MétaCan
Menu
Back to cohort
Record W3006100603 · doi:10.21037/qims.2020.01.07

Systemic ventricular strain and torsion are predictive of elevated serum NT-proBNP in Fontan patients: a magnetic resonance study

2020· article· en· W3006100603 on OpenAlexaff
Liwei Hu, Xinrong Liu, Qian Wang, Gregory P. Barton, Rongzhen Ouyang, Aimin Sun, Chen Guo, Tongtong Han, Xiaofen Yao, Christopher J. François, Yumin Zhong

Bibliographic record

VenueQuantitative Imaging in Medicine and Surgery · 2020
Typearticle
Languageen
FieldMedicine
TopicCongenital Heart Disease Studies
Canadian institutionsCircle Cardiovascular Imaging
FundersNational Institute of Allergy and Infectious DiseasesNational Key Research and Development Program of China
KeywordsMedicineInternal medicineEjection fractionCardiologyVentricleStroke volumeFontan procedureMagnetic resonance imagingCardiac magnetic resonance imagingNatriuretic peptideCardiac magnetic resonanceHeart failureRadiology

Abstract

fetched live from OpenAlex

Background: This study aimed to investigate the associations between cardiac strain, cardiac torsion, ventricular volumes, and ventricular ejection fraction, with N-terminal pro-B-type natriuretic peptide (NT-proBNP) levels in Fontan patients who were age- and gender-matched with healthy control subjects. Methods: Cardiovascular magnetic resonance (CMR) studies performed in 22 (15 male, 7 female) patients with single-ventricle physiology (all morphological left ventricles) palliated with Fontan and 17 (10 male, 7 female) age- and gender-matched healthy children volunteers were retrospectively analyzed. Serum NT-proBNP levels were obtained in Fontan subjects. Standard post-processing of CMR images included systemic ventricular end-diastolic and end-systolic volumes, stroke volume, cardiac mass, atrioventricular regurgitation, and ejection fraction. CMR tissue tracking (TT) software was used to quantify global longitudinal strain (GLS), global radial strain (GRS), and global circumferential strain (GCS) and torsion of the systemic ventricle. Pearson and Spearman correlation coefficients were used in comparisons of correlations between NT-proBNP and functional parameters in repair Fontan patients. Intra-observer and inter-observer variability of CMR strain and torsion values were determined from 10 randomly selected Fontan subjects and 10 randomly selected control subjects. Results: GLS was significantly lower in Fontan patients than in control subjects (−15.19±2.94 vs. −19.97±1.70; P<0.001). GLS was not significantly different between normal NT-proBNP levels and high NT-proBNP levels in Fontan patients (−15.59±2.72 vs. −14.62±3.32; P=0.462). The GCS of repair Fontan patients was not significantly lower than that of the control group (−16.76±3.27 vs. −17.88±2.26; P=0.235). GCS was significantly different between normal and high NT-proBNP levels group in Fontan patients (−17.95±2.43 vs. −15.04±3.67; P=0.036). The peak systolic torsion and peak systolic torsion rates were significantly lower in Fontan patients than in control subjects (0.81±0.41 vs. 1.07±0.36, P=0.044; 7.36±3.41 vs. 9.85±2.61, P=0.017). Peak systolic torsion was significantly lower in Fontan patients with normal NT-proBNP levels than in high NT-proBNP subjects (0.67±0.43 vs. 1.01±0.29; P=0.036). GCS and torsion were more strongly correlated with NT-proBNP in the patient group (r=0.541 for GCS; r=0.588 for torsion, P<0.01). The parameters of strain and torsion could be reproduced with sufficient accuracy by intra-observer agreement(biases =0.04 for GLS; biases =0.66 for GCS; biases =1.03 for GRS; biases =0.04 for torsion) and inter−observer agreement (biases =0.32 for GLS; biases =0.85 for GCS; biases =1.52 for GRS; biases =0.18 for torsion). Conclusions: GLS is an earlier marker of contractile dysfunction in repair Fontan patients. Peak systolic torsion may be a biomarker for determining subclinical dysfunction, as it is more strongly correlated with serum biomarkers of ventricular function than ventricular size or ejection fraction.

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)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.042
Threshold uncertainty score0.625

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.033
GPT teacher head0.293
Teacher spread0.260 · 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.

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

Citations8
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueQuantitative Imaging in Medicine and SurgerySame topicCongenital Heart Disease StudiesFrench-language works237,207