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Impact of Clinical Characteristics and Biomarkers on Asthma Impairment and Risk Questionnaire Exacerbation Prediction
Kevin R Murphy1, David A Beuther2, Bradley E Chipps3
1Boys Town National Research Hospital, Boys Town, Neb.
Background:
Complex models combining impairment-based control assessments with clinical characteristics and biomarkers have been developed to predict asthma exacerbations. The composite Asthma Impairment and Risk Questionnaire (AIRQ) with adjustments for demographics (age, sex, race, and body mass index) predicts 12-month exacerbation occurrence similarly to these more complex models.
Objective:
To examine whether AIRQ exacerbation prediction is enhanced when models are adjusted for a wider range of clinical characteristics and biomarkers.
Methods:
Patients aged 12 years and older completed monthly online surveys regarding exacerbation-related oral corticosteroid use, emergency department or urgent care visits, and hospitalizations. Univariate logistic regressions to predict exacerbations were performed with sociodemographics, comorbidities, exacerbation history, lung function, blood eosinophils, IgE, and FeNO. Significant (P ≤ .05) variables were included in multivariable logistic regressions with and without AIRQ control categories to predict 12-month exacerbations (log odds ratio [95% Wald confidence interval]). Model performances were compared.
Results:
Over 12 months, 1,070 patients (70% female; mean [SD] age, 43.9 [19.4] years; 22% non-White; body mass index [SD], 30.6 [8.7]) completed one or more survey (mean [SD], 10.5 [2.8] surveys). In the multivariable analysis, AIRQ control category adjusted for significant clinical characteristics and biomarkers was predictive of one or more exacerbations: odds ratio (95% CI) not well-controlled versus well-controlled: 1.93 (1.41-2.62), very poorly controlled versus well-controlled: 3.81 (2.65-5.47). Receiver operating characteristic area under the curve (AUC) for this more complex model of exacerbation prediction (AUC = 0.72) did not differ from AIRQ (AUC = 0.70). Models with AIRQ performed better than those without AIRQ (AUC = 0.67; P < .05).
Conclusion:
Costly and time-consuming complex modeling with clinical characteristics and biomarkers does not enhance the strong exacerbation prediction ability of AIRQ.
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