Invasive Management of Coronary Artery Disease in Advanced Renal Disease
Bibliographic record
Abstract
Coronary artery disease (CAD) is highly prevalent in chronic kidney disease (CKD). CKD modifies the effects of traditional risk factors on atherosclerosis, with CKD-specific mechanisms, such as inflammation and altered mineral metabolism, playing a dominant pathophysiological role as kidney function declines. Traditional risk models and cardiovascular screening tests perform relatively poorly in the CKD population, and medical treatments including lipid-lowering therapies have reduced efficacy. Clinical presentation of cardiac ischemia in CKD is atypical, whereas invasive therapies are associated with higher rates of complications than in with patients with normal or near normal kidney function. The main focus of the present review is on the invasive approach to management of CAD in late-stage CKD, with an in-depth discussion of the findings of the International Study of Comparative Health Effectiveness With Medical and Invasive Approaches (ISCHEMIA)-CKD trial, and their implications for therapeutic approach and future research in this area. We also briefly discuss the existing evidence in the epidemiology, pathogenesis, diagnosis, and medical management of CAD in late-stage CKD, end-stage kidney disease (ESKD), and kidney transplant recipients. We enumerate the evidence gap left by the frequent exclusion of patients with CKD from randomized controlled trials and highlight the priority areas for future research in the CKD population.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".