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Record W2982257202 · doi:10.1093/eurheartj/ehz748.1060

P2743Right ventricular size in repaired tetralogy of Fallot: correlations between transthoracic echocardiography and cardiovascular magnetic resonance

2019· article· en· W2982257202 on OpenAlexaff
Charlène Bredy, François Simard, F Marcotte, Annie Dore, Blandine Mondésert, R. Ibrahim, Anita Asgar, Marie Chaix, Paul Khairy, François Pierre Mongeon

Bibliographic record

VenueEuropean Heart Journal · 2019
Typearticle
Languageen
FieldMedicine
TopicCongenital Heart Disease Studies
Canadian institutionsMontreal Heart Institute
Fundersnot available
KeywordsMedicineTetralogy of FallotCardiologyInternal medicineCardiac magnetic resonanceDiastoleSystolePopulationMagnetic resonance imagingCardiac magnetic resonance imagingRadiologyHeart diseaseBlood pressure

Abstract

fetched live from OpenAlex

Abstract Introduction Right ventricular (RV) size informs about prognosis and need for pulmonary valve replacement in patients with repaired tetralogy of Fallot (rTOF). Cardiac magnetic resonance (CMR) is considered the reference standard for measurement of RV volumes. Despite known limitations for RV evaluation, 2D transthoracic echocardiography (TTE) remains the primary and most available imaging modality in the rTOF population. Purpose To determine which TTE RV size parameters best correlate with CMR-derived indexed RV end-diastolic (RVEDVi) and end-systolic (RVESVi) volumes in the rTOF population. We sought to determine the best TTE measurement thresholds to predict normal RV volume (RVEDVi ≤110 mL/m2) and significant RV dilatation by CMR (RVEDVi ≥150ml/m2). Method We retrospectively enrolled all rTOF patients followed at a single-center between 2010 and 2018 who had both TTE and CMR exams performed within a 12-month interval. All TTE exams were reviewed by an observer measuring RV areas, RV inlet and RV outlet at end-diastole and end-systole. Analyses of CMR studies were performed by 3 observers who measured RV area, RV inlet, RV outlet and RV volumes at end-diastole and end-systole. Correlations between TTE and CMR parameters were performed using Pearson correlation coefficients. Using the TTE RV parameters with the strongest correlation with CMR, we subsequently determined thresholds to predict a CMR RVEDVi ≤110ml/m2 and ≥150ml/m2 using ROC analysis. Results We enrolled 130 patients (59 women [45%], mean age 43±12.8 years). Median age at TOF repair was 4 [3–6] years; 18 patients (14%) had subsequent pulmonary valve replacement. Median interval between TTE and CMR exams was 114 [59–239] days. There were significant correlations between all TTE parameters and CMR RVEDVi. TTE indexed RV end-diastolic area (RVEDAi) most strongly correlated with CMR RVEDVi (r=0.73, p<0.0001). All TTE RV parameters significantly correlated with CMR RVESVi but indexed RV end-systolic area had the strongest correlation (r=0.77, p<0.0001). ROC analysis performed to predict RVEDVi of ≤110ml/m2 and ≥150ml/m2 using TTE RVEDAi revealed areas under the curve of 0.86±0.04 and 0.90±0.03, respectively. A TTE RVEDAi ≤17cm2/m2 predicted a normal CMR RV volume (≤110ml/m2) with a sensitivity of 90% and a specificity of 74%. A TTE RVEDAi ≥19cm2/m2 predicted a CMR RVEDVi ≥150 mL/m2 with a sensitivity of 93% and a specificity of 76%. Conclusion In rTOF patients, both diastolic and systolic TTE RV area best correlate with CMR-derived RV end-diastolic and end-systolic volumes. A cut-off value of TTE RVEDAi of 19cm2/m2 is 93% sensitive and 76% specific to predict a CMR RVEDVi ≥150ml/m2. Simple RV size measurement using TTE may help inform the need and frequency of CMR evaluations in rTOF patients.

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)0.004
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 score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.018
GPT teacher head0.254
Teacher spread0.237 · 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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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