Department of Medicine, 12277Duke University School of Medicine, Durham, NC, USA.
Department of Population Health Sciences, 12277Duke University School of Medicine, Durham, NC, USA.
Lupus. 2022 May;31(6):697-705. doi: 10.1177/09612033221090885. Epub 2022 Mar 28.
We developed a model that categorizes systemic lupus erythematosus (SLE) activity into two dimensions: Type 1 SLE consists of inflammatory activity, including arthritis, nephritis, and rashes; Type 2 SLE includes fatigue, myalgia, mood disturbance, and cognitive dysfunction. Patient-reported outcome (PRO) measures have received attention as a way to capture symptomatology of SLE. The objective of this study was to explore the use of existing PRO measures to classify Type 1 and 2 SLE activity.
Systemic lupus erythematosus patients completed three questionnaires: Systemic Lupus Activity Questionnaire (SLAQ), Polysymptomatic Distress Scale (PSD), and Patient Health Questionnaire (PHQ-2). SLE Disease Activity Index (SLEDAI) and physician global assessments (PGA; 0-3) for Type 1 and Type 2 activity were also recorded. High Type 1 SLE activity was defined as cSLEDAI ≥4 (scored without labs), SLEDAI ≥6, active nephritis, or Type 1 PGA ≥1.0. High Type 2 SLE activity was defined as Type 2 PGA ≥1.0. Patients with both high Type 1 and 2 activity were defined as Mixed SLE, and patients with low Type 1 and 2 activity were defined as Minimal SLE. Data were reduced with a factor analysis. Using a reduced set of 13 variables, multinomial logistic regression models estimated the probability of Minimal, Type 1, Type 2, and Mixed SLE classification.
The study included 208 patients with SLE. The model accurately predicted the clinician-based Type 1 and 2 SLE classification in 63% of patients; 73% of patients had their Type 1 SLE activity accurately predicted; and 83% had their Type 2 SLE activity accurately predicted. Performance varied by group: 87% of Minimal patients were correctly predicted to be in the Minimal SLE group, yet only about one-third of patients in the Type 1 group were correctly predicted to be in the Type 1 group.
Our findings indicate Type 2 SLE activity can be identified by patient-reported data. The use of PROs was not as accurate at predicting Type 1 activity. These findings highlight the challenges of using PROs to categorize and classify SLE symptoms since some manifestations of Type 1 activity (e.g., nephritis) may be essentially clinically silent while other Type 1 manifestations may cause severe symptoms.
我们建立了一个模型，将系统性红斑狼疮（SLE）的活动分为两个维度：1 型 SLE 由炎症活动组成，包括关节炎、肾炎和皮疹；2 型 SLE 包括疲劳、肌痛、情绪障碍和认知功能障碍。患者报告的结局（PRO）测量作为一种捕捉 SLE 症状的方法受到关注。本研究的目的是探讨使用现有的 PRO 测量来分类 1 型和 2 型 SLE 活动。
系统性红斑狼疮患者完成了三个问卷：系统性红斑狼疮活动问卷（SLAQ）、多症状困扰量表（PSD）和患者健康问卷（PHQ-2）。SLE 疾病活动指数（SLEDAI）和医生总体评估（PGA；0-3）也记录了 1 型和 2 型活动。高 1 型 SLE 活动定义为 cSLEDAI≥4（无实验室评分）、SLEDAI≥6、活动性肾炎或 1 型 PGA≥1.0。高 2 型 SLE 活动定义为 2 型 PGA≥1.0。同时存在高 1 型和 2 型活动的患者定义为混合性 SLE，低 1 型和 2 型活动的患者定义为最小型 SLE。数据通过因子分析进行简化。使用简化后的 13 个变量，多变量逻辑回归模型估计了最小、1 型、2 型和混合性 SLE 分类的概率。
该研究纳入了 208 例 SLE 患者。该模型准确预测了 63%的患者的临床基于的 1 型和 2 型 SLE 分类；73%的患者准确预测了他们的 1 型 SLE 活动；83%的患者准确预测了他们的 2 型 SLE 活动。表现因组而异：87%的最小型患者被正确预测为最小型 SLE 组，但只有约三分之一的 1 型患者被正确预测为 1 型组。
我们的研究结果表明，2 型 SLE 活动可以通过患者报告的数据识别。PRO 用于预测 1 型活动的准确性并不高。这些发现突出了使用 PRO 来分类和分类 SLE 症状的挑战，因为 1 型活动的一些表现（如肾炎）可能在本质上临床无明显症状，而其他 1 型表现可能会引起严重症状。
