Development and Validation of the Asthma Exacerbation Risk Score Using Claims Data

Using Electronic Health Records to Identify Asthma-Related Acute Care Encounters

OBJECTIVE

Leveraging "big data" to improve care requires that clinical concepts be operationalized using available data. Electronic health record (EHR) data can be used to evaluate asthma care, but relying solely on diagnosis codes may misclassify asthma-related encounters. We created streamlined, feasible and transparent prototype algorithms for EHR data to classify emergency department (ED) encounters and hospitalizations as "asthma-related."

METHODS

As part of an asthma program evaluation, expert clinicians conducted a multi-phase iterative chart review to evaluate 467 pediatric ED encounters and 136 hospitalizations with asthma diagnosis codes from calendar years 2017 and 2019, rating the likelihood that each encounter was actually asthma-related. Using this as a reference standard, we developed rule-based algorithms for EHR data to classify visits. Accuracy was evaluated using sensitivity, specificity, and positive and negative predictive values (PPV, NPV).

RESULTS

Clinicians categorized 38% of ED encounters as "definitely" or "probably" asthma-related; 13% as "possibly" asthma-related; and 49% as "probably not" or "definitely not" related to asthma. Based on this reference standard, we created two rule-based algorithms to identify "definitely" or "probably" asthma-related encounters, one using text and non-text EHR fields and another using non-text fields only. Sensitivity, specificity, PPV, and NPV were >95% for the algorithm using text and non-text fields and >87% for the algorithm using only non-text fields compared to the reference standard. We created a two-rule algorithm to identify asthma-related hospitalizations using only non-text fields.

CONCLUSIONS

Diagnostic codes alone are insufficient to identify asthma-related visits, but EHR-based prototype algorithms that include additional methods of identification can predict clinician-identified visits with sufficient accuracy.

Prediction of Asthma Exacerbations in Children

Asthma exacerbations are common in asthmatic children, even among those with good disease control. Asthma attacks result in the children and their parents missing school and work days; limit the patient's social and physical activities; and lead to emergency department visits, hospital admissions, or even fatal events. Thus, the prompt identification of asthmatic children at risk for exacerbation is crucial, as it may allow for proactive measures that could prevent these episodes. Children prone to asthma exacerbation are a heterogeneous group; various demographic factors such as younger age, ethnic group, low family income, clinical parameters (history of an exacerbation in the past 12 months, poor asthma control, poor adherence to treatment, comorbidities), Th2 inflammation, and environmental exposures (pollutants, stress, viral and bacterial pathogens) determine the risk of a future exacerbation and should be carefully considered. This paper aims to review the existing evidence regarding the predictors of asthma exacerbations in children and offer practical monitoring guidance for promptly recognizing patients at risk.

Predicting emergency department visits among children with asthma in two academic medical systems

Objective: To develop and validate a predictive algorithm that identifies pediatric patients at risk of asthma-related emergencies, and to test whether algorithm performance can be improved in an external site via local retraining.Methods: In a retrospective cohort at the first site, data from 26 008 patients with asthma aged 2-18 years (2012-2017) were used to develop a lasso-regularized logistic regression model predicting emergency department visits for asthma within one year of a primary care encounter, known as the Asthma Emergency Risk (AER) score. Internal validation was conducted on 8634 patient encounters from 2018. External validation of the AER score was conducted using 1313 pediatric patient encounters from a second site during 2018. The AER score components were then reweighted using logistic regression using data from the second site to improve local model performance. Prediction intervals (PI) were constructed via 10 000 bootstrapped samples.Results: At the first site, the AER score had a cross-validated area under the receiver operating characteristic curve (AUROC) of 0.768 (95% PI: 0.745-0.790) during model training and an AUROC of 0.769 in the 2018 internal validation dataset (p = 0.959). When applied without modification to the second site, the AER score had an AUROC of 0.684 (95% PI: 0.624-0.742). After local refitting, the cross-validated AUROC improved to 0.737 (95% PI: 0.676-0.794; p = 0.037 as compared to initial AUROC).Conclusions: The AER score demonstrated strong internal validity, but external validity was dependent on reweighting model components to reflect local data characteristics at the external site.

Primary care provider perceptions of an asthma home visiting program

INTRODUCTION

Pediatric asthma home visiting programs have improved clinical outcomes, but little is known about how providers perceive these programs. The purpose of this study was to understand how primary care providers and their colleagues in a medical home perceive an asthma home visiting program that is available at no cost to their patients.

METHODS

After several years of running an asthma home visiting program using community health workers (CHW) in 10 pediatric primary care offices in the South Coast of Massachusetts, we surveyed the providers of patients who had enrolled in the program. An anonymous online survey was developed by the program leaders, the program analytics team, and the CHWs for quality improvement purposes. Survey domains included the perceived utility of various aspects of the program, impact on patients, and interaction with CHWs, as well as demographic information about the providers.

RESULTS

Of the 24 providers asked to complete the survey from eight primary care practices, 21 completed the survey (88%). Respondents perceived that the most beneficial aspects were environmental assessment (95%), asthma education (91%), and addressing environmental issues (86%). In addition to numerous positive free-text responses, suggestions for improvement were in the areas of referral completion, post-visit communication, and patient identification in the medical record. All respondents would continue to refer to the program.

CONCLUSIONS

Primary care providers and medical home staff perceived an asthma home visiting program to have high utility, particularly the environmental assessment, asthma education, and mitigation of environmental issues. Additional opportunities for improvement were identified.

Association of comorbidities and medications with risk of asthma exacerbation in pediatric patients: a retrospective study using Japanese claims data

Asthma exacerbation impairs the quality of life of pediatric patients and negatively impacts future respiratory function and health economics. Several risk factors associated with exacerbations have been identified; however, most studies report the risk of each factor. Therefore, this study aimed to evaluate the risk of each factor and a combination of factors. We performed a retrospective cohort study using Japanese claims data and extracted factors associated with exacerbations using multivariate Cox proportional hazards regression and stepwise method. Risk scores were then calculated from the extracted factors and validated by tenfold cross validation. Of the 1,748,111 asthma patients in the database, the data of 14,980 were extracted, and 1988 (13.3%) had exacerbation. Factors associated with asthma exacerbation were age of 3–5 years, exacerbation history before cohort entry date, allergic rhinitis, chronic sinusitis, otitis externa, blepharitis, upper respiratory infections, urticaria, LTRA prescription, were determined. A four-level risk score was calculated from 9-factors and the AUC derived from cross validation was 0.700. Most factors extracted in our study are consistent with those of previous studies. We showed that combining each factor is more helpful in assessing the increased risk of asthma exacerbation than assessing each factor alone.