A novel predicted model for hypertension based on a large cross-sectional study.
Sci Rep
; 10(1): 10615, 2020 06 30.
Article
em En
| MEDLINE
| ID: mdl-32606332
ABSTRACT
Hypertension is a global public health issue and leading risk for death and disability. It is urgent to search novel methods predicting hypertension. Herein, we chose 73158 samples of physical examiners in central China from June 2008 to June 2018. After strict exclusion processes, 33570 participants with hypertension and 35410 healthy controls were included. We randomly chose 70% samples as the train set and the remaining 30% as the test set. Clinical parameters including age, gender, height, weight, body mass index, triglyceride, total cholesterol, low-density lipoprotein, blood urea nitrogen, uric acid, and creatinine were significantly increased, while high-density lipoprotein was decreased in the hypertension group versus controls. Nine optimal markers were identified by a logistic regression model, and achieved AUC value of 76.52% in the train set and 75.81% in the test set for hypertension. In conclusions, this study is the first to establish predicted models for hypertension using the logistic regression model in Central China, which provide risk factors and novel prediction method to predict and prevent hypertension.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Pressão Sanguínea
/
Hipertensão
/
Modelos Biológicos
Tipo de estudo:
Etiology_studies
/
Observational_studies
/
Prevalence_studies
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Adult
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Aged
/
Female
/
Humans
/
Male
/
Middle aged
País/Região como assunto:
Asia
Idioma:
En
Revista:
Sci Rep
Ano de publicação:
2020
Tipo de documento:
Article
País de afiliação:
China