Influence of the practice setting on diagnostic prediction rules using FENO measurement in combination with clinical signs and symptoms of asthma

Objectives: To evaluate the influence of the practice setting on diagnostic accuracy of fractional exhaled nitric oxide (FENO) for diagnosing asthma; and to develop prediction rules for diagnostic decision-making including clinical signs and symptoms (CSS).

Setting: Patients from 10 general practices and 1 private practice of 5 pneumologists in ambulatory care.

Participants: 553 patients, 57.9% female. Consecutive inclusion of diagnostic-naive patients suspected of suffering from obstructive airway disease. Exclusion criteria were respiratory tract infections within the last 6 weeks.

Interventions: The index test was FENO measurement. Reference standard was the Tiffeneau ratio (forced expiratory volume in 1 s/vital capacity) or airway resistance as assessed by whole body plethysmography, with additional bronchoprovocation or bronchodilator testing.

Primary and secondary outcome measures: Asthma as determined by pneumologists, who were blind to FENO measurement results. Prediction rules were derived from multiple logistic regression analysis. A freely available calculator that allows computing all combinations was developed.

Results: The practice setting only had minor influence on sensitivities of FENO cut-off points. In the final model (n=472), allergic rhinitis, wheezing and previous medication were positively associated with asthma. Increasing age and recurrent respiratory tract infections were negatively associated. The area under the curve (AUC) of FENO (AUC=0.650; 95% CI 0.599 to 0.701) increased significantly (p<0.0001) when combined with CSS (AUC=0.753; 95% CI 0.707 to 0.798). Presence of wheezing and allergic rhinitis allowed ruling in asthma with FENO >30 ppb. Ruling out with FENO <16 ppb in patients <43 years was only possible without allergic symptoms when recurrent respiratory tract infections were present.

Conclusions: FENO results should be interpreted in the context of CSS to enhance their diagnostic value in primary care. The final diagnostic model appears as a sound algorithm fitting well to the established diagnostic rules related to CSS of asthma. FENO appears more effective for ruling in asthma than for ruling it out.