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Record W3109359857 · doi:10.1093/ehjci/ehaa946.3191

Differences in lipid treatment patterns in women versus men in a large cohort of patients with atherosclerotic cardiovascular disease in Ontario, Canada

2020· article· en· W3109359857 on OpenAlexaffabout
Mary Fairbairn, Ron Goeree, Shaun G. Goodman, Raina M. Rogoza, Millicent Packalen, Ponda Motsepe-Ditshego, L. Pericleous, Paul Oh

Bibliographic record

VenueEuropean Heart Journal · 2020
Typearticle
Languageen
FieldMedicine
TopicLipoproteins and Cardiovascular Health
Canadian institutionsToronto Rehabilitation InstituteUniversity of TorontoMcMaster UniversitySt. Michael's HospitalAmgen (Canada)
Fundersnot available
KeywordsMedicineCohortInternal medicineGuidelineStatinAtherosclerotic cardiovascular diseaseCohort studyDiseaseCardiologyPathology

Abstract

fetched live from OpenAlex

Abstract Background/Introduction The prevalence of ischaemic heart disease is lower in women vs men in Canada. Studies have shown that women are more likely to be underdiagnosed and less likely to receive guideline-recommended treatments than men. Women receiving lipid-lowering therapies (LLTs) are also less likely to attain treatment goals vs men. Purpose We analysed use of LLTs and attainment of low-density lipoprotein cholesterol (LDL-C) treatment goals in a recent longitudinal cohort of patients with ASCVD with public drug coverage in Ontario to describe differences observed between female and male patients. Methods Patients ≥65 years with a primary ASCVD event/procedure between 1 Apr 2005 and 31 Mar 2016, treated with an LLT and with index and follow up LDL-C values were identified from claims data at the Institute for Clinical Evaluative Sciences data repository. Patients were assessed over a 1 year follow up period for LDL-C goal attainment (<2.0 mmol/L or 50% reduction from index LDL-C as per Canadian Cardiovascular Society Guidelines) and analysed by LLT category. Results 143,302 patients with ASCVD ≥65 years on LLTs were identified of which 41% were female. A higher proportion of female vs male patients were prescribed low (3% vs 2%) and medium intensity statins (51% vs 44%) compared with high intensity statins (43% vs 52%). A higher proportion of women failed to attain LDL-C goal compared to men (33% vs 24%) (Figure). When analysed by low, moderate or high intensity statin, 65%, 35%, and 27% of female patients and 49%, 25% and 19% of male patients failed to attain LDL-C goal at follow up. Conclusions In this retrospective study, women with a diagnosis of ASCVD were more frequently treated with low/moderate intensity statins whereas men were more frequently treated with high intensity statins. Approximately 2 of 3 women and 3 of 4 men receiving statin treatment attained LDL-C goal during the 1-year follow up period. Overall, there appear to be treatment differences between female and male patients with ASCVD, with males receiving higher intensity statin therapy and attaining LDL-C goal more frequently. Further research is needed to determine why these discrepancies exist. This study made use of de-identified data from the ICES Data Repository, which is managed by the Institute for Clinical Evaluative Sciences with support from its funders and partners: Canada's Strategy for Patient-Oriented Research (SPOR), the Ontario SPOR Support Unit, the Canadian Institutes of Health Research and the Government of Ontario. The opinions, results and conclusions reported are those of the authors. No endorsement by ICES or any of its funders or partners is intended or should be inferred. Parts of this material are based on data and/or information compiled and provided by CIHI. However, the analyses, conclusions, opinions and statements expressed in the material are those of the author(s), and not necessarily those of CIHI. Figure 1 Funding Acknowledgement Type of funding source: Private company. Main funding source(s): Amgen Canada Inc

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)0.000
metaresearch head score (Gemma)0.001
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 score0.020
Threshold uncertainty score0.148

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.003
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.025
GPT teacher head0.216
Teacher spread0.191 · 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".

Quick stats

Citations1
Published2020
Admission routes2
Has abstractyes

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