Predicting Vasovagal Reactions to Needles from Facial Action Units

Judita Rudokaite*, Itir Önal Ertuğrul, L. L. Sharon Ong, Mart P. Janssen, Elisabeth Huis in't Veld

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Background: Merely the sight of needles can cause extreme emotional and physical (vasovagal) reactions (VVRs). However, needle fear and VVRs are not easy to measure nor prevent as they are automatic and difficult to self-report. This study aims to investigate whether a blood donors’ unconscious facial microexpressions in the waiting room, prior to actual blood donation, can be used to predict who will experience a VVR later, during the donation. Methods: The presence and intensity of 17 facial action units were extracted from video recordings of 227 blood donors and were used to classify low and high VVR levels using machine-learning algorithms. We included three groups of blood donors as follows: (1) a control group, who had never experienced a VVR in the past (n = 81); (2) a ‘sensitive’ group, who experienced a VVR at their last donation (n = 51); and (3) new donors, who are at increased risk of experiencing a VVR (n = 95). Results: The model performed very well, with an F1 (=the weighted average of precision and recall) score of 0.82. The most predictive feature was the intensity of facial action units in the eye regions. Conclusions: To our knowledge, this study is the first to demonstrate that it is possible to predict who will experience a vasovagal response during blood donation through facial microexpression analyses prior to donation.
Original languageEnglish
Article number1644
Number of pages14
JournalJournal of Clinical Medicine
Volume12
Issue number4
DOIs
Publication statusPublished - 18 Feb 2023

Bibliographical note

Funding Information:
This research was funded by the ZonMW Veni project “FAINT” (project reference: 016.186.020) and Stichting Sanquin Bloedvoorziening (grant PPOC19-12/L2409).

Publisher Copyright:
© 2023 by the authors.

Keywords

  • vasovagal reactions
  • needle fear
  • machine learning
  • facial action units
  • blood donors

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