Ruxue Yin1, Gangjian Wang2, Xiaopei Yang1, Lei Zhang1, Shuolin Wang1, Tianfang Li1, Shengyun Liu3. 1. Department of Rheumatology, The First Affiliated Hospital of Zhengzhou University, No. 1 Jianshe East Road, Zhengzhou, 450052, China. 2. Department of Oncology, The First Affiliated Hospital of Zhengzhou University, No. 1 Jianshe East Road, Zhengzhou, 450052, China. 3. Department of Rheumatology, The First Affiliated Hospital of Zhengzhou University, No. 1 Jianshe East Road, Zhengzhou, 450052, China. z18339909891@163.com.
Abstract
OBJECTIVES: 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. METHOD: 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. RESULTS: 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. CONCLUSIONS: 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.
OBJECTIVES: 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. METHOD: 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. RESULTS: 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. CONCLUSIONS: 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.