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.

Keywords: Mixed connective tissue disease (MCTD); autoimmune disorders; classification criteria; diagnosis; systemic lupus erythematosus (SLE).

Figure 1. SLE and MCTD cohorts are mainly composed of unclear (non-classical) samples

The diagram illustrates the methodology used to diagnosed SLE and MCTD patients. The expert clinical rheumatology diagnoses were performed by Drs. Robert W. Hoffman and Eric L. Greidinger who are versed in lupus and MCTD. Both clinicians agreed on the diagnosis with their independent evaluations in all but 5 cases. In these cases, the clinicians met together, discussed the cases, and agreed on the most appropriate diagnosis. “n” represents total amount of samples, “+” and “-“ indicates positive or negative for a given criteria set, “*” includes all SLE classification criteria sets (ACR and SLICC) and “†” comprises all MCTD classification criteria rules (Alarcon-Segovia, Sharp, Kasukawa and Kahn).

Figure 2. SLE and MCTD populations show contrasting prevalence of clinical characteristics and molecular factors

In each graph, the inner circle represents the MCTD cohort while the outside circle is SLE population. Positive and negative values for each of the variables are represented by green and red, respectively. History of proximal muscle weakness, observed proximal muscle weakness, observed joints swelling, symmetric joints swelling, lower extremity swelling are denoted by “H prox muscle weakness”, “O prox muscle weakness”, “O joints swelling”, “S joints swelling” and “L extremity swelling”, respectively. Positive laboratory tests for ribonucleoprotein (RNP), Smith proteins (Sm), double stranded DNA (dsDNA), Fluorescent Antinuclear Antibodies (FANA), anti-La antibodies (SSB) and topoisomerase (Scl) are indicated with “+”. Experimental assays as opposed to traditional laboratory tests are in italics.

Figure 3. Proposed biomarker panel for clinical manifestations observed in SLE patients

In the plot, the clinical symptoms and laboratory tests are represented on the “x” and “y” axis, respectively. The white, blue and red boxes indicate significant correlations, significant correlations after Bonferroni corrections, and no correlations, respectively (p ≤ 0.05). Hand swelling, proximal scleroderma, any clot, valvular heart diseases, pulmonary hypertension, pulmonary fibrosis, gastric reflux, lymph nodes swelling, morning stiffness, myocardial infarction and interleukin receptor BAFFR were initially considered in the analysis but not included due to the reduced sample size for each of these variables.

Figure 4. Specific blood markers correlate with clinical symptoms in MCTD patients

Laboratory tests performed in MCTD subjects are displayed on the “y” axis while clinical manifestations exhibit in this patient population are on the “x” axis. The white, blue and red boxes indicate significant correlations, significant correlations after Bonferroni corrections and no correlations, respectively (p ≤ 0.05). Proteinuria, hematuria and cellular casts; stroke; venous clot; lung disease; synovitis; symmetric swelling of the joints; observed proximal muscle weakness and calcinosis were initially considered for the correlations but not included in the final analyses given the reduced sample size available in the MCTD cohort for these variables.

Figure 5. Reduced models for each SLE and MCTD classification criteria exhibit better power in discriminating between SLE and MCTD patients

Each of the reduced models were obtained by applying binomial logistic regression (BLR) in SPSS (version 18) when all the variables per classification criteria were considered in SLE (n = 110) and MCTD (n = 56) patients. The Systemic Lupus International Collaborating Clinics (SLICC) and American College of Rheumatology (ACR) classification criteria sets for SLE diagnosis are denoted with A and B, respectively. The Alarcόn-Segovia (C), Sharp (D), Kasukawa (E) and Kahn (F) represent the classification criteria sets for diagnosing MCTD patients. In each plot, the area under the curve (AUC) from Receiving Operating Characteristics (ROC) curves are on the y axis. Each of the columns represent characteristics and the reduced model included per classification criteria. The lines on top of each column are standard error. Significant difference between SLE and MCTD patients with p-values ≤ 0.05, ≤ 0.005 and ≤ 0.0001 are denoted with “*”, “**” and “***”, respectively.

Figure 6. Proposed novel classification DT rule for segregating between SLE and MCTD patients

The diagram represents a decision tree (Model 1 in supplementary file 3) where each variable represent a decision. If it is true, follow the right branch to the next decision; else follow the left branch. Additional trees were created for comparison purposes and are included in Supplementary file 3.