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Record W2970616726 · doi:10.4244/eij-d-19-00362

Quantitative aortography for assessing aortic regurgitation after transcatheter aortic valve implantation: results of the multicentre ASSESS-REGURGE Registry

2019· article· en· W2970616726 on OpenAlexaboutno aff
Rodrigo Modolo, Chun‐Chin Chang, Hiroki Tateishi, Yosuke Miyazaki, Michele Pighi, Mohammad Abdelghani, Martin Roos, Quinten Wolff, Joanna J. Wykrzykowska, Robbert J. de Winter, Nicolò Piazza, Gert Richardt, Mohamed Abdel‐Wahab, Osama Soliman, Yoshinobu Onuma, Nicolas M. Van Mieghem, Patrick W. Serruys

Bibliographic record

VenueEuroIntervention · 2019
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAortographyRegurgitation (circulation)RadiologyConfidence intervalAortic valve regurgitationCardiac catheterizationSurgeryInternal medicineAorta

Abstract

fetched live from OpenAlex

AIMS: Quantitative aortography using videodensitometry is a valuable tool for quantifying paravalvular regurgitation after TAVI, especially in the minimalist approach - without general anaesthesia. However, retrospective assessment of aortograms showed moderate feasibility of assessment. We sought to determine the prospective feasibility of quantitative aortography after a protocol of acquisition. METHODS AND RESULTS: This was a multicentre registry in Japan, Canada, the Netherlands and Germany including consecutive patients with Heart Team indication to undergo TAVI over a median period of 12 months. Operators performed final aortograms according to a pre-planned projection (either by CT or visually - Teng's rule). An independent core laboratory (Cardialysis) analysed all images for feasibility and for regurgitation assessment. From the four centres included in the present analysis, a total of 354 patients underwent TAVI following the acquisition protocol and all the aortograms were analysed by the core lab. The analyses were feasible in 95.5% (95% confidence interval [CI]: 93.2% to 97.5%) of the cases. This rate of analysable assessment was significantly higher than the feasibility in previous validation studies, such as in the RESPOND population (95.5% vs. 57.5%, p<0.0001). No differences were observed among different planning strategies (CT 96.5% vs. Teng's rule 93%, p=0.159; or Circle 98.5% vs. 3mensio 95.8% vs. Teng's rule 93%, p=0.247). CONCLUSIONS: ASSESS-REGURGE showed a high feasibility of assessment of regurgitation with quantitative aortography with protocoled acquisition. This may be of great importance for quantifying regurgitation in TAVI procedures (optimisation, guidance of post-dilatation), and in future clinical trials, in order to address sealing features of novel devices for TAVI objectively. ClinicalTrials.gov Identifier: NCT03644784.

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)0.009
metaresearch head score (Gemma)0.011
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 score0.009
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.028
GPT teacher head0.367
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

Citations19
Published2019
Admission routes1
Has abstractyes

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