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Record W3109125965 · doi:10.1093/ehjci/ehaa946.3170

Increased mortality in women with severe aortic stenosis and low ejection fraction

2020· article· en· W3109125965 on OpenAlexfundaboutno aff
Albert J. Paquin, Mohamed‐Salah Annabi, David Bienjonetti-Boudreau, Philippe Pîbarot, Marie‐Annick Clavel

Bibliographic record

VenueEuropean Heart Journal · 2020
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsnot available
FundersHeart and Stroke Foundation of Canada
KeywordsMedicineEjection fractionInternal medicineCardiologyvalvular heart diseaseStenosisHeart failureCoronary artery diseaseAortic valve replacementProportional hazards modelUnivariate analysisAortic valve stenosisRetrospective cohort studyMultivariate analysis

Abstract

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Abstract Background Women with severe aortic stenosis (AS) tend to be operated with more advanced valvular heart disease. They also have worse survival after aortic valve replacement (AVR). This difference between sexes in surgical referral and its relationship to mortality have not been previously studied in patients with severe AS and low left ventricular ejection fraction (LVEF). Purpose To assess sex differences in patients with severe AS and low LVEF, and to evaluate their potential association with mortality. Methods This is a retrospective study of consecutive patients presenting with a diagnosis of severe AS and low LVEF (≤50%) on echocardiogram, at the Quebec Heart and Lung Institute between 2004–2015. Patients were excluded if they had congenial or rheumatic heart disease, and/or more than moderate aortic regurgitation. Patients were compared according to sex for incidence of AVR and long-term all-cause mortality with univariate (Kaplan-Meyer with log-rank test), and multivariate analysis (Cox regression model) adjusting for baseline comorbidities, LVEF and severity of valvular disease. Results Our database had a total of 1129 patients, of which 578 were included in the analysis. There were 149 women (26%). Mean follow-up time was 3.10±3.04 years (1795 patient-years). There were 279 AVR and 284 deaths. Women were older (77±11 vs 74±10 years, p=0.004), and had less coronary artery disease (60% vs 79%, p<0.001) and dyslipidemia (55% vs 72%, p<0.001). Women had comparable severity of AS at baseline, with better LVEF (37% vs 35%, p=0.02). In univariate analysis, there was a strong tendency for increased mortality in women compared to men (p=0.06). After comprehensive baseline adjustement, female sex was predictive of mortality (HR 1.33 [1.00–1.75], p=0.04). Women were also less likely to be operated than men (HR 0.70 [0.51–0.94], p=0.02). After further adjustment for AVR as a time-dependent variable, there was only a trend toward the significance of sex as a predictor of mortality (p=0.12). In a sex-specific analysis of predictors of mortality, LVEF was predictive of mortality in women (HR 0.97 [0.94–0.99], p=0.007), while not in men (0.99 [0.98–1.01], p=0.51). Conclusions Despite adjustment for comorbidities and AS severity, women were less likely to be referred to AVR and had a higher overall mortality. After adjustment for AVR, female sex was no longer a predictor of mortality, underlying the negative impact of the lack of treatment in women with decreased LVEF. Thus underrecognition and suboptimal management of severe AS in female patients with low LVEF should be further investigated to identify contributing factors and possible solutions. Funding Acknowledgement Type of funding source: Public Institution(s). Main funding source(s): Dr Clavel has received a grant from the Heart and Stroke Foundation of Canada

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 score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.025
GPT teacher head0.309
Teacher spread0.284 · 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
Published2020
Admission routes2
Has abstractyes

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