Predictive variables and diagnostic performance of cross-sectional models for hypertension detection: a systematic review.

ABSTRACT:

Introduction

Hypertension (HTN) affects approximately 1.3 billion people worldwide, nearly half of whom remain undiagnosed, underscoring the urgent need for efficient diagnostic models to enable timely detection. This study aimed to identify and critically evaluate cross-sectional predictive models developed for diagnosing hypertension in adults globally.

Methods

A systematic review was conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and TRIPOD guidelines, searching MEDLINE/PubMed, Scopus, Web of Science, and EMBASE from January 2000 to March 2024. We included observational studies that developed or validated cross-sectional models for adult HTN detection. The risk of bias was assessed using the PROBAST (Prediction

SUBMITTER: Vera-Ponce VJ 

PROVIDER: S-EPMC12698555 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature