The Association of Ejection Fraction With Cardiac Arrest and Myocardial Infarction Differs by eGFR
Bibliographic record
Abstract
Background: Advanced chronic kidney disease (CKD) and left ventricular (LV) systolic dysfunction are potent risk factors for cardiovascular events. Here we explore if the association of LV ejection fraction (EF) with cardiac arrest, myocardial infarction (MI), ischemic stroke, and all-cause mortality differs by eGFR across earlier stages of CKD. Methods: Using registry data from 2004-2014 from five Mass General Brigham hospitals, we performed an observational cohort study of 17,962 patients with eGFR 30-90mL/min/1.73m2. Cardiovascular outcomes were ascertained from ICD-9 codes. Cox regression models, incorporating an interaction term for continuous eGFR and LVEF, were fit and adjusted for age, sex, race, hypertension, diabetes mellitus, coronary artery disease, and left ventricular mass index. Results: Mean age was 67 years, and 51% were male. The mean eGFR was 66±16 mL/min/1.73m2 and LVEF 54±13%. Over a median of 0.96 (0.14-4.84) years there were 437 cardiac arrests, 4,634 MIs, 1,549 ischemic strokes, and 6,282 deaths. The association of LVEF with cardiac arrest differed according to eGFR (P-interaction <0.01). While there was no evidence of association of LVEF with cardiac arrest in the lowest quartile of eGFR (adjusted hazard ratio (aHR) 1.02; 95% CI 0.92-1.13), for each 5% increase in LVEF there was a 21% lower risk of cardiac arrest in the highest quartile of eGFR (aHR 0.79; 95% CI 0.66-0.94; Table). The association of LVEF with MI also decreased as eGFR declined (Table). There was no evidence of effect modification of LVEF by eGFR for ischemic stroke or mortality (P-interaction >0.3 for both). Conclusions: Among patients with eGFR 30-90mL/min/1.73m2, the association of LVEF with cardiac arrest disappears at lower (vs. higher) levels of kidney function and is less pronounced for MI at lower (vs. higher) levels of kidney function. Further research is required to elucidate what factors beyond LVEF drive these outcomes in the setting of more advanced kidney disease. Funding: NIDDK Support - Risk of cardiac arrest and myocardial infarction per each increase in left ventricular ejection fraction of 5% according to eGFR quartile Outcome eGFR (CKD-EPI, ml/min/1.72 m2) aHR (95% CI) P-Interaction Quartile 1 (30-54) N=4,598 Quartile 2 (55-68) N=4,400 Quartile 3 (69-80) N=4,671 Quartile 4 (81-90) N=4,293 Cardiac Arrest 1.02 (0.92-1.13) P=0.72 0.97 (0-86-1.10) P=0.64 0.76 (0.63-0.92) P=0.01 0.79 (0.66-0.94) P=0.01 <0.01 Myocardial Infarction 0.94 (0.91-0.98) P<0.01 0.91 (0.87-0.95) P<0.01 0.90 (0.86-0.95) P<0.01 0.83 (0.79-0.88) P<0.01 <0.01 Abbreviations. eGFR; estimated glomerular filtration rate, CKD-EPI: Chronic Kidney Disease Epidemiology Collaboration, aHR: adjusted hazard ratio, CI: confidence interval, N: number.Cox regression model adjusted for age, gender, Black race, hypertension, diabetes mellitus, coronary artery disease, and left ventricular mass index.P-interaction reported for eGFR quartile.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".