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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".