Journal of managed care & specialty pharmacy 23(1) 44-50 doi 10.18553/jmcp.2017.23.1.44
Recently published asthma guidelines by the European Respiratory Society and the American Thoracic Society (ERS-ATS) define severe disease based on medication use and control level. These guidelines also emphasize that asthma severity involves certain biomarker phenotypes, one of them being eosinophilic phenotype. The quantification of the influence of eosinophil level toward predicting disease severity can help decision makers manage therapy better earlier.
To develop a risk-scoring algorithm to identify patients at greater risk of developing uncontrolled severe asthma as defined by ERS-ATS guidelines.
Data on asthma patients were extracted from the EMRClaims + database from January 2004 to July 2011. Patients with continuous enrollment 12 months before and after the date of the first encounter with a diagnosis of asthma (index date) with at least 1 blood eosinophil test result in the 12 months after the index date, but before the development of uncontrolled severe asthma or the study end date, were included. Uncontrolled severe asthma was defined as the first date on which all criteria of the ERS-ATS definition were first satisfied in the 12 months after the index date. Age (≥ 50 years vs. < 50 years), race, and sex were measured at index, and the Charlson Comorbidity Index (CCI) score (> 0 vs. 0) was measured in the pre-index period. Elevated eosinophil level was defined as a test result with ≥ 400 cells/µL. The study cohort was randomly split 50-50 into derivation and validation samples. Cox proportional hazards regression was used to develop the risk score for uncontrolled severe asthma using the derivation cohort with independent variables of eosinophil level, age, sex, race, and CCI. A bootstrapping procedure was used to generate 1,000 samples from the derivation cohort. Variables significant in ≥ 50% of the samples were retained in the final regression model. A risk score was then calculated based on the coefficient estimates of the final model. C-statistic was used to test the model’s discrimination power.
The study included 2,405 patients, 147 (6%) of whom developed uncontrolled severe asthma. Higher eosinophil level and CCI score > 0 were significantly and independently associated with an increased risk of uncontrolled severe asthma in the derivation cohort (HR = 1.90, 95% CI = 1.17-3.08 and HR = 2.00, 95% CI = 1.28-3.13, respectively); findings were similar in the validation cohort. Total risk score was categorized as 0, 2, and 4. All models showed good C-statistics (0.79-0.80), indicating favorable model discrimination. There was a significantly greater number of patients with uncontrolled severe asthma in the risk score segments of 2 and 4 compared with 0 (each P < 0.0001). CONCLUSIONS
A risk stratification tool using peripheral eosinophil counts and CCI can be used to predict the development of uncontrolled severe asthma.
This study was funded by Teva Pharmaceuticals. eMAX Health Systems was a consultant to Teva Pharmaceuticals for this study and received payment from Teva Pharmaceuticals for work on this study. Casciano and Dotiwala are employed by eMAX Health Systems. Krishnan, Li, and Martin received payment from eMAX Health Systems for work on this study. Small was employed by Teva Pharmaceuticals at the time of this study. Study concept and design were contributed primarily by Casciano, Krishnan, Small, and Martin, along with Li and Dotiwala. Dotiwala, Casciano, Small, and Li collected the data, along with Martin and Li and Krishnan. Data interpretation was provided by Martin, Casciano, and Li, with assistance from the other authors. The manuscript was written by Li, Casciano, Dotiwala, and Small, with assistance from the other authors, and revised by Dotiwala, Small, Li, and Martin, with assistance from Krishnan and Casciano.