EBioMedicine · 2025
Beyond mild, moderate, and severe traumatic brain injury: modelling severity from clinical, neuroimaging, and blood-based indicators.
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Nelson Lindsay D, Magnus Brooke E, Yue John K, Balsis Steve, Patrick Christopher J, Temkin Nancy, Yuh Esther L, Diaz-Arrastia Ramon, Ryu Ehri, Maas Andrew I R, Menon David K, Wilson Lindsay, Manley Geoffrey T, TRACK-TBI Investigators, CENTER-TBI Participants and Investigators
Аннотация
BACKGROUND: The conventional clinical approach to characterising traumatic brain injuries (TBIs) as mild, moderate, or severe using the Glasgow Coma Scale (GCS) total score has well-known limitations, prompting calls for more sophisticated strategies.
METHODS: We used item response theory (IRT) to develop a new method for quantifying TBI severity using 24 clinical, head computed tomography, and blood-based biomarker variables familiar to clinicians and researchers. IRT uses individuals' response patterns across indicators to estimate relationships between the indicators and a latent continuum of TBI severity. Model parameters were used to assign severity scores in two large cohorts, and associations with traditional GCS categories and 6-month functional outcomes (Glasgow Outcome Scale-Extended [GOSE]) were tested with correlational and logistic regression analyses.
FINDINGS: In the prospective Transforming Research and Clinical Knowledge in TBI (TRACK-TBI) cohort (N = 2545), modelling showed the 24 indicators index a common latent continuum of TBI severity. IRT enabled us to identify the relative contribution of these features to estimate an individual's TBI severity. Finally, within both the TRACK-TBI derivation sample and an external validation sample (Collaborative European NeuroTrauma Effectiveness Research in TBI [CENTER-TBI]), TBI severity scores generated using this novel IRT-based method incrementally predicted functional (GOSE) outcome better than classic clinical (mild, moderate, severe) or International Mission for Prognosis and Analysis of Clinical Trials in TBI (IMPACT) classification methods.
INTERPRETATION: Our findings directly inform ongoing international efforts to refine and deploy new pragmatic, empirically-supported strategies for characterising TBI, while illustrating a strategy that may be useful to improve staging systems for other diseases.
FUNDING: This secondary analysis project was funded by the U.S. National Institute of Neurological Disorders and Stroke (Grant No. R01 NS110856).
METHODS: We used item response theory (IRT) to develop a new method for quantifying TBI severity using 24 clinical, head computed tomography, and blood-based biomarker variables familiar to clinicians and researchers. IRT uses individuals' response patterns across indicators to estimate relationships between the indicators and a latent continuum of TBI severity. Model parameters were used to assign severity scores in two large cohorts, and associations with traditional GCS categories and 6-month functional outcomes (Glasgow Outcome Scale-Extended [GOSE]) were tested with correlational and logistic regression analyses.
FINDINGS: In the prospective Transforming Research and Clinical Knowledge in TBI (TRACK-TBI) cohort (N = 2545), modelling showed the 24 indicators index a common latent continuum of TBI severity. IRT enabled us to identify the relative contribution of these features to estimate an individual's TBI severity. Finally, within both the TRACK-TBI derivation sample and an external validation sample (Collaborative European NeuroTrauma Effectiveness Research in TBI [CENTER-TBI]), TBI severity scores generated using this novel IRT-based method incrementally predicted functional (GOSE) outcome better than classic clinical (mild, moderate, severe) or International Mission for Prognosis and Analysis of Clinical Trials in TBI (IMPACT) classification methods.
INTERPRETATION: Our findings directly inform ongoing international efforts to refine and deploy new pragmatic, empirically-supported strategies for characterising TBI, while illustrating a strategy that may be useful to improve staging systems for other diseases.
FUNDING: This secondary analysis project was funded by the U.S. National Institute of Neurological Disorders and Stroke (Grant No. R01 NS110856).