Variability independent of mean blood pressure as a real-world measure of cardiovascular risk

Abstract

<p>Background<br></p><p>Individual-level blood pressure (BP) variability, independent of mean BP levels, has been associated with increased risk for cardiovascular events in cohort studies and clinical trials using standardized BP measurements. The extent to which BP variability relates to cardiovascular risk in the real-world clinical practice setting is unclear. We sought to determine if BP variability in clinical practice is associated with adverse cardiovascular outcomes using clinically generated data from the electronic health record (EHR).<br></p><p><br>Methods<br></p><p>We identified 42,482 patients followed continuously at a single academic medical center in Southern California between 2013 and 2019 and calculated their systolic and diastolic BP variability independent of the mean (VIM) over the first 3 years of the study period. We then performed multivariable Cox proportional hazards regression to examine the association between VIM and both composite and individual outcomes of interest (incident myocardial infarction, heart failure, stroke, and death).<br></p><p><br>Findings<br></p><p>Both systolic (HR, 95% CI 1.22, 1.17–1.28) and diastolic VIM (1.24, 1.19–1.30) were positively associated with the composite outcome, as well as all individual outcome measures. These findings were robust to stratification by age, sex and clinical comorbidities. In sensitivity analyses using a time-shifted follow-up period, VIM remained significantly associated with the composite outcome for both systolic (1.15, 1.11–1.20) and diastolic (1.18, 1.13–1.22) values.<br></p><p><br>Interpretation<br></p><p>VIM derived from clinically generated data remains associated with adverse cardiovascular outcomes and represents a risk marker beyond mean BP, including in important demographic and clinical subgroups. The demonstrated prognostic ability of VIM derived from non-standardized BP readings indicates the utility of this measure for risk stratification in a real-world practice setting, although residual confounding from unmeasured variables cannot be excluded.<br></p>