Validation of a predictive model for asthma admission in children: how accurate is it for predicting admissions?

Improving Guideline-Based Care of Acute Asthma in a Pediatric Emergency Department

BACKGROUND AND OBJECTIVE

Rapid repetitive administration of short-acting β-agonists (SABA) is the most effective means of reducing acute airflow obstruction in asthma. Little evidence exists that assesses process measures (ie, timeliness) and outcomes for asthma. We used quality improvement (QI) methods to improve emergency department care in accordance with national guidelines including timely SABA administration and use of asthma severity scores.

METHODS

The Model for Improvement was used and interventions were targeted at 4 key drivers: knowledge, engagement, decision support, and workflow enhancement. Time series analysis was performed and outcomes assessed on statistical process control charts.

RESULTS

Asthma severity scoring increased from 0% to >95% in triage and to >75% for repeat scores. Time to first SABA (T1) improved by 32.8 minutes (47%). T1 for low severity patients improved by 17.6 minutes (28%). T1 for high severity patients improved by 3.1 minutes to 18.1 minutes (15%). Time to third SABA (T3) improved by 30 minutes (24%). T3 for low severity patients improved by 42.5 minutes (29%) and T3 for high severity patients improved by 21 minutes (23%). Emergency department length of stay for low severity patients discharged to home improved by 29.3 minutes (15%). The number of asthma-related visits between 48-hour return hospitalizations increased from 114 to 261. The admission rate decreased 6.0%.

CONCLUSIONS

We implemented standardized asthma severity scoring with high rates of compliance, improved timely administration of β-agonist treatments, demonstrated early improvements in Emergency department length of stay, and reduced admission rates without increasing unplanned return admissions.

Exhaled nitric oxide levels during treatment of pediatric acute asthma exacerbations and association with the need for hospitalization

OBJECTIVES

To examine how exhaled nitric oxide (eNO) levels measured before and after treatment of asthma exacerbations relate to emergency department (ED) disposition.

METHODS

We enrolled children 6 to 17 years old treated for asthma exacerbations in a pediatric ED. Using an offline single-breath eNO sampling technique, we collected replicate initial samples before treatment and replicate final samples when disposition was decided. We determined correlations and coefficients of variability of eNO values (parts per billion, ppb) of samples and compared by disposition (hospitalization or discharge) mean initial and final eNO levels and initial-to-final change.

RESULTS

Eighty-one subjects had initial and final eNO values; 24 subjects with more severe presentations had final values only. Replicate eNO samples were correlated (initial r = 0.98, final r = 0.99) and had low coefficients of variability (initial, 0.059 ± 0.057; final, 0.061 ± 0.070). For subjects with initial and final values, initial eNO levels were similar by disposition (mean difference, -8.0 ppb; 95% confidence interval [CI], -24.8 to 8.9 ppb), as were final levels (mean difference, -2.8 ppb; 95% CI, -23.8 to 18.2 ppb). Overall, final eNO was higher than initial (36.3 ± 29.7 vs 31.5 ± 23.9 ppb), but only 63% of subjects had any increase. Change in eNO was similar by disposition (mean difference, 4.6 ppb; 95% CI, -3.4 to 12.6). For more severe subjects with final eNO only, eNO was similar by disposition (P = 0.47).

CONCLUSIONS

For children aged 6 to 17 years with asthma exacerbations, eNO levels can be reliably measured. However, eNO levels measured before treatment or when disposition was determined did not distinguish children needing hospitalization.

A tree-based decision model to support prediction of the severity of asthma exacerbations in children

This paper describes the development of a tree-based decision model to predict the severity of pediatric asthma exacerbations in the emergency department (ED) at 2 h following triage. The model was constructed from retrospective patient data abstracted from the ED charts. The original data was preprocessed to eliminate questionable patient records and to normalize values of age-dependent clinical attributes. The model uses attributes routinely collected in the ED and provides predictions even for incomplete observations. Its performance was verified on independent validating data (split-sample validation) where it demonstrated AUC (area under ROC curve) of 0.83, sensitivity of 84%, specificity of 71% and the Brier score of 0.18. The model is intended to supplement an asthma clinical practice guideline, however, it can be also used as a stand-alone decision tool.