Risk stratification for hospitalization in acute asthma: the CHOP classification tree

Abstract

Objective

Simple risk stratification rules are limited in acute asthma. We developed and externally validated a classification tree for asthma hospitalization.

Methods

Data were obtained from 2 large, multicenter studies on acute asthma, the National Emergency Department Safety Study and the Multicenter Airway Research Collaboration cohorts. Both studies involved emergency department (ED) patients aged 18 to 54 years presenting to the ED with acute asthma. Clinical information was obtained from medical record review. The Classification and Regression Tree method was used to generate a simple decision tree. The tree was derived in the National Emergency Department Safety Study cohort and then was validated in the Multicenter Airway Research Collaboration cohort.

Results

There were 1825 patients in the derivation cohort and 1335 in the validation cohort. Admission rates were 18% and 21% in the derivation and validation cohorts, respectively.

The Classification and Regression Tree method identified 4 important variables (CHOP): change [C] in peak expiratory flow severity category, ever hospitalization [H] for asthma, oxygen [O] saturation on room air, and initial peak expiratory flow [P]. In a simple 3-step process, the decision rule risk-stratified patients into 7 groups, with a risk of admission ranging from 9% to 48%. The classification tree performed satisfactorily on discrimination in both the derivation and validation cohorts, with an area under the receiver operating characteristic curve of 0.72 and 0.65, respectively.

Conclusions

We developed and externally validated a novel classification tree for hospitalization among ED patients with acute asthma. Use of this explicit risk stratification rule may aid decision making in the emergency care of acute asthma.

Introduction

Acute asthma is a common presentation to the emergency department (ED), accounting for approximately 2 million ED visits and 500 000 hospitalizations in the United States each year [1]. Despite the significant morbidity associated with acute asthma, there has been a paucity of simple, practical, and validated tools for risk stratification in adult patients with acute asthma. Previous studies have used multivariable modeling and identified certain characteristics that are associated with asthma admissions [2], [3]. These multivariable models, however, may have limited use in clinical practice. They often involve mathematical equations or scoring systems and require access to a calculator or even a computer to convert point scores to risk estimates. Furthermore, these rules were derived from single-center studies and may be less generalizable to other EDs.

Unlike multivariable regression models, the Classification and Regression Tree (CART) method produces a simple decision tree that is intuitive and easy to apply in clinical practice [4], [5], [6]. The structure of the tree is clinically appealing and congruent with physicians' decision-making processes. As a result, the decision trees generated by CART have been used in a variety of clinical fields, such as emergency medicine [7], pulmonology [8], cardiology [9], [10], neurosurgery [11], and oncology [12]. In asthma, we are only aware of 2 studies that have used this method [13], [14]. These studies, however, have identified the factors associated with health services utilization in chronic asthma, which is not directly applicable to the acute care setting.

The objectives of this analysis were to develop and validate a practical and user-friendly classification tree for hospitalization among ED patients with acute asthma.