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Record W2125747792 · doi:10.1186/1532-429x-14-23

Cardiovascular magnetic resonance evaluation of aortic stenosis severity using single plane measurement of effective orifice area

2012· article· en· W2125747792 on OpenAlexafffund
Julio García, O. Marrufo, A. O. Rodríguez, Éric Larose, Philippe Pîbarot, Lyes Kadem

Bibliographic record

VenueJournal of Cardiovascular Magnetic Resonance · 2012
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsConcordia UniversityUniversité LavalMontreal Heart Institute
FundersCanadian Institutes of Health ResearchInstitut universitaire de cardiologie et de pneumologie de Québec, Université LavalNatural Sciences and Engineering Research Council of CanadaConsejo Nacional de Ciencia y TecnologíaFonds de Recherche du Québec - SantéFondation Institut Universitaire de Cardiologie et de Pneumologie de Québec
KeywordsMedicineAngiologyStenosisCardiologyInternal medicineAortic valveMagnetic resonance imagingAortic valve stenosisBland–Altman plotRadiologyNuclear medicineLimits of agreement

Abstract

fetched live from OpenAlex

BACKGROUND: Transthoracic echocardiography (TTE) is the standard method for the evaluation of the severity of aortic stenosis (AS). Valve effective orifice area (EOA) measured by the continuity equation is one of the most frequently used stenotic indices. However, TTE measurement of aortic valve EOA is not feasible or not reliable in a significant proportion of patients. Cardiovascular magnetic resonance (CMR) has emerged as a non-invasive alternative to evaluate EOA using velocity measurements. The objectives of this study were: 1) to validate a new CMR method using jet shear layer detection (JSLD) based on acoustical source term (AST) concept to estimate the valve EOA; 2) to introduce a simplified JSLD method not requiring vorticity field derivation. METHODS AND RESULTS: We performed an in vitro study where EOA was measured by CMR in 4 fixed stenoses (EOA = 0.48, 1.00, 1.38 and 2.11 cm²) under the same steady flow conditions (4-20 L/min). The in vivo study included eight (8) healthy subjects and 37 patients with mild to severe AS (0.72 cm² ≤ EOA ≤ 1.71 cm²). All subjects underwent TTE and CMR examinations. EOA was determinated by TTE with the use of continuity equation method (TTE(CONT)). For CMR estimation of EOA, we used 3 methods: 1) Continuity equation (CMR(CONT)); 2) Shear layer detection (CMR(JSLD)), which was computed from the velocity field of a single CMR velocity profile at the peak systolic phase; 3) Single plane velocity truncation (CMR(SPVT)), which is a simplified version of CMR(JSLD) method. There was a good agreement between the EOAs obtained in vitro by the different CMR methods and the EOA predicted from the potential flow theory. In the in vivo study, there was good correlation and concordance between the EOA measured by the TTE(CONT) method versus those measured by each of the CMR methods: CMR(CONT) (r = 0.88), CMR(JSLD) (r = 0.93) and CMR(SPVT) (r = 0.93). The intra- and inter- observer variability of EOA measurements was 5 ± 5% and 9 ± 5% for TTE(CONT), 2 ± 1% and 7 ± 5% for CMR(CONT), 7 ± 5% and 8 ± 7% for CMR(JSLD), 1 ± 2% and 3 ± 2% for CMR(SPVT). When repeating image acquisition, reproducibility of measurements was 10 ± 8% and 12 ± 5% for TTE(CONT), 9 ± 9% and 8 ± 8% for CMR(CONT), 6 ± 5% and 7 ± 4% for CMR(JSLD) and 3 ± 2% and 2 ± 2% for CMR(SPVT). CONCLUSION: There was an excellent agreement between the EOA estimated by the CMR(JSLD) or CMR(SPVT) methods and: 1) the theoretical EOA in vitro, and 2) the TTE(CONT) EOA in vivo. The CMR(SPVT) method was superior to the TTE and other CMR methods in terms of measurement variability. The novel CMR-based methods proposed in this study may be helpful to corroborate stenosis severity in patients for whom Doppler-echocardiography exam is inconclusive.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.858
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.006
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.040
GPT teacher head0.293
Teacher spread0.253 · 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.

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

Citations41
Published2012
Admission routes2
Has abstractyes

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