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Abstract 4366696: Characterizing Exercise-Induced Myocardial Perfusion Response in Hypertrophic Cardiomyopathy and Heart Failure with Preserved Ejection Fraction

2025· article· en· W4415789744 on OpenAlexaff
Alexander Schulz, Tess Wallace, Fahime Ghanbari, Jennifer Rodriguez, Kelvin Chow, Peter Kellman, Ethan J. Rowin, Martin S. Maron, Warren J. Manning, Reza Nezafat

Bibliographic record

VenueCirculation · 2025
Typearticle
Languageen
FieldMedicine
TopicCardiac Imaging and Diagnostics
Canadian institutionsSiemens (Canada)
Fundersnot available
KeywordsHypertrophic cardiomyopathyCoronary artery diseaseEjection fractionHeart failureHeart failure with preserved ejection fractionPerfusionHemodynamicsMyocardial perfusion imagingCardiac magnetic resonance imaging

Abstract

fetched live from OpenAlex

Background: Reduced myocardial blood flow (MBF) and myocardial perfusion reserve (MPR) in the absence of epicardial coronary artery stenosis are commonly attributed to microvascular dysfunction, a key pathophysiological feature of hypertrophic cardiomyopathy (HCM) and heart failure with preserved ejection fraction (HFpEF). Conventional vasodilator stress may not adequately reflect exercise capacity or the hemodynamic responses characterizing these conditions. To better understand exercise-induced myocardial perfusion response, we performed quantitative myocardial perfusion in conjunction with exercise stress cardiovascular magnetic resonance imaging (Ex-CMR). Methods: A total of 108 patients (59±13 years, 43 male) with HCM, HFpEF, or non-cardiac dyspnea (NCD) were prospectively recruited for Ex-CMR at 3T using a CMR-compatible supine ergometer. Diagnoses of HCM and HFpEF were made according to current guidelines. Dyspneic patients without cardiac abnormalities were classified as NCD. Patients with obstructive coronary artery disease were excluded. Ex-CMR was performed using a standardized protocol (Fig. 1A) , hemodynamic responses and symptoms were recorded ( Fig. 1B and C) . Quantitative CMR perfusion was acquired at three short-axis slice positions at rest and after peak exercise ( Fig. 2A) . Results: Acquisitions with impaired image quality and segments with late gadolinium enhancement were excluded. The final cohort comprised 97 patients ( Table 1). No significant differences in MBF during exercise or MPR were observed between patients with and without subjective symptoms or perceived high exertion ( Fig. 1D ). MBF at rest did not differ among the three groups (p=0.266) ( Fig. 2B ). Post-exercise MBF was higher in HFpEF patients, compared to NCD and HCM patients, while MPR was similar between HFpEF and NCD patients (1.41 vs.1.44, p=0.686), but lower in HCM compared to NCD (1.27 vs.1.44, p=0.005) and HFpEF (1.27 vs.1.41, p=0.019). Using multivariable linear regression, a higher exercise-induced heart rate was independently associated with both higher post-exercise MBF and MPR. Conclusion: Ex-CMR identified distinct responses of MBF and MPR among patients with HCM compared to HFpEF and NCD. Post-exercise MBF is lower and MPR substantially worse when compared to NCD, as well as patients with HFpEF. These data show promise in defining abnormalities in exercise performance and the mechanism for functional limitations in HCM.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.013
GPT teacher head0.255
Teacher spread0.242 · 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
Published2025
Admission routes1
Has abstractyes

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