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Record W4406958618 · doi:10.1093/ehjci/jeae333.396

Branch pulmonary artery flows calculated using velocity time integral correlate with MRI in pediatric pulmonary vein stenosis

2025· article· en· W4406958618 on OpenAlexaff
Sarah Pradhan, Juan Aguirre, Luc Mertens, Rachel D. Vanderlaan, Shi‐Joon Yoo, Andréea Dragulescu

Bibliographic record

VenueEuropean Heart Journal - Cardiovascular Imaging · 2025
Typearticle
Languageen
FieldMedicine
TopicCongenital Heart Disease Studies
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsMedicineStenosisCardiologyInternal medicinePulmonary arteryPulmonary veinRadiologyPulmonary vein stenosis

Abstract

fetched live from OpenAlex

Abstract Background/Introduction In pulmonary vein stenosis (PVS), serial echocardiograms are the mainstay in assessment of pulmonary vein disease. Development of gradients in stenotic pulmonary veins indicate progression of disease and influences timing of intervention. However, pulmonary vein gradients should be interpreted in the context of relative branch pulmonary artery (PA) flows to avoid misinterpretation of worsening PVS. To date, the comparison of branch PA flows derived from echocardiography compared to gold-standard from MRI has not been described. Purpose To determine if branch PA flows calculated using velocity time integral (VTI) correlate with MRI. Methods Single-center retrospective assessment of cardiac MRIs and echocardiograms in pediatric patients with pulmonary vein stenosis between February 2019 to May 2022. Cardiac MRIs and echocardiograms obtained within a 6-month period were compared. All causes and severity of PVS were included. Patients were excluded if echocardiographic assessment did not adequately visualize either branch PA or pulse wave Dopplers in the PAs were not performed. 20 patients with a mean age of 7.72 ± 5.93 years were included. Branch PA flows were derived using PA diameter in systole from the high parasternal view from which cross-sectional area (CSA) was calculated using CSA = pi(radius)^2. Using pulse wave Doppler in the branch PAs, VTI was measured as an averaged area under the curves over 3 beats. Relative branch PA flows were calculated using a product of CSA and VTI. We present branch PA flows indexed to body surface area. Results Mean RPA flows by VTI and MRI were 2.18 ± 1.36L/min/m2 and 2.34 ± 1.29L/min/m2, respectively. Mean LPA flows by VTI and MRI were 1.64±0.77L/min/m2 and 1.94±1.13L/min/m2, respectively. There were statistically significant correlations in branch PA flows comparing VTI to MRI (RPA correlation coefficient 0.549, p=0.02, LPA correlation coefficient 0.621, p=0.01). Conclusion Relative branch PA flows calculated using VTI correlate significantly with gold-standard from MRI. Lower correlation in the RPA may be due to Doppler angle. This is a feasible, complementary tool easily integrated as part of the standard echocardiographic assessment of PVS, that allows early detection of disease progression while decreasing the frequency of general anesthesia required for MRIs in young children. Expansion to a larger cohort is necessary to strengthen demonstrated correlations. Future work includes integration of branch PA flows in addition to pulmonary vein gradients to develop a PVS severity score in pediatrics to guide management.

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)0.002
metaresearch head score (Gemma)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.014
GPT teacher head0.250
Teacher spread0.235 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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