National Pharmaceutical Engineering Center for Solid Preparation in Chinese Herbal Medicine, Jiangxi University of Traditional Chinese Medicine.
Department of Pathology, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou.
Medicine (Baltimore). 2021 Apr 23;100(16):e25542. doi: 10.1097/MD.0000000000025542.
The disease progression of gouty arthritis (GA) is relatively clear, with the 4 stages of hyperuricemia (HUA), acute gouty arthritis (AGA), gouty arthritis during the intermittent period (GIP), and chronic gouty arthritis (CGA). This paper attempts to construct a clinical diagnostic model based on blood routine test data, in order to avoid the need for bursa fluid examination and other tedious steps, and at the same time to predict the development direction of GA.Serum samples from 579 subjects were collected within 3 years in this study and were divided into a training set (n = 379) and validation set (n = 200). After a series of multivariate statistical analyses, the serum biochemical profile was obtained, which could effectively distinguish different stages of GA. A clinical diagnosis model based on the biochemical index of the training set was established to maximize the probability of the stage as a diagnosis, and the serum biochemical data from 200 patients were used for validation.The total area under the curve (AUC) of the clinical diagnostic model was 0.9534, and the AUCs of the 5 models were 0.9814 (Control), 0.9288 (HUA), 0.9752 (AGA), 0.9056 (GIP), and 0.9759 (CGA). The kappa coefficient of the clinical diagnostic model was 0.80.This clinical diagnostic model could be applied clinically and in research to improve the accuracy of the identification of the different stages of GA. Meanwhile, the serum biochemical profile revealed by this study could be used to assist the clinical diagnosis and prediction of GA.
痛风性关节炎（GA）的疾病进展相对明确，分为高尿酸血症（HUA）、急性痛风性关节炎（AGA）、痛风间歇期（GIP）和慢性痛风性关节炎（CGA）4 期。本文尝试构建一种基于血常规检查数据的临床诊断模型，以避免需要关节液检查等繁琐步骤，同时预测 GA 的发展方向。本研究共收集了 579 例患者 3 年内的血清样本，分为训练集（n=379）和验证集（n=200）。经过一系列多变量统计分析，得到了血清生化特征谱，可有效区分 GA 的不同阶段。基于训练集生化指标建立了基于临床诊断模型，以最大化阶段诊断的概率，并使用 200 例患者的血清生化数据进行验证。该临床诊断模型的曲线下面积（AUC）总分为 0.9534，5 个模型的 AUC 分别为 0.9814（对照）、0.9288（HUA）、0.9752（AGA）、0.9056（GIP）和 0.9759（CGA）。临床诊断模型的kappa 系数为 0.80。该临床诊断模型可在临床和研究中应用，以提高 GA 不同阶段识别的准确性。同时，本研究揭示的血清生化特征谱可用于辅助 GA 的临床诊断和预测。
