Impact of the COVID-19 Pandemic on the Severity of Diabetic Ketoacidosis Presentations in a Tertiary Pediatric Emergency Department
Bibliographic record
Abstract
More severe presentations of diabetic ketoacidosis (DKA) have been reported during the coronavirus disease 2019 (COVID-19) pandemic, possibly due to avoidance of healthcare settings or reduced access to care. To date, no studies have utilized statistical process control to relate temporal COVID-19 events with DKA severity. Our objectives were (1) to determine whether the severity of pediatric DKA presentations changed during COVID-19 and (2) to temporally relate changes in severity with regional pandemic events. Methods: This study was a retrospective chart review of 175 patients younger than 18 years with DKA presenting to a pediatric emergency department in the United States between 5/1/2019 and 8/15/2020. As part of our ongoing clinical standard work in ED management of DKA, DKA severity measures, including presenting pH, the proportion of PICU admissions, and admission length of stay, were analyzed using statistical process control. Results: During COVID-19, we found special cause variation with a downward shift in the mean pH on DKA presentation from 7.2 to 7.1 for all patients. The proportion of DKA patients requiring PICU admission increased from 34.2% to 54.6%. Changes temporally corresponded to the statewide bans on large events (3/11/2020), school closures (3/13/2020), and a reduction in our institution's emergency department volumes. Admission length of stay was unchanged. Conclusions: Pediatric DKA presentations were more severe from March to June 2020, correlating with regional COVID-19 events. Future quality improvement interventions to reduce delayed presentations during COVID-19 surges or other natural disasters should target accessibility of care and public education regarding the importance of timely care for symptoms.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".