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. 2019 Jul 31;7:320. doi: 10.3389/fped.2019.00320

Table 1.

Characteristics of currently available asthma predictive models.

Original API (13) Isle of Wight (14) PIAMA (15) mAPI (16) ucAPI (17) APT (18) ademAPI (19)
Year publication 2000 2003 2009 2013 2014 2014 2015
Country US UK Netherlands US US UK Netherlands
#children survey 1,246 1,034 2,171 289 589 1,998 202
Source population General High-risk High-risk High-risk High-risk High-risk General
Age (y) asthma prediction 6, 8, 11, 13 10 7–8 6, 8, 11 7 6–8 6
Methods of building Clinical index Cumulate risk score Logistic regression Clinical index Clinical index LASSO regression Logistic regression
#predictors used 5 4 8 5 5 10 8
PREDICTORS
Age
Gender
Wheezing frequency*
Parental history of asthma or allergy
Eczema
Rhinitis
Wheezing without colds
Blood eosinophilia
Skin prick test
Specific IgE
Chest infections
Parental medication inhalation
Parental education
Post-term delivery
Activity disturbance
Shortness of breath
Exercise-related wheeze/cough
Aeroallergen-related wheeze/cough
EBC biomarkers
VOCs
Gene expression
*

As enter criteria for stringent API, mAPI, ucAPI, and adem API. API, asthma predictive index; APT, asthma predictive tool; PIAMA, Prevention and Incidence of Asthma and Mite Allergy; mAPI, modified API; ucAPI, University of Cincinnati-API; ademAPI, Asthma Detection and Monitoring-API; EBC, exhaled breath condensate; Ig, immunoglobulin; VOCs, exhaled volatile organic compounds.