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A novel predicted model for hypertension based on a large cross-sectional study
Zhigang Ren1,2, Benchen Rao1,2, Siqi Xie3
1Department of Infectious Diseases, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China.
Insights
This study identifies key clinical markers for predicting hypertension in Central China. The developed logistic regression model offers a novel method for early hypertension detection and prevention.
Area of Science:
- Cardiology
- Public Health
- Epidemiology
Background:
- Hypertension is a significant global health concern and a primary risk factor for mortality and morbidity.
- There is a critical need for innovative methods to predict hypertension accurately.
Purpose of the Study:
- To develop and validate a predictive model for hypertension using clinical parameters in a Central China population.
- To identify significant risk factors associated with hypertension in the study cohort.
Main Methods:
- Utilized data from 73,158 physical examination participants in Central China (2008-2018).
- Included 33,570 hypertension cases and 35,410 healthy controls after exclusion criteria.
- Employed a logistic regression model with 70% training and 30% testing data split.
Main Results:
- Identified nine optimal markers for hypertension prediction.
- The logistic regression model achieved an Area Under the Curve (AUC) of 76.52% in the training set and 75.81% in the test set.
- Significant differences in clinical parameters like BMI, lipids, and kidney function markers were observed between hypertensive and control groups.
Conclusions:
- This study presents the first logistic regression-based predictive model for hypertension in Central China.
- The model provides valuable insights into hypertension risk factors and offers a novel prediction strategy.
- The findings support enhanced hypertension prediction and prevention efforts in the region.
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.
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