S59 Predicting asthma in later childhood: a general and high-risk population approach. (15th November 2017)

• Record Type: Journal Article
• Authors: Colicino, S; Minelli, C; Lewin, A; Turner, S; Simpson, A; Arshad, SH; Henderson, AJW; Custovic, A; Cullinan, P
• Is Part Of: Thorax. Volume 72(2017)Supplement 3
• Page Start: A38
• Page End: A38
• Publication Date: 2017-11-15
• Subjects: Chest -- Diseases -- Periodicals; Thorax; Chest -- Diseases; Periodicals; Periodicals; 617.54
• DOI: 10.1136/thoraxjnl-2017-210983.65
• Languages: English
• ISSNs: 0040-6376

Abstract

Abstract : Introduction: Young children commonly wheeze but only some have asthma later in life. Asthma prediction tools have poor predictive performance and few have been validated. We aimed to develop a robust tool for the prediction of asthma at age 10–14 years using readily available information. Methods we studied 5 UK birth cohorts (the STELAR consortium) and considered two groups: 1. all children recruited at birth and 2. high-risk children on the basis of reported wheezing at 2/3 or 5 years. Two comparable cohorts (Ashford and ALSPAC) were used to select predictors (training sample) and the SEATON, MAAS and Isle of Wight studies to assess predictive performance (validation sample). We included 16 187 and 814 children from groups 1 and 2 respectively in the training sample and validated the developed predictive tools in 5320 and 285 children from the validation sample. We considered 40 potential predictors collected at recruitment and at 1, 2/3 and 5 years of age: demographic and perinatal information, eczema, hay-fever, respiratory symptoms, environmental and family-related factors. We defined asthma at 10–14 years by the presence of both current wheeze and asthma treatment. We compared 5 statistical methods to select variables and estimate coefficients: stepwise regression, classical (LASSO and Elastic-Net, EN), empirical Bayes (EB) and Bayesian (BM) regularisation Methods Predictive performance was assessed using calibration and discrimination measures including area under the ROC curve (AUC). Results: Asthma prevalence at age 10–14 ranged from 7%–18% in group 1 and from 32%–52% in group 2. Frequency of early wheezing, eczema, and paternal asthma were important predictors in all models and both groups. Other selected predictors included birth order, maternal asthma and domestic pets. Specificity and negative predictive value (NPV) were higher in the general population, while sensitivity and positive predictive value (PPV) were higher in high-risk group. BM (AUC 0.77, specificity 0.84 and NPV 0.93) and EN (AUC 0.74, sensitivity 0.71 and PPV 0.65) provided the highest accuracy and discriminative ability predictive ability in the 2 groups, respectively. Conclusion: The use of sophisticated statistical methods in a large, multicentre population demonstrated promising Results in developing an asthma predictive tool.