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

• Published in: Clinica chimica acta Vol. 308; no. 1; pp. 79 - 89
• Main Authors: Staib, A., Dolenko, B., Fink, D.J., Früh, J., Nikulin, A.E., Otto, M., Pessin-Minsley, M.S., Quarder, O., Somorjai, R., Thienel, U., Werner, G., Petrich, W.
• Format: Journal Article
• Language: English
• Published: Shannon Elsevier B.V 01.06.2001; Elsevier
• Subjects: Adolescent; Arthritis, Rheumatoid - blood; Arthritis, Rheumatoid - diagnosis; Biological and medical sciences; Data Display; Discriminant Analysis; Disease pattern recognition; Diseases of the osteoarticular system; Feasibility Studies; Female; Humans; Inflammatory joint diseases; Infrared spectra; Investigative techniques, diagnostic techniques (general aspects); Male; Medical sciences; Middle Aged; Osteoarticular system. Muscles; Pathology. Cytology. Biochemistry. Spectrometry. Miscellaneous investigative techniques; Pattern Recognition, Automated; Reference Values; Rheumatoid arthritis; Rheumatoid Factor - blood; ROC Curve; Sensitivity and Specificity; Spectrophotometry, Infrared - instrumentation; Disease pattern recognition; Rheumatoid arthritis; Infrared spectra; Human; Validation; Immunopathology; Diseases of the osteoarticular system; Autoimmune disease; Inflammatory joint disease; Pattern recognition; Infrared spectrometry; Chronic; Sensitivity; Specificity; Serum; Diagnosis; Infrared spectrum; Technique
• ISSN: 0009-8981; 1873-3492
• DOI: 10.1016/S0009-8981(01)00475-2

Background: In view of the importance of the diagnosis of rheumatoid arthritis, a novel diagnostic method based on spectroscopic pattern recognition in combination with laboratory parameters such as the rheumatoid factor is described in the paper. Results of a diagnostic study of rheumatoid arthritis employing this method are presented. Method: The method uses classification of infrared (IR) spectra of serum samples by means of discriminant analysis. The spectroscopic pattern yielding the highest discriminatory power is found through a complex optimization procedure. In the study, IR spectra of 384 serum samples have been analyzed in this fashion with the objective of differentiating between rheumatoid arthritis and healthy subjects. In addition, 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 classification employing a classifier designed specifically to provide robustness. Sensitivity and specificity are improved by 1% and 6%, respectively, upon inclusion of rheumatoid factor levels. Results for less robust methods are also presented and compared to the above numbers. Conclusion: The discrimination 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 advantage of the method is its potential use in RA diagnosis at early stages of the disease.