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Table 1 Categorization of existing predictive models for asthma development in children

From: A systematic review of 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. [9496] (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. [106110] (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 %