Risk Factors for Prolonged Length of Stay or Complications During Pediatric Respiratory Hospitalizations
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Respiratory illnesses are the leading cause of pediatric hospitalizations in the United States, and a major focus of efforts to improve quality of care. Understanding factors associated with poor outcomes will allow better targeting of interventions for improving care. The objective of this study was to identify patient and hospital factors associated with prolonged length of stay (LOS) or complications during pediatric hospitalizations for asthma or lower respiratory infection (LRI). METHODS: Cross-sectional study of hospitalizations of patients <18 years with asthma or LRI (bronchiolitis, influenza, or pneumonia) by using the nationally representative 2012 Kids Inpatient Database. We used multivariable logistic regression models to identify factors associated with prolonged LOS (>90th percentile) or complications (noninvasive ventilation, mechanical ventilation, or death). RESULTS: For asthma hospitalizations(n = 85 320), risks for both prolonged LOS and complications were increased with each year of age (adjusted odds ratio [AOR] 1.06, 95% confidence interval [CI] 1.05-1.07; AOR 1.05, 95% CI 1.03-1.07, respectively for each outcome) and in children with chronic conditions (AOR 4.87, 95% CI 4.15-5.70; AOR 21.20, 95% CI 15.20-29.57, respectively). For LRI hospitalizations (n = 204 950), risks for prolonged LOS and complications were decreased with each year of age (AOR 0.98, 95% CI 0.97-0.98; AOR 0.95, 95% CI 0.94-0.96, respectively) and increased in children with chronic conditions (AOR 9.86, 95% CI 9.03-10.76; AOR 56.22, 95% CI 46.60-67.82, respectively). Risks for prolonged LOS for asthma were increased in large hospitals (AOR 1.67, 95% CI 1.32-2.11) and urban-teaching hospitals (AOR 1.62, 95% CI 1.33-1.97). CONCLUSIONS: Older children with asthma, younger children with LRI, children with chronic conditions, and those hospitalized in large urban-teaching hospitals are more vulnerable to prolonged LOS and complications. Future research and policy efforts should evaluate and support interventions to improve outcomes for these high-risk groups (eg, hospital-based care coordination for children with chronic conditions).
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".