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Non-invasive assessment of left ventricular hemodynamic forces in hypertrophic cardiomyopathy

2024· article· en· W4403807742 on OpenAlexaff
Giorgia Panichella, Annamaria Del Franco, Alessandra Milazzo, Chiara Zocchi, M Garofalo, Angela Ilaria Fanizzi, M Ragagnin, Maurizio Pieroni, Mattia Zampieri, R H Chan, Gianni Pedrizzetti, Michele Emdin, Iacopo Olivotto

Bibliographic record

VenueEuropean Heart Journal · 2024
Typearticle
Languageen
FieldMedicine
TopicCardiomyopathy and Myosin Studies
Canadian institutionsToronto General Hospital
Fundersnot available
KeywordsMedicineHypertrophic cardiomyopathyCardiologyInternal medicineHemodynamicsCardiomyopathyLeft ventricular hypertrophyHeart failureBlood pressure

Abstract

fetched live from OpenAlex

Abstract Background Hemodynamic force (HDF) analysis allows non-invasive measurement of intraventricular pressure gradients, portraying cyclic spatial-temporal interaction between blood and tissues. Impaired HDFs have been implicated in early detecting of adverse cardiac remodelling and treatment response in various cardiovascular disorders, providing insights into cardiac physiology not offered by traditional cardiovascular imaging (1). Mainly explored in heart failure, HDF analysis has not been previously applied to hypertrophic cardiomyopathy (HCM). Purpose To investigate differences in systo-diastolic function in HCM patients compared to healthy controls using HDF analysis. Methods Forty patients with HCM diagnosis (20 obstructive and 20 non-obstructive) were retrospectively evaluated in comparison with 22 healthy controls. Left ventricular (LV) HDFs were derived from routine transthoracic apical 4-, 2- and 3-chamber views using a dedicated software. Apical-basal HDF curves were generated, where positive deflections represent forces directed from the LV apex to the base, whilst negative ones are directed toward the apex. Amplitude and timing parameters were derived, the latter indexed to total cardiac cycle duration. Results Table 1 shows the demographic, clinical and echocardiographic characteristics of HCM compared to controls. Patients with HCM were older, had a slower heart rate due to treatment with beta-blockers, and showed hypercontractility, as expressed by higher LV ejection fraction. Compared to controls, HCM patients showed reduced LV longitudinal force, i.e. the force during the whole cardiac cycle (4.17% [2.36-5.52] vs. 6.01% [4.73-7.78], p=0.002), shorter time interval to systolic peak (0.15 [0.12-0.16] vs. 0.19 [0.17-0.20], p <0.001), and shorter duration of LV impulse (0.29 [0.27-0.32] vs. 0.34 [0.32-0.37], p <0.001) (Figure 1). However, no differences in terms of systolic force peak (p=0.283) or LV impulse intensity (p=0.689) were detected. During the transition between systole and diastole, LV suction (i.e. the interval including late systolic deceleration and early diastolic suction) was significantly longer (0.26 [0.22-0.29] vs. 0.23 [0.21-0.25], p=0.023) but less pronounced (6.30% [4.56-8.44] vs. 8.72% [6.71-12.59], p=0.005), likely reflecting a relaxation impairment from the very beginning of diastole. Such impairment was maintained during early diastolic filling, where the amplitude of the positive deceleration flow force was severely reduced in HCM compared to controls (2.98% [1.98-4.58] vs. 7.11% [4.55-8.72], p <0.001). Conclusions HDF analysis in HCM patients revealed unique systolic and diastolic changes reflecting faster LV contraction during systole and slower and weaker LV forces during diastole. This analysis deepens our understanding of HCM mechanic abnormalities and may help assess response to innovative treatment options.

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)0.001
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.019
GPT teacher head0.300
Teacher spread0.281 · 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
Published2024
Admission routes1
Has abstractyes

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