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Systematic Review of Multivariable Prognostic Models for Mild Traumatic Brain Injury
Abstract
Prognostic models can guide clinical management and increase statistical power in clinical trials. The availability and adequacy of prognostic models for mild traumatic brain injury (MTBI) is uncertain. The present study aimed to (1) identify and evaluate multivariable prognostic models for MTBI, and (2) determine which pre-, peri-, and early post-injury variables have independent prognostic value in the context of multivariable models. An electronic search of MEDLINE, PsycINFO, PubMed, EMBASE, and CINAHL databases for English-language MTBI cohort studies from 1970–2013 was supplemented by Web of Science citation and hand searching. This search strategy identified 7789 articles after removing duplicates. Of 182 full-text articles reviewed, 26 met eligibility criteria including (1) prospective inception cohort design, (2) prognostic information collected within 1 month post-injury, and (3) 2+variables combined to predict clinical outcome (e.g., post-concussion syndrome) at least 1 month later. Independent reviewers extracted sample characteristics, study design features, clinical outcome variables, predictor selection methods, and prognostic model discrimination, calibration, and cross-validation. These data elements were synthesized qualitatively. The present review found no multivariable prognostic model that adequately predicts individual patient outcomes from MTBI. Suboptimal methodology limits their reproducibility and clinical usefulness. The most robust prognostic factors in the context of multivariable models were pre-injury mental health and early post-injury neuropsychological functioning. Women and adults with early post-injury anxiety also have worse prognoses. Relative to these factors, the severity of MTBI had little long-term prognostic value. Future prognostic studies should consider a broad range of biopsychosocial predictors in large inception cohorts.
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“Psychological factors, when considered alongside demographic variables and clinical indicators of brain injury severity, have consistently emerged as robust predictors of PPCS.”
From Acceptance and commitment therapy for mild traumatic brain injury (ACTion-mTBI): a quasiexperimental feasibility study (2025) · cites this paper as [11]
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“The more symptoms a patient has soon after mTBI, the more symptoms they tend to have weeks and months later(81,83-85) In adults, preinjury mental health problems and postinjury psychological distress (symptoms of depression and anxiety) are robust predictors of prolonged recovery.(76,81,83,85,87)”
BackgroundSelf-citation
Metadata
- Authors
- Silverberg, N. D., Gardner, A. J., Brubacher, J. R., Panenka, W. J., Li, J. J., & Iverson, G. L.
- Affiliations
- University of British Columbia (CA)
- University of Newcastle Australia (AU)
- Simon Fraser University (CA)
- Harvard University (US)
- Corresponding author
- Silverberg, Noah D.
- Venue
- Journal of Neurotrauma
- Publisher
- SAGE Publications
- Published
- 2015-04
- Volume
- 32
- Issue
- 8
- Pages
- 517-526
- ISSN
- 0897-7151 (ISSN-L)
- 1557-9042
- OpenAlex
- W1974797523
- Cited by
- 393
- References
- 70
- Citations by year
- 2026: 11 · 2025: 32 · 2024: 38 · 2023: 33 · 2022: 41 · 2021: 44 · 2020: 33 · 2019: 37 · 2018: 73 · 2017: 29 · 2016: 12 · 2015: 10
- FWCI
- 2.27
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Neurotrauma (publishedVersion)
- PubMed (publishedVersion)
- NOVA (University of Newcastle Australia) (submittedVersion)
- Topics
- Health Sciences › Medicine › Clinical Neurology › Traumatic Brain Injury Research
- Health Sciences › Medicine › Clinical Neurology › Traumatic Brain Injury and Neurovascular Disturbances
- Health Sciences › Medicine › Clinical Neurology › Long-Term Effects of COVID-19
- Keywords
- mild traumatic brain injury, concussion, prognostic model, post-concussion syndrome, prognostic factors, neuropsychological performance, injury severity, discrimination and calibration
- MeSH terms
- Brain Injuries, Humans, Models, Neurological, Prognosis
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