de Jong, Carmen CM.; Pedersen, Eva SL.; Mozun, Rebeca; Müller-Suter, Dominik; Jochmann, Anja; Singer, Florian; Casaulta, Carmen; Regamey, Nicolas; Moeller, Alexander; Ardura-Garcia, Cristina; Kuehni, Claudia E (2020). Diagnosis of asthma in children: findings from the Swiss Paediatric Airway Cohort. European respiratory journal, 56(5), p. 2000132. European Respiratory Society 10.1183/13993003.00132-2020
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INTRODUCTION
Diagnosing asthma in children remains a challenge because respiratory symptoms are not specific and vary over time.
AIM
In a real-life observational study, we assessed the diagnostic accuracy of respiratory symptoms, objective tests, and two paediatric diagnostic algorithms proposed by GINA and NICE to diagnose asthma in school-aged children.
METHODS
We studied children aged 5-17 years referred consecutively for evaluation of suspected asthma to pulmonary outpatient clinics. Symptoms were assessed by parental questionnaire. The investigations included specific IgE measurement or skin prick tests, measurement of fractional exhaled nitric oxide, spirometry, body plethysmography, and bronchodilator reversibility. Asthma was diagnosed by paediatric pulmonologists based on all available data. We assessed diagnostic accuracy of symptoms, tests, and diagnostic algorithms by calculating sensitivity, specificity, positive and negative predictive values, and area under the curve (AUC).
RESULTS
Among 514 participants, 357(70%) were diagnosed with asthma. The combined sensitivity and specificity (sensitivity/specificity) was highest for any wheeze (0.75/0.65), dyspnoea (0.56/0.76), and wheeze triggered by colds (0.58/0.78) or by exercise (0.55/0.74). Of the diagnostic tests, the AUC was highest for specific total resistance (sRtot) (0.73) and lowest for the residual volume (RV) total lung capacity (TLC) ratio (0.56). The NICE algorithm had a sensitivity of 0.69 and specificity of 0.67, whereas the GINA algorithm had a sensitivity of 0.42 and specificity of 0.90.
CONCLUSION
This study confirms the limited usefulness of single tests as well as existing algorithms for the diagnosis of asthma. It highlights the need for new and more appropriate evidence-based guidance.
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