ESRD and Death after Heart Failure in CKD
Bibliographic record
Abstract
CKD is a risk factor for heart failure, but there is no data on the risk of ESRD and death after recurrent hospitalizations for heart failure. We sought to determine how interim heart failure hospitalizations modify the subsequent risk of ESRD or death before ESRD in patients with CKD. We retrospectively identified 2887 patients with a GFR between 15 and 60 ml/min per 1.73 m2 referred between January of 2001 and December of 2008 to a nephrology clinic in Toronto, Canada. We ascertained interim first, second, and third heart failure hospitalizations as well as ESRD and death before ESRD outcomes from administrative data. Over a median follow-up time of 3.01 (interquartile range=1.56-4.99) years, interim heart failure hospitalizations occurred in 359 (12%) patients, whereas 234 (8%) patients developed ESRD, and 499 (17%) patients died before ESRD. Compared with no heart failure hospitalizations, one, two, or three or more heart failure hospitalizations increased the adjusted hazard ratio of ESRD from 4.89 (95% confidence interval [95% CI], 3.21 to 7.44) to 10.27 (95% CI, 5.54 to 19.04) to 14.16 (95% CI, 8.07 to 24.83), respectively, and the adjusted hazard ratio death before ESRD from 3.30 (95% CI, 2.55 to 4.27) to 4.20 (95% CI, 2.82 to 6.25) to 6.87 (95% CI, 4.96 to 9.51), respectively. We conclude that recurrent interim heart failure is associated with a stepwise increase in the risk of ESRD and death before ESRD in patients with CKD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".