"Clinical prediction model for masked hypertension diagnosed by 24-h ambulatory blood pressure measurements in a sample from specialized hospital."

Abstract

The conventional assessment of the relationship between arterial hypertension (AH) and cardiovascular damage has predominantly relied on office measurements. However, the diagnostic significance of ambulatory and home measurements has gained prominence, particularly in identifying distinct AH phenotypes like masked hypertension (MH), characterized by normal office values but elevated readings outside the clinical setting, carrying comparable risks to sustained AH. Current guidelines advocate for Ambulatory Blood Pressure Monitoring (ABPM) in individuals with office values exceeding 130/85 mmHg. This study aims to develop a clinical prediction model to identify masked hypertension in individuals with normal office blood pressure and to create a clinical score.A cross-sectional study was conducted in a secondary level hospital, including patients aged 18-85 years with average office blood pressure <140/90 mmHg who underwent a valid ABPM on the same day. Pregnant and postpartum women were excluded. A multivariable logistic regression model with calibration, discrimination, and stability parameters was applied to predict masked hypertension. 506 individuals with valid ABPM were analysed. The prevalence of masked hypertension was 30.8%. The selected variables were: diastolic blood pressure, pulse pressure, waist diameter and sex. The model calibrated adequately (Hosmer-Lemeshow test p = 0.35), with an AUC of 0.72 (95% CI, 0.67-0.77). Significant differences existed between the traditional and the new models (p < 0.001). A user-friendly clinical model was developed, with a clinical score achieving 90% specificity using an estimated probability of 0.4 with a 10-point score.A novel model, performed with easily collectable clinical variables, showed robust calibration, stability, and discrimination. It outperforms sole reliance on office blood pressure, exhibiting high specificity (~90%) for masked hypertension detection. Its internal validity suggests a potential for enhanced masked hypertension identification.