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Progression to Uncontrolled Severe Asthma: A Novel Risk Equation
Julian Casciano1, Jerry Krishnan2, Mary Buatti Small3
11 eMAX Health Systems, White Plains, New York.
Journal of Managed Care & Specialty Pharmacy
|December 28, 2016
Summary
A new risk score using eosinophil counts and comorbidity index can predict severe asthma. This tool helps identify patients at higher risk for better early management.
Area of Science:
- Pulmonology and Respiratory Medicine
- Biostatistics and Epidemiology
- Clinical Decision Support Systems
Background:
- Recent European Respiratory Society/American Thoracic Society (ERS-ATS) guidelines define severe asthma using medication and control levels.
- The eosinophilic phenotype is a key biomarker for severe asthma.
- Quantifying eosinophil influence aids in early therapeutic decision-making for severe asthma.
Purpose of the Study:
- To develop a predictive risk-scoring algorithm for identifying patients at high risk of developing uncontrolled severe asthma.
- The algorithm aims to align with the ERS-ATS criteria for severe asthma definition.
Main Methods:
- Retrospective analysis of electronic health records (EMR) data from 2004-2011.
- Inclusion criteria: continuous enrollment, asthma diagnosis, and eosinophil count post-index date.
- Cox proportional hazards regression used to build the risk score, incorporating eosinophil level, age, sex, race, and Charlson Comorbidity Index (CCI).
Main Results:
- 2,405 patients included; 6% developed uncontrolled severe asthma.
- Elevated eosinophil levels (≥ 400 cells/µL) and CCI score > 0 were significant predictors (HR=1.90 and HR=2.00, respectively).
- The risk score demonstrated good model discrimination (C-statistics 0.79-0.80), with higher scores correlating with increased risk.
Conclusions:
- A risk stratification tool utilizing peripheral eosinophil counts and CCI effectively predicts the development of uncontrolled severe asthma.
- This tool can assist clinicians in early identification and management of high-risk patients.
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