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.

The factors influencing weaning of preterm infants from noninvasive ventilation (NIV) are poorly defined and the weaning decisions are often driven by subjective judgement rather than objective measures. To standardize quantification of respiratory effort, the Silverman-Andersen Score (SAS) was included in our nursing routine. We investigated the factors that steer the weaning process and whether the inclusion of the SAS would lead to more stringent weaning. Following SAS implementation, we prospectively evaluated 33 neonates born ≤ 32 + 0 weeks gestational age. Age-, weight- and sex-matched infants born before routine SAS evaluation served as historic control. In 173 of 575 patient days, NIV was not weaned despite little respiratory distress (SAS ≤ 2), mainly due to bradycardias (60% of days without weaning), occurring alone (40%) or in combination with other factors such as apnea/desaturations. In addition, “soft factors” that are harder to grasp impact on weaning decisions, whereas the SAS overall played a minor role. Consequently, ventilation times did not differ between the groups. In conclusion, NIV weaning is influenced by various factors that override the absence of respiratory distress limiting the predictive value of the SAS. An awareness of the factors that influence weaning decisions is important as prolonged use of NIV has been associated with adverse outcome. Guidelines are necessary to standardize NIV weaning practice.

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To the Editor, Environmental exposures such as air pollution, farm animals, and tobacco smoke have been established as risk factors for childhood asthma. 1 Single nucleotide polymorphisms (SNPs) associated with childhood asthma have also been identified in both candidate gene and genome-wide association studies. 2,3 However, the majority of studies consider genetic and environmental factors independently and assume the degree of risk conferred is uniform across all individuals.As the impact of environmental risk factors may be influenced by an individual's genetic predisposition to the disease, a