目的 构建基于多维自身抗体指标的系统性红斑狼疮（SLE）器官特异性受累的风险预测模型，并通过时间分割队列对模型性能进行验证，从而为早期识别高危患者提供量化依据。 方法 采用回顾性单中心队列设计，并基于时间顺序进行模型构建与验证。开发队列纳入SLE患者170例；采用同院时间分割选择60例作为时间外推验证，按预先制定标准将器官/系统受累（二分类）定义为：浆膜、肾、肺、皮肤、血液、关节及肝功能受累。先行单因素比较（χ2、t检验或Mann-Whitney U）筛选候选变量，继而采用多因素logistic回归识别独立关联因素。模型性能通过ROC/AUC、灵敏度/特异度与Youden指数评估，采用分层10折交叉验证检验稳健性；校准以Hosmer-Lemeshow检验与Brier分数评价，决策曲线分析（DCA）用于评估临床效用。 结果 170例SLE患者器官受累分布为：血液79.41%、肾51.76%、肺51.17%、皮肤38.82%、关节33.53%、肝功能19.18%、浆膜18.02%。多因素回归显示抗-P0与抗-NUC与浆膜受累相关；抗-Ro52与肾脏受累相关；抗-dsDNA、ln（年龄）与ln（病程）与皮肤受累相关；抗-nRNP/Sm与血液受累相关；抗-NUC为关节受累独立危险因素，而抗-AMA-M2为独立保护性因素。模型判别力：各器官特异性模型AUC介于0.60 ~ 0.80；总体模型（全样本）AUC = 0.88（95%CI：0.81 ~ 0.94）；分层10折交叉验证平均AUC = 0.86（95%CI：0.78 ~ 0.93）。开发与验证队列的Hosmer-Lemeshow检验P值均 > 0.10，Brier分数 ≈ 0.12。DCA显示在预测阈值约10% ~ 50% 时模型具有净获益。 结论 基于多维自身抗体指标构建的器官特异性风险预测模型在基于时间顺序划分的验证队列中呈现良好的判别能力与可接受的校准性，提示自身抗体谱可作为SLE器官受累风险分层的辅助手段。

Objective To develop organ-specific risk prediction models for organ involvement in systemic lupus erythematosus （SLE） based on multidimensional autoantibody profiles， and to validate their performance using a time-split cohort， thereby providing a quantitative tool for early identification of high-risk patients. Methods A retrospective single-center cohort study was conducted with model development and validation performed in chronological order. The development cohort included 170 SLE patients， and 60 patients from the same institution were enrolled as a time-extrapolated validation cohort. Organ/system involvement was defined as a binary outcome according to predefined criteria， including serosal， renal， pulmonary， cutaneous， hematological， articular， and hepatic involvement. Candidate variables were screened by univariate analysis （χ2 test， t-test， or Mann?Whitney U test）， and independent associated factors were identified by multivariate logistic regression. Model discrimination was assessed using ROC analysis， AUC， sensitivity， specificity， and Youden index； robustness was evaluated by stratified 10-fold cross-validation. Calibration was assessed with the Hosmer-Lemeshow test and Brier score. Clinical utility was evaluated by decision curve analysis （DCA）. Results Among 170 SLE patients， the prevalence of organ involvement was： hematological 79.41%， renal 51.76%， pulmonary 51.17%， cutaneous 38.82%， articular 33.53%， hepatic 19.18%， and serosal 18.02%. Multivariate regression demonstrated that anti-P0 and anti-NUC were associated with serosal involvement； anti-Ro52 was associated with renal involvement； anti-dsDNA， ln（age）， and ln（disease duration） were associated with cutaneous involvement； anti-nRNP/Sm was associated with hematological involvement. Anti-NUC was an independent risk factor for articular involvement， whereas anti-AMA-M2 was an independent protective factor. The AUC of individual organ-specific models ranged from 0.60 to 0.80. The overall model achieved an AUC of 0.88 （95%CI： 0.81 - 0.94）， with a mean AUC of 0.86 （95%CI： 0.78 - 0.93） in stratified 10-fold cross-validation. Hosmer-Lemeshow tests in development and validation cohorts were > 0.10， overall Brier score ≈ 0.12. DCA showed a net clinical benefit of the models at threshold probabilities between approximately 10% and 50%. Conclusion Organ-specific risk prediction models based on multidimensional autoantibody profiles demonstrate favorable discrimination and acceptable calibration in a time-split validation cohort， indicating that autoantibody profiles can serve as a useful auxiliary tool for risk stratification of organ involvement in SLE patients.