Electrolytes and Mortality in Critically Ill Patients with Acute Kidney Injury: A Prospective Multi-Center Study from Sudan
Bibliographic record
Abstract
Acute kidney injury (AKI) carries a high mortality risk, especially in resource-limited settings. Electrolyte imbalances are common in AKI, but their prognostic value in sub-Saharan African populations is understudied. This study evaluated the prevalence of admission serum sodium (Na+) and potassium (K+) abnormalities and their association with 30-day mortality in critically ill AKI patients in Sudan. A prospective, multi-center, cross-sectional study was conducted across four Sudanese hospitals from July to September 2022. Forty-two critically ill adult patients with AKI were enrolled. Demographic, clinical, and biochemical data were collected. The primary outcome was 30-day all-cause mortality. Data were analyzed using SPSS version 25 with descriptive statistics, chi-square tests, and mortality outcome assessments. The mean age of participants was relatively young, with 42.9% aged 18–39 years. Male gender predominated (61.9%). The most common comorbidities were hypertension (69.0%), diabetes mellitus (42.9%), and chronic liver disease (76.2%). Serum sodium levels ranged from 89 to 156 mmol/L, and potassium levels ranged from 2.3 to 11.0 mmol/L. The 30-day mortality rate was 57.1%. Patients with hyperkalemia (K+ ≥ 5.5 mmol/L) and dysnatremia (Na+ <130 or >145 mmol/L) had significantly higher mortality rates (p < 0.05). Admission serum sodium and potassium levels are prevalent, low-cost prognostic markers in critically ill AKI patients. Severe hyperkalemia and dysnatremia were strongly associated with increased 30-day mortality. Early identification and management of these imbalances could improve survival outcomes, particularly in ICU settings with limited resources.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".