THE CLINICAL EPIDEMIOLOGY OF CARDIOVASCULAR DISEASES IN CHRONIC KIDNEY DISEASE: Clinical Epidemiology of Cardiovascular Disease in Chronic Kidney Disease Prior to Dialysis
Bibliographic record
Abstract
Cardiovascular disease (CVD) remains the leading cause of morbidity and mortality in patients with end-stage renal disease (ESRD). Both in dialysis and in transplant patients, CVD remains the leading cause of death. There is accumulating evidence that the increase in CVD burden is present in patients prior to dialysis, due to both conventional risk factors as well as those specific to kidney disease. Of importance is that even in patients with mild kidney disease, the risk of cardiovascular events and death is increased relative to patients without evidence of kidney disease. The new classification system proposed by the National Kidney Foundation as part of the Dialysis Outcomes Quality Initiative (DOQI) process describes the five stages of kidney disease, as well as those complications associated with chronic kidney disease (CKD), in particular cardiovascular risk factors and disease. Patients with kidney disease are deemed to be at highest cardiovascular risk. CVD, defined as the presence of either congestive heart failure (CHF), ischemic heart disease (IHD), or left ventricular hypertrophy (LVH), is prevalent in cohorts with established CKD (8-40%). The prevalence of hypertension, a major risk factor for coronary artery disease (CAD) and LVH is high in patients with CKD (87-90%). At least 35% of patients with CKD have evidence of an ischemic event (myocardial infarction or angina) at the time of presentation to a nephrologist. The prevalence of LVH increases at each stage of CKD, reaching 75% at the time of dialysis initiation, and the modifiable risk factors for LVH include anemia and systolic blood pressure, which are also worse at each stage of kidney disease. Even under the care of nephrologists, a change in cardiac status (worsening of heart failure or anginal symptoms) occurs in 20% of patients. The presence of CVD predicts a faster decline of kidney function and the need for dialysis, after controlling for all other factors including glomerular filtration rate (GFR), age, and the presence of LVH. This article describes the new classification system for staging of CKD, defines and describes CVD in CKD, and reviews the evidence and its limitations with respect to the current understanding of CKD and CVD. Specifically, methodologic issues related to survival and referral bias limit our current understanding of the complex interaction of conventional and nonconventional kidney disease-specific risk factors. We identify the importance of well-conducted studies of patient groups with and without CVD, with and without CKD, in order to better understand the complex physiology so that treatment strategies can be appropriately applied.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".