James G Krings1, Charles W Goss2, Daphne Lew2, Maanasi Samant1, Mary Clare McGregor1, Jonathan Boomer3, Leonard B Bacharier4, Ajay Sheshadri5, Chase Hall3, Joshua Brownell6, Ken B Schechtman2, Samuel Peterson7, Stephen McEleney7, David T Mauger8, John V Fahy9, Sean B Fain10, Loren C Denlinger6, Elliot Israel11, George Washko11, Eric Hoffman12, Sally E Wenzel13, Mario Castro14. 1. Division of Pulmonary and Critical Care Medicine, Department of Medicine, St Louis, Mo. 2. Division of Biostatistics, Washington University in St Louis School of Medicine, St Louis, Mo. 3. Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University of Kansas School of Medicine, Kansas City, Kan. 4. Department of Pediatrics, Vanderbilt University School of Medicine, Nashville, Tenn. 5. Division of Pulmonary and Critical Care Medicine, Department of Medicine, The University of Texas M.D. Anderson Cancer Center, Houston, Tex. 6. Division of Allergy, Pulmonary, and Critical Care Medicine, Department of Medicine, University of Wisconsin, Madison, Wis. 7. VIDA Diagnostics, Coralville, Iowa. 8. Division of Statistics and Bioinformatics, Department of Public Health Sciences, Pennsylvania State University, Hershey, Pa. 9. Division of Pulmonary and Critical Care Medicine, Department of Medicine, the University of California San Francisco, San Francisco, Calif. 10. Department of Radiology and Biomedical Engineering, University of Wisconsin, Madison, Wis. 11. Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, Mass. 12. Department of Radiology, Biomedical Engineering, and Medicine, University of Iowa, Iowa City, IA. 13. Division of Pulmonary, Allergy, and Critical Care, Department of Medicine, the University of Pittsburgh, Pittsburgh, Pa. 14. Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University of Kansas School of Medicine, Kansas City, Kan. Electronic address: mcastro2@kumc.edu.
Abstract
BACKGROUND: Currently, there is limited knowledge regarding which imaging assessments of asthma are associated with accelerated longitudinal decline in lung function. OBJECTIVES: We aimed to assess whether quantitative computed tomography (qCT) metrics are associated with longitudinal decline in lung function and morbidity in asthma. METHODS: We analyzed 205 qCT scans of adult patients with asthma and calculated baseline markers of airway remodeling, lung density, and pointwise regional change in lung volume (Jacobian measures) for each participant. Using multivariable regression models, we then assessed the association of qCT measurements with the outcomes of future change in lung function, future exacerbation rate, and changes in validated measurements of morbidity. RESULTS: Greater baseline wall area percent (β = -0.15 [95% CI = -0.26 to -0.05]; P < .01), hyperinflation percent (β = -0.25 [95% CI = -0.41 to -0.09]; P < .01), and Jacobian gradient measurements (cranial-caudal β = 10.64 [95% CI = 3.79-17.49]; P < .01; posterior-anterior β = -9.14, [95% CI = -15.49 to -2.78]; P < .01) were associated with more severe future lung function decline. Additionally, greater wall area percent (rate ratio = 1.06 [95% CI = 1.01-1.10]; P = .02) and air trapping percent (rate ratio =1.01 [95% CI = 1.00-1.02]; P = .03), as well as lower decline in the Jacobian determinant mean (rate ratio = 0.58 [95% CI = 0.41-0.82]; P < .01) and Jacobian determinant standard deviation (rate ratio = 0.52 [95% CI = 0.32-0.85]; P = .01), were associated with a greater rate of future exacerbations. However, imaging metrics were not associated with clinically meaningful changes in scores on validated asthma morbidity questionnaires. CONCLUSIONS: Baseline qCT measures of more severe airway remodeling, more small airway disease and hyperinflation, and less pointwise regional change in lung volumes were associated with future lung function decline and asthma exacerbations.
BACKGROUND: Currently, there is limited knowledge regarding which imaging assessments of asthma are associated with accelerated longitudinal decline in lung function. OBJECTIVES: We aimed to assess whether quantitative computed tomography (qCT) metrics are associated with longitudinal decline in lung function and morbidity in asthma. METHODS: We analyzed 205 qCT scans of adult patients with asthma and calculated baseline markers of airway remodeling, lung density, and pointwise regional change in lung volume (Jacobian measures) for each participant. Using multivariable regression models, we then assessed the association of qCT measurements with the outcomes of future change in lung function, future exacerbation rate, and changes in validated measurements of morbidity. RESULTS: Greater baseline wall area percent (β = -0.15 [95% CI = -0.26 to -0.05]; P < .01), hyperinflation percent (β = -0.25 [95% CI = -0.41 to -0.09]; P < .01), and Jacobian gradient measurements (cranial-caudal β = 10.64 [95% CI = 3.79-17.49]; P < .01; posterior-anterior β = -9.14, [95% CI = -15.49 to -2.78]; P < .01) were associated with more severe future lung function decline. Additionally, greater wall area percent (rate ratio = 1.06 [95% CI = 1.01-1.10]; P = .02) and air trapping percent (rate ratio =1.01 [95% CI = 1.00-1.02]; P = .03), as well as lower decline in the Jacobian determinant mean (rate ratio = 0.58 [95% CI = 0.41-0.82]; P < .01) and Jacobian determinant standard deviation (rate ratio = 0.52 [95% CI = 0.32-0.85]; P = .01), were associated with a greater rate of future exacerbations. However, imaging metrics were not associated with clinically meaningful changes in scores on validated asthma morbidity questionnaires. CONCLUSIONS: Baseline qCT measures of more severe airway remodeling, more small airway disease and hyperinflation, and less pointwise regional change in lung volumes were associated with future lung function decline and asthma exacerbations.
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