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Exploring the effects of mavacamten through hemodynamic force analysis in patients with obstructive hypertrophic cardiomyopathy

2024· article· en· W4403842894 on OpenAlexaff
Alessandra Milazzo, Chiara Zocchi, Annamaria Del Franco, Giorgia Panichella, M Garofalo, Angela Ilaria Fanizzi, M Ragagnin, Maurizio Pieroni, Mattia Zampieri, Michele Emdin, R H Chan, Gianni Pedrizzetti, Iacopo Olivotto

Bibliographic record

VenueEuropean Heart Journal · 2024
Typearticle
Languageen
FieldMedicine
TopicCardiomyopathy and Myosin Studies
Canadian institutionsToronto General Hospital
Fundersnot available
KeywordsMedicineHypertrophic cardiomyopathyCardiologyHemodynamicsInternal medicineCardiomyopathyHeart failure

Abstract

fetched live from OpenAlex

Abstract Introduction Mavacamten is a first-in-class allosteric myosin inhibitor effective in relieving left ventricular (LV) outflow tract obstruction in patients with obstructive hypertrophic cardiomyopathy (oHCM). To date, however, its effect on cardiac mechanics is still unclear. Hemodynamic force (HDF) analysis represents the fluid dynamics correlate of deformation imaging, showing a greater sensitivity in detecting early mechanical abnormalities or response to treatment [1]. Purpose To explore the in vivo effect of mavacamten on cardiac mechanics in patients with HCM after a 3 year-treatment, using HDF analysis. Methods LV HDF analysis was performed in 6 patients with oHCM (representing a cohort of the MAVA-LTE study) at baseline and after 3 years of treatment. Apical 4-, 2-, and 3-chamber echocardiographic views were analysed using a dedicated and sofisticated software. Base-apex forces (where positive deflections represent a force directed from the apex to the base of LV, whilst negative ones are directed toward the LV apex) were assessed in terms of timing indexed to cardiac cycle duration. HDF curves from the 6 patients treated with macavamten were compared to those obtained from oHCM patients (n=20), non-obstructive HCM (noHCM) patients (n=20), and healthy controls (n=22). Results Figure 1 shows a different pattern of transition between diastole and systole in HCM patients compared to controls. Specifically, HCM patients exhibited an earlier onset of the systolic phase, expressed as a fusion of late diastolic deceleration and systolic thrust. This was numerically represented as a decrease in diastolic-systolic transition time, which is 0.22 [0.20-0.24] for oHCM, 0.23 [0.21-0.25] for noHCM and 0.26 [0.26-0.30] for controls (p ≤0.001 for control vs oHCM, p ≤0.01 for control vs noHCM). Additionally, HCM patients showed an increased rate of force generation. The time to systolic peak (i.e., time interval from the onset of systole to the maximum force) was reduced in HCM patients compared to controls, and was shorter in oHCM than in noHCM patients (0.13 [0.11-0.15] vs 0.16 [0.14-0.17], p ≤0.01), thus reflecting an increased clinotropism in HCM. After 3 years of mavacamten treatment, the diastolic-systolic transition time and the time to systolic peak returned to a level similar to that of healthy controls (0.30 [0.27-0.30] in mavacamten group vs 0.26 [0.26-0.30], p >0.05, and 0.21 [0.20-0.22] vs 0.19[0.17-0.20], p=0.04, respectively) (Figure 2). Conclusion HDF analysis reveals that mavacamten affects the duration of transition from diastole to systole and prolongs time from the onset to the peak in systole. These findings may explain the removal of the mechanical coupling substrate responsible for the systolic anterior movement of mitralic leaflets and the subsequent resolution of LV outflow tract obstruction by mavacamten.Distribution of time parameters

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 score0.001
Threshold uncertainty score0.003

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)0.0010.000

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 head0.026
GPT teacher head0.248
Teacher spread0.223 · 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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