Modifiable Practices Associated with Sudden Death among Hemodialysis Patients in the Dialysis Outcomes and Practice Patterns Study
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Sudden death is common in hemodialysis patients, but whether modifiable practices affect the risk of sudden death remains unclear. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: This study analyzed 37,765 participants in 12 countries in the Dialysis Outcomes and Practice Patterns Study to explore the association of the following practices with sudden death (due to cardiac arrhythmia, cardiac arrest, and/or hyperkalemia): treatment time [TT] <210 minutes, Kt/V <1.2, ultrafiltration volume >5.7% of postdialysis weight, low dialysate potassium [K(D) <3]), and prescription of Q wave/T wave interval-prolonging drugs. Cox regression was used to estimate effects on mortality, adjusting for potential confounders. An instrumental variable approach was used to further control for unmeasured patient-level confounding. RESULTS: There were 9046 deaths, 26% of which were sudden (crude mortality rate, 15.3/100 patient-years; median follow-up, 1.59 years). Associations with sudden death included hazard ratios of 1.13 for short TT, 1.15 for large ultrafiltration volume, and 1.10 for low Kt/V. Compared with K(D) ≥3 mEq/L, the sudden death rate was higher for K(D) ≤1.5 and K(D)=2-2.5 mEq/L. The instrumental variable approach yielded generally consistent findings. The sudden death rate was elevated for patients taking amiodarone, but not other Q wave/T wave interval-prolonging drugs. CONCLUSIONS: This study identified modifiable dialysis practices associated with higher risk of sudden death, including short TT, large ultrafiltration volume, and low K(D). Because K(D) <3 mEq/L is common and easy to change, K(D) tailoring may prevent some sudden deaths. This hypothesis merits testing in clinical trials.
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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".