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Clinical and Diagnostic Laboratory Immunology, December 2005, p. 1353-1357, Vol. 12, No. 12
1071-412X/05/$08.00+0     doi:10.1128/CDLI.12.12.1353-1357.2005
Copyright © 2005, American Society for Microbiology. All Rights Reserved.

Computer-Assisted Pattern Recognition of Autoantibody Results

Bio-Rad Laboratories, Hercules, California 94547,¹ Division of Immunology and Rheumatology, Stanford University, Stanford, California 94305,² Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma 73104,³ RDL Reference Laboratory, Los Angeles, California 90034⁴

Received 29 July 2005/ Returned for modification 6 September 2005/ Accepted 5 October 2005

Immunoassay-based anti-nuclear antibody (ANA) screens are increasingly used in the initial evaluation of autoimmune disorders, but these tests offer no "pattern information" comparable to the information from indirect fluorescence assay-based screens. Thus, there is no indication of "next steps" when a positive result is obtained. To improve the utility of immunoassay-based ANA screening, we evaluated a new method that combines a multiplex immunoassay with a k nearest neighbor (kNN) algorithm for computer-assisted pattern recognition. We assembled a training set, consisting of 1,152 sera from patients with various rheumatic diseases and nondiseased patients. The clinical sensitivity and specificity of the multiplex method and algorithm were evaluated with a test set that consisted of 173 sera collected at a rheumatology clinic from patients diagnosed by using standard criteria, as well as 152 age- and sex-matched sera from presumably healthy individuals (sera collected at a blood bank). The test set was also evaluated with a HEp-2 cell-based enzyme-linked immunosorbent assay (ELISA). Both the ELISA and multiplex immunoassay results were positive for 94% of the systemic lupus erythematosus (SLE) patients. The kNN algorithm correctly proposed an SLE pattern for 84% of the antibody-positive SLE patients. For patients with no connective tissue disease, the multiplex method found fewer positive results than the ELISA screen, and no disease was proposed by the kNN algorithm for most of these patients. In conclusion, the automated algorithm could identify SLE patterns and may be useful in the identification of patients who would benefit from early referral to a specialist, as well as patients who do not require further evaluation.