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Decoding accelerometry for classification and prediction of critically ill patients with severe brain injury.
Bhattacharyay, Shubhayu; Rattray, John; Wang, Matthew; Dziedzic, Peter H; Calvillo, Eusebia; Kim, Han B; Joshi, Eshan; Kudela, Pawel; Etienne-Cummings, Ralph; Stevens, Robert D.
Afiliación
  • Bhattacharyay S; Laboratory of Computational Intensive Care Medicine, Johns Hopkins University, Baltimore, MD, USA. sb2406@cam.ac.uk.
  • Rattray J; Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK. sb2406@cam.ac.uk.
  • Wang M; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA. sb2406@cam.ac.uk.
  • Dziedzic PH; Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA. sb2406@cam.ac.uk.
  • Calvillo E; Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA.
  • Kim HB; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
  • Joshi E; Laboratory of Computational Intensive Care Medicine, Johns Hopkins University, Baltimore, MD, USA.
  • Kudela P; Department of Neurology, Johns Hopkins University, Baltimore, MD, USA.
  • Etienne-Cummings R; Department of Neurology, Johns Hopkins University, Baltimore, MD, USA.
  • Stevens RD; Laboratory of Computational Intensive Care Medicine, Johns Hopkins University, Baltimore, MD, USA.
Sci Rep ; 11(1): 23654, 2021 12 08.
Article en En | MEDLINE | ID: mdl-34880296
ABSTRACT
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.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Lesiones Encefálicas / Enfermedad Crítica Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Aged / Female / Humans / Male / Middle aged Idioma: En Revista: Sci Rep Año: 2021 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Lesiones Encefálicas / Enfermedad Crítica Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Aged / Female / Humans / Male / Middle aged Idioma: En Revista: Sci Rep Año: 2021 Tipo del documento: Article País de afiliación: Estados Unidos