Severe viral respiratory tract infections in children

Respiratory tract infections (RTIs) are estimated to cause 703.000 deaths annually in children below five years. The majority of RTIs in children are caused by viruses, yet the number of antivirals approved for treatment of these infections is very limited. Moreover, it is sometimes complicated to distinguish between bacterial and viral RTIs, which results in overuse of antibiotics. The aim of this thesis is to improve the understanding of the causative role of respiratory viruses in children with severe RTI, with the long-term goal to improve diagnostics, facilitate the development of new antiviral drugs and reduce unnecessary antibiotic use. To achieve this, a number of specific objectives have been assessed. The spread of the Influenza A H1N1(pdm09) i.e. the swine flu pandemic was slower than expected when it reached Europe during Spring 2009. This was suggested to be due to negative viral interference by circulating rhinovirus (RV). In Paper I, children with influenza-like illness were assessed during the swine flu pandemic in 2009. Co-infections were specifically assessed in influenza-positive patients with regard to disease severity. No significant difference was found between patients with single versus viral co-infection. Co-infection with influenza and RV was not uncommon, which contradicted the proposed hypothesis of viral interference. Moreover, the study showed that several different viruses were present in the children with suspected influenza, underscoring the overlap of disease presentation of different respiratory viruses. PCR is a very sensitive method for detecting viruses, yet the significance of a finding in upper respiratory specimens has been questioned. In Paper II, we assessed the role of viruses in acute respiratory illness in a case-control study. Respiratory syncytial virus (RSV), human metapneumovirus (hMPV) and parainfluenza virus were highly associated with acute respiratory illness. In contrast, detection of other viruses was common in asymptomatic controls, showing the complexity in interpreting PCR-positivity for these viruses. Community-acquired pneumonia (CAP) is a disease that traditionally has been considered a predominantly bacterial disease. Nevertheless, successful immunization against the two major bacterial causes, Streptococcus pneumoniae and Haemophilus influenza, has contributed to a declining incidence of the disease and has likely also led to a relative increase of other etiologic agents. In Paper III, the role of viruses in CAP was assessed in another case-control study. Viruses were detected in the majority of cases and RSV, hMPV and influenza were highly associated with CAP. The study suggests that viruses have a major role in childhood CAP and indicates that viral CAP is an underdiagnosed disease. Viral RTIs affect also immunosuppressed children. Neutropenia is a common adverse effect in children receiving chemotherapeutic treatment for malignancies. The condition highly increases the risk for septicemia, and fever is sometimes the only symptom. However, in the majority of episodes of febrile neutropenia, no causative agent can be identified. In Paper IV, respiratory viruses were assessed in immunosuppressed children during episodes of febrile neutropenia. Interestingly, respiratory viruses were detected in almost half of the episodes, whereas laboratory confirmed septicemia was infrequent (9%). Moreover, the majority of children had cleared their virus at follow-up suggesting a causal relationship between the detected viruses and the episodes of febrile neutropenia. This thesis has contributed to an improved understanding of the role of viruses in severe RTIs in children stressing the urgent need for new diagnostic tests that better distinguish between viral and bacterial disease. It also forwards the need for improved treatment options and new vaccines against viral RTIs in children

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 myxovims 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

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.Peer reviewe