Predictors of Diffusing Capacity in Children With Sickle Cell Disease: A Longitudinal Study - Mondal, Pritish; Midya, Vishal; Khokhar, Arshjot; Sathianathan, Shyama; Forno, Erick (Frontiers in Pediatrics, 2021)
Rationale: Gas exchange abnormalities in Sickle Cell Disease (SCD) may represent cardiopulmonary deterioration. Identifying predictors of these abnormalities in children with SCD (C-SCD) may help us understand disease progression and develop informed management decisions. Objectives: To identify pulmonary function tests (PFT) estimates and biomarkers of disease severity that are associated with and predict abnormal diffusing capacity (DLCO) in C-SCD. Methods: We obtained PFT data from 51 C-SCD (median age:12.4 years, male: female=29:22) (115 observations) and 22 controls (median age:11.1 years, male: female=8:14), formulated a rank list of DLCO predictors based on machine learning algorithms (XGBoost) or linear mixed-effect models, and compared estimated DLCO to the measured values. Finally, we evaluated the association between measured or estimated DLCO and clinical outcomes, including SCD crises, pulmonary hypertension, and nocturnal desaturation. Results: Hemoglobin-adjusted DLCO(%) and several PFT indices were diminished in C-SCD compared to controls. Both statistical approaches ranked FVC(%), neutrophils(%), and FEF25-75(%) as the top three predictors of DLCO. XGBoost had superior performance compared to the linear model. Both measured and estimated DLCO demonstrated a significant association with SCD severity: higher DLCO, estimated by XGBoost, was associated with fewer SCD crises (beta=-0.084 [95%CI: -0.13, -0.033]) and lower TRJV (beta=-0.009 [-0.017, -0.001]), but not with nocturnal desaturation (p=.12). Conclusions: In this cohort of C-CSD, DLCO was associated with PFT estimates representing restrictive lung disease (FVC, TLC), airflow obstruction (FEF25-75, FEV1/FVC, R5), and inflammation (neutrophilia). We used these indices to estimate DLCO, and show association with disease outcomes, underscoring the prediction models’ clinical relevance.