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32 Myocardial fibrosis predicts ventricular arrhythmias and sudden death after cardiac electronic device implantation

2023· article· en· W4318344200 on OpenAlex

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A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAbstracts · 2023
Typearticle
Languageen
FieldMedicine
TopicCardiac pacing and defibrillation studies
Canadian institutionsOttawa Hospital
FundersNational Institutes of HealthUniversity of ManchesterBritish Heart FoundationNational Institute for Health and Care ResearchBoston Scientific Corporation
KeywordsMedicineInternal medicineCardiologySudden cardiac deathInterquartile rangeHazard ratioVentricular tachycardiaClinical endpointConfidence intervalMyocardial fibrosisVentricular fibrillationAtrial fibrillationFibrosisRandomized controlled trial

Abstract

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<h3>Introduction</h3> Increasing evidence supports a link between myocardial fibrosis (MF) and ventricular arrhythmias. We sought to determine whether presence of MF on visual assessment (MF<sub>VA</sub>) and gray zone fibrosis (GZF) mass predicts SCD and ventricular fibrillation/sustained ventricular tachycardia after cardiac implantable electronic device (CIED) implantation. <h3>Materials and Methods</h3> In this prospective study, total fibrosis and GZF mass, quantified using cardiovascular magnetic resonance, was assessed in relation to the primary endpoint of sudden cardiac death (SCD) and the secondary, arrhythmic endpoint of SCD or ventricular arrhythmias after CIED implantation. <h3>Results</h3> Among 700 patients (age 68.0 ± 12.0yrs [mean ± SD]), 27 (3.85%) experienced a SCD and 121 (17.3%) met the arrhythmic endpoint over 6.93 yrs (median; interquartile range 5.82–9.32). MF<sub>VA</sub> predicted SCD (hazard ratio [HR]: HR: 26.3 [95% confidence interval [CI] 3.70–3337]; negative predictive value: 100%). In competing risks analyses, MF<sub>VA</sub> also predicted the arrhythmic endpoint (subdistribution [sHR]: 19.9 [95% CI 6.40–61.9]; negative predictive value: 98.6%). Compared with no MF<sub>VA</sub>,a GZF mass measured with the 5SD method (GZF<sub>5SD</sub>) &gt; 17 g was associated with highest risk of SCD (HR: 44.6;95% CI 6.12–5685) and the arrhythmic endpoint (sHR: 30.3 [95% CI 9.60–95.8]). Adding GZF<sub>5SD</sub> mass to MF<sub>VA</sub> led to reclassification of 39% for SCD and 50.2% for the arrhythmic endpoint. In contrast, LVEF did not predict either endpoint. <h3>Discussion</h3> This the largest CMR study of MF in relation to long-term clinical outcomes in patients undergoing CIED implantation. Several findings have emerged. First, all patients experiencing SCD had MF<sub>VA</sub> on preimplantation CMR. Second, absence of MF<sub>VA</sub> virtually excluded the composite, arrhythmic endpoint. Third, both TF<sub>FWHM</sub> mass and GZF<sub>5SD</sub> mass had an additional predictive value over and above MF<sub>VA</sub>, with respect to both SCD and the arrhythmic endpoint. Last, LVEF did not predict SCD or the arrhythmic endpoint. <h3>Conclusion</h3> In CIED recipients, MF<sub>VA</sub> excluded patients at risk of SCD and virtually excluded ventricular arrhythmias. Quantified GZF<sub>5SD</sub> mass added predictive value in relation to SCD and the arrhythmic endpoint. <h3>Acknowledgements</h3> We are grateful to Medtronic, Abbott and Boston Scientific for their support in funding this study, in the form of unrestricted educational grants.

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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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score0.607

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 head0.015
GPT teacher head0.269
Teacher spread0.255 · 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