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Association Between Achieved Low-Density Lipoprotein Levels and Major Adverse Cardiac Events in Patients With Stable Ischemic Heart Disease Taking Statin Treatment

2016· article· en· W2473272179 on OpenAlexaff
Morton Leibowitz, Tomas Karpati, Chandra J. Cohen-Stavi, Becca Feldman, Moshe Hoshen, Haim Bitterman, Samy Suissa, Ran D. Balicer

Bibliographic record

VenueJAMA Internal Medicine · 2016
Typearticle
Languageen
FieldMedicine
TopicLipoproteins and Cardiovascular Health
Canadian institutionsMcGill UniversityJewish General Hospital
FundersClalit Health Services
KeywordsMedicineInternal medicineHazard ratioMyocardial infarctionStatinCardiologyPropensity score matchingProportional hazards modelUnstable anginaPopulationAdverse effectCohortStroke (engine)Confidence interval

Abstract

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IMPORTANCE: International guidelines recommend treatment with statins for patients with preexisting ischemic heart disease to prevent additional cardiovascular events but differ regarding target levels of low-density lipoprotein cholesterol (LDL-C). Trial data on this question are inconclusive and observational data are lacking. OBJECTIVE: To assess the relationship between levels of LDL-C achieved with statin treatment and cardiovascular events in adherent patients with preexisting ischemic heart disease. DESIGN, SETTING, AND PARTICIPANTS: Population-based observational cohort study from 2009 to 2013 using data from a health care organization in Israel covering more than 4.3 million members. Included patients had ischemic heart disease, were aged 30 to 84 years, were treated with statins, and were at least 80% adherent to treatment or, in a sensitivity analysis, at least 50% adherent. Patients with active cancer or metabolic abnormalities were excluded. EXPOSURES: Index LDL-C was defined as the first achieved serum LDL-C measure after at least 1 year of statin treatment, grouped as low (≤70.0 mg/dL), moderate (70.1-100.0 mg/dL), or high (100.1-130.0 mg/dL). MAIN OUTCOMES AND MEASURES: Major adverse cardiac events included acute myocardial infarction, unstable angina, stroke, angioplasty, bypass surgery, or all-cause mortality. The hazard ratio of adverse outcomes was estimated using 2 Cox proportional hazards models with low vs moderate and moderate vs high LDL-C, adjusted for confounders and further tested using propensity score matching analysis. RESULTS: The cohort with at least 80% adherence included 31 619 patients, for whom the mean (SD) age was 67.3 (9.8) years. Of this population, 27% were female and 29% had low, 53% moderate, and 18% high LDL-C when taking statin treatment. Overall, there were 9035 patients who had an adverse outcome during a mean 1.6 years of follow-up (6.7 per 1000 persons per year). The adjusted incidence of adverse outcomes was not different between low and moderate LDL-C (hazard ratio [HR], 1.02; 95% CI, 0.97-1.07; P = .54), but it was lower with moderate vs high LDL-C (HR, 0.89; 95% CI, 0.84-0.94; P < .001). Among 54 884 patients with at least 50% statin adherence, the adjusted HR was 1.06 (95% CI, 1.02-1.10; P = .001) in the low vs moderate groups and 0.87 (95% CI, 0.84-0.91; P = .001) in the moderate vs high groups. CONCLUSIONS AND RELEVANCE: Patients with LDL-C levels of 70 to 100 mg/dL taking statins had lower risk of adverse cardiac outcomes compared with those with LDL-C levels between 100 and 130 mg/dL, but no additional benefit was gained by achieving LDL-C of 70 mg/dL or less. These population-based data do not support treatment guidelines recommending very low target LDL-C levels for all patients with preexisting heart disease.

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)0.003
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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.251
Teacher spread0.239 · 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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Citations75
Published2016
Admission routes1
Has abstractyes

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