Disease pattern recognition testing for rheumatoid arthritis using infrared spectra of human serum

Background: In view of the importance of the diagnosis of rheumatoid arthri tis, a novel diagnostic method based on spectroscopic pattern recognition i n combination with laboratory parameters such as the rheumatoid factor is d escribed in the paper. Results of a diagnostic study of rheumatoid arthriti s employing this method are presented. Method: The method uses classificati on of infrared (IR) spectra of serum samples by means of discriminant analy sis. The spectroscopic pattern yielding the highest discriminatory power is found through a complex optimization procedure. In the study, IR spectra o f 384 serum samples have been analyzed in this fashion with the objective o f differentiating between rheumatoid arthritis and healthy subjects. In add ition, the method integrates results from the classification with levels of the rheumatoid factor in the sample by optimized classifier weighting, in order to enhance classification accuracy, i.e. sensitivity and specificity. Results: In independent validation, sensitivity and specificity of 84% and 88%, respectively, have been obtained purely on the basis of spectra class ification employing a classifier designed specifically to provide robustnes s. Sensitivity and specificity are improved by 1% and 6%, respectively, upo n inclusion of rheumatoid factor levels. Results for less robust methods ar e also presented and compared to the above numbers. Conclusion. The discrim ination between RA and healthy by means of the pattern recognition approach presented here is feasible for IR spectra of serum samples. The method is sufficiently robust to be used in a clinical setting. A particular advantag e of the method is its potential use in RA diagnosis at early stages of the disease. (C) 2001 Elsevier Science B.V. All rights reserved.