The Association of Ejection Fraction With Hospital-Associated Cardiac Arrest and Heart Failure Hospitalization Differs According to Baseline Estimated GFR
Bibliographic record
Abstract
IntroductionChronic kidney disease (CKD) and left ventricular (LV) dysfunction are risk factors for cardiovascular events. We explore whether the association of LV ejection fraction (LVEF) with cardiac arrest, heart failure hospitalization, and all-cause mortality differs across stages of kidney impairment.MethodsWe performed an observational cohort study of 19,032 patients from 2004 to 2014 with estimated glomerular filtration rate (eGFR) ≤90 ml/min per 1.73 m2 and without end-stage kidney disease (ESKD). Cox regression models, incorporating an interaction term for eGFR and LVEF, were fit and adjusted for relevant covariates.ResultsMean age of the patients was 67 ± 14 years, and 51% were male. The mean eGFR was 64 ± 19 ml/min per 1.73 m2 and LVEF was 54 ± 13%. Over a median follow-up of 3.0 (0.7–6.0) years there were 504 cardiac arrests, 4181 heart failure hospitalizations, and 6989 deaths. The association of LVEF with cardiac arrest and heart failure hospitalization differed according to continuous eGFR (P-interaction <0.01 for both outcomes). The association of LVEF with cardiac arrest in the lowest quartile was attenuated (adjusted hazard ration [aHR] per 5% higher LVEF 0.92; 95% confidence interval [CI] 0.88–0.96) compared to the highest eGFR quartile (aHR per 5% higher LVEF 0.85; 95% CI 0.78–0.91). The association of LVEF with heart failure hospitalization was similarly attenuated in the lowest eGFR quartile. There was no effect modification of LVEF by continuous eGFR for all-cause mortality (P-interaction 0.26).ConclusionAmong non-ESKD patients with eGFR ≤90 ml/min per 1.73 m2, the association of LVEF with cardiac arrest and heart failure hospitalization is attenuated at lower levels of kidney function. Further research is required to elucidate what factors beyond LVEF drive these observations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".