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Abstract 338: Patterns of Lipid Lowering Medication Use among Patients with Atherosclerotic Cardiovascular Disease

2015· article· en· W2117184830 on OpenAlexaboutno aff
Peter P. Tóth, Xuehua Ke, Zhenxiang Zhao, Nicole Bonine, Mark J. Cziraky, Michael Grabner, John Barron, Ralph Quimbo, Debra Wertz, Diane M Flickinger, Burkhard Vangerow, Thomas P. Power

Bibliographic record

VenueCirculation Cardiovascular Quality and Outcomes · 2015
Typearticle
Languageen
FieldMedicine
TopicLipoproteins and Cardiovascular Health
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineStatinDiscontinuationAtherosclerotic cardiovascular diseaseInternal medicineCohortCanadian Cardiovascular SocietyRetrospective cohort studyCombination therapyDiseaseCardiovascular healthCohort studyPhysical therapyMyocardial infarctionAngina

Abstract

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Background: The 2013 American College of Cardiology/American Heart Association (ACC/AHA) guidelines for the treatment of hypercholesterolemia marked a departure from previously established guidelines in regards to pharmacologic therapy. This study identified patients with atherosclerotic cardiovascular disease (ASCVD) using the new guidelines, and examined their patterns of lipid lowering medication (LLM) use in a real-world environment. Methods: This retrospective cohort study utilized claims data from the HealthCore Integrated Research Database (HIRD SM ). Newly diagnosed ASCVD patients aged ≥18 years were identified between 1/1/2007 and 11/30/2012 (index date=first ASCVD diagnosis date). Patients had both ≥ 12 months pre- and post- index health plan enrollment and no LLM use at baseline. Index LLM was identified based on earliest fill of LLM within 6 months after the index date. Index LLM dose titration, discontinuation, switch, and augmentation were examined among monotherapy initiators only. Descriptive statistics were used to examine patterns of LLM use at 12 and 36 months follow-up among all patients and those with ≥36 months post-index health plan enrollment respectively. Results: The study identified 128,017 ASCVD patients with a mean age of 59 years, 43.1% (55,136 of 128,017) female, and 48.8% (62,493 of 128,017) with ≥36 months follow-up. Within 6 months after the index date, 7.8% (9,929 of 128,017) initiated high-intensity statin monotherapy; 27.8% (35,634 of 128,017) moderate/low-intensity statin monotherapy; 1.6% (2,024 of 128,017) non-statin monotherapy; 1.4% (1,770 of 128,017) combination therapy; and 61.4% (78,660 of 128,017) had no fills for any LLM. Among monotherapy initiators over 12 and 36 months follow-up, 8.8% (4,180 of 47,587) and 12.4% (2,635 of 21,198) had up-titration of index LLM, 54.5% (25,938 of 47,587) and 77.8% (16,488 of 21,198) discontinued index LLM, 9.6% (4,579 of 47,587) and 13.9% (2,935 of 21,198) switched their index LLM, and 3.0% (1,403 of 47,587) and 2.8% (586 of 21,198) augmented index LLM. Among all patients over 12 and 36 months follow-up, 41.4% (53,049 of 128,017) and 49.7% (31,057 of 62,493) had statins, 10.6% (13,522 of 128,017) and 13.0% (8,134 of 62,493) had high-intensity statins, 5.8% (7,361of 128,017) and 12.8% (7,989 of 62,493) had ≥2 types of statins, 4.9% (6,253 of 128,017) and 9.3% (5,798 of 62,493) had both statins and non-statin LLMs, and 56.9% (72,897 of 128,017) and 47.9% (29,923 of 62,493) had no LLM. Conclusions: Few patients initiated a high-intensity statin within 6 months of ASCVD diagnosis and at 12 months and 36 months follow-up. Our findings suggest that treatment for ASCVD patients was not optimal in relation to the recent ACC/AHA guideline recommendations and significant modifications in prescribing patterns should be made towards use of high-intensity statins to improve treatment outcomes.

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How this classification was reachedexpand

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
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 head0.059
GPT teacher head0.281
Teacher spread0.223 · 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 teacher head, not a consensus.

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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Citations0
Published2015
Admission routes1
Has abstractyes

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