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Clinical and radiological testing for subclinical atherosclerosis in first-degree relatives of patients with premature coronary artery disease: feasibility and diagnostic yield

2021· article· en· W3210511900 on OpenAlexaffabout
D. Vikulova, Luka Bevanda, Simon N. Pimstone, Liam R. Brunham

Bibliographic record

VenueEuropean Heart Journal · 2021
Typearticle
Languageen
FieldMedicine
TopicAcute Myocardial Infarction Research
Canadian institutionsProvidence Health Care Research InstituteSt. Paul's HospitalUniversity of British Columbia
Fundersnot available
KeywordsMedicineInternal medicineDyslipidemiaCoronary artery diseaseCardiologySubclinical infectionFamily historyStenosisFramingham Risk ScoreCoronary atherosclerosisCoronary Calcium ScoreDiseaseCoronary artery calcium

Abstract

fetched live from OpenAlex

Abstract Background Premature atherosclerotic cardiovascular disease (ASCVD) is highly heritable. The screening of first-degree relatives (FDR) of patients with premature ASCVD is recommended but not routinely performed, and the diagnostic yield of different approaches to such screening is unknown. Purpose To determine the feasibility and diagnostic yield of clinical and radiological screening of FDRs of patients with premature coronary artery disease (CAD) in clinical setting. Method We recruited FDRs of patients with angiographically-proven CAD with stenosis of ≥50% who presented at the age of ≤50 years for males and ≤55 years for females. After clinical and laboratory assessment, patients with no personal history of cardiovascular disease underwent either coronary computed tomography angiography (CCTA), coronary calcium scoring assessment (CAC), or carotid ultrasound (CUS). Subclinical atherosclerosis was defined as 1) CAC score >100 Agatston units or >75% percentile for age and sex; 2) Stenosis >50% in at least one coronary artery or segment involvement scores >50th percentile in males and >75th in females; or, 3) Carotid plaque on ultrasonography. Results We enrolled 220 FDRs between 2017 and 2020, 129 completed clinical assessment (Figure 1). Of them, 28 (21.7%) had a personal history of ASCVD and 101 were tested for subclinical atherosclerosis. The characteristics of these patients are shown in Table 1. The most prevalent cardiovascular risk factors were dyslipidemia (40.6%), hypertension (22.8%), and obesity (21.8%). When assessed with the Framingham risk score calculator without adjustment for family history, only 5.1% and 28.6%, of patients had high or moderate cardiovascular risk, respectively. After adjusting for family history and the presence of statin-indicated conditions, 39.6% and 14.9% of patients were placed in high and moderate risk groups, respectively. Subclinical atherosclerosis was found in 43.6% of all patients (Figure 1) and 57.7% of patients over 40 years of age. The diagnostic yield of procedures was 29.6% for CUS, 37.8% for CCTA and 61.1% for CAC scoring. After the radiological assessment, 13.9% of patients were reclassified to a higher risk group (Table 1). Conclusion Non-invasive cardiovascular imaging detected subclinical atherosclerosis in 43.6% of healthy patients with a family history of premature ASCVD, moving 1 in 7 patients to a higher risk group and suggesting that this screening approach may improve risk prediction in this population. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Canadian Institutes of Health Research.St. Paul's Hospital Foundation and the Vancouver General Hospital Foundation. Figure 1Table 1

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)0.008
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 score0.002
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.000
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 head0.214
GPT teacher head0.376
Teacher spread0.162 · 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

Citations0
Published2021
Admission routes2
Has abstractyes

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