Diagnosing asthma in preschool children remains an unsolved challenge, at a time when early identification would allow for better education and treatment to prevent morbidity and lung function deterioration. Objective: To evaluate if the Asthma Predictive Index (API) can be used as surrogate for asthma diagnosis in preschoolers. Methods: Birth cohort of 339 pregnant women enrolled at delivery and their offspring, who were followed for atopy, wheezing, and other respiratory illnesses through 30 months of age. The API was determined at 30 months of age by the researchers; and examined its association with physician-diagnosed asthma during the first 30 months, made independently by the primary care physician not involved in the study. Results: Among 307 offspring with complete follow-up, 44 (14.3%) were API+. Maternal body mass index, maternal education, past oral contraceptive use, birthweight, placenta weight, age of daycare at 12m, gastroesophageal reflux disease at 12m, acute otitis media at 18m, bronchiolitis, croup and pneumonia, cord blood adiponectin were all associated with API+. In the multivariable analysis, API+ was associated with almost 6-fold odds of asthma diagnosis (adjusted OR= 5.7, 95% CI [2.6-12.3]), after adjusting for the relevant covariates above including respiratory infections like bronchiolitis and pneumonia. The API sensitivity was 48%, specificity 92%, 61% PPV, 88% NPV, 6.4 LR+, 0.56 LR-, 0.84 diagnosis accuracy. The adjusted odds for asthma was 11.4. Conclusions: This longitudinal birth cohort suggests, for first time, that API could be used as a diagnostic tool, not only as a prognostic tool, in toddlers and preschoolers.
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) and biomarkers of systemic disease severity that are associated with and predict abnormal carbon monoxide diffusing capacity (DLCO) in C-SCD. Methods: We obtained PFT data from 51 C-SCD (115 observations) and 22 controls, and identified predictors of DLCO for further analyses. We 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 and estimated DLCO and clinical outcomes, including SCD crises, pulmonary hypertension, and nocturnal hypoxemia. Results: DLCO and several PFT indices were diminished in C-SCD compared to controls. Both statistical approaches ranked FVC%, neutrophils(%), and FEV25%-75% as the top three predictors of DLCO. XGBoost had superior performance compared to the linear model. Both measured and estimated DLCO demonstrated significant association with SCD severity indicators. DLCO estimated by XGBoost was associated with SCD crises (beta=-0.084 [95%CI -0.134, -0.033]) and with TRJV (beta=-0.009 [-0.017, -0.001]), but not with nocturnal hypoxia (p=0.121). Conclusions: In this cohort of C-CSD, DLCO was associated with PFT estimates representing restrictive lung disease (FVC%), airflow obstruction (FEV25%-75%), and inflammation (neutrophil%). We were able to use these indices to estimate DLCO, and show association with disease outcomes, underscoring the prediction models’ clinical relevance.
Rationale: Whether asthma constitutes a risk factor for COVID-19 is unclear. Here we aimed to assess whether asthma, the most common chronic disease in children, is a risk factor for COVID-19 in pediatric populations. Methods: We performed a systematic literature search in three stages: First, we reviewed PubMed, EMBASE and CINAHL for systematic reviews of SARS-CoC-2 and COVID-19 in pediatric populations, and reviewed their primary articles; second, we searched PubMed for studies on COVID-19 or SARS-CoV-2 and asthma/wheeze, and evaluated whether the resulting studies included pediatric populations; third, we repeated the second search in BioRxiv.org and MedRxiv.org to find pre-prints that may have information on pediatric asthma. Results: In the first search, eight systematic reviews were found, of which five were done in pediatric population; after reviewing 67 primary studies we found no data on pediatric asthma as a comorbidity for COVID-19. In the second search, we found 34 results in PubMed, of which five reported asthma in adults, but none included data on children. In the third search, 23 pre-prints in MedRxiv were identified with data on asthma, but again none with pediatric data. We found only one report by the U.S. CDC stating that 40/345 (~11.5%) children with data on chronic conditions had “chronic lung diseases including asthma”. Conclusion: There is scarcely any data on whether childhood asthma (or other pediatric respiratory diseases) constitute risk factors for SARS-CoV-2 infection or COVID-19 severity. Studies are needed that go beyond counting the number of cases in the pediatric age range.