Search

Background: Numerous risk scores have been developed to predict childhood asthma. However, they may not predict asthma beyond childhood. We aim to create childhood risk scores that predict development and persistence of asthma up to young adult life. Methods: The Isle of Wight Birth Cohort (n = 1456) was prospectively assessed up to 26 years of age. Asthma predictive scores were developed based on factors during the first 4 years, using logistic regression and tested for sensitivity, specificity and area under the curve (AUC) for prediction of asthma at (i) 18 and (ii) 26 years, and persistent asthma (PA) (iii) at 10 and 18 years, and (iv) at 10, 18 and 26 years. Models were internally and externally validated. Results: Four models were generated for prediction of each asthma outcome. ASthma PredIctive Risk scorE (ASPIRE)‐1: a 2‐factor model (recurrent wheeze [RW] and positive skin prick test [+SPT] at 4 years) for asthma at 18 years (sensitivity: 0.49, specificity: 0.80, AUC: 0.65). ASPIRE‐2: a 3‐factor model (RW, +SPT and maternal rhinitis) for asthma at 26 years (sensitivity: 0.60, specificity: 0.79, AUC: 0.73). ASPIRE‐3: a 3‐factor model (RW, +SPT and eczema at 4 years) for PA‐18 (sensitivity: 0.63, specificity: 0.87, AUC: 0.77). ASPIRE‐4: a 3‐factor model (RW, +SPT at 4 years and recurrent chest infection at 2 years) for PA‐26 (sensitivity: 0.68, specificity: 0.87, AUC: 0.80). ASPIRE‐1 and ASPIRE‐3 scores were replicated externally. Further assessments indicated that ASPIRE‐1 can be used in place of ASPIRE‐2‐4 with same predictive accuracy. Conclusion: ASPIRE predicts persistent asthma up to young adult life. [ABSTRACT FROM AUTHOR]

Data from three UK birth cohorts were used in this study: the IOWBC, Manchester Asthma and Allergy Study (MAAS), and The Avon Longitudinal Study of Parents and Children (ALSPAC) (see Supplementary Methods for cohort details, Table S1). All analyses in the IOWBC (and where data were available in MAAS and ALSPAC at their respective time points, Table S1) were adjusted for gender, maternal asthma, paternal asthma, and sibling asthma. Keywords: ALSPAC; childhood asthma; meta-analysis; parental smoking; polygenic risk score; PRS-by-environment interactions EN ALSPAC childhood asthma meta-analysis parental smoking polygenic risk score PRS-by-environment interactions 1 4 4 04/28/22 20220401 NES 220401 AUTHOR CONTRIBUTIONS B Dilini M. Kothalawala: b Conceptualization (equal); Data curation (equal); Formal analysis (equal); Investigation (equal); Methodology (lead); Project administration (lead); Software (equal); Validation (equal); Visualization (lead); Writing - original draft (lead); Writing - review & editing (lead). Covariates are gender, maternal asthma, paternal asthma, and sibling asthma. [Extracted from the article]

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. [ABSTRACT FROM AUTHOR]

To the Editor, We would like to thank Owora et al1 for their interest in our systematic review of childhood asthma prediction models and welcome their subsequent analyses and recommendations for future research in this field. To provide an illustrative example of the potential insight offered by reclassification tables, we compared two existing asthma prediction models for which data were available in the Isle of Wight Birth Cohort (IOWBC).5 The two models we compared were the updated PIAMA6 and PARS7 models. For example, although not evident from the receiver operating characteristic (ROC) plot, the reclassification table suggests that predictions made by the updated PIAMA model are skewed towards underestimating asthma risk in the IOWBC when compared against the PARS model. [Extracted from the article]

Genome-wide and epigenome-wide association studies have identified genetic variants and differentially methylated nucleotides associated with childhood asthma. Incorporation of such genomic data may improve performance of childhood asthma prediction models which use phenotypic and environmental data. Using genome-wide genotype and methylation data at birth from the Isle of Wight Birth Cohort (n = 1456), a polygenic risk score (PRS), and newborn (nMRS) and childhood (cMRS) methylation risk scores, were developed to predict childhood asthma diagnosis. Each risk score was integrated with two previously published childhood asthma prediction models (CAPE and CAPP) and were validated in the Manchester Asthma and Allergy Study. Individually, the genomic risk scores demonstrated modest-to-moderate discriminative performance (area under the receiver operating characteristic curve, AUC: PRS = 0.64, nMRS = 0.55, cMRS = 0.54), and their integration only marginally improved the performance of the CAPE (AUC: 0.75 vs. 0.71) and CAPP models (AUC: 0.84 vs. 0.82). The limited predictive performance of each genomic risk score individually and their inability to substantially improve upon the performance of the CAPE and CAPP models suggests that genetic and epigenetic predictors of the broad phenotype of asthma are unlikely to have clinical utility. Hence, further studies predicting specific asthma endotypes are warranted. [ABSTRACT FROM AUTHOR]