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Record W3109581922 · doi:10.1093/ehjci/ehaa946.3510

A longitudinal evaluation of cardiovascular risk factors, treatment patterns, and outcomes in patients with documented cardiovascular disease treated with lipid lowering therapy in the United Kingdom

2020· article· en· W3109581922 on OpenAlexaboutno aff
Y. Beaini, Mark D. Danese, Eduard Sidelnikov, Guillermo Villa, David Catterick, Mazhar Iqbal, M. Gleeson, Deborah P. Lubeck, Jay Patel

Bibliographic record

VenueEuropean Heart Journal · 2020
Typearticle
Languageen
FieldMedicine
TopicLipoproteins and Cardiovascular Health
Canadian institutionsnot available
FundersAmgen
KeywordsMedicineDyslipidemiaRevascularizationInternal medicineMyocardial infarctionCohortAnginaCanadian Cardiovascular SocietyStroke (engine)DiseaseCardiologyEmergency medicine

Abstract

fetched live from OpenAlex

Abstract Background Over time, guidelines for dyslipidemia management in patients at high risk of atherosclerotic cardiovascular disease (CVD) changed with the goal of improving patient outcomes. Guidelines have been released by the European Joint Task Force in 2007, 2012 and 2016, European Society of Cardiology in 2011, 2016 and 2019, Joint British Societies in 2014, and National Institute for Health and Care Excellence in 2014. Purpose Evaluate cardiovascular risk factors, treatment patterns, and cardiovascular outcomes over time related to dyslipidemia management. Methods Ten prevalent cohorts of patients with documented CVD receiving lipid-lowering therapy (LLT) were created using Clinical Research Practice Datalink (CPRD) records as of January 1, each year from 2008 through 2017. For each cohort, we identified CVD risk factors and LLT, and estimated the 1-year composite rate of fatal and nonfatal myocardial infarction (MI), ischemic stroke (IS), or revascularization. Patient follow-up was censored at the earliest of one year, end of data, or the outcome of interest. Patients in each cohort were required to be ≥18 years old, have ≥1 years of available medical history, and have received ≥2 LLT prescriptions in the prior year. Documented CVD was defined as MI, IS, angina, revascularization, transient ischemic attack, carotid stenosis, abdominal aortic aneurysm, or peripheral arterial disease. Patients could be in multiple cohorts. Results Annual patient counts ranged from 170,501 to 179,137 through 2013 and declined to 94,418 by 2017 (due to fewer patients in the overall CPRD data). Comparing 2008, 2011 (when ESC guidelines were revised) and 2017 showed the following for CVD risk factors: mean age was 71.6, 72.3, and 72.5 years; males were 59.9%, 61.1%, and 63.1%; current smoking was 15.1%, 15.2%, and 13.9%; type 2 diabetes was 18.4%, 20.2%, and 22.4%; stage 3–5 chronic kidney disease was 22.4%, 25.1%, and 22.8%; history of MI was 22.5%, 23.9%, and 27.4%; history of IS was 5.5%, 6.6%, and 7.9%; LDL <1.8 mmol/L was 27.8%, 29.2% and 37.2%; and LDL <1.4 mmol/L was 9.9%, 10.1%, and 15.6%. In terms of treatment, high intensity statin use increased from 12.9% to 15.7% to 30.8%; atorvastatin 40–80 mg use increased from 12.9% to 15.5% to 30.5%; while simvastatin 20–40 mg use decreased from 55.4% to 58.8% to 36.7%. The 1-year cardiovascular event rate declined from 2.54 to 2.35 to 1.96 events per 100 person-years (Figure). Conclusions After 2011 in the UK, there was an increased use of high intensity statins, a greater proportion of patients with LDL levels <1.8 and <1.4 mmol/L, and lower 1-year cardiovascular event rates. While improved CVD management likely contributed to the event rate decline, less than 40% of very high-risk patients achieved an LDL <1.8 mmol/L, and the proportion with LDL <1.4 mmol/L, as recommended by the 2019 ESC guidelines, was less than 20%. Clinicians should continue their efforts to reduce LDL in these patients. Figure 1 Funding Acknowledgement Type of funding source: Private company. Main funding source(s): Amgen

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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)0.002
metaresearch head score (Gemma)0.009
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 score0.094
Threshold uncertainty score0.187

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.061
GPT teacher head0.285
Teacher spread0.224 · 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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Citations3
Published2020
Admission routes1
Has abstractyes

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