Lupus Clinic, Reumatologia, Dipartimento di Scienze Cliniche Internistiche, Anestesiologiche e Cardiovascolari, Sapienza Università di Roma, Viale del Policlinico 155, 00161 Rome, Italy.
Lupus Clinic, Reumatologia, Dipartimento di Scienze Cliniche Internistiche, Anestesiologiche e Cardiovascolari, Sapienza Università di Roma, Viale del Policlinico 155, 00161 Rome, Italy.
Semin Arthritis Rheum. 2021 Apr;51(2):404-408. doi: 10.1016/j.semarthrit.2021.02.005. Epub 2021 Feb 19.
We evaluated a monocentric SLE cohort in order to assess the frequency of Lupus comprehensive disease control (LupusCDC), a condition defined by the achievement of remission and the absence of damage progression.
Our longitudinal analysis included SLE patients with 5-years follow-up and at least one visit per year. Disease activity was assessed by SLE Disease Activity Index 2000 (SLEDAI-2K) and three different remission levels were evaluated (Complete Remission, CR; Clinical remission off-corticosteroids; clinical remission on-corticosteroids). Chronic damage was assessed according to SLICC Damage Index (SDI). LupusCDC was defined as remission achievement for at least one year plus absence of chronic damage progression in the previous one year. A machine learning based analysis was carried out, applying and comparing Nonlinear Support Vector Machines (SVM) models and Decision Trees (DT), whereas features ranking was performed with the ReliefF algorithm.
We evaluated 172 patients [M/F 16/156, median age 49 years (IQR 16.7), median disease duration 180 months (IQR 156)]. SDI values (baseline mean±SD 0.7 ± 1.1) significantly increased during the follow-up period. In all time-points analyzed, LupusCDC including CR was the most frequently detected. The failure to reach this condition was significantly associated with renal involvement and with the intake of immunosuppressant drugs and glucocorticoid (GC). Ten patients (5.8%) have maintained LupusCDC during the whole 5-year follow-up: these patients had never presented renal involvement and showed lower prevalence of anti-phospholipid antibodies (p = 0.0001). Finally, the prevalence of GC intake was significantly lower (p = 0.0001). The application of machine learning models showed that the available features were able to provide significant information to build predictive models with an AUC score of 0.703 ± 0.02 for DT and 0.713 ± 0.02 for SVM.
Our data on a monocentric cohort suggest that the LupusCDC can efficaciously merge into one outcome SLE-related disease activity and chronic damage in order to perform an all-around evaluation of SLE patients.
我们评估了一个单中心系统性红斑狼疮（SLE）队列，以评估狼疮综合疾病控制（LupusCDC）的频率，这一状况通过达到缓解和无疾病进展来定义。
我们的纵向分析包括有 5 年随访且每年至少就诊 1 次的 SLE 患者。疾病活动度通过系统性红斑狼疮疾病活动指数 2000（SLEDAI-2K）进行评估，并评估了三种不同的缓解水平（完全缓解，CR；无皮质类固醇的临床缓解；有皮质类固醇的临床缓解）。慢性损伤根据 SLICC 损伤指数（SDI）进行评估。LupusCDC 定义为缓解持续至少 1 年且前 1 年无慢性损伤进展。进行了基于机器学习的分析，应用并比较了非线性支持向量机（SVM）模型和决策树（DT），并使用 ReliefF 算法进行特征排序。
我们评估了 172 例患者[M/F 16/156，中位年龄 49 岁（IQR 16.7），中位疾病病程 180 个月（IQR 156）]。SDI 值（基线均值±SD 0.7±1.1）在随访期间显著增加。在分析的所有时间点，包括 CR 的 LupusCDC 是最常检测到的。未能达到这一状况与肾脏受累以及免疫抑制剂和糖皮质激素（GC）的使用显著相关。10 例（5.8%）患者在整个 5 年随访期间维持了 LupusCDC：这些患者从未出现肾脏受累，且抗磷脂抗体的患病率较低（p=0.0001）。最终，GC 摄入的患病率显著降低（p=0.0001）。机器学习模型的应用表明，现有的特征能够提供有意义的信息，用于构建预测模型，DT 的 AUC 评分为 0.703±0.02，SVM 的 AUC 评分为 0.713±0.02。
我们在单中心队列中的数据表明，LupusCDC 可以有效地合并为一个结局，即 SLE 相关的疾病活动和慢性损伤，以便对 SLE 患者进行全面评估。
