Abstract
Objective Gouty arthritis is a chronic disease caused by purine metabolism disorder. The progress of the disease is relatively clear, divided into four stages. Clinical judgment of the stage of gouty arthritis is generally based on blood uric acid levels, bursa fluid examination, there are false positive cases. This paper attempts to construct a clinical diagnostic model based on routine blood test data, so as to avoid tedious procedures such as cystic fluid examination and predict the development direction of the disease. Methods Serum samples from 579 patients were collected and divided into training set (379 cases) and validation set (200 cases). Through a series of multivariate statistical analysis and correlation analysis, 12 serum biochemical indicators were selected as risk factors for gouty arthritis to obtain the serum biochemical spectrum of gouty arthritis. Based on the biochemical indicators of 379 patients, the clinical diagnosis model of gouty arthritis was established and visualized with R language to predict the probability of patients at different stages, and the stage with the highest probability was used as the diagnosis result, and the evaluation model of 200 patients was used. Results The total ROC of the clinical diagnosis model was 0.95, and the Kappa coefficient of the clinical diagnosis model was 0.80. Conclusion This model can be applied to clinical prediction and basic research to improve the accuracy of gouty arthritis staging.
Establishment of a clinical diagnostic model for gouty arthritis based on the serum biochemical profile
