Can SLE classification rules be effectively applied to diagnose unclear SLE cases?

OBJECTIVE: Develop a novel classification criteria to distinguish between unclear SLE and 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 (BLR) 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 which 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 exhibit prevalence of skin rashes and renal disease while MCTD cases show dominance of myositis and muscle weakness. Additionally decision trees 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 compare 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