Decompensated Heart Failure in the Setting of Kidney Dysfunction: A Community-Wide Perspective
Bibliographic record
Abstract
BACKGROUND: Patients with heart failure (HF) and kidney disease have a poor long-term outlook which has provided impetus for the identification of factors of prognostic importance and more fully understanding the impact of kidney dysfunction in patients with HF. OBJECTIVES: Our objectives were to describe the characteristics, hospital treatment practices, as well as hospital and long-term outcomes in patients with varying degrees of kidney dysfunction who were hospitalized with acute HF at all medical centers in a large New England metropolitan area. METHODS: Residents of the Worcester metropolitan area hospitalized with clinical findings of decompensated HF at 11 greater Worcester medical centers during 1995 and 2000 comprised the study sample. Kidney function was classified into 4 categories of estimated glomerular filtration rate (eGFR) for purposes of analysis: <30 (n = 569), 30-44 (n = 725), 45-59 (n = 763), and > or =60 (n = 2,293) ml/min per 1.73 m(2). RESULTS: The average age of the study sample was 76 years and 57% were women. Patients with severe kidney dysfunction were less likely to receive angiotensin-converting enzyme inhibitors, diuretics and digoxin during hospitalization for acute HF compared to patients with more normal kidney function. Patients with lower eGFR levels had higher in-hospital and post-discharge death rates in comparison to those with higher levels of eGFR. CONCLUSION: Our results demonstrate the impact of renal impairment on the prognosis of patients with decompensated HF. Our findings highlight the less than optimal management of these high-risk patients. Increased surveillance and enhanced treatment of patients with HF and kidney dysfunction remains warranted to improve the survival outlook of these patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".