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Updated: Feb 17, 2026

Characterization of Sickling During Controlled Automated Deoxygenation with Oxygen Gradient Ektacytometry
Published on: November 5, 2019
Age is a predictor of a small decrease in lung function in children with sickle cell anemia
Shaina M Willen1, Robyn Cohen2, Mark Rodeghier3
1Department of Pediatrics, Division of Hematology/Oncology, Vanderbilt-Meharry Center for Excellence in Sickle Cell Disease, Vanderbilt University Medical Center, Nashville, Tennessee.
Abstract:
The longitudinal pattern of lung function in children with sickle cell anemia (SCA) has shown a decrease in FEV1 % predicted, a risk factor for death in adults with SCA, but predictors for this decline are poorly characterized. In a prospective longitudinal multi-center cohort of children with SCA, we tested the hypotheses that: (1) FEV1 % predicted declines over time; and (2) SCA-specific characteristics and therapy predict this decline. At three clinical centers, children with SCA (HbSS or HbSβ0 thalassemia), unselected for respiratory disease, were enrolled in the Sleep and Asthma Cohort (SAC) study. Study-certified pulmonary function technicians performed spirometry and lung volumes. Each assessment was reviewed centrally. Predicted values were determined for TLC, FEV1 , FVC, and FEV1 /FVC ratio. A total of 197 participants, mean age 11.0 years at first testing (range 4-19.3 years), had a minimum of three spirometry measurements, over an average of 4.4 years (range 1.1-6.5 years) from baseline to endpoint. In a multivariable model, FEV1 % predicted declines by 0.3% for every additional year of age (95% CI -0.56 to -0.05, P = .020). Sex, asthma history, hemoglobin, reticulocyte count, white blood cell count, incidence rate of severe acute pain and acute chest syndrome episodes, and hydroxyurea therapy were not associated with a decline in FEV1 % predicted. In a large, rigorously evaluated, prospective cohort of an unselected group of children with SCA, FEV1 % predicted declines minimally over an average of 4 years, and none of the examined disease features predict the decline.
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