Hyponatremia causing factors and its association with disease severity and length of stay in COVID-19 patients: A retrospective study from tertiary care hospital
Bibliographic record
Abstract
The coronavirus disease-2019 (COVID-19) infection has taken the world by storm within a few months. Evidence has suggested that patients with electrolyte imbalances at baseline may have a longer duration of hospital stay. We aimed to determine the factors associated with hyponatremia on admission in COVID-19 patients and its impact on the length of stay. We conducted a retrospective study including 521 patients who tested positive for COVID-19 and had their electrolytes checked on admission from June 2020 to October 2020. Patients with sodium <135 mmol/l were included in the hyponatremic group and were compared against normonatremic patients. The severity of COVID-19 was found to be more prevalent in the case group as compared to control (38.3% vs 29.2%; 21.1% vs 17.7%). Hyponatremic patients stayed more than 5 days in hospital (56.3% vs 46.5%), and stayed longer in special care (23.4% vs 20.0%) as compared to controls. Hyponatremic patients as compared to control were more likely to have diabetes (47.9% vs 30.0%), hypertension (49.0% vs 38.5%), ischemic heart disease (20.7% vs 15.4%), chronic liver disease (2.7% vs 1.2%), and chronic kidney disease (9.6% vs 3.8%). Upon matching on the age, the adjusted odds of hyponatremia in COVID-19-positive patients were 1.9 times among diabetic patients. Moreover, COVID-19-positive patients suffering from CKD had a higher risk of developing hyponatremia (OR = 2.3, 95% CI: 1.1-5.6). The risk of hyponatremia among COVID-19-positive patients is statistically higher in patients with 1 comorbidity (OR = 1.9, 95%CI: 1.3-3.4). Hyponatremia on admission can be used to forecast the length of hospital stay and the severity of illness in COVID-19 patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".