Dialysate potassium and risk of death in chronic hemodialysis patients
Bibliographic record
Abstract
BACKGROUND: Few data guide the prescription of dialysate potassium (dK) in hemodialysis, which is usually prescribed empirically on the basis of predialysis serum potassium levels. METHODS: This was a retrospective cohort study of prospectively collected data. We studied all patients initiating chronic hemodialysis in the Northern Alberta Renal Program (NARP) between January 2001 and December 2006. Data on demographic, clinical and treatment characteristics as well as the dates of death or transplant were extracted from the NARP database. We aimed to examine the relation between dialysate potassium level and all-cause death. RESULTS: During the study, 515/1,267 of patients (41%) died. The frequency of dK of 0 or 1 mEq/L, 2, 3 and 4 mEq/L was 6%, 40%, 51% and 3%, respectively. In our base model, which considered dK as a categorical exposure, the hazard ratios associated with 0 or 1 mEq/L, 2, 3 and 4 mEq/L were 1.13 (95% confidence interval [95% CI], 0.78-1.63), 1 (referent), 1.29 (95% CI, 1.07-1.56) and 1.74 (95% CI, 1.09-2.77), respectively. When markers of inflammation or malnutrition were adjusted for separately, the association between dK and mortality was attenuated but remained significant. After simultaneous adjustment for markers of inflammation and malnutrition, the risk of death associated with the higher dK categories was attenuated, and the overall trend was eliminated. Analyses using dK as a time-varying covariate found similar results. CONCLUSIONS: Although unadjusted and partially adjusted models suggested a graded association between higher dK and the risk of all-cause death, this association was apparently due to confounding by factors suggesting malnutrition and inflammation. The relative paucity of data on the association between dK and clinical outcomes despite the biological importance of potassium suggest that further studies are needed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".