Norbert Donner-Banzhoff1, Jörg Haasenritter2, Eyke Hüllermeier3, Annika Viniol2, Stefan Bösner2, Annette Becker2. 1. Department of General Practice/Family Medicine, University of Marburg, Karl-von-Frisch Str. 4, D-35043 Marburg, Germany. Electronic address: Norbert@staff.uni-marburg.de. 2. Department of General Practice/Family Medicine, University of Marburg, Karl-von-Frisch Str. 4, D-35043 Marburg, Germany. 3. Department of Knowledge Engineering & Bioinformatics, University of Marburg, D-35043 Marburg, Germany.
Abstract
OBJECTIVES: The classical diagnostic cross-sectional study has a focus on one disease only. Generalist clinicians, however, are confronted with a wide range of diagnoses. We propose the "comprehensive diagnostic study design" to evaluate diagnostic tests regarding more than one disease outcome. STUDY DESIGN AND SETTING: We present the secondary analysis of a data set obtained from patients presenting with chest pain in primary care. Participating clinicians recorded 42 items of the history and physical examination. Diagnostic outcomes were reviewed by an independent panel after 6-month follow-up (n = 710 complete cases). We used Shannon entropy as a measure of uncertainty before and after testing. Four different analytical strategies modeling specific clinical ways of reasoning were evaluated. RESULTS: Although the "global entropy" strategy reduced entropy most, it is unlikely to be of clinical use because of its complexity. "Inductive" and "fixed-set" strategies turned out to be efficient requiring a small amount of data only. The "deductive" procedure resulted in the smallest reduction of entropy. CONCLUSION: We suggest that the comprehensive diagnostic study design is a feasible and valid option to improve our understanding of the diagnostic process. It is also promising as a justification for clinical recommendations.
OBJECTIVES: The classical diagnostic cross-sectional study has a focus on one disease only. Generalist clinicians, however, are confronted with a wide range of diagnoses. We propose the "comprehensive diagnostic study design" to evaluate diagnostic tests regarding more than one disease outcome. STUDY DESIGN AND SETTING: We present the secondary analysis of a data set obtained from patients presenting with chest pain in primary care. Participating clinicians recorded 42 items of the history and physical examination. Diagnostic outcomes were reviewed by an independent panel after 6-month follow-up (n = 710 complete cases). We used Shannon entropy as a measure of uncertainty before and after testing. Four different analytical strategies modeling specific clinical ways of reasoning were evaluated. RESULTS: Although the "global entropy" strategy reduced entropy most, it is unlikely to be of clinical use because of its complexity. "Inductive" and "fixed-set" strategies turned out to be efficient requiring a small amount of data only. The "deductive" procedure resulted in the smallest reduction of entropy. CONCLUSION: We suggest that the comprehensive diagnostic study design is a feasible and valid option to improve our understanding of the diagnostic process. It is also promising as a justification for clinical recommendations.
Authors: Michael Pentzek; Michael Wagner; Heinz-Harald Abholz; Horst Bickel; Hanna Kaduszkiewicz; Birgitt Wiese; Siegfried Weyerer; Hans-Helmut König; Martin Scherer; Steffi G Riedel-Heller; Wolfgang Maier; Alexander Koppara Journal: Br J Gen Pract Date: 2019-10-31 Impact factor: 5.386