The association between glycaemic control during hospitalization and risk of adverse events: A retrospective cohort study
Bibliographic record
Abstract
INTRODUCTION: Hyperglycaemia is common during hospitalization; glycaemic targets in non-critical care settings have not been well studied. We assessed associations between inpatient glycaemic control and adverse events. METHODS: We conducted a retrospective cohort study on non-critically ill medical patients hospitalized in a tertiary care hospital between 2015 and 2018. Mean glycaemia during the first four days of hospitalization was categorized as 4.0-7.0 mmol/L, 7.1-10.0 mmol/L and >10.0 mmol/L. The primary outcome was a composite of adverse events including mortality, infections, acute kidney injury, thromboembolic and cardiovascular events. The secondary outcome was hypoglycaemia, defined as any glycaemia <4.0 mmol/L. Logistic regression was used to assess adverse events, and a Cox proportional hazards model was used to estimate hypoglycaemia risk. RESULTS: Our cohort included 1,368 patients, of whom 407 (29.8%) experienced an adverse event. We did not find associations between glycaemia of 4.0-7.0 mmol/L (adjusted odds ratio [OR]: 0.88, 95% confidence interval [CI]: 0.63-1.23) or glycaemia of >10.0 mmol/L (adjusted OR: 0.98, 95% CI: 0.75-1.28) and the occurrence of adverse events, compared to a glycaemia of 7.1-10.0 mmol/L. Glycaemia of >10.0 mmol/L was associated with an increased risk of hypoglycaemia (adjusted hazard ratio [HR]: 1.72, 95% CI: 1.21-2.45). Hypoglycaemia was associated with adverse events (adjusted OR 1.85, 95% CI 1.31-2.60). CONCLUSIONS: Neither glycaemia of 4.0-7.0 mmol/L nor glycaemia of >10.0mmol/L during non-critical care hospitalization was associated with increased adverse events. Glycaemia of >10.0 mmol/L was associated with increased hypoglycaemia, likely due to aggressive glucose lowering. These findings highlight the need for further studies to discern optimal inpatient glycaemic targets.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".