Abstract P116: Chronic Kidney Disease And Cardiac Arrhythmias: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Background: Chronic kidney disease (CKD) strongly predicts sudden cardiac death and may elevate the risk of certain cardiac arrhythmias like atrial fibrillation; however, the relationships between CKD and various types of arrhythmia are not well-characterized. Methods: We performed a systematic review and meta-analysis by searching Embase and PubMed for prospective, cross-sectional, and case-control studies examining the associations of two key CKD measures, estimated glomerular filtration rate (eGFR) and albuminuria, with arrhythmias in adults that were published until July 2018. We performed qualitative assessment of studies using the Newcastle Ottawa Quality Assessment Scale. We pooled the results using random-effects models. Results: Among 16,245 articles, we identified 34 prospective (n=24,213,233), 21 cross-sectional (n=253,328), and 4 case-control (n=1,694) studies that included diverse study populations from 19 countries and were mostly high quality. Most prospective studies examined the relationship between eGFR and atrial fibrillation (AF), and demonstrated that lower eGFR was associated with a higher risk of AF (pooled hazard ratio [HR] 1.72 [95% CI: 1.30, 2.27] comparing reduced vs. referent eGFR groups)[ Figure ]. A few studies examined albuminuria and demonstrated its associations with AF (pooled HR 2.16 [95% CI: 1.74, 2.67] comparing high vs. low albuminuria). Results were similar for cross-sectional studies. Four prospective studies reported a higher incidence of ventricular tachycardia resulting in ICD shock according to reduced eGFR (pooled HR 2.32 [95% CI: 1.74, 3.09] comparing reduced vs. referent eGFR groups). Limited number of studies examined other types of arrhythmia. Conclusion: We identified robust data on the relationship between CKD (eGFR and albuminuria) and AF. Reduced eGFR was associated with life-threatening ventricular arrhythmias. Our review highlights the need of future studies for non-AF arrhythmias, especially in the context of albuminuria.
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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 | 0.011 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.030 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".