Association of Hypokalemia With Mortality in Patients Undergoing Hemodialysis: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
BACKGROUND: Potassium imbalance, particularly hypokalemia, is a critical risk factor for adverse outcomes in patients undergoing hemodialysis (HD). However, the association between hypokalemia and mortality is unclear. METHODS: For this systematic review and meta-analysis, we assessed the association between hypokalemia and mortality in patients undergoing HD. We performed a systematic search of electronic databases (PubMed, Embase, Cochrane Library, and Scopus) to identify relevant studies published up to April 2024. Eligible studies were prospective or retrospective cohort studies reporting hazard ratios (HRs) for mortality in association with the presence of hypokalemia among patients undergoing HD. We used the assessed study Newcastle-Ottawa Scale to assess quality of the selected studies. RESULTS: We carried out both qualitative and quantitative assessments. For the meta-analysis, we pooled the HRs for all-cause and cardiovascular mortalities. The overall pooled HR for all-cause mortality and cardiovascular mortality were 1.34 (95% CI, 1.15, 1.55) and 1.49 (95% CI, 1.12, 1.98), respectively, indicating significant associations between hypokalemia and all-cause mortality and cardiovascular mortality in patients undergoing HD. Additionally, we conducted subgroup analyses based on study design, geographical location, type of dialysis, and serum potassium levels. CONCLUSION: Our findings provide robust evidence of a significant association between hypokalemia and mortality in patients undergoing HD. Early detection and proactive management of hypokalemia are crucial for improving outcomes and reducing mortality risk in these patients.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.037 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".