A simple prediction model of hyperuricemia for use in a rural setting

Abstract

Currently, the most widely used screening methods for hyperuricemia (HUA) involves invasive laboratory tests, which are lacking in many rural hospitals in China. This study explored the use of non-invasive physical examinations to construct a simple prediction model for HUA, in order to reduce the economic burden and invasive operations such as blood sampling, and provide some help for the health management of people in poor areas with backward medical resources. Data of 9252 adults from April to June 2017 in the Affiliated Hospital of Guilin Medical College were collected and divided randomly into a training set (n = 6364) and a validation set (n = 2888) at a ratio of 7:3. In the training set, non-invasive physical examination indicators of age, gender, body mass index (BMI) and prevalence of hypertension were included for logistic regression analysis, and a nomogram model was established. The classification and regression tree (CART) algorithm of the decision tree model was used to build a classification tree model. Receiver operating characteristic (ROC) curve, calibration curve and decision curve analyses (DCA) were used to test the distinction, accuracy and clinical applicability of the two models. The results showed age, gender, BMI and prevalence of hypertension were all related to the occurrence of HUA. The area under the ROC curve (AUC) of the nomogram model was 0.806 and 0.791 in training set and validation set, respectively. The AUC of the classification tree model was 0.802 and 0.794 in the two sets, respectively, but were not statistically different. The calibration curves and DCAs of the two models performed well on accuracy and clinical practicality, which suggested these models may be suitable to predict HUA for rural setting.

The development and validation of a non-invasive prediction model of hyperuricemia based on modifiable risk factors: baseline findings of a health examination population cohort

“…6,15,19,20 have particular importance in disease prevention and control given their changeable levels developed a prediction model of HUA that contains no invasive tests as predictors, in which BMI was selected as a modifiable risk factor. 21 In addition to the well-known risk factors of HUA, such as overweight/obesity, alcohol consumption, our study found that staying up late was commonly associated with a higher risk of HUA, which is in line with other previous studies. For example, Zou et al found that short sleep is genetically associated with an increased risk of HUA among the white British population.…”

Associations of triglyceride-glucose index with hyperuricemia among Royal Thai Army personnel

“…1 ). These variables have also been previously incorporated into models predicting hyperuricemia [ 45 ]. Moreover, these variables had clinical applicability and are routinely measured in clinical settings.…”

Potential Predictive Value of miR-125b-5p, miR-155-5p and Their Target Genes in the Course of COVID-19

“…The classification tree model is a tool to represent the processing logic with a binary tree diagram In recent years, scholars have successfully applied the classification tree model to predict COVID-19 in the elderly population, in which the number of sample cases is 41, the same small sample prediction model as this study. 58 In addition, the classification tree has been applied to predict hyperuricemia, 59 predict the survival rate of lung cancer patients, 60 analyze the cause of pediatric pneumonia 61 and other medical and surgical diseases, and its application in medical-related fields has proven its effectiveness. In this study, miR-125b-5p together with miR-155-5p and target genes STAT3 and TRIM32 constituted a classification tree model.…”