Predictors of 30‐Day Recurrent Emergency Department Visits for Hyperglycemia in Patients With Diabetes: A Multicentre Prospective Cohort Study
Bibliographic record
Abstract
OBJECTIVES: Identifying predictors of increased healthcare utilization for hyperglycemia may have important implications for designing interventions to improve patient outcomes and reduce costs. Studies examining predictors of 30-day recurrent ED hyperglycemia visits have been limited due to their retrospective nature. This study's objective was to prospectively identify predictors of 30-day recurrent ED visits for hyperglycemia in patients with diabetes. METHODS: We conducted a multicentre, prospective cohort study of adults ≥ 18 years at one of four Canadian tertiary care, academic EDs with a diagnosis of hyperglycemia, diabetic ketoacidosis, or hyperosmolar hyperglycemic state. Multivariable logistic regression analysis was used to identify variables independently associated with recurrent 30-day ED visits for hyperglycemia. RESULTS: We enrolled 594 patients; 80 (13.5%) had a recurrent ED visit for hyperglycemia within 30 days. Independently associated predictors of 30-day recurrent visits on complete case analysis include substance abuse history (odds ratio [OR] 2.32, 95% confidence interval [CI]: 1.23-4.38) and initial laboratory blood glucose (OR 1.04, 95% CI: 1.01-1.07), while a new diabetes diagnosis was negatively associated (OR 0.29, 95% CI: 0.09-0.94). Sensitivity analysis using multiple imputation for missing data found the following independently associated variables: substance abuse history (OR 2.55, 95% CI: 1.34-4.85), previous ED visit within the past 14 days (OR 2.14, 95% CI: 1.02-4.48), and initial laboratory blood glucose (OR 1.04, 95% CI: 1.01-1.07). Two variables were negatively associated: recent hospitalization within the past 30 days (OR 0.40, 95% CI: 0.19-0.98) and new diabetes diagnosis (OR 0.37, 95% CI: 0.14-0.97). CONCLUSIONS: This multicentre prospective study reports predictors independently associated with 30-day recurrent ED visits for hyperglycemia. These predictors should be considered by ED clinicians when making disposition and follow-up plans for this important patient population, and future interventions should explore the interaction between hyperglycemia and substance use to prevent recurrent ED visits and reduce healthcare system costs and utilization.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".