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Petersen BA, Erickson KI, Kurowski BG, Boninger ML, Treble-Barna A. Emerging methods for measuring physical activity using accelerometry in children and adolescents with neuromotor disorders: a narrative review. J Neuroeng Rehabil 2024; 21:31. [PMID: 38419099 PMCID: PMC10903036 DOI: 10.1186/s12984-024-01327-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 02/21/2024] [Indexed: 03/02/2024] Open
Abstract
BACKGROUND Children and adolescents with neuromotor disorders need regular physical activity to maintain optimal health and functional independence throughout their development. To this end, reliable measures of physical activity are integral to both assessing habitual physical activity and testing the efficacy of the many interventions designed to increase physical activity in these children. Wearable accelerometers have been used for children with neuromotor disorders for decades; however, studies most often use disorder-specific cut points to categorize physical activity intensity, which lack generalizability to a free-living environment. No reviews of accelerometer data processing methods have discussed the novel use of machine learning techniques for monitoring physical activity in children with neuromotor disorders. METHODS In this narrative review, we discuss traditional measures of physical activity (including questionnaires and objective accelerometry measures), the limitations of standard analysis for accelerometry in this unique population, and the potential benefits of applying machine learning approaches. We also provide recommendations for using machine learning approaches to monitor physical activity. CONCLUSIONS While wearable accelerometers provided a much-needed method to quantify physical activity, standard cut point analyses have limitations in children with neuromotor disorders. Machine learning models are a more robust method of analyzing accelerometer data in pediatric neuromotor disorders and using these methods over disorder-specific cut points is likely to improve accuracy of classifying both type and intensity of physical activity. Notably, there remains a critical need for further development of classifiers for children with more severe motor impairments, preschool aged children, and children in hospital settings.
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Affiliation(s)
- Bailey A Petersen
- Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
| | - Kirk I Erickson
- AdventHealth Research Institute Department of Neuroscience, AdventHealth, Orlando, FL, USA
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA, USA
- Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA, USA
| | - Brad G Kurowski
- Division of Pediatric Rehabilitation Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Department of Neurology and Rehabilitation Medicine, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - M L Boninger
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, USA
- Clinical and Translational Science Institute, University of Pittsburgh, Pittsburgh, PA, USA
| | - A Treble-Barna
- Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, USA
- Clinical and Translational Science Institute, University of Pittsburgh, Pittsburgh, PA, USA
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Bhattacharyay S, Caruso PF, Åkerlund C, Wilson L, Stevens RD, Menon DK, Steyerberg EW, Nelson DW, Ercole A. Mining the contribution of intensive care clinical course to outcome after traumatic brain injury. NPJ Digit Med 2023; 6:154. [PMID: 37604980 PMCID: PMC10442346 DOI: 10.1038/s41746-023-00895-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 08/01/2023] [Indexed: 08/23/2023] Open
Abstract
Existing methods to characterise the evolving condition of traumatic brain injury (TBI) patients in the intensive care unit (ICU) do not capture the context necessary for individualising treatment. Here, we integrate all heterogenous data stored in medical records (1166 pre-ICU and ICU variables) to model the individualised contribution of clinical course to 6-month functional outcome on the Glasgow Outcome Scale -Extended (GOSE). On a prospective cohort (n = 1550, 65 centres) of TBI patients, we train recurrent neural network models to map a token-embedded time series representation of all variables (including missing values) to an ordinal GOSE prognosis every 2 h. The full range of variables explains up to 52% (95% CI: 50-54%) of the ordinal variance in functional outcome. Up to 91% (95% CI: 90-91%) of this explanation is derived from pre-ICU and admission information (i.e., static variables). Information collected in the ICU (i.e., dynamic variables) increases explanation (by up to 5% [95% CI: 4-6%]), though not enough to counter poorer overall performance in longer-stay (>5.75 days) patients. Highest-contributing variables include physician-based prognoses, CT features, and markers of neurological function. Whilst static information currently accounts for the majority of functional outcome explanation after TBI, data-driven analysis highlights investigative avenues to improve the dynamic characterisation of longer-stay patients. Moreover, our modelling strategy proves useful for converting large patient records into interpretable time series with missing data integration and minimal processing.
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Affiliation(s)
- Shubhayu Bhattacharyay
- Division of Anaesthesia, University of Cambridge, Cambridge, UK.
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
- Laboratory of Computational Intensive Care Medicine, Johns Hopkins University, Baltimore, MD, USA.
| | - Pier Francesco Caruso
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, Milan, 20072, Italy
| | - Cecilia Åkerlund
- Department of Physiology and Pharmacology, Section for Perioperative Medicine and Intensive Care, Karolinska Institutet, Stockholm, Sweden
| | - Lindsay Wilson
- Division of Psychology, University of Stirling, Stirling, UK
| | - Robert D Stevens
- Laboratory of Computational Intensive Care Medicine, Johns Hopkins University, Baltimore, MD, USA
- Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - David K Menon
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
| | - Ewout W Steyerberg
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - David W Nelson
- Department of Physiology and Pharmacology, Section for Perioperative Medicine and Intensive Care, Karolinska Institutet, Stockholm, Sweden
| | - Ari Ercole
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
- Cambridge Centre for Artificial Intelligence in Medicine, Cambridge, UK
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Gomez LA, Shen Q, Doyle K, Vrosgou A, Velazquez A, Megjhani M, Ghoshal S, Roh D, Agarwal S, Park S, Claassen J, Kleinberg S. Classification of Level of Consciousness in a Neurological ICU Using Physiological Data. Neurocrit Care 2023; 38:118-128. [PMID: 36109448 PMCID: PMC9935697 DOI: 10.1007/s12028-022-01586-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 08/08/2022] [Indexed: 10/14/2022]
Abstract
BACKGROUND Impaired consciousness is common in intensive care unit (ICU) patients, and an individual's degree of consciousness is crucial to determining their care and prognosis. However, there are no methods that continuously monitor consciousness and alert clinicians to changes. We investigated the use of physiological signals collected in the ICU to classify levels of consciousness in critically ill patients. METHODS We studied 61 patients with subarachnoid hemorrhage (SAH) and 178 patients with intracerebral hemorrhage (ICH) from the neurological ICU at Columbia University Medical Center in a retrospective observational study of prospectively collected data. The level of consciousness was determined on the basis of neurological examination and mapped to comatose, vegetative state or unresponsive wakefulness syndrome (VS/UWS), minimally conscious minus state (MCS-), and command following. For each physiological signal, we extracted time-series features and performed classification using extreme gradient boosting on multiple clinically relevant tasks across subsets of physiological signals. We applied this approach independently on both SAH and ICH patient groups for three sets of variables: (1) a minimal set common to most hospital patients (e.g., heart rate), (2) variables available in most ICUs (e.g., body temperature), and (3) an extended set recorded mainly in neurological ICUs (absent for the ICH patient group; e.g., brain temperature). RESULTS On the commonly performed classification task of VS/UWS versus MCS-, we achieved an area under the receiver operating characteristic curve (AUROC) in the SAH patient group of 0.72 (sensitivity 82%, specificity 57%; 95% confidence interval [CI] 0.63-0.81) using the extended set, 0.69 (sensitivity 83%, specificity 51%; 95% CI 0.59-0.78) on the variable set available in most ICUs, and 0.69 (sensitivity 56%, specificity 78%; 95% CI 0.60-0.78) on the minimal set. In the ICH patient group, AUROC was 0.64 (sensitivity 56%, specificity 65%; 95% CI 0.55-0.74) using the minimal set and 0.61 (sensitivity 50%, specificity 80%; 95% CI 0.51-0.71) using the variables available in most ICUs. CONCLUSIONS We find that physiological signals can be used to classify states of consciousness for patients in the ICU. Building on this with intraday assessments and increasing sensitivity and specificity may enable alarm systems that alert physicians to changes in consciousness and frequent monitoring of consciousness throughout the day, both of which may improve patient care and outcomes.
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Affiliation(s)
- Louis A Gomez
- Stevens Institute of Technology, 1 Castle Point on Hudson, Hoboken, NJ, 07030, USA
| | - Qi Shen
- Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
| | - Kevin Doyle
- Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
| | - Athina Vrosgou
- Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
| | - Angela Velazquez
- Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
| | - Murad Megjhani
- Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
| | - Shivani Ghoshal
- Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
- New York Presbyterian Hospital, New York, NY, USA
| | - David Roh
- Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
- New York Presbyterian Hospital, New York, NY, USA
| | - Sachin Agarwal
- Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
- New York Presbyterian Hospital, New York, NY, USA
| | - Soojin Park
- Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
- New York Presbyterian Hospital, New York, NY, USA
| | - Jan Claassen
- Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
- New York Presbyterian Hospital, New York, NY, USA
| | - Samantha Kleinberg
- Stevens Institute of Technology, 1 Castle Point on Hudson, Hoboken, NJ, 07030, USA.
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Bhattacharyay S, Milosevic I, Wilson L, Menon DK, Stevens RD, Steyerberg EW, Nelson DW, Ercole A. The leap to ordinal: Detailed functional prognosis after traumatic brain injury with a flexible modelling approach. PLoS One 2022; 17:e0270973. [PMID: 35788768 PMCID: PMC9255749 DOI: 10.1371/journal.pone.0270973] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2022] [Accepted: 06/21/2022] [Indexed: 11/30/2022] Open
Abstract
When a patient is admitted to the intensive care unit (ICU) after a traumatic brain injury (TBI), an early prognosis is essential for baseline risk adjustment and shared decision making. TBI outcomes are commonly categorised by the Glasgow Outcome Scale–Extended (GOSE) into eight, ordered levels of functional recovery at 6 months after injury. Existing ICU prognostic models predict binary outcomes at a certain threshold of GOSE (e.g., prediction of survival [GOSE > 1]). We aimed to develop ordinal prediction models that concurrently predict probabilities of each GOSE score. From a prospective cohort (n = 1,550, 65 centres) in the ICU stratum of the Collaborative European NeuroTrauma Effectiveness Research in TBI (CENTER-TBI) patient dataset, we extracted all clinical information within 24 hours of ICU admission (1,151 predictors) and 6-month GOSE scores. We analysed the effect of two design elements on ordinal model performance: (1) the baseline predictor set, ranging from a concise set of ten validated predictors to a token-embedded representation of all possible predictors, and (2) the modelling strategy, from ordinal logistic regression to multinomial deep learning. With repeated k-fold cross-validation, we found that expanding the baseline predictor set significantly improved ordinal prediction performance while increasing analytical complexity did not. Half of these gains could be achieved with the addition of eight high-impact predictors to the concise set. At best, ordinal models achieved 0.76 (95% CI: 0.74–0.77) ordinal discrimination ability (ordinal c-index) and 57% (95% CI: 54%– 60%) explanation of ordinal variation in 6-month GOSE (Somers’ Dxy). Model performance and the effect of expanding the predictor set decreased at higher GOSE thresholds, indicating the difficulty of predicting better functional outcomes shortly after ICU admission. Our results motivate the search for informative predictors that improve confidence in prognosis of higher GOSE and the development of ordinal dynamic prediction models.
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Affiliation(s)
- Shubhayu Bhattacharyay
- Division of Anaesthesia, University of Cambridge, Cambridge, United Kingdom
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
- Laboratory of Computational Intensive Care Medicine, Johns Hopkins University, Baltimore, MD, United States of America
- * E-mail:
| | - Ioan Milosevic
- Division of Anaesthesia, University of Cambridge, Cambridge, United Kingdom
| | - Lindsay Wilson
- Division of Psychology, University of Stirling, Stirling, United Kingdom
| | - David K. Menon
- Division of Anaesthesia, University of Cambridge, Cambridge, United Kingdom
| | - Robert D. Stevens
- Laboratory of Computational Intensive Care Medicine, Johns Hopkins University, Baltimore, MD, United States of America
- Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University, Baltimore, MD, United States of America
| | - Ewout W. Steyerberg
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - David W. Nelson
- Department of Physiology and Pharmacology, Section for Perioperative Medicine and Intensive Care, Karolinska Institutet, Stockholm, Sweden
| | - Ari Ercole
- Division of Anaesthesia, University of Cambridge, Cambridge, United Kingdom
- Cambridge Centre for Artificial Intelligence in Medicine, Cambridge, United Kingdom
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