Department of Rheumatology, The First Affiliated Hospital of Zhengzhou University, No. 1 Jianshe East Road, Zhengzhou, 450052, China.
Department of Oncology, The First Affiliated Hospital of Zhengzhou University, No. 1 Jianshe East Road, Zhengzhou, 450052, China.
Clin Rheumatol. 2021 Oct;40(10):3951-3960. doi: 10.1007/s10067-021-05722-7. Epub 2021 May 18.
This study aimed to identify the risk factors for relapse/refractory adult-onset Still's disease (AOSD) and to construct and validate a prognostic nomogram for predicting the individual risk of relapse/refractory disease.
A total of 174 patients were included in our study. Univariate and multivariate logistic regression analyses were used to identify relapse/refractory-associated factors, which were used to construct nomograms. Receiver operating characteristic (ROC) curve analysis, calibration curves, and decision curve analysis (DCA) were used to assess the predictive ability of the nomograms.
Univariate and multivariate logistic analyses showed that age, fever, disease duration, platelet count, serum ferritin level, and erythrocyte sedimentation rate were independent unfavourable factors for relapse/refractory AOSD (p < 0.05). We constructed a 6-factor nomogram based on univariate and multivariate logistic analyses. ROC analysis indicated that the area under the curve of the 6-factor nomogram in the training set and test set was 0.765 and 0.714, respectively. In addition, the calibration curves showed excellent prediction accuracy, and DCA showed superior net benefit in the 6-factor nomograms. Moreover, we evaluated the predictive effectiveness of our nomogram in females and young adults. The results showed that our 6-factor nomogram has the same predictive ability in both subgroups.
Novel nomograms based on clinical characteristics were developed and may be applied to help predict the individual risk of poor prognosis of patients. Key Points • Logistic regression was used to identify risk factors for relapse/refractory adult-onset Still's disease. • We then constructed a nomogram for predicting disease risk. • ROC analysis, calibration curves, and DCA all showed that the nomogram exerted good prediction ability in both the training set and test set. • The nomogram has the same predictive ability in both female and young adult subgroups.
本研究旨在确定成人Still 病(AOSD)复发/难治的危险因素,并构建和验证预测个体复发/难治疾病风险的预后列线图。
本研究共纳入 174 例患者。采用单因素和多因素逻辑回归分析确定与复发/难治相关的因素,并构建列线图。采用接收者操作特征(ROC)曲线分析、校准曲线和决策曲线分析(DCA)评估列线图的预测能力。
单因素和多因素逻辑分析表明,年龄、发热、病程、血小板计数、血清铁蛋白水平和红细胞沉降率是 AOSD 复发/难治的独立不利因素(p<0.05)。我们基于单因素和多因素逻辑分析构建了一个 6 因素列线图。ROC 分析表明,该列线图在训练集和测试集中的曲线下面积分别为 0.765 和 0.714。此外,校准曲线显示出优异的预测准确性,DCA 显示在 6 因素列线图中具有更高的净获益。此外,我们评估了该列线图在女性和年轻患者中的预测效果。结果表明,我们的 6 因素列线图在这两个亚组中具有相同的预测能力。
基于临床特征的新列线图被开发出来,可能有助于预测患者预后不良的个体风险。
• 逻辑回归用于确定成人Still 病复发/难治的危险因素。
• 然后构建了预测疾病风险的列线图。
• ROC 分析、校准曲线和 DCA 均表明,该列线图在训练集和测试集中均具有良好的预测能力。
• 该列线图在女性和年轻成年亚组中具有相同的预测能力。