Coronary Artery Disease Is a Predictor of Progression to Dialysis in Patients With Chronic Kidney Disease, Type 2 Diabetes Mellitus, and Anemia: An Analysis of the Trial to Reduce Cardiovascular Events With Aranesp Therapy (TREAT)
Bibliographic record
Abstract
BACKGROUND: Although clear evidence shows that chronic kidney disease is a predictor of cardiovascular events, death, and accelerated coronary artery disease (CAD) progression, it remains unknown whether CAD is a predictor of progression of chronic kidney disease to end-stage renal disease. We sought to assess whether CAD adds prognostic information to established predictors of progression to dialysis in patients with chronic kidney disease, diabetes, and anemia. METHODS AND RESULTS: Using the previously described Trial to Reduce Cardiovascular Events With Aranesp Therapy (TREAT) population, we compared baseline characteristics of patients with and without CAD. Cox proportional hazards models were used to assess the association between CAD and the outcomes of end-stage renal disease and the composite of death or end-stage renal disease. Of the 4038 patients, 1791 had a history of known CAD. These patients were older (mean age 70 versus 65 years, P<0.001) and more likely to have other cardiovascular disease. CAD patients were less likely to have marked proteinuria (29% versus 39%, P<0.001), but there was no significant difference in estimated glomerular filtration rate between the 2 groups. After adjusting for age, sex, race, estimated glomerular filtration rate, proteinuria, treatment group, and 14 other renal risk factors, patients with CAD were significantly more likely to progress to end-stage renal disease (adjusted hazard ratio 1.20 [95% CI 1.01-1.42], P=0.04) and to have the composite of death or end-stage renal disease (adjusted hazard ratio 1.15 [95% CI 1.01-1.30], P=0.03). CONCLUSIONS: In patients with chronic kidney disease, diabetes, and anemia, a history of CAD is an independent predictor of progression to dialysis. In patients with diabetic nephropathy, a history of CAD contributes important prognostic information to traditional risk factors for worsening renal disease.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".