title: Analysis of obstetricians' decision making on CTG recordings.
abstract: Interpretation of cardiotocogram (CTG) is a difficult task since its evaluation is complicated by a great inter- and intra-individual variability. Previous studies have predominantly analyzed clinicians' agreement on CTG evaluation based on quantitative measures (e.g. kappa coefficient) that do not offer any insight into clinical decision making. In this paper we aim to examine the agreement on evaluation in detail and provide data-driven analysis of clinical evaluation. For this study, nine obstetricians provided clinical evaluation of 634 CTG recordings (each ca. 60min long). We studied the agreement on evaluation and its dependence on the increasing number of clinicians involved in the final decision. We showed that despite of large number of clinicians the agreement on CTG evaluations is difficult to reach. The main reason is inherent inter- and intra-observer variability of CTG evaluation. Latent class model provides better and more natural way to aggregate the CTG evaluation than the majority voting especially for larger number of clinicians. Significant improvement was reached in particular for the pathological evaluation - giving a new insight into the process of CTG evaluation. Further, the analysis of latent class model revealed that clinicians unconsciously use four classes when evaluating CTG recordings, despite the fact that the clinical evaluation was based on FIGO guidelines where three classes are defined.
authors.affiliation: Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic. Electronic address: spilka.jiri@fel.cvut.cz.
authors.forename: Jiří
authors.initials: J
authors.lastname: Spilka
authors.affiliation: Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic.
authors.forename: Václav
authors.initials: V
authors.lastname: Chudáček
authors.affiliation: Department of Gynecology and Obstetrics, Teaching Hospital of Masaryk University in Brno, Czech Republic.
authors.forename: Petr
authors.initials: P
authors.lastname: Janků
authors.affiliation: Department of Gynecology and Obstetrics, Teaching Hospital of Masaryk University in Brno, Czech Republic.
authors.forename: Lukáš
authors.initials: L
authors.lastname: Hruban
authors.affiliation: Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic.
authors.forename: Miroslav
authors.initials: M
authors.lastname: Burša
authors.affiliation: Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic.
authors.forename: Michal
authors.initials: M
authors.lastname: Huptych
authors.affiliation: Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic.
authors.forename: Lukáš
authors.initials: L
authors.lastname: Zach
authors.affiliation: Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic.
authors.forename: Lenka
authors.initials: L
authors.lastname: Lhotská
mesh_terms: D001185:Artificial Intelligence; D015148:Cardiotocography / Q000706:statistics & numerical data*; D020000:Decision Support Systems, Clinical; D003661:Decision Support Techniques; D006801:Humans; D015588:Observer Variation; D009774:Obstetrics / Q000706:statistics & numerical data*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: Biomedical informatics; Cardiotocography; Decision making; Fetal heart rate; Latent class analysis; Observer variation