Methods:
Study population: We completed a retrospective chart review on 140 C-SCD, ages 6-19 years, followed at the Penn State Pediatric Comprehensive SCD clinic between 2010-2020. PFTs (spirometry, IOS, plethysmography, and DLCO) are typically obtained annually along with pertinent laboratory data. We accessed the charts and extracted demographic characteristics, anthropometric measures, PFT data, pertinent laboratory results, and measures of clinical outcomes.
Control group: We identified 22 race‐matched children (African American and Hispanic) without SCD from our patient pool, who performed DLCO between 2018-2020, primarily due to dyspnea of unknown origin. Children with pre-existing cardiovascular, hematological, oncological, or pulmonary conditions that could affect DLCO were excluded. Since data on total hemoglobin were unavailable for most control subjects, we compared DLCO adjusted for alveolar ventilation (DLCO/VA) between cases and controls (the rest of the analyses in C-SCD were performed using hemoglobin-adjusted DLCO, as described above).
Predictors of adjusted DLCO: DLCO was adjusted for hemoglobin concentration and age using sex-specific predictive equations and expressed as a percent of predicted (%pred)19. We selected the following potential predictors of DLCO: 1) Pulmonary function test estimates: PFT estimates representing obstructive and restrictive airway disease were considered as potential predictors of DLCO. Spirometry data included forced vital capacity (FVC), forced expiratory volume in 1 second (FEV1), FEV1/FVC, and the forced expiratory volume between 25th-75thof FVC (FEV25%-75%). Plethysmography data included total lung capacity (TLC), vital capacity (VC), residual volume (RV), and RV/TLC. Spirometry and plethysmography indices were expressed as %pred (FEV1/FVC and RV/TLC were expressed as a percent). NHANES III equations were used to calculate %predicted values. Measures of total airway resistance (R5) and reactance (X5, Fres, and AX) were obtained from the IOS reports and were expressed as %pred using Berdel/Lechtenbörger equations (except AX, which does not have standard reference values)20. Subjects were instructed not to take bronchodilator therapy for at least 12 hours prior to the PFTs. 2) Laboratory values: the degree of anemia and biomarkers of hemolysis (LDH, total bilirubin, reticulocyte count)– is known to be correlated with SCD related complications. Systemic diseases, including liver and renal function abnormalities, also known to affect DLCO. Neutrophilia and renal failure has been reported as major predictors of death in SCD5. Thus, we adjusted the study analyses for SCD biomarkers, including a complete blood count (CBC) with differential, fetal hemoglobin (HbF), and lactate dehydrogenase (LDH) levels, along with liver and renal function test results (e-Table 1).
Indicators of disease severity and clinical outcomes: Number of ACS has been reported to have an association with risk of early death as early as age of 10 years in C-SCD5,21.Clinical severity indicators considered in this study include lifetime number of hospitalizations with ACS and VOC; sleep-related nocturnal hypoxemia (defined as the percent of total sleep time spent with SpO2 of <90%)22. Additionally, tricuspid regurgitation jet velocity (TRJV) >2.5 m/s, measured by echocardiography, was considered as a surrogate marker of pulmonary hypertension23.
Statistical analyses: We used R (version 3.6.1) and SPSS (version 26.0) for data analysis. DLCO estimates falling outside three times the mean Cook’s Distance and two-standard deviation of Studentized t-values were considered to be outliers and were excluded from further analysis. We compared case and control groups with Mann-Whitney U-tests, and used Pearson correlations to estimate the association between potential predictors and DLCO. We added with bootstrap correction to Pearson correlation to adjust for non-normality24.
Prediction models: Variables with a statistically significant association with DLCO were then examined for relative strength estimation using both a machine learning (ML) based tool, XGBoost, and a linear mixed-effects regression model. XGBoost is a precise and resourceful instrument that can be used for any type of regression analysis or ranking of the predictors, as programmed by a user-built prediction model25. We hypothesized that the ML tool would perform better compared to linear regression since it can further adjust for non-linear associations. Both models were adjusted for age, sex, race, hemoglobin genotype as they affect pulmonary function in children with SCD 26,27. Models were adjusted for hydroxyurea, which increases HbF and improves clinical outcomes in SCD28; and asthma medications like LABA and ICS, which can significantly elevate PFT estimates. Finally, models were also controlled for the diagnosis of asthma (yes vs. no) since asthma is one of the major comorbidities in C-SCD29. We built the XGBoost model based on the five-fold cross-validation (CV) method. Subjects were randomly divided into five equal groups; four of those five groups were selected at a time as training data and the remaining one as test data, and the process was repeated five times. Based on the results, the predictors of DLCO were selected, and the algorithm was built. We discuss further details in e-Appendix 1 .
Multicollinearity adjustment: We estimated the degree of multicollinearity between different PFT indices based on simple linear regression analyses by including all indices in the model with hemoglobin-adjusted DLCO as the dependent variable. In this analysis, FEV1(%) had a high variance inflation factor (VIF) of 5.92 and was therefore removed from further analyses to minimize multicollinearity and stabilize the standard error estimates30; the rest of the predictor variables were included in the final models for both XGBoost and regression analysis.
Ranking of the predictors: Predictors were ranked based on their relative importance determined by “gain” measure in XGBoost and by p-values in the linear mixed model. To quantify the performance of both models in terms of predictive accuracy, we calculated the mean absolute percentage error (MAPE) and correlation coefficient between measured and eDLCO. MAPE values <10% and between 10%-20% are considered as ‘excellent’ and ‘good’ forecasting, respectively31.
Association between DLCO and clinical outcome measures of SCD:To confirm the prognostic importance of DLCO, we analyzed its association with SCD clinical outcomes using linear regression adjusted for age and sex. For the correlational analyses between lifetime events (numbers) of VOC/ACS and DLCO, we used the median values of DLCO for the subjects with multiple data points. We also conducted correlation analyses between DLCO and other disease severity indicators, including TRJV and the degree of nocturnal hypoxemia. First, we examined measured DLCO, and then we used our prediction models (XGBoost and mixed-effect model) to calculate eDLCO, and further analyzed the association between eDLCO values and outcome measures using linear regression to cross-examine the accuracy and clinical relevance of the prediction models.
Validation of the prediction model: Leave-one-out performance (LOOP) cross-validation was used for the model validation32. Using ‘LOOP’ function, predicted DLCO was estimated for each study subject while the remaining data (111 in this case) was used to train the XGBoost algorithm. This process was repeated to predict DLCO for all of study participants. The forecast’s strength was estimated with MAPE and the Pearson correlation coefficient between observed vs. predicted DLCO.