporary representative data sources, the recalibrated LIFE-CVD2 model provides a more accurate tool for the prediction of lifetime risk and CVD-free life expectancy for individuals without previous CVD, facilitating shared decision-making for cardiovascular prevention as recommended by 2021 European guidelines.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.897
Threshold uncertainty score0.516

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.033
GPT teacher head0.239
Teacher spread0.206 · 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