RT Journal Article
SR Electronic
T1 Development and validation of a predictive model combining patient-reported outcome measures (PROMs), spirometry and FeNO for asthma diagnosis
JF ERJ Open Research
JO erjor
FD European Respiratory Society
SP 00451-2022
DO 10.1183/23120541.00451-2022
A1 Gilles Louis
A1 Florence Schleich
A1 Michèle Guillaume
A1 Delphine Kirkove
A1 Halehsadat Nekoee Zahrei
A1 Anne-Françoise Donneau
A1 Monique Henket
A1 Virginie Paulus
A1 Françoise Guissard
A1 Renaud Louis
A1 Benoit Pétré
YR 2022
UL http://openres.ersjournals.com/content/early/2022/11/03/23120541.00451-2022.abstract
AB INTRODUCTION Although asthma is a common disease, its diagnosis remains a challenge in clinical practice with both over/under-diagnosis. Here, we performed a prospective observational study investigating the value of symptom intensity scales alone or combined with spirometry and FeNO to aid in asthma diagnosis.METHODS We recruited, over a 38-month period, 303 untreated patients complaining with symptoms suggestive of asthma (cough, chest tightness, dyspnea, airway secretion and wheezing). The whole cohort was split in a training cohort (n=166) for patients recruited in odd months and a validation cohort (n=137) for the patients recruited in even months. Asthma was diagnosed either by a positive reversibility test (≥12% and 200 ml) and/or a positive bronchial challenge test (PC20M≤8 mg·ml−1). In order to assess the diagnostic performance of symptoms, spirometric indices and FeNO, we performed ROC 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% in the validation cohort were diagnosed as being asthmatics. After logistic regression wheezing was the only symptom to be significantly associated with asthma. Similarly, FEV1% predicted, FEV1/FVC% and FeNO were significantly associated with asthma. A predictive model combining these four parameters yielded an AUC 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 wheezing intensity scale with spirometry and FeNO may help in improving asthma diagnosis accuracy in clinical practice.FootnotesThis manuscript has recently been accepted for publication in the ERJ Open Research. It is published here in its accepted form prior to copyediting and typesetting by our production team. After these production processes are complete and the authors have approved the resulting proofs, the article will move to the latest issue of the ERJOR online. Please open or download the PDF to view this article.Conflict of interests: Outside of this submitted work, RL received unrestricted research grants from GSK, AstraZeneca and Chiesi and lecture or adboard fees from GSK, AZ, Novartis and Sonafi. Outside of this submitted work, FS received lecture or adboard fees from Chiesi, AZ, GSK, and Novartis.Conflict of interests: The rest of the authors declare that they have no relevant conflicts of interest.