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Risk factors for cardiac death in women with coronary artery disease

2025· article· en· W7127634628 on OpenAlexaboutno aff
I S King, M A E Eskuri, L Holmstrom, A M Kiviniemi, E S Lepojarvi, M P Tulppo, O P Piira, T V Kentta, J S Perkiomaki, O H Ukkola, H V Huikuri, M J Junttila

Bibliographic record

VenueEuropean Heart Journal · 2025
Typearticle
Languageen
FieldMedicine
TopicAcute Myocardial Infarction Research
Canadian institutionsnot available
Fundersnot available
KeywordsSudden cardiac deathEjection fractionCoronary artery diseaseUnivariate analysisMyocardial infarctionAtrial fibrillationCause of deathSudden deathProportional hazards model

Abstract

fetched live from OpenAlex

Abstract Background Coronary artery disease (CAD) is a leading cause of mortality, with women accounting for approximately 43% of cases. Emerging evidence suggests that CAD differs in its presentation and progression in women, potentially affecting detection, management, and prognosis. Aim This study aimed to identify factors associated with cardiac death in women with CAD. Methods This study utilized data from the ARTEMIS cohort, comprising 1,946 patients (619 women) with angiographically confirmed CAD, of whom we included individuals who experienced cardiac death or aborted sudden cardiac arrest during a 10-year follow-up. The primary endpoint was cardiac death, encompassing both sudden (SCD) and non-sudden (NSCD) cases, which were analyzed separately as secondary endpoints. We examined associations between cardiac death and traditional CAD risk factors, echocardiographic parameters (left ventricular ejection fraction [LVEF] and mass), electrocardiographic (ECG) abnormalities, Canadian Cardiovascular Society (CCS) grade, creatine clearance (CrCl), high-sensitivity troponin T (hs-TnT), B-type natriuretic peptide (BNP), and high-sensitivity C-reactive protein (hs-CRP). Factors identified as statistically significant in the univariate analysis were included in a multivariate Cox regression model to determine factors with independent associations with cardiac death. Results Cardiac death occurred in 7.6% (N=47) of women during follow-up. In univariate analysis, factors significantly associated with cardiac death in women with CAD included age, systolic BP, Syntax Score (both pre- and post-revascularization), CrCl, BNP, hs-TnT, type 2 diabetes, higher CCS grades, Q waves, atrial fibrillation (AF), and T wave inversions. In contrast, low-density lipoprotein levels, hs-CRP, LVEF, LVM, and ECG abnormalities including left bundle branch block, left ventricular hypertrophy, and abnormal QTc did not show a statistically significant association with cardiac death. Independent risk factors for cardiac death included age (HR 1.10 per year, 95% CI 1.04–1.17, p=0.001), post-revascularization Syntax Score (HR 1.05 per unit increase, 95% CI 1.01–1.08, p=0.007), systolic blood pressure (BP) (HR 1.01 per unit increase, 95% CI 1.00–1.03, p=0.030), hs-TnT (HR 1.05 per unit increase, 95% CI 1.02–1.07, p<0.001), and AF (HR 5.20, 95% CI 1.72-15.77, p=0.004). Post-revascularization Syntax Score was significantly associated only with SCD, while elevated systolic BP and higher hs-TnT levels were specifically linked with NSCD. AF was particularly emphasized in women with CAD and NSCD. Conclusions Higher post-revascularization Syntax Scores, systolic BP levels and hs-TnT levels are associated with elevated risk of cardiac death in women with CAD. AF demonstrated a particularly strong association with cardiac death in this population, suggesting that prioritizing the treatment and prevention of AF could help reduce cardiac mortality in women with CAD.Table 1 Figure 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)0.000
metaresearch head score (Gemma)0.001
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 score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0020.000

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 head0.036
GPT teacher head0.323
Teacher spread0.287 · 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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Citations0
Published2025
Admission routes1
Has abstractyes

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