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系统性红斑狼疮患者新发血小板减少症的风险预测:一项基于中国系统性红斑狼疮治疗与研究协作组(CSTAR)登记系统的多中心前瞻性队列研究

Risk prediction of new-onset thrombocytopenia in patients with systemic lupus erythematosus: a multicenter prospective cohort study based on Chinese SLE treatment and research group (CSTAR) registry.

作者信息

Zhang Yupei, Jiang Nan, Duan Xinwang, Xu Jian, Wu Lijun, Wei Wei, Xiao Weiguo, Luo Li, Jiang Zhenyu, Wang Yanhong, Zhao Jiuliang, Wang Qian, Tian Xinping, Li Mengtao, Zeng Xiaofeng

机构信息

Department of Rheumatology and Clinical Immunology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, National Clinical Research Center for Dermatologic and Immunologic Diseases (NCRC-DID), Ministry of Science & Technology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Key Laboratory of Rheumatology and Clinical Immunology, Ministry of Education, Beijing, China.

Department of Rheumatology, The Second Affiliated Hospital of Nanchang University, Nanchang, China.

出版信息

Arthritis Res Ther. 2024 Dec 27;26(1):229. doi: 10.1186/s13075-024-03460-0.

DOI:10.1186/s13075-024-03460-0
PMID:39731190
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11674330/
Abstract

BACKGROUND

Thrombocytopenia (TP) is a hematological manifestation of systemic lupus erythematosus (SLE) and is associated with unfavorable prognostic outcomes. This study aimed to develop a risk prediction model for new-onset TP in SLE patients.

METHODS

Based on the multicenter prospective Chinese SLE Treatment and Research Group (CSTAR) registry, newly diagnosed SLE patients without TP at registration were enrolled. The least absolute shrinkage and selection operator (LASSO) method was used for variable selection. The final model was developed using multivariate Cox regression and displayed as a nomogram. Internal validation was achieved using enhanced Bootstrap resampling.

RESULTS

During follow-up, thrombocytopenia developed in 80 (3.52%) of 2270 lupus patients. The final risk prediction model incorporated six predictors: baseline SDI score ≥ 1 (HR 2.207, 95% CI 1.350-3.609, p = 0.002), hemolytic anemia (HR 1.953, 95% CI 1.017-3.750, p = 0.044), low complement level (HR 2.351, 95% CI 1.004-5.505, p = 0.049), positive anti-β2GPI antibody (HR 1.805, 95% CI 1.084-3.004, p = 0.024), positive Coombs test (HR 1.878, 95% CI 1.123-3.141, p = 0.017), and positive anti-histone antibody (HR 1.595, 95% CI 1.017-2.587, p = 0.059). The model's performance was indicated by C-index values for risk prediction at one, two, and three years, which were 0.741 (0.660-0.823), 0.730 (0.655-0.805), and 0.710 (0.643-0.777), respectively; and Brier scores of 0.018 (0.012-0.024), 0.025 (0.017-0.032), and 0.037 (0.027-0.046), respectively. Calibration curves were drawn and situated near the diagonal line.

CONCLUSIONS

This study developed the first risk prediction model for TP onset in lupus patients. Patients with baseline organ damage, hemolytic anemia, low complement, positive anti-histone antibody, positive anti-β2GPI antibody, or positive Coombs test were identified as being at high risk for thrombocytopenia and require further clinical attention.

摘要

背景

血小板减少症(TP)是系统性红斑狼疮(SLE)的一种血液学表现,与不良预后相关。本研究旨在建立SLE患者新发TP的风险预测模型。

方法

基于多中心前瞻性中国SLE治疗与研究组(CSTAR)登记系统,纳入登记时无TP的新诊断SLE患者。采用最小绝对收缩和选择算子(LASSO)方法进行变量选择。最终模型通过多变量Cox回归建立并以列线图展示。使用增强Bootstrap重抽样进行内部验证。

结果

随访期间,2270例狼疮患者中有80例(3.52%)发生血小板减少症。最终的风险预测模型纳入了六个预测因素:基线SDI评分≥1(HR 2.207,95%CI 1.350 - 3.609,p = 0.002)、溶血性贫血(HR 1.953,95%CI 1.017 - 3.750,p = 0.044)、补体水平低(HR 2.351,95%CI 1.004 - 5.505,p = 0.049)、抗β2GPI抗体阳性(HR 1.805,95%CI 1.084 - 3.004,p = 0.024)、Coombs试验阳性(HR 1.878,95%CI 1.123 - 3.141,p = 0.017)以及抗组蛋白抗体阳性(HR 1.595,95%CI 1.017 - 2.587,p = 0.059)。该模型在1年、2年和3年的风险预测C指数值分别为0.741(0.660 - 0.823)、0.730(0.655 - 0.805)和0.710(0.643 - 0.777),Brier评分分别为0.018(0.012 - 0.024)、0.025(0.017 - 0.032)和0.037(0.027 - 0.046)。绘制了校准曲线且位于对角线附近。

结论

本研究建立了首个狼疮患者TP发病的风险预测模型。基线有器官损害、溶血性贫血、补体水平低、抗组蛋白抗体阳性、抗β2GPI抗体阳性或Coombs试验阳性的患者被确定为血小板减少症的高危人群,需要进一步的临床关注。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb01/11674330/fa59dd3edb28/13075_2024_3460_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb01/11674330/feae6d84b6d9/13075_2024_3460_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb01/11674330/de2af64fc339/13075_2024_3460_Fig2_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb01/11674330/dc8f60d22e93/13075_2024_3460_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb01/11674330/fa59dd3edb28/13075_2024_3460_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb01/11674330/feae6d84b6d9/13075_2024_3460_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb01/11674330/de2af64fc339/13075_2024_3460_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb01/11674330/dc2f9aee1a9b/13075_2024_3460_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb01/11674330/dc8f60d22e93/13075_2024_3460_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb01/11674330/fa59dd3edb28/13075_2024_3460_Fig5_HTML.jpg

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