Low Serum Potassium Levels and Clinical Outcomes in Peritoneal Dialysis—International Results from PDOPPS
Bibliographic record
Abstract
INTRODUCTION: Hypokalemia, including normal range values <4 mEq/l, has been associated with increased peritonitis and mortality in patients with peritoneal dialysis. This study sought to describe international variation in hypokalemia, potential modifiable hypokalemia risk factors, and the covariate-adjusted relationship of hypokalemia with peritonitis and mortality. METHODS: Baseline serum potassium was determined in 7421 patients from 7 countries in the Peritoneal Dialysis Outcomes and Practice Patterns Study (2014-2017). Association of baseline patient and treatment factors with subsequent serum potassium <4 mEq/l was evaluated by logistic regression, whereas baseline serum potassium levels (4-month average and fraction of 4 months having hypokalemia) on clinical outcomes was assessed by Cox regression. RESULTS: Hypokalemia was more prevalent in Thailand and among black patients in the United States. Characteristics/treatments associated with potassium <4 mEq/l included protein-energy wasting indicators, lower urine volume, lower blood pressure, higher dialysis dose, greater diuretic use, and not being prescribed a renin-angiotensin system inhibitor. Persistent hypokalemia (all 4 months vs. 0 months over the 4-month exposure period) was associated with 80% higher subsequent peritonitis rates (at K <3.5 mEq/l) and 40% higher mortality (at K <4.0 mEq/l) after extensive case mix/potential confounding adjustments. Furthermore, adjusted peritonitis rates were higher if having mean serum K over 4 months <3.5 mEq/l versus 4.0-4.4 mEq/l (hazard ratio, 1.15 [95% confidence interval, 0.96-1.37]), largely because of Gram-positive/culture-negative infections. CONCLUSIONS: Persistent hypokalemia is associated with higher mortality and peritonitis even after extensive adjustment for patient factors. Further studies are needed to elucidate mechanisms of these poorer outcomes and modifiable risk factors for persistent hypokalemia.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".