Renal Insufficiency and Heart Failure
Bibliographic record
Abstract
BACKGROUND: The prevalence, prognostic import, and impact of renal insufficiency on the benefits of ACE inhibitors and beta-blockers in community-dwelling patients with heart failure are uncertain. METHODS AND RESULTS: We analyzed data from a prospective cohort of 754 patients with heart failure who had ejection fraction, serum creatinine, and weight measured at baseline. Median age was 69 years, and 43% had an ejection fraction > or =35%. By the Cockcroft-Gault equation, 118 patients (16%) had creatinine clearances < or =30 mL/min and 301 (40%) had creatinine clearances between 30 and 59 mL/min. During follow-up (median 926 days), 385 patients (37%) died. Even after adjustment for all other prognostic factors, survival was significantly associated with renal function (P=0.002) in patients with either systolic or diastolic dysfunction; patients exhibited a 1% increase in mortality for each 1-mL/min decrease in creatinine clearance. The associations with 1-year mortality reductions were similar for ACE inhibitors (OR 0.46 [95% CI 0.26 to 0.82] versus OR 0.28 [95% CI 0.11 to 0.70]) and beta-blockers (OR 0.40 [95% CI 0.23 to 0.70] versus OR 0.41 [95% CI 0.19 to 0.85]) in patients with creatinine clearances <60 mL/min versus > or =60 mL/min, although these drugs were used less frequently in patients with renal insufficiency. CONCLUSIONS: Renal insufficiency is more prevalent in patients with heart failure than previously reported and is an independent prognostic factor in diastolic and systolic dysfunction. ACE inhibitors and beta-blockers were associated with similar reductions in mortality in patients with and without renal insufficiency.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".