Kidney Function and Mortality among Patients with Left Ventricular Systolic Dysfunction
Bibliographic record
Abstract
Kidney disease has emerged as a risk factor for mortality in heart failure populations. The objective of this study was to determine the impact of different stages of kidney dysfunction (defined using the Kidney Disease Outcomes Quality Initiative [K/DOQI] classification system) and changes in kidney function on mortality in a cohort of patients with heart failure. A retrospective analysis was conducted of data from the randomized controlled trials Studies of Left Ventricular Dysfunction. A total of 6640 participants with asymptomatic and symptomatic heart failure were studied. Estimated GFR (eGFR) were calculated and then categorized according to the K/DOQI classification system into the following categories: > or =90, 60 to 89, 30 to 59, and 15 to 29 ml/min per 1.73 m2. Reduction in eGFR from baseline was calculated and subsequently categorized according to rate of decline (<5, 5 to 10, 11 to 15, and >15 ml/min per 1.73 m2 per year). Independent of baseline differences, lower levels of eGFR were associated with a higher total mortality compared with those with eGFR > or =90 ml/min (30 to 59 ml/min per 1.73 m2: hazard ratio [HR] 1.32, 95% confidence interval [CI] 1.10 to 1.59, P = 0.004; 15 to 29 ml/min per 1.73 m2: HR 2.54, 95% CI 1.54 to 4.19, P < 0.001). eGFR deteriorated rapidly (>15 ml/min per 1.73 m2 per year) in 12% of participants. This decline was associated with a significant increase in mortality compared with slower decline (<5 ml/min per 1.73 m2 per year), despite adjustments for baseline kidney function, baseline heart failure, or change in heart failure (HR 5.63; 95% CI 4.90 to 6.46; P < 0.0001). The levels of eGFR from the K/DOQI classification system are associated with mortality in a well-characterized heart failure population. Rate of decline in kidney function is a strong predictor of increased mortality in this population, independent of worsening heart failure and baseline kidney function.
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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 | 0.000 | 0.000 |
| 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".