Development and validation of a predictive model combining patient-reported outcome measures, spirometry and exhaled nitric oxide fraction for asthma diagnosis

INTRODUCTIONAlthough 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.METHODSWe 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.RESULTS63% 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.CONCLUSIONCombining wheezing intensity scale with spirometry and FeNO may help in improving asthma diagnosis accuracy in clinical practice.