Coronary Artery Disease in patients with End‐Stage Kidney Disease; Current perspective and gaps of knowledge
Bibliographic record
Abstract
Coronary artery disease (CAD) is very common in dialysis patients. One third have preexisting CAD and another one third have significant occult disease at the time of starting dialysis. Symptoms are often absent or are atypical, emphasizing the need for vigorous screening, specifically in patients awaiting transplant. The lesions tend to be heavily calcified, diffuse, and involve multiple vessels, consequently, percutaneous coronary interventions are more complicated to perform, and are less successful in achieving and maintaining short- and long-term patency. Dialysis patients have been excluded from the randomized controlled trials on which the current standards for managing CAD have been established. Due to differences in pathobiology and risks and benefits, it is uncertain that the results of these clinical trials extrapolate to patients with advanced chronic kidney disease (CKD). Here we review the data from observational studies and identify special considerations concerning the diagnosis and management of CAD in dialysis patients, including the use of noninvasive functional testing vs anatomical testing, the management of acute coronary syndromes and of stable coronary artery disease, the role for percutaneous revascularization vs coronary artery bypass grafting, and of platelet inhibitor therapy after coronary stenting. We review the preliminary results of the recently published ISCHEMIA-CKD trial, the only trial to date to involve large numbers of dialysis patients. This is the first of, hopefully, many trials in the pipeline that will examine therapies for CAD specifically in patients with advanced CKD, a growing population that is at particularly high risk for poor outcomes.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".