Mifetika Lukitasari, Jitendra Jonnagaddala, Siaw-Teng Liaw, Bin Jalaludin
Aims: Visit-to-visit blood pressure variability (VVV BPV) is a recognized risk factor for cardiovascular disease (CVD) that is underutilized in clinical practice. The reliability of electronic health record (EHR) data in estimating BPV and predicting CVD remains uncertain. This study compared BPV estimation methodologies using EHR vs. non-EHR data and examined dose-response associations with CVD. Methods and results: A systematic review and meta-analysis was conducted across five databases (MEDLINE, Scopus, EMBASE, CINAHL, and Web of Science) for studies published from January 2012 to August 2024. Studies assessing VVV BPV in adults and its association with CVD outcomes (e.g. myocardial infarction, stroke, heart failure, and cardiovascular mortality) were included. A dose–response meta-analysis (DRMA) evaluated BPV thresholds linked to increased CVD risk using standard deviation (SD) and coefficient of variation (CV). A total of 4926 studies were screened, 49 of which met the inclusion criteria. No consensus has emerged on BPV estimation methodologies, although non-EHR studies have followed stricter protocols. The meta-analysis showed that VVV BPV predicted any CVD outcome. Effect sizes were comparable between EHR [the hazard ratio (HR): 1.17, 95% confidence interval (CI): 1.09–1.24] and non-EHR (HR: 1.14, 95% CI: 1.10–1.17) studies (P-value = 0.468). A BPV threshold of SD 6.72 mmHg or CV 9.05% was linked to a 10% higher CVD risk. Conclusion: The EHR data reliably estimate BPV, yielding effect sizes similar to those of non-EHR sources. A non-linear dose–response relationship suggests that a higher BPV increases CVD risk. Visit-to-visit blood pressure variability needs to be incorporated into clinical practice, and further research is required to identify strategies to implement and scale up into routine workflow. © The Author(s) 2025. Published by Oxford University Press on behalf of the European Society of Cardiology. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.
School of Population Health, Health Translation Hub, Level 5, UNSW Kensington Campus, 2052, NSW, Australia; School of Nursing, Faculty of Health Science, Brawijaya University, Puncak Dieng Eksklusif, Kunci, Kalisongo, Kec. Dau, East Java, Malang, 65151, Indonesia; School of Population Health, Health Translation Hub, Level 5, UNSW Kensington Campus, 2052, NSW, Australia; SREDH Consortium, UNSW Sydney, Health Translation Hub, Level 5, UNSW Kensington Campus, 2052, NSW, Australia; School of Population Health, Health Translation Hub, Level 5, UNSW Kensington Campus, 2052, NSW, Australia; SREDH Consortium, UNSW Sydney, Health Translation Hub, Level 5, UNSW Kensington Campus, 2052, NSW, Australia; School of Population Health, Health Translation Hub, Level 5, UNSW Kensington Campus, 2052, NSW, Australia; SREDH Consortium, UNSW Sydney, Health Translation Hub, Level 5, UNSW Kensington Campus, 2052, NSW, Australia
