Antonius Schneider1, Klaus Linde2, Johannes B Reitsma3, Susanne Steinhauser4, Gerta Rücker5. 1. Institute of General Practice, University Hospital Klinikum rechts der Isar, Technische Universität München, Orleansstraße 47, 81667 München, Germany. Electronic address: antonius.schneider@mri.tum.de. 2. Institute of General Practice, University Hospital Klinikum rechts der Isar, Technische Universität München, Orleansstraße 47, 81667 München, Germany. 3. Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, PO Box 85500, 3508 GA, Utrecht, The Netherlands. 4. Institute of Medical Statistics, Informatics and Epidemiology, University of Cologne, Bachemer Str. 86, 50931 Köln, Germany. 5. Institute for Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Stefan-Meier-Strasse 26, 79104 Freiburg, Germany.
Abstract
OBJECTIVES: Measurement of fractional exhaled nitric oxide (FENO) might substitute bronchial provocation for diagnosing asthma. However, optimal FENO thresholds for diagnosing asthma remain unclear. We reanalyzed data collected for a systematic review investigating the diagnostic accuracy of FENO measurement to exploit all available thresholds under consideration of pretest probabilities using a newly developed statistical model. STUDY DESIGN AND SETTING: One hundred and fifty data sets for a total of 53 different cutoffs extracted from 26 studies with 4,518 participants were analyzed with the multiple thresholds model. This model allows identifying thresholds at which the test is likely to perform best. RESULTS: Diagnosing asthma might only be possible in a meaningful manner when the pretest probability of asthma is at least 30%. In that case, FENO > 50 ppb leads to a positive predictive value of 0.76 [95% confidence interval (CI): 0.29-0.96]. Excluding asthma might only be possible, when the pretest probability of asthma is 30% at maximum. Then, FENO < 20 ppb leads to a negative predictive value of 0.86 (95% CI 0.66-0.95). CONCLUSION: The multiple thresholds model generates a more comprehensive and more clinically useful picture of the effects of different thresholds, which facilitates the determination of optimal thresholds for diagnosing or excluding asthma with FENO measurement.
OBJECTIVES: Measurement of fractional exhaled nitric oxide (FENO) might substitute bronchial provocation for diagnosing asthma. However, optimal FENO thresholds for diagnosing asthma remain unclear. We reanalyzed data collected for a systematic review investigating the diagnostic accuracy of FENO measurement to exploit all available thresholds under consideration of pretest probabilities using a newly developed statistical model. STUDY DESIGN AND SETTING: One hundred and fifty data sets for a total of 53 different cutoffs extracted from 26 studies with 4,518 participants were analyzed with the multiple thresholds model. This model allows identifying thresholds at which the test is likely to perform best. RESULTS: Diagnosing asthma might only be possible in a meaningful manner when the pretest probability of asthma is at least 30%. In that case, FENO > 50 ppb leads to a positive predictive value of 0.76 [95% confidence interval (CI): 0.29-0.96]. Excluding asthma might only be possible, when the pretest probability of asthma is 30% at maximum. Then, FENO < 20 ppb leads to a negative predictive value of 0.86 (95% CI 0.66-0.95). CONCLUSION: The multiple thresholds model generates a more comprehensive and more clinically useful picture of the effects of different thresholds, which facilitates the determination of optimal thresholds for diagnosing or excluding asthma with FENO measurement.
Authors: Antonius Schneider; Benjamin Brunn; Alexander Hapfelmeier; Konrad Schultz; Christina Kellerer; Rudolf A Jörres Journal: EClinicalMedicine Date: 2022-07-01
Authors: Christina Kellerer; Alexander Hapfelmeier; Rudolf A Jörres; Konrad Schultz; Benjamin Brunn; Antonius Schneider Journal: BMJ Open Date: 2021-02-12 Impact factor: 2.692
Authors: Yasaman Vali; Jenny Lee; Jérôme Boursier; René Spijker; Joanne Verheij; M Julia Brosnan; Quentin M Anstee; Patrick M Bossuyt; Mohammad Hadi Zafarmand Journal: J Clin Med Date: 2021-05-29 Impact factor: 4.241