Key result
The office-based Cox regression models for men (M1) and women (W1), along with the Artificial Neural Network model for women, demonstrated good predictive performance for 6-year hypertension risk, with C-indexes of 0.771, 0.765, and 0.756 respectively.
Why the study?
While various hypertension predictive models exist worldwide, there were no existing predictive models for hypertension among Chinese rural populations.
Cohort (n=8,319)
No
Effect estimate: C-index 0.771 (95% CI 0.750, 0.791)
Specific predictive models (W1 and ANN for women, M1 for men) can effectively predict hypertension risk in rural Chinese populations, providing a tailored tool where none previously existed.
May support office-based hypertension risk prediction in rural Chinese adults; leaves open external validation before clinical adoption.
BACKGROUND: Various hypertension predictive models have been developed worldwide; however, there is no existing predictive model for hypertension among Chinese rural populations. METHODS: This is a 6-year population-based prospective cohort in rural areas of China. Data was collected in 2007-2008 (baseline survey) and 2013-2014 (follow-up survey) from 8319 participants ranging in age from 35 to 74 years old. Specified gender hypertension predictive models were established based on multivariate Cox regression, Artificial Neural Network (ANN), Naive Bayes Classifier (NBC), and Classification and Regression Tree (CART) in the training set. External validation was conducted in the testing set. The estimated models were assessed by discrimination and calibration, respectively. RESULTS: = 0.3145), respectively. CONCLUSIONS: Not all machine-learning models performed better than the traditional Cox regression models. The W1 and ANN models for women and M1 model for men have better predictive performance which could potentially be recommended for predicting hypertension risk among rural populations.
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Xu et al. (2019) conducted a cohort in Hypertension (n=8,319). Hypertension risk prediction models (Cox regression, ANN, NBC, CART) was evaluated on Discrimination (C-index) for predicting 6-year hypertension incidence (M1 model in men, testing set) (C-index 0.771, 95% CI 0.750, 0.791). The office-based Cox regression models for men (M1) and women (W1), along with the Artificial Neural Network model for women, demonstrated good predictive performance for 6-year hypertension risk, with C-indexes of 0.771, 0.765, and 0.756 respectively.
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