PDF(Pubmed)
Abstract：
UNASSIGNED: Rheumatoid arthritis (RA) is an autoinflammatory disease, its core treatment principle is to achieve remission as soon as possible. There is no good prediction model that can accurately predict the remission rate of patients to choose a good treatment scheme. Here, we aimed to verify the prognostic value of some inflammatory indicators in RA and establish a prediction model to predict the remission rate after treatment.
UNASSIGNED: A total of 223 patients were enrolled at Qilu Hospital from June 2014 to June 2020. Baseline clinical data were collected and plasma was obtained to detect the inflammatory indicators. All patients were treated with conventional synthetic disease-modifying antirheumatic drugs (csDMARDs). All patients were followed up and were recorded the time to reach the disease activity score-28 with erythrocyte sedimentation rate (DAS28-ESR) of <2.6. A total of 156 patients were randomly assigned to the development cohort, and 67 patients were assigned to the validation cohort. Inflammatory indicators in plasma were detected by enzyme-linked immunosorbent assay (ELISA). The predictive factors were screeded by using least absolute shrinkage and selection operator (LASSO) and Cox regression. The model was created and verified by using the standard method. A total of 6 independent risk factors were analyzed to construct a nomogram to predict the remission rate in 3, 6 and 12 months.
UNASSIGNED: The remission rates after treatment in 3, 6 and 12 months were 38.76%, 58.91%, and 81.40%, respectively. Patient age, C-reactive protein (CRP), interleukin (IL)-6, galectin-9 (Gal-9), health assessment questionnaire (HAQ), and DAS28-ESR were included in the prognostic model to predict the remission rate. The resulting model had good discrimination ability in both the development cohort (C-index, 0.729) and the validation cohort (C-index, 0.710). Time-dependent receiver operating characteristic (ROC) curve, calibration analysis, and decision curve analysis (DCA) showed that the model has significant discriminant power and clinical practicability in predicting the remission rate.
UNASSIGNED: We established a new predictive model and validated it. The model can predict the remission rate in 3, 6 and 12 months after receiving csDMARDs treatment. By using this model, we can facilitate the identification of high-risk patients early and intervene with them as soon as possible.
UNASSIGNED: A total of 223 patients were enrolled at Qilu Hospital from June 2014 to June 2020. Baseline clinical data were collected and plasma was obtained to detect the inflammatory indicators. All patients were treated with conventional synthetic disease-modifying antirheumatic drugs (csDMARDs). All patients were followed up and were recorded the time to reach the disease activity score-28 with erythrocyte sedimentation rate (DAS28-ESR) of <2.6. A total of 156 patients were randomly assigned to the development cohort, and 67 patients were assigned to the validation cohort. Inflammatory indicators in plasma were detected by enzyme-linked immunosorbent assay (ELISA). The predictive factors were screeded by using least absolute shrinkage and selection operator (LASSO) and Cox regression. The model was created and verified by using the standard method. A total of 6 independent risk factors were analyzed to construct a nomogram to predict the remission rate in 3, 6 and 12 months.
UNASSIGNED: The remission rates after treatment in 3, 6 and 12 months were 38.76%, 58.91%, and 81.40%, respectively. Patient age, C-reactive protein (CRP), interleukin (IL)-6, galectin-9 (Gal-9), health assessment questionnaire (HAQ), and DAS28-ESR were included in the prognostic model to predict the remission rate. The resulting model had good discrimination ability in both the development cohort (C-index, 0.729) and the validation cohort (C-index, 0.710). Time-dependent receiver operating characteristic (ROC) curve, calibration analysis, and decision curve analysis (DCA) showed that the model has significant discriminant power and clinical practicability in predicting the remission rate.
UNASSIGNED: We established a new predictive model and validated it. The model can predict the remission rate in 3, 6 and 12 months after receiving csDMARDs treatment. By using this model, we can facilitate the identification of high-risk patients early and intervene with them as soon as possible.
摘要：
未经证实：类风湿性关节炎（RA）是一种自身炎症性疾病，其核心治疗原则是尽快达到缓解。目前还没有很好的预测模型能够准确预测患者的缓解率,从而选择好的治疗方案。这里,目的验证部分炎性指标在RA中的预后价值,并建立预测治疗后缓解率的预测模型。
UNASSIGNED：2014年6月至2020年6月齐鲁医院共纳入223例患者。收集基线临床数据并获得血浆以检测炎症指标。所有患者均接受常规合成疾病缓解抗风湿药（csDMARDs）治疗。对所有患者进行随访,记录患者达到疾病活动评分-28、红细胞沉降率(DAS28-ESR)<2.6的时间。共有156名患者被随机分配到发展队列中，67例患者被分配到验证队列.采用酶联免疫吸附试验(ELISA)检测血浆炎症指标。通过使用最小绝对收缩和选择算子（LASSO）和Cox回归来筛选预测因素。采用标准方法建立模型并进行验证。对6个独立危险因素进行分析，构建列线图，预测3、6和12个月的缓解率。
UNASSIGNED：治疗后3、6、12个月的缓解率为38.76%，58.91%,和81.40%，分别。患者年龄,C反应蛋白(CRP),白细胞介素（IL）-6，半乳糖凝集素-9（Gal-9），健康评估问卷（HAQ），将DAS28-ESR纳入预后模型以预测缓解率。所得模型在两个发展队列中都具有良好的判别能力(C指数，0.729)和验证队列(C指数，0.710）。随时间变化的接收机工作特性(ROC)曲线,校准分析,和决策曲线分析（DCA）表明，该模型在预测缓解率方面具有显着的判别力和临床实用性。
UNASSIGNED：我们建立了一个新的预测模型并对其进行了验证。该模型可以预测接受csDMARDs治疗后3、6和12个月的缓解率。通过使用这个模型，我们可以促进早期识别高危患者，并尽快对他们进行干预。
UNASSIGNED：2014年6月至2020年6月齐鲁医院共纳入223例患者。收集基线临床数据并获得血浆以检测炎症指标。所有患者均接受常规合成疾病缓解抗风湿药（csDMARDs）治疗。对所有患者进行随访,记录患者达到疾病活动评分-28、红细胞沉降率(DAS28-ESR)<2.6的时间。共有156名患者被随机分配到发展队列中，67例患者被分配到验证队列.采用酶联免疫吸附试验(ELISA)检测血浆炎症指标。通过使用最小绝对收缩和选择算子（LASSO）和Cox回归来筛选预测因素。采用标准方法建立模型并进行验证。对6个独立危险因素进行分析，构建列线图，预测3、6和12个月的缓解率。
UNASSIGNED：治疗后3、6、12个月的缓解率为38.76%，58.91%,和81.40%，分别。患者年龄,C反应蛋白(CRP),白细胞介素（IL）-6，半乳糖凝集素-9（Gal-9），健康评估问卷（HAQ），将DAS28-ESR纳入预后模型以预测缓解率。所得模型在两个发展队列中都具有良好的判别能力(C指数，0.729)和验证队列(C指数，0.710）。随时间变化的接收机工作特性(ROC)曲线,校准分析,和决策曲线分析（DCA）表明，该模型在预测缓解率方面具有显着的判别力和临床实用性。
UNASSIGNED：我们建立了一个新的预测模型并对其进行了验证。该模型可以预测接受csDMARDs治疗后3、6和12个月的缓解率。通过使用这个模型，我们可以促进早期识别高危患者，并尽快对他们进行干预。
