Background: Blood pressure variability (BPV) is associated with outcome after endovascular thrombectomy in acute large vessel occlusion stroke. We aimed to provide the optimal sampling frequency and BPV index for outcome prediction by using high-resolution blood pressure (BP) data.
Methods: Patient characteristics, 3-month outcome, and BP values measured intraarterially at 1 Hz for up to 24 h were extracted from 34 patients treated at a tertiary care center neurocritical care unit. Outcome was dichotomized (modified Rankin Scale 0-2, favorable, and 3-6, unfavorable) and associated with systolic BPV (as calculated by using standard deviation, coefficient of variation, averaged real variability, successive variation, number of trend changes, and a spectral approach using the power of specific BP frequencies). BP values were downsampled by either averaging or omitting all BP values within each prespecified time bin to compare the different sampling rates.
Results: Out of 34 patients (age 72 ± 12.7 years, 67.6% men), 10 (29.4%) achieved a favorable functional outcome and 24 (70.6%) had an unfavorable functional outcome at 3 months. No group differences were found in mean absolute systolic BP (SBP) (130 ± 18 mm Hg, p = 0.82) and diastolic BP (DBP) (59 ± 10 mm Hg, p = 1.00) during the monitoring time. BPV only reached predictive significance when using successive variation extracted from downsampled (averaged over 5 min) SBP data (median 4.8 mm Hg [range 3.8-7.1]) in patients with favorable versus 7.1 mmHg [range 5.5-9.7] in those with unfavorable outcome, area under the curve = 0.74 [confidence interval (CI) 0.57-0.85; p = 0.031], or the power of midrange frequencies between 1/20 and 1/5 min [area under the curve = 0.75 (CI 0.59-0.86), p = 0.020].
Conclusions: Using high-resolution BP data of 1 Hz, downsampling by averaging all BP values within 5-min intervals is essential to find relevant differences in systolic BPV, as noise can be avoided (confirmed by the significance of the power of midrange frequencies). These results demonstrate how high-resolution BP data can be processed for effective outcome prediction.
Keywords: Big data; Blood pressure; Critical care; Ischemic stroke; Thrombectomy.
© 2022. The Author(s).