Higher Pulmonary Disease Burden among Hispanic Children with Sickle Cell Disease

Chen, L.; Gong, Jacqueline; Matta, Esther; Morrone, Kerry; Manwani, Deepa; Rastogi, Deepa; De, Aliva

doi:10.1164/ajrccm-conference.2019.199.1_meetingabstracts.a4387

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“…Biltagi et al (2) reported DLCO impairment in C-SCD compared to controls, and we previously reported an annual DLCO decline of 1.5% in C-SCD (12). Moreover, DLCO impairment in SCD may differ by race/ethnicity (13). However, to date, no studies have focused on the determinants of DLCO in the SCD population.…”

Section: Introductionmentioning

confidence: 69%

“…XGBoost is a machine learning instrument that can be used for any type of regression analysis or ranking of the predictors, as programmed by a userbuilt prediction model (27), while mixed-model is useful for longitudinal data; both approaches can account for variables with repeated measures within participants. Both models were adjusted for potential confounders or effect modifiers such as age, sex, race, hemoglobin genotype (13,25). Models were also adjusted for hydroxyurea, which increases HbF (28).…”

Section: Prediction Modelsmentioning

confidence: 99%

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Predictors of Diffusing Capacity in Children With Sickle Cell Disease: A Longitudinal Study

et al. 2021

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Background: 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 = 0.12).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.

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Section: Introductionmentioning

confidence: 69%