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Record W2970115505 · doi:10.1001/jamacardio.2019.2851

Cost-effectiveness of Low-density Lipoprotein Cholesterol Level–Guided Statin Treatment in Patients With Borderline Cardiovascular Risk

2019· article· en· W2970115505 on OpenAlexaff
Ciaran Kohli‐Lynch, Brandon K. Bellows, George Thanassoulis, Yiyi Zhang, Mark J. Pletcher, Eric Vittinghoff, Michael Pencina, Dhruv S. Kazi, Allan D. Sniderman, Andrew E. Moran

Bibliographic record

VenueJAMA Cardiology · 2019
Typearticle
Languageen
FieldMedicine
TopicLipoproteins and Cardiovascular Health
Canadian institutionsMcGill University
FundersNational Heart, Lung, and Blood Institute
KeywordsMedicineStatinInternal medicineCohortNational Health and Nutrition Examination SurveyCohort studyEmergency medicinePhysical therapyEnvironmental healthPopulation

Abstract

fetched live from OpenAlex

Importance: American College of Cardiology/American Heart Association cholesterol guidelines prioritize primary prevention statin therapy based on 10-year absolute risk (AR10) of atherosclerotic cardiovascular disease (ASCVD). However, given the same AR10, patients with higher levels of low-density lipoprotein cholesterol (LDL-C) experience greater absolute risk reduction from statin therapy. Objectives: To estimate the cost-effectiveness of expanding preventive statin treatment eligibility from standard care to patients at borderline risk (AR10, 5.0%-7.4%) for ASCVD and with high levels of LDL-C and to estimate cost-effectiveness of statin treatment across ranges of age, sex, AR10, and LDL-C levels. Design, Setting, and Participants: This study evaluated 100 simulated cohorts, each including 1 million ASCVD-free survey respondents (50% men and 50% women) aged 40 years at baseline. Cohorts were created by probabilistic sampling of the 1999-2014 US National Health and Nutrition Examination Surveys from the perspective of the US health care sector. The CVD Policy Model microsimulation version projected lifetime health and cost outcomes. Probability of first-ever coronary heart disease or stroke event was estimated by analysis of 6 pooled US cohort studies and recalibrated to match contemporary event rates. Other model variables were derived from national surveys, meta-analyses, and published literature. Data were analyzed from May 15, 2018, through June 10, 2019. Exposures: Four statin treatment strategies were compared: (1) treat all patients with AR10 of at least 7.5%, diabetes, or LDL-C of at least 190 mg/dL (standard care); (2) add treatment for borderline risk and LDL-C levels of 160 to 189 mg/dL; (3) add treatment for borderline risk and LDL-C levels of 130 to 159 mg/dL; and (4) add treatment for remainder of patients with AR10 of at least 5.0%. Statin treatment was also compared with no statin treatment in age, sex, AR10, and LDL-C strata. Main Outcomes and Measures: Lifetime quality-adjusted life-years (QALYs) and costs (2019 US dollars) were projected and discounted 3.0% annually. The primary outcome was the incremental cost-effectiveness ratio. Results: In these 100 simulated cohorts, each with 1 million patients aged 40 years at baseline (50% women and 50% men), adding preventive statins to individuals with borderline AR10 and LDL-C levels of 160 to 189 mg/dL would be cost-saving; further treating borderline AR10 and LDL-C levels of 130 to 159 mg/dL would also be cost-saving; and treating all individuals with AR10 of at least 5.0% would be highly cost-effective ($33 558/QALY) and would prevent the most ASCVD events. Within age, AR10, and sex categories, individuals with higher baseline LDL-C levels gained more QALYs from statin therapy. Cost-effectiveness increased with LDL-C level and AR10. Conclusions and Relevance: In this study, lifetime statin treatment of patients in a hypothetical cohort with borderline ASCVD risk and LDL-C levels of 160 to 189 mg/dL was found to be cost-saving. Results suggest that treating all patients at borderline risk regardless of LDL-C level would likely be highly cost-effective.

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 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)0.002
metaresearch head score (Gemma)0.000
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 score0.127
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
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 head0.023
GPT teacher head0.269
Teacher spread0.246 · 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".

Quick stats

Citations47
Published2019
Admission routes1
Has abstractyes

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