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

@entity1 @entity2023 @entity565 @entity842 @entity1729 @entity56 @entity1224
OBJECTIVES: To evaluate the influence of the practice setting on diagnostic accuracy of fractional exhaled @entity842 (FENO) for diagnosing @entity565 ; and to develop prediction rules for diagnostic decision-making including clinical signs and symptoms (CSS). SETTING: @entity1 from 10 general practices and 1 private practice of 5 pneumologists in ambulatory care. @entity1 : 553 @entity1 , 57.9% female. Consecutive inclusion of diagnostic-naive @entity1 suspected of suffering from @entity1224 . Exclusion criteria were @entity1729 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), @entity2023 , wheezing and previous medication were positively associated with @entity565 . Increasing age and recurrent @entity1729 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 @entity2023 allowed ruling in @entity565 with FENO >30   ppb. Ruling out with FENO <16   ppb in @entity1 <43   years was only possible without @entity56 when recurrent @entity1729 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 @entity565 . FENO appears more effective for ruling in @entity565 than for ruling it out.
