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dc.contributor.authorBhattacharyay, Shubhayu
dc.contributor.authorRattray, John
dc.contributor.authorWang, Matthew
dc.contributor.authorDziedzic, Peter H
dc.contributor.authorCalvillo, Eusebia
dc.contributor.authorKim, Han B
dc.contributor.authorJoshi, Eshan
dc.contributor.authorKudela, Pawel
dc.contributor.authorEtienne-Cummings, Ralph
dc.contributor.authorStevens, Robert D
dc.date.accessioned2021-12-24T14:38:48Z
dc.date.available2021-12-24T14:38:48Z
dc.date.issued2021-12-08
dc.date.submitted2021-05-25
dc.identifier.issn2045-2322
dc.identifier.others41598-021-02974-w
dc.identifier.other2974
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/331819
dc.descriptionFunder: Gates Cambridge Trust; doi: http://dx.doi.org/10.13039/501100005370
dc.descriptionFunder: Office of the Provost, Johns Hopkins University; doi: http://dx.doi.org/10.13039/100012800
dc.description.abstractOur goal is to explore quantitative motor features in critically ill patients with severe brain injury (SBI). We hypothesized that computational decoding of these features would yield information on underlying neurological states and outcomes. Using wearable microsensors placed on all extremities, we recorded a median 24.1 (IQR: 22.8-25.1) hours of high-frequency accelerometry data per patient from a prospective cohort (n = 69) admitted to the ICU with SBI. Models were trained using time-, frequency-, and wavelet-domain features and levels of responsiveness and outcome as labels. The two primary tasks were detection of levels of responsiveness, assessed by motor sub-score of the Glasgow Coma Scale (GCSm), and prediction of functional outcome at discharge, measured with the Glasgow Outcome Scale-Extended (GOSE). Detection models achieved significant (AUC: 0.70 [95% CI: 0.53-0.85]) and consistent (observation windows: 12 min-9 h) discrimination of SBI patients capable of purposeful movement (GCSm > 4). Prediction models accurately discriminated patients of upper moderate disability or better (GOSE > 5) with 2-6 h of observation (AUC: 0.82 [95% CI: 0.75-0.90]). Results suggest that time series analysis of motor activity yields clinically relevant insights on underlying functional states and short-term outcomes in patients with SBI.
dc.languageen
dc.publisherSpringer Science and Business Media LLC
dc.subjectArticle
dc.subject/692/699/375/1345
dc.subject/692/699/375/1370
dc.subject/692/699/375/380
dc.subject/692/699/375/534
dc.subject/692/617/375/1345
dc.subject/692/617/375/1370
dc.subject/692/617/375/380
dc.subject/692/617/375/534
dc.subject/639/166/985
dc.subject/639/705/1042
dc.subject/631/114/2415
dc.subjectarticle
dc.titleDecoding accelerometry for classification and prediction of critically ill patients with severe brain injury.
dc.typeArticle
dc.date.updated2021-12-24T14:38:47Z
prism.issueIdentifier1
prism.publicationNameSci Rep
prism.volume11
dc.identifier.doi10.17863/CAM.79268
dcterms.dateAccepted2021-11-25
rioxxterms.versionofrecord10.1038/s41598-021-02974-w
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
dc.contributor.orcidBhattacharyay, Shubhayu [0000-0001-7428-5588]
dc.identifier.eissn2045-2322
cam.issuedOnline2021-12-08


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