The epidemiology of intensive care unit-acquired hyponatraemia and hypernatraemia in medical-surgical intensive care units
Bibliographic record
Abstract
INTRODUCTION: Although sodium disturbances are common in hospitalised patients, few studies have specifically investigated the epidemiology of sodium disturbances in the intensive care unit (ICU). The objectives of this study were to describe the incidence of ICU-acquired hyponatraemia and hypernatraemia and assess their effects on outcome in the ICU. METHODS: We identified 8142 consecutive adults (18 years of age or older) admitted to three medical-surgical ICUs between 1 January 2000 and 31 December 2006 who were documented to have normal serum sodium levels (133 to 145 mmol/L) during the first day of ICU admission. ICU acquired hyponatraemia and hypernatraemia were respectively defined as a change in serum sodium concentration to below 133 mmol/L or above 145 mmol/L following day one in the ICU. RESULTS: A first episode of ICU-acquired hyponatraemia developed in 917 (11%) patients and hypernatraemia in 2157 (26%) patients with an incidence density of 3.1 and 7.4 per 100 days of ICU admission, respectively, during 29,142 ICU admission days. The incidence of both ICU-acquired hyponatraemia (age, admission diagnosis, Acute Physiology and Chronic Health Evaluation (APACHE) II score, length of ICU stay, level of consciousness, serum glucose level, body temperature, serum potassium level) and ICU-acquired hypernatraemia (baseline creatinine, APACHE II score, mechanical ventilation, length of ICU stay, body temperature, serum potassium level, level of care) varied according to patients' characteristics. Compared with patients with normal serum sodium levels, hospital mortality was increased in patients with ICU-acquired hyponatraemia (16% versus 28%, p < 0.001) and ICU-acquired hypernatraemia (16% versus 34%, p < 0.001). CONCLUSIONS: ICU-acquired hyponatraemia and hypernatraemia are common in critically ill patients and are associated with increased risk of hospital mortality.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".