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Record W1781169408 · doi:10.5603/kp.2015.0149

Electromechanical mapping of the left ventricle for stem cell injection in a patient with permanent atrial fibrillation

2015· article· en· W1781169408 on OpenAlexaboutno aff
Zofia Parma, Tomasz Jadczyk, Wojciech Wojakowski

Bibliographic record

VenueKardiologia Polska · 2015
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neural Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCardiologyEjection fractionInternal medicineVentricleCanadian Cardiovascular SocietyAtrial fibrillationHeart failurePercutaneous coronary interventionMyocardial infarctionHypokinesiaAngina

Abstract

fetched live from OpenAlex

Stem cell therapies for improvement of the ischaemic myocardium are an emerging and promising therapeutic option. Three-dimensional NOGA mapping allows simultaneous registration of left ventricle (LV) mechanical and electrical activity, enabling assessment of myocardial viability and targeted cell delivery. However, LV mapping in patients during atrial fibrillation (AF) is considered time consuming and difficult. A 77-year-old man was admitted to our centre presenting with refractory angina. His risk factors included hypertension, hyperlipidaemia, and advanced chronic renal failure, he also had a history of permanent AF. In the past he had undergone coronary artery bypass grafting twice: in 1985 and 2002, he had also suffered from a non-ST-elevation myocardial infarction treated with percutaneous coronary intervention with a drug eluting stent implantation in 2010. At the moment of admission, he was in CCS class III despite optimal medical treatment. The patient had previously been disqualified from any further revascularisation by the Heart Team. Echocardiography revealed a mild impairment of the LV ejection fraction (LVEF 45%), with hypokinesia of the intraventricular septum and inferior wall. Single-photon emission computed tomography (SPECT) showed reversible perfusion defects in the anterolateral region. AF with short QRS duration (80 ms) and relatively good rate control (80 bpm) was observed in an electrocardiogram. The patient was enrolled to the REGENT (autologous CD133+ cells vs. placebo, double-blind, placebo-controlled RCT) trial to undergo targeted transendocardial treatment. We used a NOGA-XP System to perform electromechanical mapping and direct transendocardial cell injection. A diagnostic NOGA STAR catheter was placed in the LV under fluoroscopic guidance, and LV electromechanical mapping was performed. Completing the data necessary to build the map and localise the target area took about 60 min. The regions of hibernating myocardium defined by preserved electrical and decreased mechanical activity correlated with reversible perfusion defects detected by SPECT. Time volume graphs showed evident dyssynchrony of the hibernating areas (Fig. 1). Following the standardised NOGA injection criteria, twelve 0.2 mL injections of autologous CD133+ bone marrow stem cells or placebo (procedure double-blinded) were placed into the anterolateral viable area (> 5 mV unipolar) with low wall movement (< 6% LLS) (Fig. 2). Only limited premature ventricular contractions were detectable at injections, which can probably be explained by a reduced time window for excitation after the electrical refractory period. Total injection time was 20 min. Mapping and injection were completed within 80 min despite permanent AF. No raise in creatinine levels, pericardial effusion, and no local complications were observed in the days following the procedure. To conclude, despite technical difficulties due to irregular rhythm, electromechanical mapping on AF is feasible. Completing the procedure in patients with AF and good heart rate control does not have to be more time consuming than in patients on sinus rhythm. Additionally, time volume graphs display differences in wall movement of hibernating and normal tissue even during AF, providing us with additional information on segmental ventricular contraction synchrony.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.225

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.040
GPT teacher head0.239
Teacher spread0.199 · 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 designBench or experimental
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
Published2015
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