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Early improvement in left ventricular microvascular flow post sonothrombolysis is associated with better left ventricular systolic function in st elevation myocardial infarction patients

2024· article· en· W4403846629 on OpenAlexaff
Luís Filipe Azevedo, J P E Rolim, Maria Cristina Donadio Abduch, David Le Bihan, Rodrigo B. M. Barretto, Márcio Silva Miguel Lima, Carolina Stangenhaus, Henrique Barbosa Ribeiro, Miguel Aguiar, Bruno G. Tavares, João Sbano, Otto Kamp, Harald Becher, Thomas R. Porter, Wilson Mathias

Bibliographic record

VenueEuropean Heart Journal · 2024
Typearticle
Languageen
FieldEngineering
TopicUltrasound and Hyperthermia Applications
Canadian institutionsUniversity of Alberta Hospital
Fundersnot available
KeywordsMedicineCardiologyInternal medicineMyocardial infarctionVentricular functionElectrocardiography in myocardial infarction

Abstract

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Abstract Background/Introduction Acute coronary syndromes are the main cause of death worldwide and the timely application of proper therapy is critical for patient outcome. However, even when state of the art treatment is implemented, significant no-reflow phenomenon may occur. In this scenario, sonothrombolysis, which consists of a continuous infusion of an ultrasound-enhancing agent associated with high-energy intermittent ultrasound impulses, may restore cardiac microcirculation and improve left ventricular (LV) systolic function. Purpose We aimed to evaluate LV segmental microvascular flow, LV mechanics and function in patients with ST-elevation myocardial infarction (STEMI) after sonothrombolysis therapy and evaluate whether an early (immediately post percutaneous coronary intervention – PCI or PCI+Sonothrombolysis) improvement in myocardial microvascular flow could predict better LV systolic function at 1-month. Methods STEMI patients receiving conventional treatment (PCI with or without previous fibrinolytic therapy) were randomized to sonothrombolysis (therapy group - TG) or conventional treatment (control group - CG). This study included 143 patients (interim analysis) who compose the HUBBLE I databank (High-intensity Ultrasonic impulses and microbuBBles to Limit the Extent of acute myocardial infarction I) and were randomized from two clinical trials (NCT04732091 and NCT02410330): CG, N=74 and TG, N=69 patients. There were 25 patients treated previously with fibrinolytic therapy (14 in CG and 11 in TG) and the percentage of left anterior descending artery as culprit vessel was similar between groups (59 vs. 54%, P=0.482). Sonothrombolysis was initiated after patient admission to the emergency room and continued after PCI for a total of 50 minutes. Echocardiography was carried out immediately after PCI or PCI+Sonothrombolysis and at 1-month for assessing LV ejection fraction (LVEF) and perfusion score index (PSI) in contrasted imaging and global longitudinal strain (GLS) in 2D imaging, that was presented in [module]. Results Demographic characteristics, cardiovascular risk factors and echocardiographic variables immediately after PCI or PCI+Sonothrombolysis were similar between groups (P>0.05). At 1-month follow up, TG presented higher LVEF (46.4±10.5 vs. 50.9±11.1%, P=0.018) and GLS (13.0±3.6 vs. 14.9±4.3%, P=0.008) and lower PSI (1.56±0.4 vs. 1.39±0.3, P=0.007) and number of segments with perfusion defects (5.8±3.7 vs. 4.6±3.3, P=0.050) compared with CG. There was a negative correlation between early PSI and 1-month LVEF (r=-0.701, p<0.0001). Conclusions The preliminary data indicates that sonothrombolysis can be an effective adjuvant treatment in STEMI patients previously treated or not with fibrinolytic therapy, contributing to a better myocardial microvascular flow and LV systolic performance. Early myocardial microvascular reperfusion improvement can predict whether cardiac function will improve in this setting.

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)0.000
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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)0.0020.000

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 head0.005
GPT teacher head0.185
Teacher spread0.180 · 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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