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Record W2948697173 · doi:10.1093/ehjci/jez118.016

P429Combining systolic and diastolic feature tracking myocardial strain parameters for a more comprehensive assessment of the different characteristics in HFpEF

2019· article· en· W2948697173 on OpenAlexaffabout
Kady Fischer, Dominik P. Guensch, Melanie Artho, Silvia Luescher, Bernd Jung, Hendrik von Tengg‐Kobligk, Balthasar Eberle, MG Friedrich

Bibliographic record

VenueEuropean Heart Journal - Cardiovascular Imaging · 2019
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Function and Risk Factors
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsDiastoleCardiologyFeature trackingInternal medicineStrain (injury)Feature (linguistics)MedicineArtificial intelligenceBlood pressureComputer sciencePattern recognition (psychology)

Abstract

fetched live from OpenAlex

Funding Acknowledgements: Swiss National Science Foundation, McGill University Health Centre Foundation, Department of Anaesthesiology and Pain Therapy Inselspital Background: Heart Failure with Preserved Ejection Fraction (HFpEF) is a disease commonly associated with impaired myocardial relaxation, stiffening of the ventricles and consecutive diastolic dysfunction. Due to a left ventricular ejection fraction (LVEF) greater than 50%, the severity of heart failure or progression of systolic dysfunction may be underappreciated. Feature-tracking (FT) CMR is becoming a diagnostic and prognostic tool in cardiovascular disease. This technique has been most validated for peak strain, a marker of systolic function. Current software now allows for a variety of systolic and diastolic parameters to be extrapolated from these images, which in combination with tissue character­ization and myocardial function assessments may provide a more comprehensive assessment of HFpEF. Purpose: To investigate ventricular systolic and diastolic strain measurements obtained by FT-CMR in relation to other CMR characteristics of HFpEF patients. Methods: HFpEF patients with diastolic dysfunction (LVEF > 50%) underwent standard cine acquisition of the ventricles at rest. These images were analyzed with FT software for systolic parameters of peak strain, time to peak strain, along with systolic and diastolic strain rates (SR) in a circumferential and longitudinal orientation. T2 mapping and T1-based contrast enhanced extracellular volume (ECV) maps were acquired, while the myocardial oxygenation response was assessed with an oxygenation-sensitive image during a vasoactive stimulus induced by 60s of rapid-paced breathing and subsequent apnea. Results: Thirty patients (47% female) with a mean age of 61 ± 14 (SD) completed the CMR exam. Global peak circumferential strain was related to EF (r=-0.409, p = 0.025) for circumferential only, while peak strain and systolic SR in both orientations were associated with an enlarged mass index (r > 0.365, p < 0.050). A poor myocardial oxygenation response was associated with an increase in longitudinal time to peak strain (r=-0.383, p = 0.044). T2 was not associated with peak strain, but with circumferential time to peak (r = 0.533, p = 0.004) and an attenuated systolic SR (r = 0.440, p = 0.015). On the other hand, ECV was associated with the circumferential diastolic SR ratio of the early and atrial peaks (r = 0.576, p = 0.001). Conclusion: Using an array of strain parameters from FT-CMR may add more insight into various presentations of HFpEF, and into the progressive deterioration of myocardial relaxation and ventricular compliance. Diastolic parameters were found to be associated with measurements indicating ventricular stiffness, whereas inducible myocardial ischaemia and edema were associated with signs of systolic dysfunc­ti­on, especially time to peak strain. This parameter and abnormal myocardial oxygenation could be early mar­kers for progressive systolic involvement with diastolic dysfunction in HFpEF. Larger samples as well as myocardial segmental analysis may help to better characterize HFpEF pathophysiology Abstract P429 Figure. Feature Tracking in HFpEF

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)0.001
metaresearch head score (Gemma)0.000
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 score0.032
Threshold uncertainty score0.897

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.026
GPT teacher head0.283
Teacher spread0.257 · 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".

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Citations0
Published2019
Admission routes2
Has abstractyes

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