Cardiac MR modelling of systolic and diastolic blood pressure

• Published in: Open heart Vol. 10; no. 2; p. e002484
• Main Authors: Assadi, Hosamadin, Matthews, Gareth, Zhao, Xiaodan, Li, Rui, Alabed, Samer, Grafton-Clarke, Ciaran, Mehmood, Zia, Kasmai, Bahman, Limbachia, Vaishali, Gosling, Rebecca, Yashoda, Gurung-Koney, Halliday, Ian, Swoboda, Peter, Ripley, David Paul, Zhong, Liang, Vassiliou, Vassilios S, Swift, Andrew J, Geest, Rob J van der, Garg, Pankaj
• Format: Journal Article
• Language: English
• Published: England British Cardiovascular Society 18.12.2023; BMJ Publishing Group LTD; BMJ Publishing Group
• Series: Original research
• Subjects: Aortic Diseases; Blood pressure; Blood Pressure - physiology; Cardiac Imaging Techniques; Cardiac Risk Factors and Prevention; Cardiovascular disease; Coronary vessels; Heart failure; Humans; Hypertension; Magnetic Resonance Imaging; Mortality; Patients; Prospective Studies; Retrospective Studies; Software; Stroke Volume; Ventricular Function, Left; United Kingdom > UK; Netherlands; Hypertension; Magnetic Resonance Imaging; Cardiac Imaging Techniques; Aortic Diseases
• ISSN: 2053-3624; 2398-595X; 2053-3624
• DOI: 10.1136/openhrt-2023-002484

AimsBlood pressure (BP) is a crucial factor in cardiovascular health and can affect cardiac imaging assessments. However, standard outpatient cardiovascular MR (CMR) imaging procedures do not typically include BP measurements prior to image acquisition. This study proposes that brachial systolic BP (SBP) and diastolic BP (DBP) can be modelled using patient characteristics and CMR data.MethodsIn this multicentre study, 57 patients from the PREFER-CMR registry and 163 patients from other registries were used as the derivation cohort. All subjects had their brachial SBP and DBP measured using a sphygmomanometer. Multivariate linear regression analysis was applied to predict brachial BP. The model was subsequently validated in a cohort of 169 healthy individuals.ResultsAge and left ventricular ejection fraction were associated with SBP. Aortic forward flow, body surface area and left ventricular mass index were associated with DBP. When applied to the validation cohort, the correlation coefficient between CMR-derived SBP and brachial SBP was (r=0.16, 95% CI 0.011 to 0.305, p=0.03), and CMR-derived DBP and brachial DBP was (r=0.27, 95% CI 0.122 to 0.403, p=0.0004). The area under the curve (AUC) for CMR-derived SBP to predict SBP>120 mmHg was 0.59, p=0.038. Moreover, CMR-derived DBP to predict DBP>80 mmHg had an AUC of 0.64, p=0.002.ConclusionCMR-derived SBP and DBP models can estimate brachial SBP and DBP. Such models may allow efficient prospective collection, as well as retrospective estimation of BP, which should be incorporated into assessments due to its critical effect on load-dependent parameters.