目的·基于实验室指标，建立早期系统性红斑狼疮（systemic lupus erythmatosus，SLE）与其他自身免疫性疾病鉴别诊断的列线图并评估其效能。方法·选择2017年1月—2021年12月在兰州大学第一医院就诊的535例SLE患者（SLE组）以及同时期的535例其他自身免疫性疾病患者（对照组）。收集并比较2组患者的基础信息及实验室检查指标（共116项）。将SLE组和对照组分别按7∶3的比例随机分为训练集和验证集，采用LASSO回归、多因素Logistic回归筛选SLE的主要危险因子，并建立早期SLE鉴别诊断列线图（简称“SLE列线图”）。使用Bootstrap法行内部重复抽样1 000次对列线图进行校准，分别利用受试者操作特征曲线（receiver operator characteristic curve，ROC曲线）、决策曲线分析（decision curve analysis，DCA）评估SLE列线图的鉴别诊断的能力以及在临床应用中的价值。采用R语言“DynNom”包将列线图转换为电子计算器，并通过3组患者数据对其与SLE列线图的一致性进行验证。结果·LASSO回归和多因素Logistic回归共筛选出6个SLE的主要危险因子，即抗核抗体（antinuclear antibody，ANA）、抗双链DNA（anti-double-stranded DNA，anti-dsDNA）抗体、抗核糖核蛋白抗体/史密斯抗体（anti-ribonucleoprotein antibody/anti-Simth antibody，anti-nRNP/Sm）、抗核糖体P蛋白（anti-ribosomal P protein，anti-P）抗体、抗核小体抗体（anti-nucleosome antibody，ANuA）、尿蛋白（urinary protein，PRO），并由该6个因子共同构建SLE列线图。该列线图的校准曲线在训练集和验证集的标准误分别为0.009和0.015，其ROC曲线下面积分别为0.889和0.869。DCA的结果显示，当SLE列线图的风险阈概率在0.15~0.95时，该列线图取得的净获益较高。电子计算器的预测结果显示，1号SLE患者的ANA（滴度1∶100）为阳性，其患病率为0.166；2号患者的ANA（滴度1∶100）、ANuA（滴度1∶100）均为阳性，其患病率为0.676；3号患者的PRO、ANA（滴度1∶100）、ANuA（滴度1∶100）、anti-P抗体（滴度1∶100）均为阳性，其患病率为0.990，这与SLE列线图鉴别诊断的结果相一致。结论·基于ANA、anti-dsDNA抗体、anti-nRNP/Sm、anti-P抗体、ANuA、PRO建立的SLE列线图以及转换成的电子计算器可较好地鉴别SLE早期和其他自身免疫性疾病，具有重要的临床应用价值。

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.