title: Can SLE classification rules be effectively applied to diagnose unclear SLE cases?
abstract: Objective The objective of this paper is to develop novel classification criteria to distinguish between unclear systemic lupus erythematosus (SLE) and mixed connective tissue disease (MCTD) cases. Methods A total of 205 variables from 111 SLE and 55 MCTD patients were evaluated to uncover unique molecular and clinical markers for each disease. Binomial logistic regressions (BLRs) were performed on currently used SLE and MCTD classification criteria sets to obtain six reduced models with power to discriminate between unclear SLE and MCTD patients that were confirmed by receiving operating characteristic (ROC) curve. Decision trees were employed to delineate novel classification rules to discriminate between unclear SLE and MCTD patients. Results SLE and MCTD patients exhibited contrasting molecular markers and clinical manifestations. Furthermore, reduced models highlighted SLE patients exhibiting prevalence of skin rashes and renal disease while MCTD cases show dominance of myositis and muscle weakness. Additionally decision tree analyses revealed a novel classification rule tailored to differentiate unclear SLE and MCTD patients (Lu-vs-M) with an overall accuracy of 88%. Conclusions Validation of our novel proposed classification rule (Lu-vs-M) includes novel contrasting characteristics (calcinosis, CPK elevated and anti-IgM reactivity for U1-70K, U1A and U1C) between SLE and MCTD patients and showed a 33% improvement in distinguishing these disorders when compared to currently used classification criteria sets. Pending additional validation, our novel classification rule is a promising method to distinguish between patients with unclear SLE and MCTD diagnosis.
authors.affiliation: 1 Department of Surgery, University of Miami Miller School of Medicine, Miami, FL, USA.
authors.forename: A
authors.initials: A
authors.lastname: Mesa
authors.affiliation: 2 Bioinformatics Research Group (BioRG), School of Computing and Information Sciences, Florida International University, Miami, FL, USA.
authors.forename: M
authors.initials: M
authors.lastname: Fernandez
authors.affiliation: 3 Department of Mathematics and Statistics, Florida International University, Miami, FL, USA.
authors.forename: W
authors.initials: W
authors.lastname: Wu
authors.affiliation: 2 Bioinformatics Research Group (BioRG), School of Computing and Information Sciences, Florida International University, Miami, FL, USA.
authors.forename: G
authors.initials: G
authors.lastname: Narasimhan
authors.affiliation: 4 Bruce W Carter Department of Veterans Affairs Medical Center, and University of Miami Miller School of Medicine, Miami, FL, USA.
authors.forename: E L
authors.initials: EL
authors.lastname: Greidinger
authors.affiliation: 5 Department of Biological Sciences, Florida International University, Miami, FL, USA; International Forensic Research Institute, Florida International University, Miami, FL, USA.
authors.forename: D K
authors.initials: DK
authors.lastname: Mills
mesh_terms: D019540:Area Under Curve; D015415:Biomarkers / Q000097:blood; D016009:Chi-Square Distribution; D003661:Decision Support Techniques; D003663:Decision Trees; D003937:Diagnosis, Differential; D006801:Humans; D016015:Logistic Models; D008180:Lupus Erythematosus, Systemic / Q000097:blood / Q000145:classification / Q000175:diagnosis* / Q000453:epidemiology; D008947:Mixed Connective Tissue Disease / Q000097:blood / Q000145:classification / Q000175:diagnosis* / Q000453:epidemiology; D010641:Phenotype; D011237:Predictive Value of Tests; D015995:Prevalence; D012372:ROC Curve; D015203:Reproducibility of Results
chemical_list: D015415:Biomarkers
keywords: Mixed connective tissue disease (MCTD); autoimmune disorders; classification criteria; diagnosis; systemic lupus erythematosus (SLE)