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Snider SB, Temkin NR, Barber J, Edlow BL, Giacino JT, Hammond FM, Izzy S, Kowalski RG, Markowitz AJ, Rovito CA, Shih SL, Zafonte RD, Manley GT, Bodien YG. Predicting Functional Dependency in Patients with Disorders of Consciousness: A TBI-Model Systems and TRACK-TBI Study. Ann Neurol 2023; 94:1008-1023. [PMID: 37470289 PMCID: PMC10799195 DOI: 10.1002/ana.26741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 06/27/2023] [Accepted: 06/29/2023] [Indexed: 07/21/2023]
Abstract
OBJECTIVE It is not currently possible to predict long-term functional dependency in patients with disorders of consciousness (DoC) after traumatic brain injury (TBI). Our objective was to fit and externally validate a prediction model for 1-year dependency in patients with DoC ≥ 2 weeks after TBI. METHODS We included adults with TBI enrolled in TBI Model Systems (TBI-MS) or Transforming Research and Clinical Knowledge in TBI (TRACK-TBI) studies who were not following commands at rehabilitation admission or 2 weeks post-injury, respectively. We fit a logistic regression model in TBI-MS and validated it in TRACK-TBI. The primary outcome was death or dependency at 1 year post-injury, defined using the Disability Rating Scale. RESULTS In the TBI-MS Discovery Sample, 1,960 participants (mean age 40 [18] years, 76% male, 68% white) met inclusion criteria, and 406 (27%) were dependent 1 year post-injury. In a TBI-MS held out cohort, the dependency prediction model's area under the receiver operating characteristic curve was 0.79 (95% CI 0.74-0.85), positive predictive value was 53% and negative predictive value was 86%. In the TRACK-TBI external validation (n = 124, age 40 [16] years, 77% male, 81% white), the area under the receiver operating characteristic curve was 0.66 (0.53, 0.79), equivalent to the standard IMPACTcore + CT score (p = 0.8). INTERPRETATION We developed a 1-year dependency prediction model using the largest existing cohort of patients with DoC after TBI. The sensitivity and negative predictive values were greater than specificity and positive predictive values. Accuracy was diminished in an external sample, but equivalent to the IMPACT model. Further research is needed to improve dependency prediction in patients with DoC after TBI. ANN NEUROL 2023;94:1008-1023.
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Affiliation(s)
- Samuel B. Snider
- Division of Neurocritical Care, Department of Neurology, Brigham and Women’s Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Nancy R. Temkin
- Department of Neurological Surgery, University of Washington, Seattle, Washington, USA
- Department of Biostatistics, University of Washington, Seattle, Washington, USA
| | - Jason Barber
- Department of Neurological Surgery, University of Washington, Seattle, Washington, USA
| | - Brian L. Edlow
- Harvard Medical School, Boston, MA, USA
- Center for Neurotechnology and Neurorecovery and Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Joseph T. Giacino
- Harvard Medical School, Boston, MA, USA
- Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Boston, MA USA
| | - Flora M. Hammond
- Department of Physical Medicine and Rehabilitation, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Saef Izzy
- Division of Neurocritical Care, Department of Neurology, Brigham and Women’s Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Robert G. Kowalski
- Departments of Neurosurgery and Neurology, University of Colorado School of Medicine, Aurora CO, USA
| | | | - Craig A. Rovito
- Harvard Medical School, Boston, MA, USA
- Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Boston, MA USA
| | - Shirley L. Shih
- Harvard Medical School, Boston, MA, USA
- Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Boston, MA USA
| | - Ross D. Zafonte
- Harvard Medical School, Boston, MA, USA
- Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Boston, MA USA
| | - Geoffrey T. Manley
- Department of Neurological Surgery, UCSF, San Francisco, CA USA
- Brain and Spinal Cord Injury Center, Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, CA USA
| | - Yelena G. Bodien
- Harvard Medical School, Boston, MA, USA
- Center for Neurotechnology and Neurorecovery and Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Boston, MA USA
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