Abstract：
BACKGROUND: 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.
METHODS: 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.
RESULTS: 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 15 years), 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).
CONCLUSIONS: 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.
METHODS: 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.
RESULTS: 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 15 years), 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).
CONCLUSIONS: 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.
摘要：
背景：单基因狼疮被定义为系统性红斑狼疮（SLE）/SLE样患者，在单个基因中具有高外显率的显性或隐性遗传致病变异。然而,因为单基因SLE的临床表型广泛且与经典SLE重叠，它会导致诊断和治疗的延迟。目前,缺乏早期识别模型为临床医师提供早期识别线索。我们的目标是建立一个早期识别儿童单基因狼疮的临床模型，从而促进患者的早期和精确诊断和治疗。
方法：这项回顾性队列研究包括2012年6月至2022年12月在北京协和医院儿科治疗的41例单基因狼疮患者。对照组由按1:2比例招募的经典SLE患者组成。患者以7:3的比例随机分为训练组和验证组。基于最小绝对收缩和选择算子建立逻辑回归模型以生成系数图。使用受试者操作员特征曲线和曲线下面积（AUC）指数评估模型的预测能力。
结果：共纳入41例单基因狼疮患者和82例经典SLE患者。在单基因性狼疮病例中（男女比例为1:1.05，发病年龄从出生到15岁），共鉴定出18个基因突变.系数图中包括的变量是发病年龄，反复感染,颅内钙化,生长和发育迟缓，肌肉张力异常,淋巴结病/肝脾肿大，还有冻疮样皮疹.我们的模型通过内部验证证明了令人满意的诊断性能，AUC值为0.97(95%置信区间=0.92-0.97)。
结论：我们总结并分析了儿童单基因狼疮的临床特征，并建立了临床医生早期识别的预测模型。当评分超过-9.032299时，临床医生应高度警惕单基因狼疮。
方法：这项回顾性队列研究包括2012年6月至2022年12月在北京协和医院儿科治疗的41例单基因狼疮患者。对照组由按1:2比例招募的经典SLE患者组成。患者以7:3的比例随机分为训练组和验证组。基于最小绝对收缩和选择算子建立逻辑回归模型以生成系数图。使用受试者操作员特征曲线和曲线下面积（AUC）指数评估模型的预测能力。
结果：共纳入41例单基因狼疮患者和82例经典SLE患者。在单基因性狼疮病例中（男女比例为1:1.05，发病年龄从出生到15岁），共鉴定出18个基因突变.系数图中包括的变量是发病年龄，反复感染,颅内钙化,生长和发育迟缓，肌肉张力异常,淋巴结病/肝脾肿大，还有冻疮样皮疹.我们的模型通过内部验证证明了令人满意的诊断性能，AUC值为0.97(95%置信区间=0.92-0.97)。
结论：我们总结并分析了儿童单基因狼疮的临床特征，并建立了临床医生早期识别的预测模型。当评分超过-9.032299时，临床医生应高度警惕单基因狼疮。
