Development and Validation of a Bayesian Network for Supporting the Etiological Diagnosis of Uveitis.
J Clin Med
; 10(15)2021 Jul 30.
Article
em En
| MEDLINE
| ID: mdl-34362175
ABSTRACT
The etiological diagnosis of uveitis is complex. We aimed to implement and validate a Bayesian belief network algorithm for the differential diagnosis of the most relevant causes of uveitis. The training dataset (n = 897) and the test dataset (n = 154) were composed of all incident cases of uveitis admitted to two internal medicine departments, in two independent French centers (Lyon, 2003-2016 and Dijon, 2015-2017). The etiologies of uveitis were classified into eight groups. The algorithm was based on simple epidemiological characteristics (age, gender, and ethnicity) and anatomoclinical features of uveitis. The cross-validated estimate obtained in the training dataset concluded that the etiology of uveitis determined by the experts corresponded to one of the two most probable diagnoses in at least 77% of the cases. In the test dataset, this probability reached at least 83%. For the training and test datasets, when the most likely diagnosis was considered, the highest sensitivity was obtained for spondyloarthritis and HLA-B27-related uveitis (76% and 63%, respectively). The respective specificities were 93% and 54%. This algorithm could help junior and general ophthalmologists in the differential diagnosis of uveitis. It could guide the diagnostic work-up and help in the selection of further diagnostic investigations.
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1
Base de dados:
MEDLINE
Tipo de estudo:
Diagnostic_studies
/
Etiology_studies
Idioma:
En
Ano de publicação:
2021
Tipo de documento:
Article