RT Journal Article
SR Electronic
T1 Differentiation Between Wegener’s Granulomatosis and Microscopic Polyangiitis by an Artificial Neural Network and by Traditional Methods
JF The Journal of Rheumatology
JO J Rheumatol
FD The Journal of Rheumatology
SP 1039
OP 1047
DO 10.3899/jrheum.100814
VO 38
IS 6
A1 ROLAND LINDER
A1 ISABELLE ORTH
A1 E. CHRISTIAN HAGEN
A1 FOKKO J. van der WOUDE
A1 WILHELM H. SCHMITT
YR 2011
UL http://www.jrheum.org/content/38/6/1039.abstract
AB Objective. To investigate the operating characteristics of the American College of Rheumatology (ACR) traditional format criteria for Wegener’s granulomatosis (WG), the Sørensen criteria for WG and microscopic polyangiitis (MPA), and the Chapel Hill nomenclature for WG and MPA. Further, to develop and validate improved criteria for distinguishing WG from MPA by an artificial neural network (ANN) and by traditional approaches [classification tree (CT), logistic regression (LR)]. Methods. All criteria were applied to 240 patients with WG and 78 patients with MPA recruited by a multicenter study. To generate new classification criteria (ANN, CT, LR), 23 clinical measurements were assessed. Validation was performed by applying the same approaches to an independent monocenter cohort of 46 patients with WG and 21 patients with MPA. Results. A total of 70.8% of the patients with WG and 7.7% of the patients with MPA from the multicenter cohort fulfilled the ACR criteria for WG (accuracy 76.1%). The accuracy of the Chapel Hill criteria for WG and MPA was only 35.0% and 55.3% (Sørensen criteria: 67.2% and 92.4%). In contrast, the ANN and CT achieved an accuracy of 94.3%, based on 4 measurements (involvement of nose, sinus, ear, and pulmonary nodules), all associated with WG. LR led to an accuracy of 92.8%. Inclusion of antineutrophil cytoplasmic antibodies did not improve the allocation. Validation of methods resulted in accuracy of 91.0% (ANN and CT) and 88.1% (LR). Conclusion. The ACR, Sørensen, and Chapel Hill criteria did not reliably separate WG from MPA. In contrast, an appropriately trained ANN and a CT differentiated between these disorders and performed better than LR.