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Record W4403806038 · doi:10.1093/eurheartj/ehae666.128

Core laboratory versus center-reported echocardiographic assessment of the native and bioprosthetic aortic valve

2024· article· en· W4403806038 on OpenAlexaff
Bart J.J. Velders, Michiel D. Vriesendorp, Neil J. Weissman, Joseph F. Sabik, Michael J. Reardon, François Dagenais, Michael G. Moront, Vivek Rao, Shunichi Fukuhara, Ralf Günzinger, Morris Brown, Rolf H. H. Groenwold, Robert J.M. Klautz, Federico M. Asch

Bibliographic record

VenueEuropean Heart Journal · 2024
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsToronto General Hospital
Fundersnot available
KeywordsMedicineAortic valveCardiologyInternal medicineCore (optical fiber)

Abstract

fetched live from OpenAlex

Abstract Background The use of a central echocardiographic core laboratory (ECL) is advised to minimize measurement variation and heterogeneity in clinical trials. Insights into quantitative differences between ECL and center-reported echocardiographic assessment of the native and bioprosthetic aortic valve are lacking. We aimed to explore clinically relevant differences between these evaluations. Methods Data were used from the PERIcardial SurGical AOrtic Valve ReplacemeNt (PERIGON) Pivotal Trial for the Avalus valve. In this trial, patients with an indication for surgical aortic valve replacement (SAVR) due to aortic stenosis or regurgitation (AR) were enrolled. Serial echocardiographic examinations were performed at each center and reanalyzed by an independent ECL. For the bioprosthetic valve analysis, postoperative data throughout 5-year follow-up were pooled. Differences between the ECL and the centers in continuous parameters were quantified in mean differences and intraclass correlation coefficients (ICCs). Between-center differences were illustrated by plotting standardized mean differences (SMDs) between centers and the ECL for the 10 sites that implanted the most prostheses. Agreement on AR, paravalvular leak (PVL), and prosthesis-patient mismatch (PPM) classification was investigated using Cohen’s kappa coefficients. Results The analysis on the native aortic valve was performed on 1118 patients. The relative mean difference was largest for the left ventricular outflow tract (LVOT) area, followed by stroke volume and effective orifice area (index), with center-reported values being 11-7% higher (Table 1). High ICCs of around 0.90 were observed for peak aortic jet velocity, mean pressure gradient, and the velocity-time integral across the aortic valve. More than 5000 echocardiograms were available for the bioprosthetic valve analysis. Therein, comparable results were observed. In Figure 1, differences in assessment on center level are illustrated. The SMDs for peak velocity were between 0 and 0.5 for all centers, while there was more heterogeneity between centers for other parameters. The kappa coefficient was 0.59 (95% confidence interval [CI] 0.56, 0.63) for agreement on native AR, 0.28 (95% CI 0.18, 0.37) for PVL, and 0.42 (95% CI 0.40, 0.44) for PPM. Conclusions For echocardiographic assessment of the native and bioprosthetic aortic valve, agreement between the ECL and the clinical centers varies by parameter and by center. There is high agreement on continuous-wave Doppler-related measurements. On the contrary, agreement is low for parameters that involve measurement of the LVOT diameter. These results provide important context for the interpretation of aortic valve performance in studies that lack central ECL evaluation.

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)0.013
metaresearch head score (Gemma)0.017
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 score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
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 head0.052
GPT teacher head0.390
Teacher spread0.338 · 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
Published2024
Admission routes1
Has abstractyes

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