Improved differentiation between Churg-Strauss syndrome and Wegener's granulomatosis by an artificial neural network.

Abstract

To examine the operating characteristics of the American College of Rheumatology (ACR) classification criteria for Churg-Strauss syndrome (CSS) and Wegener's granulomatosis (WG), and to develop and validate improved criteria for distinguishing CSS from WG. The ACR classification criteria for WG and CSS were applied to 40 consecutive CSS patients age- and sex-matched with 40 patients with WG. Forty-three clinical, laboratory, and biopsy parameters were assessed. Artificial neural networks (ANNs) were trained and tested with all 43 parameters (set A) and with 15 solely clinical parameters documented at the initial manifestation of the disease (set B). The ANNs were trained with data from the first 27 CSS and 27 WG patients and validated with data from the next 13 consecutive CSS and 13 WG patients. To compare the ANNs with established methods, traditional format and classification tree criteria were generated using the same data sets. Fourteen of 40 CSS patients fulfilled the ACR criteria for WG, while 4 WG patients met the ACR criteria for CSS. The ANN, in contrast, reliably distinguished all CSS cases from WG cases (parameter set A, accuracy 100%). For parameter set B, the ANN achieved an accuracy of 100% in the training phase and 96% for validation. The newly formulated traditional format and classification tree criteria reached an accuracy of 81% and 88%, respectively. The ACR criteria for WG do not reliably differentiate between CSS and WG (specificity 65%). An ANN, however, could be trained to correctly allocate all but 1 patient on the basis of clinical data. Indeed, the ANN applied in this study proved superior to established methods of classification. We suggest that an ANN may be effectively applied in the classification of systemic vasculitides.