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Decoding accelerometry for classification and prediction of critically ill patients with severe brain injury
- Source :
- Scientific Reports, Scientific Reports, Vol 11, Iss 1, Pp 1-17 (2021)
- Publication Year :
- 2021
- Publisher :
- Nature Publishing Group UK, 2021.
-
Abstract
- Funder: Gates Cambridge Trust; doi: http://dx.doi.org/10.13039/501100005370<br />Funder: Office of the Provost, Johns Hopkins University; doi: http://dx.doi.org/10.13039/100012800<br />Our 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.
- Subjects :
- Male
639/705/1042
Cerebrovascular disorders
Statistical methods
Critical Illness
Science
692/699/375/1370
Glasgow Outcome Scale
Pilot Projects
Brain injuries
692/699/375/380
Severity of Illness Index
Article
692/617/375/534
Accelerometry
Humans
Author Correction
GeneralLiterature_REFERENCE(e.g.,dictionaries,encyclopedias,glossaries)
ComputingMilieux_MISCELLANEOUS
692/699/375/1345
Neurovascular disorders
Aged
692/617/375/1345
Multidisciplinary
Computational science
Middle Aged
631/114/2415
Stroke
639/166/985
692/617/375/1370
Medicine
Female
692/617/375/380
692/699/375/534
Biomedical engineering
Subjects
Details
- Language :
- English
- ISSN :
- 20452322
- Volume :
- 11
- Database :
- OpenAIRE
- Journal :
- Scientific Reports
- Accession number :
- edsair.doi.dedup.....e3ecd7dbc086661ab18605553f00b05a