Predictors of erythropoietin use in patients with cardiorenal anaemia syndrome
Bibliographic record
Abstract
OBJECTIVES: Chronic kidney disease (CKD) and anemia are common in patients with heart failure (HF) - these 3 conditions have been coined the Cardiorenal Anemia Sydrome (CRAS). The National Kidney Foundation Kidney Disease Outcomes Quality Initiative (NKF-K/DOQI) guidelines do not specifically address patients with CRAS, creating uncertainty in erythropoietin (EPO) prescribing. We sought to determine predictors of EPO use in patients with CRAS. METHODS: We conducted a retrospective cohort study at the Veteran's Affairs Greater Los Angeles Healthcare System (VAGLAHS), a 300+ bed facility that provides primary and tertiary inpatient, and ambulatory care services, between January 1, 2003 to December 31, 2006. A multiple logistic regression model was constructed to identify predictors of EPO use among CRAS patients. KEY FINDINGS: Of 2058 patients with CRAS, 213 (10.3%) were prescribed EPO. There were significant differences in baseline characteristics between the EPO and non-EPO groups. The following predictors were found to be associated with EPO prescription: iron supplementation (odds ratio [OR] 52.70, 95% confidence interval [CI] 11.70-237.46), renal clinic appointment (OR 2.60, 95% CI 1.79-3.76), malignancy (OR 1.52, 95% CI 1.07-2.16) and use of hydralazine/nitrates (OR 1.41, 95% CI 1.03-1.92). There was an inverse association found between EPO prescription and baseline hemoglobin (OR 0.61, 95% CI 0.53-0.70) and eGFR (OR 0.96, 95% CI 0.94-0.97). CONCLUSION: A small proportion of patients eligible for EPO therapy according to guidelines at the time of the study were prescribed the indicated therapy. Markers of declining renal function or those suggesting need for anemia therapy were identified as EPO predictors.
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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 | 0.001 |
| 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 | 0.001 |
| 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".