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Record W2910757759 · doi:10.1001/jamacardio.2018.4537

Cardiovascular Risk Factors Associated With Venous Thromboembolism

2019· article· en· W2910757759 on OpenAlexafffund
John Gregson, Stephen Kaptoge, Thomas Bolton, Lisa Pennells, Peter Willeit, Stephen Burgess, Steven Bell, Michael Sweeting, Eric B. Rimm, Christopher Kabrhel, Bengt Zöller, Gerd Assmann, Vilmundur Guðnason, Aaron R. Folsom, Volker Arndt, Astrid Fletcher, Paul E. Norman, Børge G. Nordestgaard, Akihiko Kitamura, Bakhtawar K. Mahmoodi, Peter H. Whincup, Matthew Knuiman, Veikko Salomaa, Christa Meisinger, Wolfgang Köenig, Maryam Kavousi, Henry Völzke, Jackie A. Cooper, Toshiharu Ninomiya, Edoardo Casiglia, Beatriz L. Rodríguez, Yoav Ben‐Shlomo, Jean‐Pierre Després, Leon A. Simons, Elizabeth Barrett‐Connor, Cecilia Björkelund, Marlene Notdurfter, Daan Kromhout, Jackie F. Price, Susan E. Sutherland, Johan Sundström, Jussi Kauhanen, John Gallacher, Joline W. J. Beulens, Rachel Dankner, Cyrus Cooper, Simona Giampaoli, Jason F. Deen, Agustı́n Gómez de la Cámara, Lewis H. Kuller, Annika Rosengren, Peter J. Svensson, Dorothea Nagel, Carlos J. Crespo, Hermann Brenner, Juan Rafael Albertorio‐Diaz, Robert C. Atkins, Eric J. Brunner, Martin J. Shipley, Inger Njølstad, Yvonne T. van der Schouw, Randi Selmer, Maurizio Trevisan, W. M. Monique Verschuren, Philip Greenland, Sylvia Wassertheil‐Smoller, Gordon Lowe, Angela Wood, Adam S. Butterworth, Simon G. Thompson, John Danesh, Emanuele Di Angelantonio, Tom Meade

Bibliographic record

VenueJAMA Cardiology · 2019
Typearticle
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsUniversité Laval
FundersNorwegian Institute of Public HealthErasmus Universitair Medisch Centrum RotterdamUppsala UniversitetMedical Research CouncilRijksinstituut voor Volksgezondheid en MilieuGraduate School of Public Health, University of PittsburghUniversität GreifswaldVersus ArthritisStrongNovo Nordisk FondenWageningen University and ResearchUniversität UlmHelmholtz Zentrum MünchenGöteborgs UniversitetUniversity of California, San FranciscoTechnische Universität MünchenWestfälische Wilhelms-Universität MünsterUniversity of New South WalesUniversity of PittsburghTel Aviv UniversityLunds UniversitetFeinberg School of MedicineUniversiteit UtrechtUniversity of GlasgowLondon School of Hygiene and Tropical MedicineCleveland ClinicRijksuniversiteit GroningenBritish Heart FoundationHáskóli ÍslandsCity University of New YorkUniversity of BristolWeill Cornell Medical CollegeNational Institute for Health and Care ResearchDeutsches Zentrum für Herz-KreislaufforschungLundbeckfondenUniversity College LondonUniversity of SouthamptonWellcome TrustUniversità degli Studi di PadovaUniversity of WashingtonVrije Universiteit AmsterdamUniversity of DundeeDeutsches KrebsforschungszentrumCenters for Disease Control and PreventionPortland State UniversityMonash UniversityUniversité LavalUniversity of California, San DiegoNational Heart, Lung, and Blood InstituteItä-Suomen YliopistoTrakya ÜniversitesiUniversity of South CarolinaNorthwestern University
KeywordsMedicineVenous thromboembolismCardiologyInternal medicineIntensive care medicineThrombosis

Abstract

fetched live from OpenAlex

Importance: It is uncertain to what extent established cardiovascular risk factors are associated with venous thromboembolism (VTE). Objective: To estimate the associations of major cardiovascular risk factors with VTE, ie, deep vein thrombosis and pulmonary embolism. Design, Setting, and Participants: This study included individual participant data mostly from essentially population-based cohort studies from the Emerging Risk Factors Collaboration (ERFC; 731 728 participants; 75 cohorts; years of baseline surveys, February 1960 to June 2008; latest date of follow-up, December 2015) and the UK Biobank (421 537 participants; years of baseline surveys, March 2006 to September 2010; latest date of follow-up, February 2016). Participants without cardiovascular disease at baseline were included. Data were analyzed from June 2017 to September 2018. Exposures: A panel of several established cardiovascular risk factors. Main Outcomes and Measures: Hazard ratios (HRs) per 1-SD higher usual risk factor levels (or presence/absence). Incident fatal outcomes in ERFC (VTE, 1041; coronary heart disease [CHD], 25 131) and incident fatal/nonfatal outcomes in UK Biobank (VTE, 2321; CHD, 3385). Hazard ratios were adjusted for age, sex, smoking status, diabetes, and body mass index (BMI). Results: Of the 731 728 participants from the ERFC, 403 396 (55.1%) were female, and the mean (SD) age at the time of the survey was 51.9 (9.0) years; of the 421 537 participants from the UK Biobank, 233 699 (55.4%) were female, and the mean (SD) age at the time of the survey was 56.4 (8.1) years. Risk factors for VTE included older age (ERFC: HR per decade, 2.67; 95% CI, 2.45-2.91; UK Biobank: HR, 1.81; 95% CI, 1.71-1.92), current smoking (ERFC: HR, 1.38; 95% CI, 1.20-1.58; UK Biobank: HR, 1.23; 95% CI, 1.08-1.40), and BMI (ERFC: HR per 1-SD higher BMI, 1.43; 95% CI, 1.35-1.50; UK Biobank: HR, 1.37; 95% CI, 1.32-1.41). For these factors, there were similar HRs for pulmonary embolism and deep vein thrombosis in UK Biobank (except adiposity was more strongly associated with pulmonary embolism) and similar HRs for unprovoked vs provoked VTE. Apart from adiposity, these risk factors were less strongly associated with VTE than CHD. There were inconsistent associations of VTEs with diabetes and blood pressure across ERFC and UK Biobank, and there was limited ability to study lipid and inflammation markers. Conclusions and Relevance: Older age, smoking, and adiposity were consistently associated with higher VTE risk.

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.015
GPT teacher head0.228
Teacher spread0.213 · 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 designObservational
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".

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Citations362
Published2019
Admission routes2
Has abstractyes

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