What is the clinical evidence from this study?

Study design: Cross-Sectional. Population: Hypertension (n=68980). Intervention: Logistic regression prediction model (9 clinical markers) vs. Healthy controls. Primary outcome: Area under the curve (AUC) for predicting hypertension in the test set (95% CI 75.16-76.46%, p=<0.001).

June 30, 2020Open Access

A novel predicted model for hypertension based on a large cross-sectional study

Q: What are the key findings of this study?

A logistic regression model using nine clinical markers achieved an AUC of 75.81% for predicting hypertension in the test set.

ZRZhigang RenShandong University BRBenchen RaoFirst Affiliated Hospital of Zhengzhou University SXSiqi XieNanjing Agricultural University

Key Result

A logistic regression model using nine clinical markers achieved an AUC of 75.81% for predicting hypertension in the test set.

Study Design

Type

Cross-Sectional (n=68,980)

Multicenter

Structured PICO

Can a logistic regression model based on routine clinical markers accurately predict hypertension in a central Chinese population?

Population

68,980 physical examiners in central China (33,570 participants with hypertension and 35,410 healthy controls)

Intervention

Logistic regression prediction model based on nine optimal clinical markers

Outcome

Model discrimination for hypertension (Area Under the Curve / AUC)

A logistic regression model using nine routine clinical markers demonstrated moderate discriminative ability (AUC ~76%) for predicting hypertension in a large Chinese cohort.

Main Result

p-value: p=<0.001

Limitations

Data was collected from a single center in Central China, which may limit generalizability to other regions or populations.
Prediction power needs to be validated in other large cohorts.

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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