摘要 22 - 基于多种生物标志物的银屑病关节炎患者疾病活动性预测评分的推导和内部验证

Yingzhao Jin, Isaac T Cheng, Ho So, T. Yip, CK Wong, L. Tam
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Least absolute shrinkage and selection operator (LASSO) was used to select biomarkers which were associated with moderate/high disease activity in the derivation cohort. Receiver operating characteristic (ROC) curve, GiViTI calibration belt were used to assess the performance of the model in both cohorts. Results The cohort [age: 55.5 (44.0-62.75) years, male: 80 (45.5%)] had moderate disease activity [DAPSA: 15.9 (8.3-26.9); PASI: 3.2 (0.5-6.8)]. 101 PsA patients (57.4%) had moderate/high disease activity. Biomarker levels associated with moderate/high disease activity included SAA (Serum amyloid A), IL8 (Interleukin 8), IP10 (Interferon gamma-induced protein 10), M-CSF (Macrophage colony-stimulating factor), SCGF-[Formula: see text] (Stem cell growth factor), SDF-1[Formula: see text] (Stromal cell-derived factor 1[Formula: see text]) (Figure 1A, B). The model’s equation including the 6 biomarker levels was applied to the validation-cohort. 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引用次数: 0

摘要

背景 C反应蛋白(CRP)通常用于监测银屑病关节炎(PsA)的疾病活动性,但中度至高度疾病活动性患者中有一半以上的CRP水平正常。我们的研究旨在探讨 PsA 患者血清蛋白生物标志物与疾病活动性的相关性。方法 在这项横断面研究中,共招募了 176 名符合 CASPAR(银屑病关节炎分类标准)的患者。疾病活动度通过银屑病关节炎临床疾病活动度(cDAPSA)进行测量。对 45 种蛋白质生物标志物、软骨和骨转换标志物水平进行了评估(表 1)。患者按 7:3 的比例随机分为衍生队列和验证队列。使用最小绝对收缩和选择算子(LASSO)来选择与衍生队列中的中度/高度疾病活动相关的生物标记物。使用接收者操作特征曲线(ROC)和 GiViTI 校准带评估模型在两个队列中的表现。结果 该队列[年龄:55.5(44.0-62.75)岁,男性:80(45.5%)]具有中度疾病活动性[DAPSA:15.9(8.3-26.9);PASI:3.2(0.5-6.8)]。101名PsA患者(57.4%)有中度/高度疾病活动。与中度/高度疾病活动相关的生物标志物水平包括:SAA(血清淀粉样蛋白A)、IL8(白细胞介素8)、IP10(γ干扰素诱导蛋白10)、M-CSF(巨噬细胞集落刺激因子)、SCGF-[公式:见正文](干细胞生长因子)、SDF-1[公式:见正文](基质细胞衍生因子1[公式:见正文])(图1A、B)。包括 6 个生物标记物水平的模型方程被应用于验证队列。衍生队列和验证队列在判别中度/高度疾病活动性时的 ROC 曲线下面积(AUC)分别为 0.802 和 0.835(图 1C、D)。与 CRP 相比,多生物标记物面板模型的 AUC 更高(AUC=0.727,P=0.022)。两组校准图的 P 值分别为 0.902 和 0.123(图 1E、F)。结论 多生物标记物面板在区分中度/高度疾病活动患者与低度疾病活动/缓解患者方面表现出色。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Abstract 22 — Derivation and Internal Validation of a Multi-Biomarker-Based Disease Activity Prediction Score for Psoriatic Arthritis Patients
Background While C-reactive protein (CRP) is commonly used to monitor disease activity in Psoriatic Arthritis (PsA), over half of the patients with moderate-to-high disease activity had normal CRP level. Our study aims to investigate the correlation of serum protein biomarkers and disease activity in patients with PsA. Methods 176 patients fulfilled the CASPAR (ClASsification criteria for Psoriatic ARthritis) were recruited in this cross-sectional study. Disease activity was measured by the clinical Disease Activity in Psoriatic Arthritis (cDAPSA). 45 protein biomarkers, cartilage and bone turn-over markers level were assessed (Table 1). The patients were randomly divided into a derivation-cohort and a validation-cohort at a ratio of 7:3. Least absolute shrinkage and selection operator (LASSO) was used to select biomarkers which were associated with moderate/high disease activity in the derivation cohort. Receiver operating characteristic (ROC) curve, GiViTI calibration belt were used to assess the performance of the model in both cohorts. Results The cohort [age: 55.5 (44.0-62.75) years, male: 80 (45.5%)] had moderate disease activity [DAPSA: 15.9 (8.3-26.9); PASI: 3.2 (0.5-6.8)]. 101 PsA patients (57.4%) had moderate/high disease activity. Biomarker levels associated with moderate/high disease activity included SAA (Serum amyloid A), IL8 (Interleukin 8), IP10 (Interferon gamma-induced protein 10), M-CSF (Macrophage colony-stimulating factor), SCGF-[Formula: see text] (Stem cell growth factor), SDF-1[Formula: see text] (Stromal cell-derived factor 1[Formula: see text]) (Figure 1A, B). The model’s equation including the 6 biomarker levels was applied to the validation-cohort. The area under the ROC curve (AUC) for discriminating moderate/high disease activity was 0.802 and 0.835 for the derivation-and-validation-cohorts, respectively (Figure 1C, D). The multi-biomarkers panel model had higher-AUC when compared with that of CRP (AUC=0.727, p=0.022). The P-values of calibration charts in the two sets were 0.902 and 0.123 (Figure 1E, F). Conclusions The multi-biomarkers panel had excellent performance in discriminating patients with moderate/high disease activity from those with low disease activity/remission.
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