MO493PATTERNS OF HYPERKALEMIA AND ASSOCIATED ADVERSE HEALTH OUTCOMES IN PERSONS WITH CHRONIC KIDNEY DISEASE
Bibliographic record
Abstract
Abstract Background and Aims Patients with CKD are often described as having chronic hyperkalemia, but earlier studies have relied on a single potassium measurement. Whether the hyperkalemia in CKD is chronic or transient, and whether temporal patterns have different outcome implications, is unknown. Method Observational study from the Stockholm Creatinine Measurements (SCREAM) project of patients with confirmed CKD G3-5. We extracted all data on potassium measured during routine outpatient care and estimated its longitudinal trajectories. At each month, we created a rolling assessment of the proportion of time in which potassium was abnormal during the previous year, defining patterns of normokalemia (100% of time with potassium 3.5-5.0 mmol/L), transient (<50% of time with potassium >5.0 mmol/L) and chronic hyperkalemia (≥50% of time with potassium >5.0 mmol/L). We described the presence of chronic and transient hyperkalemia throughout the spectrum of CKD G3-5 and identified clinical predictors by logistic regression. Through time-dependent Cox models we examined whether previous hyperkalemia patterns offer prognostic gain beyond that of the current potassium value. Results We included 36,511 participants (56% women) with confirmed CKD G3-5, median age 81 years, and eGFR 46 ml/min/1.73 m2. During 3-year-median follow-up, patterns of transient and chronic hyperkalemia were observed in 15% and 4% of patients with CKD G3a, increasing to 50% and 17% of patients with CKD G5. Factors associated with chronic hyperkalemia were younger age, male sex, more severe CKD category, presence of diabetes or heart failure, use of renin-angiotensin system inhibitors, and use of potassium binders. Major cardiovascular events (MACE) occurred in 13,104 (36%) patients and 13,570 (37%) died. In time-dependent models, independent of identified confounders and of time-updated potassium values, compared with the normokalemic pattern, patients with transient (HR 1.37, 95% CI 1.29-1.46) or chronic (HR 1.17, 95% CI 1.04-1.32) hyperkalemia patterns were at higher risk of MACE. Transient hyperkalemia pattern (HR 1.43, 95% CI 1.35-1.52) and time-updated elevated potassium, but not a state of chronic hyperkalemia (HR 1.07, 95% CI 0.95-1.20), predicted the risk of death. Conclusion Chronic hyperkalemia occurs in 4-17% of patients with CKD G3-5. We did not observe a clear dose-response for the association between hyperkalemia pattern (normal, transient, chronic) with either MACE or death. There was modest incremental information in the previous potassium pattern, beyond potassium measured at a single time point.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".