Use of inhaled corticosteroids and the risk of hospitalisation for pneumonia in children with asthma: a nationwide cohort study.

BACKGROUND: The potential association between the use of inhaled corticosteroids (ICS) and the risk of pneumonia among adults is disputed and paediatric-specific evidence is scarce. AIM: To assess the potential association between ICS, use and the risk of hospitalisation for pneumonia among children (age 2-17 years) with asthma. METHODS: This was a cohort study based on nationwide data from routine clinical practice in Sweden (January 2007 to November 2021). From 425 965 children with confirmed asthma, episodes of new ICS use and no use were identified using records of dispensed drugs. We adjusted for potential confounders with propensity score overlap weighting and the risk of a hospitalisation with pneumonia as primary diagnosis was estimated. Multiple subgroup and sensitivity analyses were also performed. RESULTS: We identified 249 351 ICS (mean follow-up of 0.9 years) and 214 840 no-use (mean follow-up of 0.7 years) episodes. During follow-up, 369 and 181 events of hospitalisation for pneumonia were observed in the ICS and no-use episodes, respectively. The weighted incidence rates of hospitalisation for pneumonia was 14.5 per 10 000 patient-years for ICS use episodes and 14.6 for no-use episodes. The weighted HR for hospitalisation for pneumonia associated with ICS use was 1.06 (95% CI 0.88 to 1.28) and the absolute rate difference was -0.06 (95% CI -2.83 to 2.72) events per 10 000 patient-years, compared with no use. CONCLUSIONS: In this nationwide cohort study, we found no evidence of an association between ICS use and the risk of hospitalisation for pneumonia among children with asthma, as compared with no use.

Risk factors for severe respiratory syncytial virus infection during the first year of life: development and validation of a clinical prediction model.

BACKGROUND: Novel immunisation methods against respiratory syncytial virus (RSV) are emerging, but knowledge of risk factors for severe RSV disease is insufficient for optimal targeting of interventions against them. Our aims were to identify predictors for RSV hospital admission from registry-based data and to develop and validate a clinical prediction model to guide RSV immunoprophylaxis for infants younger than 1 year. METHODS: In this model development and validation study, we studied all infants born in Finland between June 1, 1997, and May 31, 2020, and in Sweden between June 1, 2006, and May 31, 2020, along with the data for their parents and siblings. Infants were excluded if they died or were admitted to hospital for RSV within the first 7 days of life. The outcome was hospital admission due to RSV bronchiolitis during the first year of life. The Finnish study population was divided into a development dataset (born between June 1, 1997, and May 31, 2017) and a temporal hold-out validation dataset (born between June 1, 2017, and May 31, 2020). The development dataset was used for predictor discovery and selection in which we screened 1511 candidate predictors from the infants', parents', and siblings' data, and developed a logistic regression model with the 16 most important predictors. This model was then validated using the Finnish hold-out validation dataset and the Swedish dataset. FINDINGS: In total, there were 1 124 561 infants in the Finnish development dataset, 130 352 infants in the Finnish hold-out validation dataset, and 1 459 472 infants in the Swedish dataset. In addition to known predictors such as severe congenital heart defects (adjusted odds ratio 2·89, 95% CI 2·28-3·65), we confirmed some less established predictors for RSV hospital admission, most notably oesophageal malformations (3·11, 1·86-5·19) and lower complexity congenital heart defects (1·43, 1·25-1·63). The prediction model's C-statistic was 0·766 (95% CI 0·742-0·789) in Finnish data and 0·737 (0·710-0·762) in Swedish validation data. The infants in the highest decile of predicted RSV hospital admission probability had 4·5 times higher observed risk compared with others. Calibration varied according to epidemic intensity. The model's performance was similar to a machine learning (XGboost) model using all 1511 candidate predictors (C-statistic in Finland 0·771, 95% CI 0·754-0·788). The prediction model showed clinical utility in decision curve analysis and in hypothetical number needed to treat calculations for immunisation, and its C-statistic was similar across different strata of parental income. INTERPRETATION: The identified predictors and the prediction model can be used in guiding RSV immunoprophylaxis in infants, or as a basis for further immunoprophylaxis targeting tools. FUNDING: Sigrid Jusélius Foundation, European Research Council, Pediatric Research Foundation, and Academy of Finland.

Introducing a New Algorithm for Classification of Etiology in Studies on Pediatric Pneumonia: Protocol for the Trial of Respiratory Infections in Children for Enhanced Diagnostics Study.

BACKGROUND: There is a need to better distinguish viral infections from antibiotic-requiring bacterial infections in children presenting with clinical community-acquired pneumonia (CAP) to assist health care workers in decision making and to improve the rational use of antibiotics. OBJECTIVE: The overall aim of the Trial of Respiratory infections in children for ENhanced Diagnostics (TREND) study is to improve the differential diagnosis of bacterial and viral etiologies in children aged below 5 years with clinical CAP, by evaluating myxovirus resistance protein A (MxA) as a biomarker for viral CAP and by evaluating an existing (multianalyte point-of-care antigen detection test system [mariPOC respi] ArcDia International Oy Ltd.) and a potential future point-of-care test for respiratory pathogens. METHODS: Children aged 1 to 59 months with clinical CAP as well as healthy, hospital-based, asymptomatic controls will be included at a pediatric emergency hospital in Stockholm, Sweden. Blood (analyzed for MxA and C-reactive protein) and nasopharyngeal samples (analyzed with real-time polymerase chain reaction as the gold standard and antigen-based mariPOC respi test as well as saved for future analyses of a novel recombinase polymerase amplification-based point-of-care test for respiratory pathogens) will be collected. A newly developed algorithm for the classification of CAP etiology will be used as the reference standard. RESULTS: A pilot study was performed from June to August 2017. The enrollment of study subjects started in November 2017. Results are expected by the end of 2019. CONCLUSIONS: The findings from the TREND study can be an important step to improve the management of children with clinical CAP. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/12705.