Patterns of chronic and transient hyperkalaemia and clinically important outcomes in patients with chronic kidney disease
Bibliographic record
Abstract
BACKGROUND: Whether hyperkalaemia in CKD is chronic or transient, and whether this has different outcome implications, is not known. METHODS: This was an observational study of adults with CKD G3-5 from Stockholm, Sweden 2006-11. We examined individual trajectories of potassium from all measurements obtained through routine outpatient care. For each month of follow-up, we created a rolling assessment of the proportion of time in which potassium was abnormal during the previous 12 months. We defined patterns of hyperkalaemia as transient (≤50% of time during the previous year with potassium >5.0 mmol/L) and chronic (>50% of time with potassium >5.0 mmol/L), and examined whether previous hyperkalaemia pattern offers additional predictive value beyond that provided by the most recent (current) potassium value. RESULTS: . Transient and chronic hyperkalaemia, respectively, were observed in 15% and 4% of patients with CKD G3a, and in 50% and 17% of patients with CKD G5. In fully adjusted models, transient (hazard ratio 1.36, 95% confidence interval 1.29-1.46) or chronic (1.16, 1.04-1.32) hyperkalaemia patterns, but not current hyperkalaemia, were associated with major adverse cardiovascular events (MACE), compared with normokalaemia. Transient hyperkalaemia (1.43, 1.35-1.52) and current potassium values, but not chronic hyperkalaemia, were associated with the risk of death. CONCLUSIONS: Between 4% and 17% of patients with CKD G3-5 develop chronic hyperkalaemia. In general, hyperkalaemia predicted MACE and death; however, the lack of effect of current potassium on MACE when adjusted for the previous pattern, and the stronger effects on death than on MACE, lead us to question whether hyperkalaemia is causal in these relationships.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".