A diagnostic prediction model for hypertension in Han and Yugur population from the China National Health Survey (CNHS)

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.

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“…9,16 Hypertension was defined as a SBP ≥140 mmHg or a DBP ≥90 mmHg or being diagnosed with hypertension in the past or currently taking antihypertensive medications. 17 Hyperuricemia was defined as serum uric acid (SUA) levels higher than 360 μmol L −1 (approximately 6 mg dL −1 ) in women and 420 μmol L −1 (approximately 7 mg dL −1 ) in men, consistent with previous studies. 18,19 CVDs included CHD and stroke in the current study and were identified based on selfreported diagnosis history by a physician.…”

A nomograph model for predicting the risk of diabetes nephropathy

“…Subsequently, logistic regression was used to develop a hypertension risk classi cation diagnostic model, which was visualized using a column chart. At the same time, a website was developed to calculate the exact probability of hypertension, in order to facilitate personalized prediction of hypertension [23] . JoonNyung Heo et al developed three machine learning models for a retrospective study to predict longterm outcomes in patients with ischemic stroke, and evaluated the accuracy of the models through ASTRAL scores [24] .…”

The development and validation of a non-invasive prediction model of hyperuricemia based on modifiable risk factors: baseline findings of a health examination population cohort

“…13 Hypertension was defined as a SBP ≥ 140 mmHg and/or DBP ≥ 90 mmHg, and/or in the case of physician-diagnosed hypertension. 14…”

“…13 Hypertension was defined as a SBP ≥ 140 mmHg and/or DBP ≥ 90 mmHg, and/or in the case of physician-diagnosed hypertension. 14 Hyperuricemia was defined as a SUA higher than 360 μmol L −1 (approximately 6 mg dl −1 ) in women and 420 μmol L −1 (approximately 7.0 mg dl −1 ) in men, or current use of uric acid lowering drugs. 5,15 Data pre-processing…”