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.
Highlights
Recording lifestyle characteristics and blood biochemical indicators[20]
Taking into account the large population base in China, the large proportion of the rural inhabitants, the imbalance of medical resources and the lack of corresponding facilities and equipment in many rural hospitals, coupled with the need to reduce the financial burden and avoid invasive procedures such as taking blood, we attempted to predict the risk of HUA based on some basic information of the patients and non-invasive examinations, including age, gender, body mass index (BMI) and the prevalence of hypertension
In this study, the nomogram and the classification tree models were combined to fully explore the physical examination database and construct a HUA prediction model suitable for people living in rural settings
Summary
Recording lifestyle characteristics and blood biochemical indicators[20]. Taking into account the large population base in China, the large proportion of the rural inhabitants, the imbalance of medical resources and the lack of corresponding facilities and equipment in many rural hospitals, coupled with the need to reduce the financial burden and avoid invasive procedures such as taking blood, we attempted to predict the risk of HUA based on some basic information of the patients and non-invasive examinations, including age, gender, body mass index (BMI) and the prevalence of hypertension. Logistic regression is often used to look for risk factors associated with disease as well as to predict the probability of the occurrence of certain diseases. It is a specific and simple traditional prediction method, and it can visually present the results pictorially through the nomogram model. The decision tree model is a simple and easy-to-use non-parametric classifier, among which the classification and regression tree (CART) algorithm is the most widely used algorithm for this model. In this study, the nomogram and the classification tree models were combined to fully explore the physical examination database and construct a HUA prediction model suitable for people living in rural settings. It was anticipated that this procedure would produce a high accuracy, low cost and easy to use model which can provide assistance for the health management of rural community populations by helping in the prevention of HUA
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