ArticleJournal of human hypertension2021
Prediction model and assessment of probability of incident hypertension: the Rural Chinese Cohort Study.
Article in Journal of human hypertension, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
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Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it, 22 citations in OpenAlex.
- Prognostic risk models for incident hypertension: A PRISMA systematic review and meta-analysis.PloS one · 2024Pooled it
- Prediction of hypertension using traditional regression and machine learning models: A systematic review and meta-analysis.PloS one · 2022Pooled it
- Development and validation of hypertension prediction models: The Korean Genome and Epidemiology Study_Cardiovascular Disease Association Study (KoGES_CAVAS).Journal of human hypertension · 2023Trial
- Development and validation of a hypertension risk prediction model using electronic health records data from general practices in Hampshire and Isle of Wight integrated care system.BMC cardiovascular disorders · 2026Article
- Differential risk profiles for geriatric depression, anxiety, and sleep disturbances in rural China: insights from the Taierzhuang cohort.Frontiers in public health · 2026Article
- Predicting Metabolic Syndrome Using Supervised Machine Learning: A Multivariate Parameter Approach.International journal of molecular sciences · 2025Article
- Further insights into influence factors of hypertension in older patients with obstructive sleep apnea syndrome: a model based on multiple centers.Aging clinical and experimental research · 2025Article
- Risk Assessment Score and Chi-Square Automatic Interaction Detection Algorithm for Hypertension Among Africans: Models From the SIREN Study.Hypertension (Dallas, Tex. : 1979) · 2023Article
- A comparison of machine learning algorithms and traditional regression-based statistical modeling for predicting hypertension incidence in a Canadian population.Scientific reports · 2023Article
- Development and validation of a model to predict the risk of hypertension using anthropometric indicators in the Chinese population: a retrospective cohort study.American journal of translational research · 2023Article
- Developing and Validating Risk Algorithm for Hypertension in South Africa: Results from a Nationally Representative Cohort (2008-2017).High blood pressure & cardiovascular prevention : the official journal of the Italian Society of Hypertension · 2022Article
- Development and validation of a hypertension risk prediction model and construction of a risk score in a Canadian population.Scientific reports · 2022Article
- Is the Efficiency Score an Indicator for Incident Hypertension in the Community Population of Western China?International journal of environmental research and public health · 2021Article
- A risk scoring system to predict the risk of new-onset hypertension among patients with type 2 diabetes.Journal of clinical hypertension (Greenwich, Conn.) · 2021Article
- Exploration of the risk factors of essential hypertension with hyperhomocysteinemia: A hospital-based study and nomogram analysis.Clinics (Sao Paulo, Brazil) · 2021Article
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We aimed to develop a hypertension risk-prediction model among rural Chinese people. We included data for 9034 participants aged 18-70 years without baseline hypertension, diabetes, myocardial infarction, stroke, or heart failure in a rural Chinese cohort. The sample was randomly divided into a training set (60%) and testing set (40%). We used shrinkage estimates by the least absolute shrinkage and selection operator method in fitting a logistic model to explore the possibility of predicting the risk of hypertension in the training set. On multivariable analysis, age, parental hypertension, systolic and diastolic blood pressure, body mass index (BMI), and age by BMI were significant predictors of hypertension. After bootstrap validation, the corrected C-index, calibration intercept, and calibration slope were 0.7932, -0.0041, and 0.9938, respectively for the training set. Our model also had good discrimination (C-index, 0.7914 [95% CI 0.773-0.809]) and calibration (Hosmer-Lemeshow χ
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