Table 1.

Categorization of existing predictive models for asthma development in children

Category	Articles (year)	Targeted population	Population size	Prediction target (the dependent variable)	Methods for building the predictive models	Predictors included in the final model	Prediction accuracy
For the general child population	Castro-Rodríguez et al. [94–96] (2000, 2011)	Children at age three	986 in [94], 1954 in [95], 93 in [96]	Asthma development at age 6–13	Clinical index	Seven predictors collected from a parental questionnaire: early wheeze, early frequent wheeze, parental asthma, eczema, blood eosinophilia, wheezing without colds, and allergic rhinitis	The loose asthma predictive index: sensitivity = 57 %, specificity = 81 %, positive predictive value = 26 %, negative predictive value = 94 % [94]
							The stringent asthma predictive index: sensitivity = 28 %, specificity = 96 %, positive predictive value = 48 %, negative predictive value = 92 % [94]
	Chang et al. [97] (2013)	Children at age 1–3	289	Asthma development at age 6–11	Clinical index	Early wheeze, early frequent wheeze, parental asthma, eczema, blood eosinophilia, wheezing without colds, allergic sensitization to aeroallergens, allergic sensitization to milk, eggs, or peanuts	Sensitivity = 17 %, specificity = 99 %, positive predictive value = 72 %, negative predictive value = 91 %
	Amat et al. [100] (2011)	Children under age three with a history of ≥3 wheezing episodes and having been assessed for respiratory wheezing disease using a standardized allergy testing program and a doctor-led ISAAC questionnaire [179, 180]	227	Asthma development at age 13			Sensitivity = 87 %, specificity = 37 %, positive predictive value = 61 %, negative predictive value = 71 %, AUC = 0.62, accuracy = 69 %
	Singer et al. [98] (2013)	Children aged 3 months–4 years with recurrent coughing or wheezing	166	Asthma development six years later	Clinical index	Early wheeze, early frequent wheeze, parental asthma, eczema, elevated fraction of exhaled nitric oxide (FeNO), wheezing without colds, allergic rhinitis	Sensitivity = 75 %, specificity = 62 %, positive predictive value = 58 %, negative predictive value = 78 %
	Amin et al. [99] (2014)	Children at age three with ≥1 parent with a positive skin prick test	589	Objectively confirmed asthma at age seven	Clinical index	Frequent wheezing, parental asthma, allergic sensitization to ≥1 aeroallergens, a history of eczema, wheezing without a cold, allergic rhinitis, allergic sensitization to milk or egg	Sensitivity = 44 %, specificity = 94 %, positive predictive value = 60 %, negative predictive value = 89 %
	Klaassen et al. [101] (2015)	Children aged 2–4 years with recurrent wheezing	198	Asthma development at age six	Logistic regression	The original asthma predictive index [94], exhaled volatile organic compounds, gene expression	AUC = 0.86
	Zhang et al. [88] (2014)	Children aged 2–20 months with the first episode of wheezing	128	Multi-trigger wheezing in the next two years	Logistic regression	Wheezing severity score computed using the Preschool Respiratory Assessment Measure scoring scale, number of shed exfoliated airway epithelial cells, family or personal history of atopic disease	Sensitivity = 95 %, specificity = 74 %, positive predictive value = 59 %, negative predictive value = 94 %
	Kurukulaaratchy et al. [89, 103] (2003, 2010)	Children at age four	1034 in [89], 936 in [103]	Persistent wheezing at age 10 (wheezing onset by age four and still wheezing at age 10)	Cumulative risk score	Four predictors collected from a parental questionnaire: family history of asthma, recurrent chest infections at age two, atopic skin prick testing at age four, and absence of nasal symptoms at age one	Sensitivity = 53 %, specificity = 85 %, positive predictive value = 68 %, negative predictive value = 74 % [89]
							Sensitivity = 22 %, specificity = 97 %, positive predictive value = 65 %, negative predictive value = 81 % [103]
	Balemans et al. [90] (2006)	Children at age two	693	Asthma development at age 21	Logistic regression	Four predictors collected from a parental questionnaire: female gender, smoking mother, lower respiratory tract illness before age two, and atopic parents	AUC = 0.66, sensitivity = 53 %, specificity = 70 %, positive predictive value = 20 %, negative predictive value = 91 %
		Children at age four				Four predictors collected from a parental questionnaire: female gender, smoking mother, lower respiratory tract illness between ages two and four, and atopic parents	AUC = 0.68, sensitivity = 71 %, specificity = 53 %, positive predictive value = 18 %, negative predictive value = 93 %
	Clough et al. [17] (1999)	Children aged 3–36 months with first wheezing in the previous 12 weeks and at least one atopic parent	97	Receiving prophylactic antiasthma treatment one year later	Logistic regression	Age, serum soluble interleukin-2 receptor (IL-2R) level	Accuracy = 71 %, sensitivity = 57 %, specificity = 84 %, positive predictive value = 76 %, negative predictive value = 68 %
	Devulapalli et al. [104] (2008)	Children at age two	449	Asthma development at age 10	Severity score	Three predictors collected from a parental questionnaire: number of episodes of bronchial obstruction, number of months with persistent bronchial obstruction, and number of hospital admissions due to bronchial obstruction	AUC = 0.78, sensitivity = 56 %, specificity = 86 %, positive predictive value = 53 %, negative predictive value = 88 % when the severity score was cut off at 5
	Marenholz et al. [105] (2009)	Infants	871	Asthma development between ages 7 and 13	Combination of two attributes	Filaggrin gene mutation, increased immunoglobulin E (IgE) levels to food allergens	Sensitivity = 9 %, specificity = 99 %, positive predictive value = 73 %, negative predictive value = 80 %
		Infants with eczema					Sensitivity = 17 %, specificity = 100 %, positive predictive value = 100 %, negative predictive value = 72 %
	Chatzimichail et al. [106–110] (2010–2013)	Children at age five with an asthma diagnosis	112	Continued asthma diagnosis at age 7–14	Evolutionary algorithm consisting of a neural network and a genetic algorithm [106]	Four predictors collected from a questionnaire: cough, bronchiolitis episodes until age five, wheezing, and asthma diagnosis [106]	Accuracy = 95 % [106]
					Principle component analysis for feature extraction, least square support vector machine for classification [107]	46 predictors collected from a questionnaire [107]	Accuracy = 96 %, sensitivity = 95 %, specificity = 96 % [107]
					Partial least square regression for feature selection, neural network for classification [108]	Nine predictors collected from a questionnaire: wheezing episodes until age five, wheezing episodes between ages three and five, wheezing episodes until age three, weight, waist’s perimeter, seasonal symptoms, FEF25/75, number of family members, and corticosteroids inhaled [108]	Accuracy = 97 %, sensitivity = 96 %, specificity = 100 % [108]
					Correlation analysis for feature selection, neural network for classification [109, 110]	Eight predictors collected from a questionnaire: cough, bronchiolitis episodes until age five, until age three, between ages three and five, at age two, at age three, at age four, and at age five [109]	Accuracy = 100 %, sensitivity = 100 %, specificity = 100 % [109, 110]
						Ten predictors collected from a questionnaire: cough, asthma diagnosis, total number of bronchiolitis episodes until age five, bronchiolitis episodes until age three, between ages three and five, until age four, at age one, at age two, at age three, and at age five [110]
	Lødrup Carlsen et al. [91] (2010)	Children at birth	614	Asthma development by age 10	Logistic regression	Female gender, family network, alcohol in pregnancy, parental rhinoconjunctivis, parental education, lung function at birth (resistance ≤ median, Ve ≤ median, tPTEF/tE ≤ 0.2)	AUC = 0.74, sensitivity = 75 %, specificity = 64 %, positive predictive value = 35 %, negative predictive value = 91 %
	Spycher et al. [92] (2012)	Children at birth	5677	Asthma development at age 7–8	Logistic regression	Genetic information	AUC < 0.6
	van der Werff et al. [102] (2013)	Children aged 4–14 without asthma	1042	Asthma development three years later	Logistic regression	Antibiotic use in the child’s first year of life, family history of atopic diseases, allergic sensitization, and municipality	AUC = 0.69
	Smolinska et al. [111] (2014)	Children aged 2–4 with recurrent wheezing symptoms	252	Asthma development at age six	Random forest for feature selection, dissimilarity partial least squares discriminant analysis for classification	Measurements of volatile organic compounds excreted in breath	Accuracy = 80 %
For the primary care setting	Vial Dupuy et al. [18] (2011)	Children under two presenting recurrent wheezing (≥3 wheezing episodes) to a pediatric pulmonology and allergy center’s outpatient department through primary care physicians’ referral	200	Development of persistent asthma at age six	Logistic regression	Family history of asthma, atopic dermatitis, multiple allergen sensitizations	AUC = 0.66, sensitivity = 42 %, specificity = 90 %, positive predictive value = 67 %, negative predictive value = 76 %
	Caudri et al. [9, 27] (2013, 2009)	Children aged 0–4 at the first time of having asthma-like symptoms in the primary care setting	2171 in [27]	Asthma development at age 7–8	Logistic regression	Eight predictors collected from a parental questionnaire: male gender, post-term delivery, parental education, parental inhaled medication, wheezing frequency, wheeze/dyspnea apart from colds, respiratory infections, and eczema	AUC = 0.74, sensitivity = 36 %, specificity = 91 %, positive predictive value = 32 %, negative predictive value = 92 %
			2877 in [9]	Asthma development at age six		Male gender, pre-term birth, parental education, parental inhaled medication, wheezing frequency, wheeze/dyspnea apart from colds, respiratory infections, eczema	AUC = 0.75, sensitivity = 37 %, specificity = 92 %, positive predictive value = 22 %, negative predictive value = 96 % when the asthma risk score corresponding to the model was cut off at 12
	van der Mark et al. [71] (2014)	Children aged 1–5 previously presented to primary care clinic for recurrent coughing, wheezing, and/or shortness of breath	438	Asthma diagnosis at age six	Logistic regression	Age, family history of asthma or allergy, wheezing-induced sleep disturbances, wheezing in the absence of common colds, specific IgE for cat, dog, and house dust mite	AUC = 0.73, positive predictive value = 22 %, negative predictive value = 78 % when the asthma prediction score corresponding to the model was cut off at 3
	Eysink et al. [65] (2005)	Children aged 1–4 who visited their primary care physicians for persistent coughing of ≥5 days	123	Asthma development at age six	Logistic regression	Age, family history of pollen allergy, wheezing, specific IgE for cat, dog, and house dust mite	AUC = 0.87
	Pescatore et al. [113, 114] (2014)	Children aged 1–3 who visited their primary care physicians for wheeze or cough	1226 in [113], 140 in [114]	Asthma development 5 years later	Logistic regression	Gender, age, wheeze without colds, wheeze frequency, activity disturbance, shortness of breath, exercise-related wheeze/cough, aeroallergen-related wheeze/cough, eczema, parental history of asthma/bronchitis	AUC = 0.76, sensitivity = 72 %, specificity = 71 %, positive predictive value = 49 %, negative predictive value = 86 % when the asthma prediction score corresponding to the model was cut off at 5
For bronchiolitis patients	Mikalsen et al. [93] (2013)	Children at age two previously hospitalized for bronchiolitis during infancy	93	Asthma diagnosis at age 11	Logistic regression	Four predictors collected from a parental questionnaire: recurrent wheezing, parental atopy, parental asthma, and atopic dermatitis	Sensitivity = 65 %, specificity = 82 %, positive predictive value ≈ 50 %, negative predictive value ≈ 89 %