123 Reduction of asthma hospitalizations in children in a tertiary care center in Quebec during the early confinement period of the COVID-19 pandemic
Bibliographic record
Abstract
Abstract Primary Subject area Respirology Background Asthma in children and adolescents is a major cause of urgent visits and hospitalizations. In preschoolers, viral infections remain the main cause of these exacerbations, although environmental factors may also play a role. In older children and adolescents, many other risk factors are present including respiratory allergies, exercise, strong emotions, active and passive smoking and poor adherence to recommended treatments. In the context of the COVID-19 pandemic, clinicians have been concerned about the possibility of a significant increase in urgent consultations for asthma in children. However, due to the confinement imposed on a large part of the population from March 2020, we have suspected a reduction in the number of viral infections leading to urgent visits and hospitalizations in pediatric patients. Objectives Our hypothesis was that the number of children hospitalized with asthma exacerbations during the pandemic lockdown declined significantly. Our objective was to determine if the number and severity of hospitalizations at the CME of the CHU de Québec for asthma exacerbations in children aged 1 to 17 had significantly decreased during the confinement period from April 1 to July 1, 2020 and to what extent, according to various clinical features. Design/Methods Retrospective study reviewing episodes of care in medical records in children aged 1 to 17 and hospitalized on all wards at the CME-CHU de Québec. All patients with primary diagnosis of asthmatic exacerbation according to the summary sheets between April 1 to July 1, 2020 for the study group and from April 1 to July 1, 2019 for the control group were included. We aimed to determine the number of hospitalizations for this diagnosis for each period and determine the length of stay on the various pediatric wards for each episode of care according to the established criteria. We have determined the presence of risk factors (asthma diagnosis, underlying condition, regular asthma medication, allergies, and other factors relevant to asthma).We have further determined the presence of gravity markers during the course of hospitalization (need for: IV corticosteroids, Mg sulfate, IV bronchodilators, non-invasive ventilation, intubation, ICU stay and radiological pneumonia) for each episode of care. The data was denominated and collated in an encoded file, and shareable only between the investigators. Results After exclusions, a total of 97 charts were analyzed. Between April 1 and July 1 2019, a total of 89 patients were admitted with a diagnosis of asthma exacerbation while only eight patients were admitted during the same period in 2020. The median age of patients admitted in 2020 was higher than in 2019 (53 vs 25 months). In 2019, 40 children (48%) had a previous diagnosis of asthma, of whom 28 used regular controller medications compared to 2 children (25%) in 2020, both taking regular controller medications. More patients had an associated diagnosis of upper respiratory tract infection in 2019 than in 2020 (92% vs 63%). Nine patients required intensive care in 2019 compared to none in 2020. Conclusion Compared to 2019, hospital admissions for asthma exacerbations in our tertiary care centre in Quebec City were significantly lower during the early phase of the 2020 pandemic. More research is required to determine the exact causes of this significant reduction.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".