Risk of End-Stage Renal Disease and Death After Cardiovascular Events in Chronic Kidney Disease
Bibliographic record
Abstract
BACKGROUND: Patients with chronic kidney disease stages 3 to 5 (glomerular filtration rate <60 mL/min/1.73m(2)) are at increased risk of cardiovascular (CV) disease when compared with patients with less severe chronic kidney disease. How CV events modify the subsequent risk of progression to end-stage-renal disease (ESRD) or all-cause mortality (ACM) before ESRD is not well known. METHODS AND RESULTS: This retrospective cohort study involved 2964 chronic kidney disease subjects referred between January 2001 and December 2008 to the nephrology clinic at Sunnybrook Health Sciences Center, Toronto, Ontario. Interim CV events (heart failure, myocardial infarction, and stroke), ESRD, and ACM were ascertained from administrative data. Over a median follow-up time of 2.76 years (interquartile range, 1.45-4.62), 447 (15%) subjects had a CV event. In the same time period, 318 (11%) developed ESRD, and 446 (15%) experienced ACM before ESRD (156 [5%] from a CV and 290 [10%] from a non-CV-related cause). When analyzed as a time-dependent variable, an interim CV event was associated with a higher risk of subsequent ESRD (hazard ratio, 5.33; 95% confidence interval, 3.74-7.58) and ACM before ESRD (hazard ratio, 4.15, hazard ratio, 3.30-5.23). The hazard ratio for CV-related death versus non-CV-related death before ESRD was 12.38 (95% confidence interval, 8.30-18.45) versus 2.13 (95% confidence interval, 1.57-2.87). CONCLUSIONS: CV events are common in patients with chronic kidney disease stages 3 to 5 and are associated with a substantial increase in the risk of ESRD and ACM before ESRD. Intensive primary and secondary prevention strategies may help attenuate this risk.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".