Background The inability to objectively diagnose childhood asthma before age five often results in both under‐treatment and over‐treatment of asthma in preschool children. Prediction tools for estimating a child's risk of developing asthma by school‐age could assist physicians in early asthma care for preschool children. This review aimed to systematically identify and critically appraise studies which either developed novel or updated existing prediction models for predicting school‐age asthma. Methods Three databases (MEDLINE, Embase and Web of Science Core Collection) were searched up to July 2019 to identify studies utilizing information from children ≤5 years of age to predict asthma in school‐age children (6‐13 years). Validation studies were evaluated as a secondary objective. Results Twenty‐four studies describing the development of 26 predictive models published between 2000 and 2019 were identified. Models were either regression‐based (n = 21) or utilized machine learning approaches (n = 5). Nine studies conducted validations of six regression‐based models. Fifteen (out of 21) models required additional clinical tests. Overall model performance, assessed by area under the receiver operating curve (AUC), ranged between 0.66 and 0.87. Models demonstrated moderate ability to either rule in or rule out asthma development, but not both. Where external validation was performed, models demonstrated modest generalizability (AUC range: 0.62‐0.83). Conclusion Existing prediction models demonstrated moderate predictive performance, often with modest generalizability when independently validated. Limitations of traditional methods have shown to impair predictive accuracy and resolution. Exploration of novel methods such as machine learning approaches may address these limitations for future school‐age asthma prediction.

Background Omalizumab and Mepolizumab are biologic drugs with proven efficacy in clinical trials. However, a better understanding of their real‐world effectiveness in severe asthma management is needed. Objectives To better understand the real‐world effectiveness of Omalizumab and Mepolizumab, elucidate the clinical phenotypes of patients treated with these drugs, identify baseline characteristics associated with biologic response and assess the spectrum of responses to these medications. Methods Using real‐world clinical data, we retrospectively phenotyped biologic naïve patients from the Wessex AsThma CoHort of difficult asthma (N = 478) commenced on Omalizumab (N = 105) or Mepolizumab (N = 62) compared to severe asthma patients not receiving biologics (SNB, N = 178). We also assessed multiple clinical endpoints and identified features associated with response. Results Compared to SNB, Omalizumab patients were younger, diagnosed with asthma earlier, and more likely to have rhinitis. Conversely, compared to SNB, Mepolizumab patients were predominantly older males, diagnosed with asthma later, and more likely to have nasal polyposis but less dysfunctional breathing. Both treatments reduced exacerbations, Acute Healthcare Encounters [AHE] (emergency department or hospital admissions), maintenance oral corticosteroid dose, and improved Asthma Control Questionnaire 6 (ACQ6) scores. Omalizumab response was independently associated with more baseline exacerbations (p = .024) but fewer AHE (p = .050) and absence of anxiety (p = .008). Lower baseline ACQ6 was independently associated with Mepolizumab response (p = .007). A composite group of non‐responders demonstrated significantly more psychopathologies and worse baseline subjective disease compared to responder groups. Conclusions and Clinical Relevance In a difficult asthma cohort, Omalizumab and Mepolizumab were used in distinct clinical phenotypes but were both multidimensionally efficacious. Certain baseline clinical characteristics were associated with poorer biologic responses, such as psychological co‐morbidity, which may assist clinicians in biologic selection. These characteristics also emphasize the need for comprehensive approaches to support these patients.