Establishment and evaluation of nomogram for differential diagnosis of systemic lupus erythematosus based on laboratory indications

Abstract

Objective·To establish a nomogram for the differential diagnosis of early systemic lupus erythematosus (SLE) and other autoimmune diseases based on laboratory indications, and to evaluate its efficacy.Methods·A total of 535 SLE patients admitted to the First Hospital of Lanzhou University from January 2017 to December 2021 were selected as SLE group, and 535 patients with other autoimmune diseases during the same period were selected as control group. Basic information and laboratory test indicators of the SLE group and control group were collected and compared. The SLE group and control group were randomly assigned to the training set and the validation set at a ratio of 7∶3, respectively. LASSO regression method and multivariate Logistic regression were used to select the main risk factors of SLE. The nomogram for differential diagnosis of early SLE (SLE nomogram) was established according to the selected main risk factors. Bootstrap method was used to conduct internal repeated sampling for 1 000 times to calibrate the nomogram. The receiver operator characteristic curve (ROC curve) and decision curve analysis (DCA) were performed to evaluate the differential diagnosis ability and the value in clinical application of SLE nomogram, respectively. The "DynNom" package of R language was used to convert the nomogram into an electronic calculator, and its consistency with SLE nomogram was verified by data from 3 groups of patients.Results·LASSO regression and multivariate Logistic regression identified six major risk factors for SLE, including antinuclear antibody (ANA), anti-double-stranded DNA (anti-dsDNA) antibody, anti-ribonucleoprotein antibody/anti-Simth antibody (anti-nRNP/Sm), anti-ribosomal P protein (anti-P) antibody, anti-nucleosome antibody (ANuA) and urinary protein (PRO), which were used to construct the SLE nomogram. The calibration curve of the SLE nomogram had standard errors of 0.009 and 0.015 in the training set and validation set, respectively, and its area under the curve (AUC) was 0.889 and 0.869, respectively. The results of DCA showed that when the risk threshold of SLE nomogram was 0.15‒0.95, the model achieved more net benefit. The prediction results of the electronic calculator showed that when ANA (titer 1∶100) was positive in SLE patient No.1, the prevalence was 0.166; when both ANA (titer 1∶100) and ANuA (titer 1∶100) were positive in patient No.2, the prevalence was 0.676; when all of PRO, ANA (titer 1∶100), ANuA (titer 1∶100) and anti-P antibody (titer 1∶100) were positive in patient No.3, the prevalence was 0.990, which was consistent with the differential diagnosis results of the SLE nomogram.Conclusion·The established SLE nomogram based on ANA, anti-dsDNA antibody, anti-nRNP/Sm, anti-P antibody, ANuA and PRO and its conversion into an electronic calculator can effectively distinguish early SLE from other autoimmune diseases, and have important clinical application value.

Keywords

• nomogram
• systemic lupus erythematosus (sle)
• autoimmune disease
• differential diagnosis
• electronic calculator