In young children with asthma, obesity and uncontrolled disease are highly associated with peripheral airway impairment

摘要

? 2023 American College of Allergy, Asthma ImmunologyBackground: Despite the fact that impulse oscillometry (IOS)-determined peripheral airway impairment (PAI) phenotype is a major risk factor for uncontrolled asthma, IOS is seldom used clinically. Objective: To identify clinical characteristics that can best identify the PAI phenotype. Methods: Clinical characteristics and spirometry results were compared in 227 patients with asthma with the PAI phenotype determined by resistance and reactance values that exceeded IOS?predictive normal values using Gochicoa-Rangel equations. Logistic regression analyses determined factors associated with PAI phenotype, with risk classification based on predicted probability from the final adjusted model. Results: Analysis for identifying PAI, present in 37 of our population, revealed statistically significant odds ratio (OR) for age (4-7 years), of 3.75 (1.47-9.55) (P =.006), obesity OR of 2.59 (1.36-4.96) (P =.004), uncontrolled asthma OR of 2.77 (1.34-5.74) (P =.006), and abnormal forced expiratory flow between 25 and 75 (FEF25-75) (<65) OR of 4.22 (1.59-11.20) (P =.004). For identifying PAI in those considered well controlled, key characteristics were age (4-7 years), OR of 2.81 (1.10-7.18) (P =.03), and obesity, OR of 2.18 (1.09-4.39) (P =.03). For those 4 to 7 years old, who were obese and had uncontrolled disease, probability of PAI was greater than or equal to 80, regardless of FEF 25-75. Probabilities from logistic regression analyses to identify PAI were associated with an area under the curve of 0.750, and applying standard threshold of greater than or equal to 0.50 probability for identification produced sensitivity at 49.4, specificity at 85.3, positive predictive value at 66.1, negative predictive value at 74.4, and accuracy at 72.1. Conclusion: Clinical characteristics of age at 4 to 7 years, obesity, uncontrolled asthma, and FEF 25-75 (<65) identify PAI with high specificity and accuracy. This approach offers the clinician a practical method for strongly considering the presence of PAI when IOS is not available.