de Jong Carmen C M, Pedersen Eva S L, Mozun Rebeca, Müller-Suter Dominik, Jochmann Anja, Singer Florian, Casaulta Carmen, Regamey Nicolas, Moeller Alexander, Ardura-Garcia Cristina, Kuehni Claudia E
Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland.
Paediatric Respiratory Medicine, Kantonsspital Aarau, Aarau, Switzerland.
Eur Respir J. 2020 Nov 5;56(5). doi: 10.1183/13993003.00132-2020. Print 2020 Nov.
Diagnosing asthma in children remains a challenge because respiratory symptoms are not specific and vary over time.
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.
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 ( ), 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).
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 (sR; 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%.
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.
由于呼吸道症状不具有特异性且随时间变化，儿童哮喘的诊断仍然是一项挑战。
在一项现实生活中的观察性研究中，我们评估了呼吸道症状、客观检查以及两种儿科诊断算法（由全球哮喘防治创议组织（GINA）和英国国家卫生与临床优化研究所（NICE）提出）在学龄儿童哮喘诊断中的诊断准确性。
我们研究了年龄在5至17岁之间因疑似哮喘而连续转诊至肺部门诊进行评估的儿童。通过家长问卷评估症状。检查包括特异性IgE测量或皮肤点刺试验、呼出一氧化氮分数（ ）测量、肺功能测定、体容积描记法和支气管扩张剂可逆性（BDR）。儿科肺科医生根据所有可用数据诊断哮喘。我们通过计算敏感性、特异性、阳性预测值（PPV）、阴性预测值（NPV）和曲线下面积（AUC）来评估症状、检查和诊断算法的诊断准确性。
在514名参与者中，357名（70%）被诊断为哮喘。任何喘息（敏感性=75%，特异性=65%）、呼吸困难（敏感性=56%，特异性=76%）以及由感冒（敏感性=58%，特异性=78%）或运动（敏感性=55%，特异性=74%）引发的喘息，其综合敏感性和特异性最高。在诊断检查中，特异性总气道阻力（sR；AUC=0.73）的AUC最高，残气量（RV）/肺总量（TLC）比值（AUC=0.56）的AUC最低。NICE算法的敏感性=69%，特异性=67%，而GINA算法的敏感性=42%，特异性=90%。
本研究证实了单一检查和现有算法在哮喘诊断中的作用有限。它凸显了对新的、更合适的循证指南的需求。
