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Abstract 4142314: ECG predictors for readmission in patients with heart failure: A meta-analysis

2024· article· en· W4404359858 on OpenAlexaff
Reha Kumar, Shijie Zhou, Douglas Lee, Andrew C.T. Ha

Bibliographic record

VenueCirculation · 2024
Typearticle
Languageen
FieldMedicine
TopicHeart Rate Variability and Autonomic Control
Canadian institutionsUniversity Health NetworkUniversity of Toronto
Fundersnot available
KeywordsMedicineHeart failureMeta-analysisInternal medicineCardiologyIntensive care medicine

Abstract

fetched live from OpenAlex

Background: Reducing heart failure (HF) readmission rates remains a global healthcare priority. Current strategies for preventing HF hospitalizations are limited by a lack of accurate predictive models to stratify readmission risk. Electrocardiograms (ECG) provide pertinent information and are broadly available as part of routine cardiac care. We conducted a meta-analysis to identify ECG predictors of readmission in patients with HF. Methods: PubMed, Embase, Cochrane Central and CINAHL were searched, from inception to December 2023. The search strategy was comprised of indexed terms and keywords related to HF, readmission , and electrocardiography. Two reviewers screened relevant studies and extracted data around study design, patient demographics and outcomes. Statistical analysis : Odds Ratio (OR) data and 95% confidence intervals (CI) were calculated on a logarithmic scale using inverse variance method. Pooled OR and 95% CIs were computed using a random-effects model. Results: Of the 409 articles screened, 21 were included for final review. 32,337 patients were analyzed across 8 retrospective studies, 10 prospective cohort studies, 2 registry trials and 1 post-hoc analysis of a randomized control trial. Outcome data were collected for 28 individual ECG variables, which were classified into 5 categories: conduction delay - defined as QRS >120ms (n=7), atrial fibrillation/flutter (n=6), sinus rhythm (n=3), heart rate (n=3), QT C interval (n=3) and QRS-T angle (n=2). Four remaining variables did not fit into these categories (1D). On pooled analysis, presence of atrial fibrillation/flutter [OR 1.54, 95% CI 1.22-1.95, p <0.01] and conduction delay [OR 1.13, 95% CI 1.05-1.22, p <0.01] were associated with higher readmission risk (1A, 1B). Presence of sinus rhythm was associated with lower readmission risk [OR 0.62, 95% CI 0.53-0.72, p <0.01] (1C). Pooled data for heart rate, QRS-T angle and QT C interval were not statistically significant. Conclusion: Presence of atrial fibrillation/flutter and conduction delay on ECG are associated with increased risk while sinus rhythm is associated with lower risk of HF readmission. These findings should be explored further to identify those with HF at risk of readmission.

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)0.013
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.025
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0200.058
Bibliometrics0.0050.006
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0070.001

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 head0.030
GPT teacher head0.276
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
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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