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Record W4392446659 · doi:10.1159/000536392

Optimizing Post-Acute Coronary Syndrome Dyslipidemia Management: Insights from the North American Acute Coronary Syndrome Reflective III

2024· article· en· W4392446659 on OpenAlexaffabout
Meshal Alanezi, Andrew T. Yan, Mary Tan, Ronald Bourgeois, Peiman Malek-Marzban, Rani Beharry, Suhaib Alkurtass, Gábor Gyenes, Pierre-Louis Nadeau, Nduka Francis Nwadiaro, Sean Jedrzkiewicz, Dongsheng Gao, Harish Chandna, W. Bradley Nelson, Shaun G. Goodman

Bibliographic record

VenueCardiology · 2024
Typearticle
Languageen
FieldMedicine
TopicLipoproteins and Cardiovascular Health
Canadian institutionsCape Breton Regional HospitalWindsor Regional HospitalUniversity of Alberta HospitalMisericordia Community HospitalOakville-Trafalgar Memorial HospitalAlberta Hospital EdmontonOccupational and Environmental Medical Association of CanadaUniversity of TorontoMoncton HospitalSt. Michael's HospitalCentre hospitalier universitaire de QuébecCanadian Heart Research Centre
FundersSanofi
KeywordsAcute coronary syndromeDyslipidemiaMedicineInternal medicineCardiologyIntensive care medicineMyocardial infarctionObesity

Abstract

fetched live from OpenAlex

INTRODUCTION: Despite contemporary practice guidelines, a substantial number of post-acute coronary syndrome (ACS) patients fail to achieve guideline-recommended LDL-C thresholds. Our study aimed to investigate this guideline recommendations-to-practice care gap. Specifically, we aimed to identify opportunities where additional lipid-lowering therapies are indicated and explore reasons for the non-prescription of guideline-recommended therapies. METHODS: ACS patients with LDL-C ≥1.81 mmol/L (70 mg/dL) despite maximally tolerated statin ± ezetimibe therapy (including those intolerant of ≥2 statins) were enrolled 1-12 months post-event from 27 Canadian and US sites from September 2018 to October 2020 and followed up for three visits during the 12 months post-event. We determined the proportion of patients who did not achieve Canadian/US guideline-recommended LDL-C thresholds, the number of patients who would have been eligible for additional lipid-lowering therapies, and reasons behind lack of escalation in lipid-lowering therapies when indicated. Individual patient and aggregate practice feedback, including guideline-recommended intensification suggestions, were provided to each physician. RESULTS: Of the 248 patients enrolled in the pilot study (median age 64 [57, 73] years, 31.5% female and STEMI 27.4%), 75.4% were on high-intensity statins on the first visit. A total of 18.5% of those who attended all 3 visits had an LDL-C measured only at the first visit which was above the threshold. After 1 year of follow-up, 51.9% of patients achieved LDL-C thresholds at either visit 2 or 3. In the context of feedback reminding physicians about guideline-directed LDL-C-modifying therapy in their individual participating patients, we observed an increase in the use of ezetimibe and PCSK9 inhibitor therapy at 3-12 months. This was associated with a significant lowering of the mean LDL-C (from 2.93 mmol/L [baseline] to 2.09 mmol/L [3-6 months] to 1.87 mmol/L [6-12 months]) and a significantly greater proportion of patients (from 0% [baseline] to 38.6% [3-6 months] to 53.4% [6-12 months]) achieving guideline-recommended LDL-C thresholds. The most prevalent reasons behind the non-intensification of LDL-C-lowering therapy with ezetimibe and/or PCSK9i were LDL-C levels being close to target, the pre-existing use of other lipid-lowering therapies, patient refusal, and cost. CONCLUSION: Although most patients post-ACS were on high-intensity statin therapy, almost 50% failed to achieve guideline-recommended LDL-C thresholds by 1-year follow-up. Furthermore, additional lipid-lowering therapies in this high-risk group were underprescribed, and this might be linked to several factors including potential gaps in physician knowledge, treatment inertia, patient refusal, and cost.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.110
Threshold uncertainty score0.218

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.013
GPT teacher head0.268
Teacher spread0.255 · 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

Citations9
Published2024
Admission routes2
Has abstractyes

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