Literature DB >> 17027432

Prediction of asthma in young adults using childhood characteristics: Development of a prediction rule.

Walter A F Balemans1, Cornelis K van der Ent, Anne G M Schilder, Elisabeth A M Sanders, Gerhard A Zielhuis, Maroeska M Rovers.   

Abstract

OBJECTIVE: To develop an easily applicable prediction rule for asthma in young adulthood using childhood characteristics.
METHODS: A total of 1,055 out of 1,328 members of a Dutch birth cohort were followed from 2 to 21 years of age. Univariate and multivariate logistic regression analyses were used to evaluate the predictive value of childhood characteristics on asthma at 21 years of age. A prognostic function was developed, and the area under the receiving operating characteristic (ROC) curve was used to estimate the predictive ability of the prognostic models.
RESULTS: Of the 693 responding subjects, 86 (12%) were diagnosed with asthma. Independent prognostic factors at ages 2 and 4 years were female gender (odds ratios (OR) 1.9 and 2.1; 95% confidence intervals (CI) 1.2-3.2 and 1.3-2.5), smoking mother (OR 1.6 and 1.6; CI 1.0-2.7 and 1.0-2.6), lower respiratory tract illness (OR 1.9 and 2.4; CI 1.0-3.6 and 1.4-4.0), and atopic parents (OR 2.1 and 1.9; CI 1.3-3.4 and 1.2-3.1). The predictive power of both models was poor; area under ROC curve was 0.66 and 0.68, respectively.
CONCLUSION: Asthma in young adulthood could not be predicted satisfactorily based on childhood characteristics. Nevertheless, we propose that this method is further tested as a tool to predict development of asthma.

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Year:  2006        PMID: 17027432     DOI: 10.1016/j.jclinepi.2006.02.011

Source DB:  PubMed          Journal:  J Clin Epidemiol        ISSN: 0895-4356            Impact factor:   6.437


  5 in total

1.  Risk for asthma in offspring of asthmatic mothers versus fathers: a meta-analysis.

Authors:  Robert H Lim; Lester Kobzik; Morten Dahl
Journal:  PLoS One       Date:  2010-04-12       Impact factor: 3.240

2.  Prediction and treatment of asthma in preschool children at risk: study design and baseline data of a prospective cohort study in general practice (ARCADE).

Authors:  Karina E van Wonderen; Lonneke B van der Mark; Jacob Mohrs; Ronald B Geskus; Willem M van der Wal; Wim M C van Aalderen; Patrick J E Bindels; Gerben ter Riet
Journal:  BMC Pulm Med       Date:  2009-04-15       Impact factor: 3.317

3.  An intelligent system approach for asthma prediction in symptomatic preschool children.

Authors:  E Chatzimichail; E Paraskakis; M Sitzimi; A Rigas
Journal:  Comput Math Methods Med       Date:  2013-03-14       Impact factor: 2.238

4.  Predicting asthma in preschool children with asthma symptoms: study rationale and design.

Authors:  Esther Hafkamp-de Groen; Hester F Lingsma; Daan Caudri; Alet Wijga; Vincent Wv Jaddoe; Ewout W Steyerberg; Johan C de Jongste; Hein Raat
Journal:  BMC Pulm Med       Date:  2012-10-16       Impact factor: 3.317

Review 5.  A systematic review of predictive models for asthma development in children.

Authors:  Gang Luo; Flory L Nkoy; Bryan L Stone; Darell Schmick; Michael D Johnson
Journal:  BMC Med Inform Decis Mak       Date:  2015-11-28       Impact factor: 2.796

  5 in total

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