Developing and Externally Validating the Multivariable Prediction Model for White-Coat Hypertension.

Area of Science:

• Cardiology
• Hypertension Research
• Clinical Prediction Modeling

Background:

• White-coat hypertension (WCH) diagnosis requires out-of-office blood pressure (BP) monitoring, which is resource-intensive.
• Developing a predictive model using readily available clinical data can streamline WCH identification.

Purpose of the Study:

• To develop and validate a prediction model for identifying white-coat hypertension (WCH) based on patient clinical characteristics.
• To create a tool that assists clinicians in distinguishing WCH from sustained hypertension (SH).

Main Methods:

• A prediction model was developed using multivariate logistic regression on clinical data from 233 outpatients with elevated office BP.
• Independent predictors for WCH were identified, and a nomogram was constructed.
• The model was externally validated on 150 patients from an independent study site.

Main Results:

• Six key predictors for WCH were identified: office systolic blood pressure, body mass index, sex, total cholesterol, homocysteine, and heart rate.
• The prediction model achieved an AUC of 0.792 for the training set and 0.692 for the external validation set.
• Calibration and decision curve analyses confirmed the model's good performance in differentiating WCH from SH.

Conclusions:

• The developed prediction model effectively identifies individuals with WCH using clinical data.
• This tool can guide personalized management strategies for abnormal blood pressure.
• It offers a more efficient approach compared to solely relying on out-of-office BP monitoring.