Abstract 4142314: ECG predictors for readmission in patients with heart failure: A meta-analysis
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.058 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".