Predicting vasovagal syncope from heart rate and blood pressure: A prospective study in 140 subjects
Nathalie Virag, Mark Erickson, Patricia Taraborrelli, Rolf Vetter, Phang Boon Lim, Richard Sutton, Nathalie Virag, Mark Erickson, Patricia Taraborrelli, Rolf Vetter, Phang Boon Lim, Richard Sutton
Abstract
Background: We developed a vasovagal syncope (VVS) prediction algorithm for use during head-up tilt with simultaneous analysis of heart rate (HR) and systolic blood pressure (SBP). We previously tested this algorithm retrospectively in 1155 subjects, showing sensitivity 95%, specificity 93%, and median prediction time 59 seconds.
Objective: The purpose of this prospective, single-center study of 140 subjects was to evaluate this VVS prediction algorithm and to assess whether retrospective results were reproduced and clinically relevant. The primary endpoint was VVS prediction: sensitivity and specificity >80%.
Methods: In subjects referred for 60° head-up tilt (Italian protocol), noninvasive HR and SBP were supplied to the VVS prediction algorithm: simultaneous analysis of RR intervals, SBP trends, and their variability represented by low-frequency power-generated cumulative risk, which was compared with a predetermined VVS risk threshold. When cumulative risk exceeded threshold, an alert was generated. Prediction time was duration between first alert and syncope.
Results: Of the 140 subjects enrolled, data were usable for 134. Of 83 tilt-positive subjects (61.9%), 81 VVS events were correctly predicted by the algorithm, and of 51 tilt-negative subjects (38.1%), 45 were correctly identified as negative by the algorithm. Resulting algorithm performance was sensitivity 97.6% and specificity 88.2%, meeting the primary endpoint. Mean VVS prediction time was 2 minutes 26 seconds ± 3 minutes 16 seconds (median 1 minute 25 seconds). Using only HR and HR variability (without SBP), mean prediction time reduced to 1 minute 34 seconds ± 1 minute 45 seconds (median 1 minute 13 seconds).
Conclusion: The VVS prediction algorithm is a clinically relevant tool and could offer applications, including providing a patient alarm, shortening tilt-test time, and triggering pacing intervention in implantable devices.
Trial registration: ClinicalTrials.gov NCT02140567.
Keywords: Autonomic nervous system; Blood pressure; Heart rate; Syncope Prediction Study; Tilt test; Vasovagal syncope.
Copyright © 2018 Heart Rhythm Society. Published by Elsevier Inc. All rights reserved.
Source: PubMed