Novel pediatric-automated respiratory score using physiologic data and machine learning in asthma.

OBJECTIVES

Manual clinical scoring systems are the current standard used for acute asthma clinical care pathways. No automated system exists that assesses disease severity, time course, and treatment impact in pediatric acute severe asthma exacerbations.

WORKING HYPOTHESIS

machine learning applied to continuous vital sign data could provide a novel pediatric-automated asthma respiratory score (pARS) by using the manual pediatric asthma score (PAS) as the clinical care standard.

METHODS

Continuous vital sign monitoring data (heart rate, respiratory rate, and pulse oximetry) were merged with the health record data including a provider-determined PAS in children between 2 and 18 years of age admitted to the pediatric intensive care unit (PICU) for status asthmaticus. A cascaded artificial neural network (ANN) was applied to create an automated respiratory score and validated by two approaches. The ANN was compared with the Normal and Poisson regression models.

RESULTS

Out of an initial group of 186 patients, 128 patients met inclusion criteria. Merging physiologic data with clinical data yielded >37 000 data points for model training. The pARS score had good predictive accuracy, with 80% of the pARS values within ±2 points of the provider-determined PAS, especially over the mid-range of PASs (6-9). The Poisson and Normal distribution regressions yielded a smaller overall median absolute error.

CONCLUSIONS

The pARS reproduced the manually recorded PAS. Once validated and studied prospectively as a tool for research and for physician decision support, this methodology can be implemented in the PICU to objectively guide treatment decisions.