LASSO-derived nomogram for early identification of pediatric monogenic lupus.

Abstract

Monogenic lupus is defined as systemic lupus erythematosus (SLE)/SLE-like patients with either dominantly or recessively inherited pathogenic variants in a single gene with high penetrance. However, because the clinical phenotype of monogenic SLE is extensive and overlaps with that of classical SLE, it causes a delay in diagnosis and treatment. Currently, there is a lack of early identification models for clinical practitioners to provide early clues for recognition. Our goal was to create a clinical model for the early identification of pediatric monogenic lupus, thereby facilitating early and precise diagnosis and treatment for patients. This retrospective cohort study consisted of 41 cases of monogenic lupus treated at the Department of Pediatrics at Peking Union Medical College Hospital from June 2012 to December 2022. The control group consisted of classical SLE patients recruited at a 1:2 ratio. Patients were randomly divided into a training group and a validation group at a 7:3 ratio. A logistic regression model was established based on the least absolute shrinkage and selection operator to generate the coefficient plot. The predictive ability of the model was evaluated using receiver operator characteristic curves and the area under the curve (AUC) index. A total of 41 cases of monogenic lupus patients and 82 cases of classical SLE patients were included. Among the monogenic lupus cases (with a male-to-female ratio of 1:1.05 and ages of onset ranging from birth to 15years), a total of 18 gene mutations were identified. The variables included in the coefficient plot were age of onset, recurrent infections, intracranial calcifications, growth and developmental delay, abnormal muscle tone, lymphadenopathy/hepatosplenomegaly, and chilblain-like skin rash. Our model demonstrated satisfactory diagnostic performance through internal validation, with an AUC value of 0.97 (95% confidence interval = 0.92-0.97). We summarized and analyzed the clinical characteristics of pediatric monogenic lupus and developed a predictive model for early identification by clinicians. Clinicians should exercise high vigilance for monogenic lupus when the score exceeds - 9.032299.