Trends in Intensive Care Unit Admissions and Ventilation in Patients With Asthma: A 10 Year Population-Based Study
Bibliographic record
Abstract
Abstract Rationale: Intensive care unit (ICU) admissions are a key marker of asthma severity, and signal patients at highest risk for asthma-related morbidity and mortality. We aimed to assess trends in ICU admissions and use of invasive ventilation over the past 10 years in pediatric and adult patients with asthma in Ontario, Canada. Methods: We assembled a 10-year cross-sectional cohort of individuals with prevalent asthma (aged 0-59 years) using the OASIS (Ontario Asthma Surveillance Information System) population-based health administrative databases from 2013-2022. All-cause ICU admissions were identified for admissions where asthma was listed as the most responsible diagnosis. Yearly ICU admission rate was calculated per 1,000 asthma prevalence. Invasive ventilation (IV) rates per 1000 asthma patients who had at least one ICU admission were calculated. All analyses were stratified by age groups (children aged 0-18 and adults aged 19-39 and 40-59). Results: From 2013 to 2022, there were 7,870 ICU admissions among patients admitted to hospital with asthma or asthma-related conditions as the most responsible diagnosis. Over 1/3 (4318) of these ICU admissions were in children under 6 years old. Among adults aged 19-59, the ICU admission rates showed a 14% decline over time. In contrast, among children aged 0-18, a steady increasing trend in ICU admission was observed from 0.64 in 2013 to 1.67 per 1000 in 2022, a nearly 3-fold increase in 10 years (Rate Ratio=2.59, 95%CI: 2.28-2.94). The highest increase was in children aged 0-5 years with a rebound seen in the post COVID-pandemic years (Figure 1). There were 2,112 admissions requiring IV (295.2 per 1000 ICU asthma patients). A decrease in the IV rate (-64%) in children under 19 years of age was seen; whereas in adults 19-59 years old, there was a slight increase in IV rates (+25.6%). Conclusions: There has been an increase in ICU admission rates in pediatric patients with asthma, most notably in patients under 6 years of age, although rates of invasive ventilation have decreased in children. In adults, ICU admission and ventilation rates have been relatively stable. Further analysis is planned to identify trends in non-invasive ventilation, and risk factors for ICU admissions. Future studies are needed to assess the trend in ICU admissions in children, particularly in the post-COVID era.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| 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".