A diagnostic prediction model for hypertension in Han and Yugur population from the China National Health Survey (CNHS).
10.1097/CM9.0000000000001989
- Author:
Chengdong YU
1
;
Xiaolan REN
2
;
Ze CUI
3
;
Li PAN
1
;
Hongjun ZHAO
2
;
Jixin SUN
3
;
Ye WANG
1
;
Lijun CHANG
2
;
Yajing CAO
3
;
Huijing HE
1
;
Jin'en XI
2
;
Ling ZHANG
4
;
Guangliang SHAN
1
Author Information
1. Department of Epidemiology and Statistics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, Beijing 100005, China.
2. Institute of Chronic and Noncommunicable Disease Control and Prevention, Gansu Provincial Centre for Disease Control and Prevention, Lanzhou, Gansu 730000, China.
3. Hebei Provincial Center for Disease Control and Prevention, Shijiazhuang, Hebei 050000, China.
4. Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China.
- Publication Type:Journal Article
- MeSH:
Adult;
Humans;
Asian People;
China/epidemiology*;
Cross-Sectional Studies;
Health Surveys;
Hypertension/epidemiology*;
Nomograms;
Ethnicity
- From:
Chinese Medical Journal
2023;136(9):1057-1066
- CountryChina
- Language:English
-
Abstract:
BACKGROUND:The prevalence of hypertension is high among Chinese adults, thus, identifying non-hypertensive individuals at high risk for intervention will help to improve the efficiency of primary prevention strategies.
METHODS:The cross-sectional data on 9699 participants aged 20 to 80 years were collected from the China National Health Survey in Gansu and Hebei provinces in 2016 to 2017, and they were nonrandomly split into the training set and validation set based on location. Multivariable logistic regression analysis was performed to develop the diagnostic prediction model, which was presented as a nomogram and a website with risk classification. Predictive performances of the model were evaluated using discrimination and calibration, and were further compared with a previously published model. Decision curve analysis was used to calculate the standardized net benefit for assessing the clinical usefulness of the model.
RESULTS:The Lasso regression analysis identified the significant predictors of hypertension in the training set, and a diagnostic model was developed using logistic regression. A nomogram with risk classification was constructed to visualize the model, and a website ( https://chris-yu.shinyapps.io/hypertension_risk_prediction/ ) was developed to calculate the exact probabilities of hypertension. The model showed good discrimination and calibration, with the C-index of 0.789 (95% confidence interval [CI]: 0.768, 0.810) through internal validation and 0.829 (95% CI: 0.816, 0.842) through external validation. Decision curve analysis demonstrated that the model was clinically useful. The model had a higher area under receiver operating characteristic curves in training and validation sets compared with a previously published diagnostic model based on Northern China population.
CONCLUSION:This study developed and validated a diagnostic model for hypertension prediction in Gansu Province. A nomogram and a website were developed to make the model conveniently used to facilitate the individualized prediction of hypertension in the general population of Han and Yugur.
