| piama_risk_score | R Documentation |
Calculates the Prevention and Incidence of Asthma and Mite Allergy (PIAMA) Risk Score. This tool predicts the probability of a preschool-aged child (tested around age 4) developing asthma by school age (age 8), based on eight clinical and demographic variables.
piama_risk_score(sex, gestational_age_gt_40_weeks,
parental_education_low_intermediate, parental_asthma,
eczema, wheezing_frequency, wheezing_apart_from_colds,
inhaled_medication)
sex |
String. Child's sex ("Male" or "Female"). (Male adds 2 points). |
gestational_age_gt_40_weeks |
Numeric (0 or 1). Was the child born post-term (> 40 weeks gestation)? (1 = Yes, +1 point). |
parental_education_low_intermediate |
Numeric (0 or 1). Is maternal/paternal education level low or intermediate (i.e., not high/tertiary)? (1 = Yes, +1 point). |
parental_asthma |
Numeric (0 or 1). Does a parent have asthma? (1 = Yes, +2 points). |
eczema |
Numeric (0 or 1). Does the child have eczema? (1 = Yes, +1 point). |
wheezing_frequency |
String. Frequency of wheezing in the past year. "none": 0 points. "1_to_3": 1-3 episodes (+1 point). "4_or_more": >= 4 episodes (+5 points). |
wheezing_apart_from_colds |
Numeric (0 or 1). Does wheezing occur apart from colds? (1 = Yes, +2 points). |
inhaled_medication |
Numeric (0 or 1). Did the child use inhaled medication (bronchodilators or steroids) in the last year? (1 = Yes, +4 points). |
A list containing:
PIAMA_Score |
The calculated risk score (Range 0-18). |
Asthma_Risk_Age_8 |
Estimated probability of having asthma at age 8 years. |
Caudri D, Wijga A, A Schipper CM, et al. Predicting the long-term prognosis of children with symptoms suggestive of asthma at preschool age. J Allergy Clin Immunol. 2009;124(5):903-910.e1-7. doi:10.1016/j.jaci.2009.06.045
# Example 1: High Risk
# Male (+2), Post-term (+1), Parent Asthma (+2), Wheeze >4x (+5), Meds (+4)
# Score = 14
piama_risk_score("male", 1, 0, 1, 0, "4_or_more", 0, 1)
# Example 2: Low Risk
# Female, Term, High Education, No Hx, Wheeze 1-3x (+1)
# Score = 1
piama_risk_score("female", 0, 0, 0, 0, "1_to_3", 0, 0)
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