| Title |
Application of the Asthma Phenotype Algorithm from the Severe Asthma Research Program to an Urban Population
|
|---|---|
| Published in |
PLOS ONE, September 2012
|
| DOI | 10.1371/journal.pone.0044540 |
| Pubmed ID | |
| Authors |
Paru Patrawalla, Angeliki Kazeros, Linda Rogers, Yongzhao Shao, Mengling Liu, Maria-Elena Fernandez-Beros, Shulian Shang, Joan Reibman |
| Abstract |
Identification and characterization of asthma phenotypes are challenging due to disease complexity and heterogeneity. The Severe Asthma Research Program (SARP) used unsupervised cluster analysis to define 5 phenotypically distinct asthma clusters that they replicated using 3 variables in a simplified algorithm. We evaluated whether this simplified SARP algorithm could be used in a separate and diverse urban asthma population to recreate these 5 phenotypic clusters. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 50% |
| Australia | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 60 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Portugal | 1 | 2% |
| Germany | 1 | 2% |
| France | 1 | 2% |
| Brazil | 1 | 2% |
| India | 1 | 2% |
| Unknown | 55 | 92% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 11 | 18% |
| Student > Master | 10 | 17% |
| Student > Bachelor | 7 | 12% |
| Student > Ph. D. Student | 7 | 12% |
| Other | 5 | 8% |
| Other | 9 | 15% |
| Unknown | 11 | 18% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 20 | 33% |
| Agricultural and Biological Sciences | 7 | 12% |
| Nursing and Health Professions | 4 | 7% |
| Biochemistry, Genetics and Molecular Biology | 3 | 5% |
| Engineering | 3 | 5% |
| Other | 9 | 15% |
| Unknown | 14 | 23% |