Development of a Computable Phenotype for Prehospital Pediatric Asthma Encounters

Abstract

Introduction

Asthma exacerbations are a common cause of pediatric Emergency Medical Services (EMS) encounters. Accordingly, prehospital management of pediatric asthma exacerbations has been designated an EMS research priority. However, accurate identification of pediatric asthma exacerbations from the prehospital record is nuanced and difficult due to the heterogeneity of asthma symptoms, especially in children. Therefore, this study’s objective was to develop a prehospital-specific pediatric asthma computable phenotype (CP) that could accurately identify prehospital encounters for pediatric asthma exacerbations.

Methods

This is a retrospective observational study of patient encounters for ages 2–18 years from the ESO Data Collaborative between 2018 and 2021. We modified two existing rule-based pediatric asthma CPs and created three new CPs (one rule-based and two machine learning-based). Two pediatric emergency medicine physicians independently reviewed encounters to assign labels of asthma exacerbation or not. Taking that labeled encounter data, a 50/50 train/test split was used to create training and test sets from the labeled data. A 90/10 split was used to create a small validation set from the training set. We used specificity, sensitivity, positive predictive value (PPV), negative predictive value (NPV) and macro F₁ to compare performance across all CP models.

Results

After applying the inclusion and exclusion criteria, 24,283 patient encounters remained. The machine-learning models exhibited the best performance for the identification of pediatric asthma exacerbations. A multi-layer perceptron-based model had the best performance in all metrics, with an F₁ score of 0.95, specificity of 1.00, sensitivity of 0.91, negative predictive value of 0.98, and positive predictive value of 1.00.

Conclusion

We modified existing and developed new pediatric asthma CPs to retrospectively identify prehospital pediatric asthma exacerbation encounters. We found that machine learning-based models greatly outperformed rule-based models. Given the high performance of the machine-learning models, the development and application of machine learning-based CPs for other conditions and diseases could help accelerate EMS research and ultimately enhance clinical care by accurately identifying patients with conditions of interest.

Authors Contributions

IH contributed to the study design, data collection, data analysis, drafting and revising the manuscript. JB contributed to data analysis, drafting and revising the manuscript. ISC contributed to data collection, data analysis, and critical revision of the manuscript. JF contributed to the study design, data collection, data analysis, and critical revision of the manuscript.

Disclosure Statement

IH, JB, ISC, and JF report no conflicts of interest. The authors alone are responsible for the content and writing of this manuscript.

Additional information

Funding

Dr. Fishe’s activities were supported by a career development award from NIH/NHLBI K23HL149991. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.