PT  - JOURNAL ARTICLE
AU  - Gilles Louis
AU  - Florence Schleich
AU  - Michèle Guillaume
AU  - Delphine Kirkove
AU  - Halehsadat Nekoee Zahrei
AU  - Anne-Françoise Donneau
AU  - Monique Henket
AU  - Virginie Paulus
AU  - Françoise Guissard
AU  - Renaud Louis
AU  - Benoit Pétré
TI  - Development and validation of a predictive model combining patient-reported outcome measures, spirometry and exhaled nitric oxide fraction for asthma diagnosis
AID  - 10.1183/23120541.00451-2022
DP  - 2023 Jan 01
TA  - ERJ Open Research
PG  - 00451-2022
VI  - 9
IP  - 1
4099  - http://openres.ersjournals.com/content/9/1/00451-2022.short
4100  - http://openres.ersjournals.com/content/9/1/00451-2022.full
SO  - erjor2023 Jan 01; 9
AB  - Introduction Although asthma is a common disease, its diagnosis remains a challenge in clinical practice with both over- and underdiagnosis. Here, we performed a prospective observational study investigating the value of symptom intensity scales alone or combined with spirometry and exhaled nitric oxide fraction (FENO) to aid in asthma diagnosis.Methods Over a 38-month period we recruited 303 untreated patients complaining of symptoms suggestive of asthma (wheezing, dyspnoea, cough, sputum production and chest tightness). The whole cohort was split into a training cohort (n=166) for patients recruited during odd months and a validation cohort (n=137) for patients recruited during even months. Asthma was diagnosed either by a positive reversibility test (≥12% and ≥200 mL in forced expiratory volume in 1 s (FEV1)) and/or a positive bronchial challenge test (provocative concentration of methacholine causing a 20% fall in FEV1 ≤8 mg·mL−1). In order to assess the diagnostic performance of symptoms, spirometric indices and FENO, we performed receiver operating characteristic curve analysis and multivariable logistic regression to identify the independent factors associated with asthma in the training cohort. Then, the derived predictive models were applied to the validation cohort.Results 63% of patients in the derivation cohort and 58% of patients in the validation cohort were diagnosed as being asthmatic. After logistic regression, wheezing was the only symptom to be significantly associated with asthma. Similarly, FEV1 (% pred), FEV1/forced vital capacity (%) and FENO were significantly associated with asthma. A predictive model combining these four parameters yielded an area under the curve of 0.76 (95% CI 0.66–0.84) in the training cohort and 0.73 (95% CI 0.65–0.82) when applied to the validation cohort.Conclusion Combining a wheezing intensity scale with spirometry and FENO may help in improving asthma diagnosis accuracy in clinical practice.Misdiagnosis of asthma is common in clinical practice. Here, a predictive model was developed and validated, combining symptom intensity scales, spirometry and FENO, that offers a new simple and minimally invasive way to aid in diagnosing asthma. https://bit.ly/3hdpmvz