Imperial College London
Alternate

Dr P Boon Lim

Faculty of Medicine, National Heart & Lung Institute

Honorary Clinical Senior Lecturer
 
 
 
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Contact

 

+44 (0)20 3313 2115p.b.lim Website

 
 
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Location

 

Cardiology DepartmentBlock B Hammersmith HospitalHammersmith Campus

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Summary

 

Publications

Citation

BibTex format

@article{Virag:2018:10.1016/j.hrthm.2018.04.032,
author = {Virag, N and Erickson, M and Taraborrelli, P and Vetter, R and Lim, PB and Sutton, R},
doi = {10.1016/j.hrthm.2018.04.032},
journal = {Heart Rhythm},
pages = {1404--1410},
title = {Predicting vasovagal syncope from heart rate and blood pressure: A prospective study in 140 subjects},
url = {http://dx.doi.org/10.1016/j.hrthm.2018.04.032},
volume = {15},
year = {2018}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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 of 59s. OBJECTIVE: This study was prospective, single center, on 140 subjects to evaluate this VVS prediction algorithm and assess if retrospective results were reproduced and clinically relevant. Primary endpoint was VVS prediction: sensitivity and specificity >80%. METHODS: In subjects, referred for 60° head-up tilt (Italian protocol), non-invasive 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 140 subjects enrolled, data was usable for 134. Of 83 tilt+ve (61.9%), 81 VVS events were correctly predicted and of 51 tilt-ve subjects (38.1%), 45 were correctly identified as negative by the algorithm. Resulting algorithm performance was sensitivity 97.6%, specificity 88.2%, meeting primary endpoint. Mean VVS prediction time was 2min 26s±3min16s with median 1min 25s. Using only HR and HR variability (without SBP) the mean prediction time reduced to 1min34s±1min45s with median 1min13s. CONCLUSION: The VVS prediction algorithm, is clinically-relevant tool and could offer applications including providing a patient alarm, shortening tilt-test time, or triggering pacing intervention in implantable devices.
AU  - Virag,N
AU  - Erickson,M
AU  - Taraborrelli,P
AU  - Vetter,R
AU  - Lim,PB
AU  - Sutton,R
DO  - 10.1016/j.hrthm.2018.04.032
EP  - 1410
PY  - 2018///
SN  - 1547-5271
SP  - 1404
TI  - Predicting vasovagal syncope from heart rate and blood pressure: A prospective study in 140 subjects
T2  - Heart Rhythm
UR  - http://dx.doi.org/10.1016/j.hrthm.2018.04.032
UR  - https://www.ncbi.nlm.nih.gov/pubmed/29715516
UR  - http://hdl.handle.net/10044/1/59747
VL  - 15
ER  -
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