Chapter, 2024

Chapter 24 Estimated Arterial Stiffness

Early Vascular Aging (EVA) 9780443155123, Pages 305-315

Editors:

Publisher: Elsevier

DOI: 10.1016/b978-0-443-15512-3.00046-5

Contributors

Vishram-Nielsen, Julie K K 0000-0003-1762-7750 [1] Terentes-Printzios, Dimitrios G 0000-0003-4039-8263 [2] Olsen, Michael Hecht 0000-0003-2230-5780 [3] [4] Vlachopoulos, Charalambos V 0000-0002-9904-3558 [2]

Affiliations

  1. [1] Zealand University Hospital Roskilde
    2. [NORA names: Region Zealand; Hospital; Denmark; Europe, EU; Nordic; OECD];
  2. [2] National and Kapodistrian University of Athens
    3. [NORA names: Greece; Europe, EU; OECD];
  3. [3] Holbæk Sygehus
    4. [NORA names: Region Zealand; Hospital; Denmark; Europe, EU; Nordic; OECD];
  4. [4] University of Southern Denmark
    5. [NORA names: SDU University of Southern Denmark; University; Denmark; Europe, EU; Nordic; OECD]

Abstract

Since arterial stiffness assessment is not widely available, several measures have been proposed to facilitate its introduction in everyday clinical practice either through equations or by using artificial intelligence. Indeed, machine learning algorithms may improve accessibility and performance of vascular age assessment. In this line, an estimated PWV (ePWV), which is calculated exclusively from age and mean arterial blood pressure, has been proposed. This parameter was found to predict MACE independently of conventional risk scores (namely FRS and SCORE) but seldomly independently of the traditional cardiovascular risk factors. Hence, introduction of ePWV in risk stratification, when cfPWV measurements are unavailable, can be of clinical value as a pedagogic tool to convey the concept of early vascular aging to subjects in need of cardiovascular prevention. Nevertheless, the amount of clinical information derived from the direct measurement of cfPWV cannot be replaced with ePWV, and thus measuring cfPWV is still recommended in cases where risk prediction is challenging.

Keywords

MACE, PWV, access, age, age assessment, algorithm, amount, amount of clinical information, arterial blood pressure, arterial stiffness assessment, artificial intelligence, assessment, blood pressure, cardiovascular prevention, cardiovascular risk factors, cases, cfPWV, cfPWV measurements, clinical information, clinical practice, clinical value, concept, conventional risk scores, equations, estimated PWV, everyday clinical practice, factors, improve access, information, intelligence, introduction, learning algorithms, machine, machine learning algorithms, measurements, parameters, pedagogical tool, performance, practice, prediction, pressure, prevention, risk, risk factors, risk prediction, risk score, risk stratification, scores, stiffness assessment, stratification, subjects, tools, traditional cardiovascular risk factors, values, vascular aging assessment

Data Provider: Digital Science

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