Repurposing electroencephalogram monitoring of general anaesthesia for building biomarkers of brain ageing: an exploratory study
Background: Electroencephalography (EEG) is increasingly used for monitoring the depth of general anaesthesia, but EEG data from general anaesthesia monitoring are rarely reused for research. Here, we explored repurposing EEG monitoring from general anaesthesia for brain-age modelling using machine...
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-09-01
|
| Series: | BJA Open |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772609623000242 |
| _version_ | 1827810076193193984 |
|---|---|
| author | David Sabbagh Jérôme Cartailler Cyril Touchard Jona Joachim Alexandre Mebazaa Fabrice Vallée Étienne Gayat Alexandre Gramfort Denis A. Engemann |
| author_facet | David Sabbagh Jérôme Cartailler Cyril Touchard Jona Joachim Alexandre Mebazaa Fabrice Vallée Étienne Gayat Alexandre Gramfort Denis A. Engemann |
| author_sort | David Sabbagh |
| collection | DOAJ |
| description | Background: Electroencephalography (EEG) is increasingly used for monitoring the depth of general anaesthesia, but EEG data from general anaesthesia monitoring are rarely reused for research. Here, we explored repurposing EEG monitoring from general anaesthesia for brain-age modelling using machine learning. We hypothesised that brain age estimated from EEG during general anaesthesia is associated with perioperative risk. Methods: We reanalysed four-electrode EEGs of 323 patients under stable propofol or sevoflurane anaesthesia to study four EEG signatures (95% of EEG power <8–13 Hz) for age prediction: total power, alpha-band power (8–13 Hz), power spectrum, and spatial patterns in frequency bands. We constructed age-prediction models from EEGs of a healthy reference group (ASA 1 or 2) during propofol anaesthesia. Although all signatures were informative, state-of-the-art age-prediction performance was unlocked by parsing spatial patterns across electrodes along the entire power spectrum (mean absolute error=8.2 yr; R2=0.65). Results: Clinical exploration in ASA 1 or 2 patients revealed that brain age was positively correlated with intraoperative burst suppression, a risk factor for general anaesthesia complications. Surprisingly, brain age was negatively correlated with burst suppression in patients with higher ASA scores, suggesting hidden confounders. Secondary analyses revealed that age-related EEG signatures were specific to propofol anaesthesia, reflected by limited model generalisation to anaesthesia maintained with sevoflurane. Conclusions: Although EEG from general anaesthesia may enable state-of-the-art age prediction, differences between anaesthetic drugs can impact the effectiveness and validity of brain-age models. To unleash the dormant potential of EEG monitoring for clinical research, larger datasets from heterogeneous populations with precisely documented drug dosage will be essential. |
| first_indexed | 2024-03-11T22:47:44Z |
| format | Article |
| id | doaj.art-4e04acc5046e42b79dd4b32f642f6651 |
| institution | Directory Open Access Journal |
| issn | 2772-6096 |
| language | English |
| last_indexed | 2024-03-11T22:47:44Z |
| publishDate | 2023-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | BJA Open |
| spelling | doaj.art-4e04acc5046e42b79dd4b32f642f66512023-09-22T04:40:12ZengElsevierBJA Open2772-60962023-09-017100145Repurposing electroencephalogram monitoring of general anaesthesia for building biomarkers of brain ageing: an exploratory studyDavid Sabbagh0Jérôme Cartailler1Cyril Touchard2Jona Joachim3Alexandre Mebazaa4Fabrice Vallée5Étienne Gayat6Alexandre Gramfort7Denis A. Engemann8INSERM, Université de Paris, Paris, France; Inria, CEA, Université Paris-Saclay, Palaiseau, France; Corresponding author. INSERM, Université de Paris, Paris, France.INSERM, Université de Paris, Paris, France; Department of Anesthesia and Critical Care Medicine, AP-HP, Hôpital Lariboisière, Paris, FranceDepartment of Anesthesia and Critical Care Medicine, AP-HP, Hôpital Lariboisière, Paris, FranceDepartment of Anesthesia and Critical Care Medicine, AP-HP, Hôpital Lariboisière, Paris, FranceINSERM, Université de Paris, Paris, France; Department of Anesthesia and Critical Care Medicine, AP-HP, Hôpital Lariboisière, Paris, FranceINSERM, Université de Paris, Paris, France; Inria, CEA, Université Paris-Saclay, Palaiseau, France; Department of Anesthesia and Critical Care Medicine, AP-HP, Hôpital Lariboisière, Paris, FranceINSERM, Université de Paris, Paris, France; Department of Anesthesia and Critical Care Medicine, AP-HP, Hôpital Lariboisière, Paris, FranceInria, CEA, Université Paris-Saclay, Palaiseau, FranceInria, CEA, Université Paris-Saclay, Palaiseau, France; Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Basel, Switzerland; Corresponding author. Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Basel, Switzerland.Background: Electroencephalography (EEG) is increasingly used for monitoring the depth of general anaesthesia, but EEG data from general anaesthesia monitoring are rarely reused for research. Here, we explored repurposing EEG monitoring from general anaesthesia for brain-age modelling using machine learning. We hypothesised that brain age estimated from EEG during general anaesthesia is associated with perioperative risk. Methods: We reanalysed four-electrode EEGs of 323 patients under stable propofol or sevoflurane anaesthesia to study four EEG signatures (95% of EEG power <8–13 Hz) for age prediction: total power, alpha-band power (8–13 Hz), power spectrum, and spatial patterns in frequency bands. We constructed age-prediction models from EEGs of a healthy reference group (ASA 1 or 2) during propofol anaesthesia. Although all signatures were informative, state-of-the-art age-prediction performance was unlocked by parsing spatial patterns across electrodes along the entire power spectrum (mean absolute error=8.2 yr; R2=0.65). Results: Clinical exploration in ASA 1 or 2 patients revealed that brain age was positively correlated with intraoperative burst suppression, a risk factor for general anaesthesia complications. Surprisingly, brain age was negatively correlated with burst suppression in patients with higher ASA scores, suggesting hidden confounders. Secondary analyses revealed that age-related EEG signatures were specific to propofol anaesthesia, reflected by limited model generalisation to anaesthesia maintained with sevoflurane. Conclusions: Although EEG from general anaesthesia may enable state-of-the-art age prediction, differences between anaesthetic drugs can impact the effectiveness and validity of brain-age models. To unleash the dormant potential of EEG monitoring for clinical research, larger datasets from heterogeneous populations with precisely documented drug dosage will be essential.http://www.sciencedirect.com/science/article/pii/S2772609623000242brain ageburst suppressionelectroencephalogram (EEG)general anaesthesiamachine learningpropofol |
| spellingShingle | David Sabbagh Jérôme Cartailler Cyril Touchard Jona Joachim Alexandre Mebazaa Fabrice Vallée Étienne Gayat Alexandre Gramfort Denis A. Engemann Repurposing electroencephalogram monitoring of general anaesthesia for building biomarkers of brain ageing: an exploratory study BJA Open brain age burst suppression electroencephalogram (EEG) general anaesthesia machine learning propofol |
| title | Repurposing electroencephalogram monitoring of general anaesthesia for building biomarkers of brain ageing: an exploratory study |
| title_full | Repurposing electroencephalogram monitoring of general anaesthesia for building biomarkers of brain ageing: an exploratory study |
| title_fullStr | Repurposing electroencephalogram monitoring of general anaesthesia for building biomarkers of brain ageing: an exploratory study |
| title_full_unstemmed | Repurposing electroencephalogram monitoring of general anaesthesia for building biomarkers of brain ageing: an exploratory study |
| title_short | Repurposing electroencephalogram monitoring of general anaesthesia for building biomarkers of brain ageing: an exploratory study |
| title_sort | repurposing electroencephalogram monitoring of general anaesthesia for building biomarkers of brain ageing an exploratory study |
| topic | brain age burst suppression electroencephalogram (EEG) general anaesthesia machine learning propofol |
| url | http://www.sciencedirect.com/science/article/pii/S2772609623000242 |
| work_keys_str_mv | AT davidsabbagh repurposingelectroencephalogrammonitoringofgeneralanaesthesiaforbuildingbiomarkersofbrainageinganexploratorystudy AT jeromecartailler repurposingelectroencephalogrammonitoringofgeneralanaesthesiaforbuildingbiomarkersofbrainageinganexploratorystudy AT cyriltouchard repurposingelectroencephalogrammonitoringofgeneralanaesthesiaforbuildingbiomarkersofbrainageinganexploratorystudy AT jonajoachim repurposingelectroencephalogrammonitoringofgeneralanaesthesiaforbuildingbiomarkersofbrainageinganexploratorystudy AT alexandremebazaa repurposingelectroencephalogrammonitoringofgeneralanaesthesiaforbuildingbiomarkersofbrainageinganexploratorystudy AT fabricevallee repurposingelectroencephalogrammonitoringofgeneralanaesthesiaforbuildingbiomarkersofbrainageinganexploratorystudy AT etiennegayat repurposingelectroencephalogrammonitoringofgeneralanaesthesiaforbuildingbiomarkersofbrainageinganexploratorystudy AT alexandregramfort repurposingelectroencephalogrammonitoringofgeneralanaesthesiaforbuildingbiomarkersofbrainageinganexploratorystudy AT denisaengemann repurposingelectroencephalogrammonitoringofgeneralanaesthesiaforbuildingbiomarkersofbrainageinganexploratorystudy |