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Record W2505044400 · doi:10.1136/heartjnl-2016-309665

Effect of age and aortic valve anatomy on calcification and haemodynamic severity of aortic stenosis

2016· article· en· W2505044400 on OpenAlexafffund
Mylène Shen, Lionel Tastet, Romain Capoulade, Éric Larose, Élisabeth Bédard, Marie Arsenault, Philippe Chétaille, Jean G. Dumesnil, Patrick Mathieu, Marie‐Annick Clavel, Philippe Pîbarot

Bibliographic record

VenueHeart · 2016
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsCentre hospitalier universitaire de QuébecUniversité LavalInstitut universitaire de cardiologie et de pneumologie de Québec
FundersCanadian Institutes of Health Research
KeywordsMedicineCardiologyInternal medicineAortic valveStenosisHemodynamicsVentricular outflow tractBicuspid aortic valveCalcificationAortic valve stenosisDoppler echocardiographyBicuspid valveEjection fractionBlood pressureHeart failureDiastole

Abstract

fetched live from OpenAlex

OBJECTIVE: To evaluate the effect of age and aortic valve anatomy (tricuspid (TAV) vs bicuspid (BAV) aortic valve) on the relationship between the aortic valve calcification (AVC) and the haemodynamic parameters of aortic stenosis (AS) severity. METHODS: Two hundred patients with AS and preserved left ventricular ejection fraction were prospectively recruited in the PROGRESSA (Metabolic Determinants of the Progression of Aortic Stenosis) study and underwent a comprehensive Doppler echocardiography and multidetector CT (MDCT). Mean transvalvular gradient (MG) measured by Doppler echocardiography was used to assess AS haemodynamic severity and AVC was evaluated by MDCT using the Agatston method and indexed to the left ventricular outflow tract area to obtain AVC density (AVCd). All analyses were adjusted for sex. RESULTS: Thirty-nine patients had a BAV and 161 a TAV. Median age was 51 and 72 years for BAV and TAV patients, respectively. There was a modest correlation between MG and AVCd (ρ=0.51, p<0.0001) in the whole cohort. After dichotomisation for valve anatomy, there was a good correlation between AVCd and MG in the TAV group (ρ=0.61, p<0.0001) but weak correlation in the BAV group (ρ=0.32, p=0.046). In the TAV group, the strength of the AVCd-MG correlation was similar in younger (<72 years old; ρ=0.59, p<0.0001) versus older (≥72 years old; ρ=0.61, p<0.0001) patients. In the BAV group, there was no correlation between AVCd and MG in younger patients (<51 years old; ρ=0.12, p=0.65), whereas there was a good correlation in older patients (≥51 years old; ρ=0.55, p=0.009). AVCd (p=0.005) and age (p=0.02) were both independent determinants of MG in BAV patients while AVCd (p<0.0001) was the only independent determinant of MG in TAV patients. CONCLUSIONS: In patients with TAV as well as in older patients with BAV, AVCd appears to be the main factor significantly associated with the haemodynamic severity of AS and so it may be used to corroborate AS severity in case of uncertain or discordant findings at echocardiography. However, among younger patients with BAV, some may have a haemodynamically significant stenosis with minimal AVCd. The results of MDCT AVCd should thus be interpreted cautiously in this subset of patients. TRIAL REGISTRATION NUMBER: NCT01679431; Pre-results.

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)0.005
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 score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.010
GPT teacher head0.336
Teacher spread0.326 · 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

Citations63
Published2016
Admission routes2
Has abstractyes

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