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Heart Rhythm. 2018 Sep;15(9):1404-1410. doi: 10.1016/j.hrthm.2018.04.032. Epub 2018 Apr 30.

Predicting vasovagal syncope from heart rate and blood pressure: A prospective study in 140 subjects.

Author information

1
Medtronic Europe, Tolochenaz, Switzerland. Electronic address: nathalie.virag@medtronic.com.
2
Medtronic Inc, Minneapolis, Minnesota.
3
Imperial Healthcare NHS Trust, Hammersmith Hospital, London, United Kingdom.
4
Bern University of Applied Sciences, Burgdorf, Switzerland.
5
Imperial Healthcare NHS Trust, Hammersmith Hospital, London, United Kingdom; National Heart & Lung Institute, Imperial College, London, United Kingdom.

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

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