Association Between Prehospital Blood Glucose Levels and Outcomes in Patients With COVID-19 Infection: A Retrospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: Hyperglycaemia is associated with worse outcomes in many settings. However, the association between dysglycaemia and adverse outcomes remains debated in COVID-19 patients. This study determined the association of prehospital blood glucose levels with acute medical unit (intensive care unit or high dependency unit) admission and mortality among COVID-19-infected patients. METHODS: This was a single-centre, retrospective cohort study based on patients cared for by the prehospital medical mobile unit from a Swiss university hospital between March 2020 and April 2021. All adult patients with confirmed or suspected COVID-19 infection during the study period were included. Data were obtained from the prehospital medical files. The main exposure was prehospital blood glucose level. A 7.8 mmol/L cut-off was used to define high blood glucose level. Restricted cubic splines were also used to analyse the exposure as a continuous variable. The primary endpoint was acute medical unit admission; secondary endpoints were 7-day and 30-day mortality. Multivariable logistic regressions were performed to compute odds ratios. RESULTS: A total of 276 patients were included. The mean prehospital blood glucose level was 8.8 mmol/l, and 123 patients presented high blood glucose levels. The overall acute medical unit admission rate was 31.2%, with no statistically significant difference according to prehospital blood glucose levels. The mortality rate was 13.8% at 7 days and 25% at 30 days. The 30-day mortality rate was higher in patients with high prehospital blood glucose levels, with an adjusted odds ratio of 2.5 (1.3-4.8). CONCLUSIONS: In patients with acute COVID-19 infection, prehospital blood glucose levels do not seem to be associated with acute medical unit admission. However, there was an increased risk of 30-day mortality in COVID-19 patients who presented high prehospital blood glucose levels.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".