Cardiovascular Magnetic Resonance Reference Ranges From the Healthy Hearts Consortium

Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved..

BACKGROUND: The absence of population-stratified cardiovascular magnetic resonance (CMR) reference ranges from large cohorts is a major shortcoming for clinical care.

OBJECTIVES: This paper provides age-, sex-, and ethnicity-specific CMR reference ranges for atrial and ventricular metrics from the Healthy Hearts Consortium, an international collaborative comprising 9,088 CMR studies from verified healthy individuals, covering the complete adult age spectrum across both sexes, and with the highest ethnic diversity reported to date.

METHODS: CMR studies were analyzed using certified software with batch processing capability (cvi42, version 5.14 prototype, Circle Cardiovascular Imaging) by 2 expert readers. Three segmentation methods (smooth, papillary, anatomic) were used to contour the endocardial and epicardial borders of the ventricles and atria from long- and short-axis cine series. Clinically established ventricular and atrial metrics were extracted and stratified by age, sex, and ethnicity. Variations by segmentation method, scanner vendor, and magnet strength were examined. Reference ranges are reported as 95% prediction intervals.

RESULTS: The sample included 4,452 (49.0%) men and 4,636 (51.0%) women with average age of 61.1 ± 12.9 years (range: 18-83 years). Among these, 7,424 (81.7%) were from White, 510 (5.6%) South Asian, 478 (5.3%) mixed/other, 341 (3.7%) Black, and 335 (3.7%) Chinese ethnicities. Images were acquired using 1.5-T (n = 8,779; 96.6%) and 3.0-T (n = 309; 3.4%) scanners from Siemens (n = 8,299; 91.3%), Philips (n = 498; 5.5%), and GE (n = 291, 3.2%).

CONCLUSIONS: This work represents a resource with healthy CMR-derived volumetric reference ranges ready for clinical implementation.

Medienart:

E-Artikel

Erscheinungsjahr:

2024

Erschienen:

2024

Enthalten in:

Zur Gesamtaufnahme - year:2024

Enthalten in:

JACC. Cardiovascular imaging - (2024) vom: 26. März

Sprache:

Englisch

Beteiligte Personen:

Raisi-Estabragh, Zahra [VerfasserIn]
Szabo, Liliana [VerfasserIn]
McCracken, Celeste [VerfasserIn]
Bülow, Robin [VerfasserIn]
Aquaro, Giovanni Donato [VerfasserIn]
Andre, Florian [VerfasserIn]
Le, Thu-Thao [VerfasserIn]
Suchá, Dominika [VerfasserIn]
Condurache, Dorina-Gabriela [VerfasserIn]
Salih, Ahmed M [VerfasserIn]
Chadalavada, Sucharitha [VerfasserIn]
Aung, Nay [VerfasserIn]
Lee, Aaron Mark [VerfasserIn]
Harvey, Nicholas C [VerfasserIn]
Leiner, Tim [VerfasserIn]
Chin, Calvin W L [VerfasserIn]
Friedrich, Matthias G [VerfasserIn]
Barison, Andrea [VerfasserIn]
Dörr, Marcus [VerfasserIn]
Petersen, Steffen E [VerfasserIn]

Links:

Volltext

Themen:

Artificial intelligence
Automated analysis
Cardiovascular magnetic resonance
Ethnicity
Healthy reference ranges
Journal Article
Sex differences

Anmerkungen:

Date Revised 13.04.2024

published: Print-Electronic

Citation Status Publisher

doi:

10.1016/j.jcmg.2024.01.009

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM371028639