Automated pupillometry to detect command following in neurological patients: a proof-of-concept study

Research output: Contribution to journalJournal articleResearchpeer-review

Documents

  • peerj-6929

    Final published version, 10.3 MB, PDF document

Background: Levels of consciousness in patients with acute and chronic brain injury are notoriously underestimated. Paradigms based on electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) may detect covert consciousness in clinically unresponsive patients but are subject to logistical challenges and the need for advanced statistical analysis.

Methods: To assess the feasibility of automated pupillometry for the detection of command following, we enrolled 20 healthy volunteers and 48 patients with a wide range of neurological disorders, including seven patients in the intensive care unit (ICU), who were asked to engage in mental arithmetic.

Results: Fourteen of 20 (70%) healthy volunteers and 17 of 43 (39.5%) neurological patients, including 1 in the ICU, fulfilled prespecified criteria for command following by showing pupillary dilations during ≥4 of five arithmetic tasks. None of the five sedated and unconscious ICU patients passed this threshold.

Conclusions: Automated pupillometry combined with mental arithmetic appears to be a promising paradigm for the detection of covert consciousness in people with brain injury. We plan to build on this study by focusing on non-communicating ICU patients in whom the level of consciousness is unknown. If some of these patients show reproducible pupillary dilation during mental arithmetic, this would suggest that the present paradigm can reveal covert consciousness in unresponsive patients in whom standard investigations have failed to detect signs of consciousness.

Original languageEnglish
Article numbere6929
JournalPeerJ
Volume7
Number of pages14
ISSN2167-8359
DOIs
Publication statusPublished - 2019

Number of downloads are based on statistics from Google Scholar and www.ku.dk


No data available

ID: 238484905