Carmen C M de Jong1, Eva S L Pedersen1, Rebeca Mozun1, Dominik Müller-Suter2, Anja Jochmann3, Florian Singer4,5, Carmen Casaulta4,6, Nicolas Regamey7, Alexander Moeller8, Cristina Ardura-Garcia1, Claudia E Kuehni1,4. 1. Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland. 2. Paediatric Respiratory Medicine, Kantonsspital Aarau, Aarau, Switzerland. 3. Paediatric Respiratory Medicine, University Children's Hospital (UKBB), University of Basel, Basel, Switzerland. 4. Paediatric Respiratory Medicine, Children's University Hospital of Bern, University of Bern, Bern, Switzerland. 5. PedNet Bern, Children's University Hospital of Bern, University of Bern, Bern, Switzerland. 6. Paediatric Respiratory Medicine, Kantonsspital Graubunden, Chur, Switzerland. 7. Division of Paediatric Pulmonology, Children's Hospital, Lucerne, Switzerland. 8. Division of Paediatric Pulmonology, University Children's Hospital of Zurich, University of Zurich, Zurich, Switzerland.
Abstract
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 the Global Initiative for Asthma (GINA) and the National Institute for Health and Care Excellence (NICE)) in the diagnosis of asthma in school-aged children. METHODS: We studied children aged 5-17 years who were referred consecutively to pulmonary outpatient clinics for evaluation of suspected asthma. Symptoms were assessed by parental questionnaire. The investigations included specific IgE measurement or skin prick tests, measurement of exhaled nitric oxide fraction (F eNO), spirometry, body plethysmography and bronchodilator reversibility (BDR). 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 predictive value (PPV), negative predictive value (NPV) and area under the curve (AUC). RESULTS: Among 514 participants, 357 (70%) were diagnosed with asthma. The combined sensitivity and specificity was highest for any wheeze (sensitivity=75%, specificity=65%), dyspnoea (sensitivity=56%, specificity=76%) and wheeze triggered by colds (sensitivity=58%, specificity=78%) or by exercise (sensitivity=55%, specificity=74%). Of the diagnostic tests, the AUC was highest for specific total airway resistance (sRtot; AUC=0.73) and lowest for the residual volume (RV)/total lung capacity (TLC) ratio (AUC=0.56). The NICE algorithm had sensitivity=69% and specificity=67%, whereas the GINA algorithm had sensitivity=42% and specificity=90%. CONCLUSION: This study confirms the limited usefulness of single tests and existing algorithms for the diagnosis of asthma. It highlights the need for new and more appropriate evidence-based guidance.
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 the Global Initiative for Asthma (GINA) and the National Institute for Health and Care Excellence (NICE)) in the diagnosis of asthma in school-aged children. METHODS: We studied children aged 5-17 years who were referred consecutively to pulmonary outpatient clinics for evaluation of suspected asthma. Symptoms were assessed by parental questionnaire. The investigations included specific IgE measurement or skin prick tests, measurement of exhaled nitric oxide fraction (F eNO), spirometry, body plethysmography and bronchodilator reversibility (BDR). 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 predictive value (PPV), negative predictive value (NPV) and area under the curve (AUC). RESULTS: Among 514 participants, 357 (70%) were diagnosed with asthma. The combined sensitivity and specificity was highest for any wheeze (sensitivity=75%, specificity=65%), dyspnoea (sensitivity=56%, specificity=76%) and wheeze triggered by colds (sensitivity=58%, specificity=78%) or by exercise (sensitivity=55%, specificity=74%). Of the diagnostic tests, the AUC was highest for specific total airway resistance (sRtot; AUC=0.73) and lowest for the residual volume (RV)/total lung capacity (TLC) ratio (AUC=0.56). The NICE algorithm had sensitivity=69% and specificity=67%, whereas the GINA algorithm had sensitivity=42% and specificity=90%. CONCLUSION: This study confirms the limited usefulness of single tests and existing algorithms for the diagnosis of asthma. It highlights the need for new and more appropriate evidence-based guidance.
Authors: Rebeca Mozun; Cristina Ardura-Garcia; Eva S L Pedersen; Myrofora Goutaki; Jakob Usemann; Florian Singer; Philipp Latzin; Alexander Moeller; Claudia E Kuehni Journal: Pediatr Pulmonol Date: 2021-10-01
Authors: Ran Wang; Stephen J Fowler; Stephen W Turner; Sarah Drake; Laura Healy; Lesley Lowe; Hannah Wardman; Miriam Bennett; Adnan Custovic; Angela Simpson; Clare S Murray Journal: ERJ Open Res Date: 2022-09-12