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Record W4403245298 · doi:10.1001/jamacardio.2024.3241

Transcatheter Aortic Valve Implantation by Valve Type in Women With Small Annuli

2024· article· en· W4403245298 on OpenAlexaffabout
Didier Tchétché, Roxana Mehran, Daniel J. Blackman, Ramzi F. Khalil, Helge Möllmann, Mohamed Abdel‐Wahab, Walid Ben Ali, Paul Mahoney, Hendrik Ruge, Sabine Bleiziffer, Lang Lin, Molly Szerlip, Kendra J. Grubb, Isida Byku, Mayra Guerrero, Linda D. Gillam, Anna Sonia Petronio, Guilherme F. Attizzani, Wayne Batchelor, Hemal Gada, Toby Rogers, Joshua D. Rovin, Brian Whisenant, Stewart M. Benton, Blake Gardner, Ratnasari Padang, Andrew D. Althouse

Bibliographic record

VenueJAMA Cardiology · 2024
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsMontreal Heart Institute
Fundersnot available
KeywordsMedicineAortic valve replacementRandomized controlled trialAortic valve stenosisAortic valveInternal medicineStenosisValve replacementCardiologyClinical endpointStroke (engine)SurgeryClinical trialIncidence (geometry)

Abstract

fetched live from OpenAlex

Importance: Historically, women with aortic stenosis have experienced worse outcomes and inadequate recognition compared to men, being both underdiagnosed and undertreated, while also facing underrepresentation in clinical trials. Objective: To determine whether women with small aortic annuli undergoing transcatheter aortic valve replacement have better clinical and hemodynamic outcomes with a self-expanding valve (SEV) or balloon-expandable valve (BEV). Design, Setting, Participants: The Small Annuli Randomized to Evolut or SAPIEN Trial (SMART) was a large-scale randomized clinical trial focusing on patients with small aortic annuli undergoing transcatheter aortic valve replacement, randomized to receive SEVs or BEVs and included 716 patients treated at 83 centers in Canada, Europe, Israel, and the US from April 2021 to October 2022. This prespecified secondary analysis reports clinical and hemodynamic findings for all 621 women enrolled in SMART. Data for this report were analyzed from February to April 2024. Interventions: Transcatheter aortic valve replacement with an SEV or a BEV. Main Outcomes and Measures: The composite coprimary clinical end point comprised death, disabling stroke, or heart failure-related rehospitalization. The coprimary valve function end point was the incidence of bioprosthetic valve dysfunction, both assessed through 12 months. Secondary end points included the incidence of moderate or severe prosthesis-patient mismatch. Results: A total of 621 women (mean [SD] age, 80.2 [6.2] years; 312 randomized to the SEV group and 309 to the BEV group) were included in the present analysis. At 12 months, there were no significant differences in the coprimary clinical end point between the SEV and BEV groups (9.4% vs 11.8%, absolute risk difference -2.3%; 95% CI -7.2 to 2.5, P = .35). However, SEV implantation was associated with less bioprosthetic valve dysfunction (8.4% vs 41.8%; absolute risk difference, -33.4%; 95% CI, -40.4 to -26.4; P < .001). SEV implantation resulted in lower aortic valve gradients and larger effective orifice areas at 30 days and 12 months and less mild or greater aortic regurgitation at 12 months compared to BEV implantation. Prosthesis-patient mismatch was significantly lower with SEVs, regardless of the definition used and adjustment for body mass index. Use of SEVs was associated with better quality of life outcomes as assessed by the Valve Academic Research Consortium-3 ordinal quality of life measure. Conclusions and Relevance: Among women with severe symptomatic aortic stenosis and small aortic annuli undergoing transcatheter aortic valve replacement, the use of SEVs, compared to BEVs, resulted in similar clinical outcomes and a markedly reduced incidence of bioprosthetic valve dysfunction through 12 months, including a lower risk of prosthesis-patient mismatch and better 12-month quality of life. Trial Registration: ClinicalTrials.gov Identifier: NCT04722250.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.163
Threshold uncertainty score0.466

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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 head0.012
GPT teacher head0.292
Teacher spread0.281 · 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.

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

Citations16
Published2024
Admission routes2
Has abstractyes

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