Cardiac MR modelling of systolic and diastolic blood pressure

Assadi, Hosamadin ORCID: https://orcid.org/0000-0002-6143-8095, Matthews, Gareth ORCID: https://orcid.org/0000-0001-8353-4806, Zhao, Xiaodan, Li, Rui, Alabed, Samer, Grafton-Clarke, Ciaran ORCID: https://orcid.org/0000-0002-8537-0806, Mehmood, Zia, Kasmai, Bahman, Limbachia, Vaishali, Gosling, Rebecca, Yashoda, Gurung-Koney, Halliday, Ian, Swoboda, Peter, Ripley, David Paul, Zhong, Liang, Vassiliou, Vassilios S. ORCID: https://orcid.org/0000-0002-4005-7752, Swift, Andrew J., van der Geest, Rob J. and Garg, Pankaj ORCID: https://orcid.org/0000-0002-5483-169X (2023) Cardiac MR modelling of systolic and diastolic blood pressure. Open Heart, 10 (2). ISSN 2053-3624

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Abstract

Aims: Blood 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.   Methods: In 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.   Results: Age 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.   Conclusion: CMR-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.

Item Type:	Article
Additional Information:	Data availability statement: Data are available on reasonable request. The datasets generated and analysed during the current study are not publicly available. Access to the raw images of patients is not permitted since specialised postprocessing imaging-based solutions can identify the study patients in the future. Data are available from the corresponding author on reasonable request. Funding information: PG and AJS are funded by Wellcome Trust Clinical Research Career Development Fellowships (220703/Z/20/Z & 205188/Z/16/Z). GM is funded by the National Institute of Health Research (NIHR). Rights retention statement: For the purpose of Open Access, the authors have applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission.
Faculty \ School:	Faculty of Medicine and Health Sciences > Norwich Medical School
UEA Research Groups:	Faculty of Science > Research Groups > Norwich Epidemiology Centre Faculty of Medicine and Health Sciences > Research Groups > Norwich Epidemiology Centre Faculty of Medicine and Health Sciences > Research Groups > Cardiovascular and Metabolic Health Faculty of Medicine and Health Sciences > Research Centres > Metabolic Health
Related URLs:	http://www.scopus.com/inward/record.url?...
Depositing User:	LivePure Connector
Date Deposited:	20 Dec 2023 02:56
Last Modified:	07 Nov 2024 00:53
URI:	https://ueaeprints.uea.ac.uk/id/eprint/94004
DOI:	10.1136/openhrt-2023-002484

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