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Record W2051241633 · doi:10.1016/j.ejheart.2004.10.016

QT Dynamicity: A Prognostic Factor for Sudden Cardiac Death in Chronic Heart Failure

2005· article· en· W2051241633 on OpenAlexaff
Atul Pathak, Daniel Curnier, J Fourcade, Jéme Roncalli, Phyllis K. Stein, Patricia Hermant, Marc Bousquet, P Massabuau, Jean‐Michel Sénard, Jean‐Louis Montastruc, Michel Galinier

Bibliographic record

VenueEuropean Journal of Heart Failure · 2005
Typearticle
Languageen
FieldMedicine
TopicCardiac electrophysiology and arrhythmias
Canadian institutionsIron Ore Company (Canada)
Fundersnot available
KeywordsMedicineCardiologyHeart failureInternal medicineSudden deathConfidence intervalSinus rhythmQT intervalSudden cardiac deathRelative riskElectrocardiographyAtrial fibrillation

Abstract

fetched live from OpenAlex

INTRODUCTION: The aim of this study was to determine whether impaired adaptation of the QT interval to changes in heart rate predicts sudden death in patients with chronic heart failure (CHF). METHODS: We prospectively included 175 CHF patients in sinus rhythm. QT dynamicity was evaluated by analyzing 24-h Holter recordings. The linear regression slope of QT interval measured to the apex and to the end of T wave plotted against RR intervals was calculated using a dedicated Holter algorithm. RESULTS: Mean follow-up was 29.9+/-17.9 months. There were 48 deaths, of which 21 were sudden. The actuarial 3-year mortality rates were 38.4% for overall mortality and 14.1% for sudden death. Of all the parameters, an increased QTe/RR slope (>0.28) was the strongest independent predictor of sudden death (relative risk 3.47, 95% confidence interval 1.43-8.40, p=0.006). CONCLUSION: Increased 24-h QTe dynamicity is independently predictive of sudden death among patients with heart failure. This simple parameter may help to stratify risk and select patients who may benefit from antiarrhythmic prophylaxis.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.500
Threshold uncertainty score0.916

Codex and Gemma teacher scores by category

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 integrity0.0000.001
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 head0.011
GPT teacher head0.257
Teacher spread0.246 · 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 designNot applicable
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".

Quick stats

Citations55
Published2005
Admission routes1
Has abstractyes

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