A simple prediction model of hyperuricemia for use in a rural setting.
Sci Rep
; 11(1): 23300, 2021 12 02.
Article
en En
| MEDLINE
| ID: mdl-34857832
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 73. 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.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Población Rural
/
Hiperuricemia
Tipo de estudio:
Etiology_studies
/
Prevalence_studies
/
Prognostic_studies
/
Risk_factors_studies
Aspecto:
Determinantes_sociais_saude
Límite:
Adult
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Aged
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Aged80
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Female
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Humans
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Male
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Middle aged
País/Región como asunto:
Asia
Idioma:
En
Revista:
Sci Rep
Año:
2021
Tipo del documento:
Article
País de afiliación:
China