Severe Asthma Research Program
A National Institutes of Health/ National Heart, Lung & Blood Institute sponsored network
1. Choi S, et al. Quantitative computed tomography imaging-based clustering differentiates asthmatic subgroups with distinctive clinical phenotypes. J Allergy Clin Immunol. 2017 Jan 28. pii: S0091-6749(17)30146-X. doi: 10.1016/j.jaci.2016.11.053. PubMed PMID: 28143694.
The unique structural and functional alterations observed in each imaging cluster provide a basis for new insights for the existing pathophysiology of asthma. The clustering membership can be used for a basis for the development of effective interventions for asthmatics in the future.
Severe asthma can be quite different from patient to patient – a new approach to “cluster” these patients into groups to understand their disease better has been done. The SARP imaging cluster study added CT scans of the chest to the information we gathered on our patients. We found unique changes in the windpipes and lungs of the patients in these clusters. Our hope is this information can be used to target treatment better in the future.
2. Altes TA, et al. Clinical correlates of lung ventilation defects in asthmatic children. J Allergy Clin Immunol. 2016 Mar;137(3):789-96.e7. doi: 10.1016/j.jaci.2015.08.045. PubMed PMID: 26521043.
This study showed that a new method using inhaled hyperpolarized helium-3 gas can be safely used to image by MRI the pattern of ventilation in the lungs of children with asthma. This method has significant advantages over standard chest CT in that there is no exposure to ionizing radiation. We learned that children with severe asthma have greater regions of the lung which do not get enough air, and that the volume of these poorly ventilated regions correlates significantly with many of the features of asthma.
3. Dunican EM, et al. National Heart Lung and Blood Institute (NHLBI) Severe Asthma Research Program (SARP). Mucus plugs in patients with asthma linked to eosinophilia and airflow obstruction. J Clin Invest. 2018 Feb 5. pii: 95693. doi: 10.1172/JCI95693. [Epub ahead of print] PubMed PMID: 29400693.
4. Georas SN. et al. All plugged up - noninvasive mucus score to assess airway dysfunction in asthma. J Clin Invest. 2018 Feb 5. pii: 99726. doi: 10.1172/JCI99726. [Epub ahead of print] PubMed PMID: 29400694.
5. Ash, et al. Pruning of the Pulmonary Vasculature in Asthma: The SARP Cohort. Am J Respir Crit Care Med. 2018 Apr 19. doi: 10.1164/rccm.201712-2426OC.PubMed PMID: 29672122.
6. Choi S, et al. Differentiation of quantitative CT imaging phenotypes in asthma versus COPD. BMJ Open Respir Res. 2017 Nov 9;4(1):e000252. doi: 10.1136/bmjresp-2017-000252. eCollection 2017. Erratum in: BMJ Open Respir Res. 2018 Mar 6;5(1):e000252corr1. PubMed PMID: 29435345; PubMed Central PMCID: PMC5687530.
7. Shim SS, et al. Lumen area change (Delta Lumen) between inspiratory and expiratory CT as a measure of severe outcomes in asthma. J Allergy Clin Immunol. 2018 Feb 10. pii: S0091-6749(18)30219-7. doi: 10.1016/j.jaci.2017.12.1004. PubMed PMID: 29438772.