Increased Serum Sodium at Acute Kidney Injury Onset Predicts In-Hospital Death
Bibliographic record
Abstract
Background: Over the last decades, acute kidney injury (AKI) has been identified as a potentially fatal diagnosis which substantially increases in-hospital mortality in the short term and morbidity/mortality in the long term. However, reliable biomarkers for predicting AKI-associated outcomes are still missing. In this study, we assessed whether serum sodium, measured at different time points during the in-hospital treatment period, provided prognostic information in AKI. Methods: This was a retrospective, observational cohort study. AKI subjects were identified via the in-hospital AKI alert system. Serum sodium and potassium levels were documented at five pre-defined time points: hospital admission, AKI onset, minimum estimated glomerular filtration rate, minimum and maximum of the respective electrolyte during the treatment period. In-hospital death, the need for kidney replacement therapy (KRT) and recovery of kidney function were defined as endpoints. Results: Patients who suffered in-hospital death (n = 37, 23.1%) showed significantly higher serum sodium levels at diagnosis of AKI (survivors: 145.7 ± 2.13 vs. non-survivors: 138.8 ± 0.636 mmol/L, P = 0.003). A logistic regression model was significant for serum sodium levels in patients with in-hospital death (X 2 , P = 0.003; odds ratio = 1.08 (1.022 - 1.141); R 2 = 0.082; d = 0.089). This suggests an increase of the relative risk for in-hospital death by 8% with every unit of serum sodium increase. Patients with a sodium above the upper normal range at AKI diagnosis were also more likely to suffer in-hospital death (P = 0.001). Conclusion: In summary, we present evidence that serum sodium, measured at time of AKI diagnosis, potentially serves as a predictor for in-hospital death in patients with AKI. J Clin Med Res. 2023;15(2):90-98 doi: https://doi.org/10.14740/jocmr4845
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".