The CBI-M framework for characterization of traumatic brain injury: Evaluation of performance using data from the CENTER-TBI and TRACK-TBI prospective cohort studies
12 affiliations
- 1Department of Public Health, Erasmus MC - University Medical Center Rotterdam, Rotterdam, The Netherlands.
- 2Department of Neurosurgery, Erasmus MC - University Medical Center Rotterdam, Rotterdam, The Netherlands.
- 3Department of Neurological Surgery, University of California, San Francisco, California, United States of America.
- 4Brain and Spinal Injury Center, Zuckerberg San Francisco General Hospital, San Francisco, California, United States of America.
- 5Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, Wisconsin, United States of America.
- 6Department of Rehabilitation and Human Performance, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America.
- 7Departments of Neuroscience and Neurology, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America.
- 8Department of Neurosurgery, Antwerp University Hospital, Edegem, Belgium.
- 9Biostatistics Research Center, Herbert Wertheim School of Public Health and Longevity Science, University of California, San Diego, California, United States of America.
- 10Department of Medicine, University of Cambridge, Cambridge, United Kingdom.
- 11Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands.
- 12Department of Translational Neuroscience, Faculty of Medicine and Health Science, University of Antwerp, Antwerp, Belgium.
Abstract
Background: The CBI-M Framework, incorporating Clinical, Biomarker, Imaging, and Modifier pillars, was recently proposed to provide a comprehensive characterization of patients with acute traumatic brain injury (TBI). Such detailed characterization reflects the clinical and biological heterogeneity of TBI, and is considered essential to inform appropriate clinical management. Still unclear is the extent to which CBI-M captures clinically relevant heterogeneity in relation to diagnosis, clinical care pathways, and longer-term outcome. Prior to broad implementation into clinical practice and research, its diagnostic and prognostic performance needs to be ascertained. In this study, we aim to explore how CBI-M and its constituent pillars relate to diagnosis, clinical care pathways, and outcome in two large contemporary TBI cohorts. Methods and findings: We analyzed data of patients with acute TBI enrolled in the European-based CENTER-TBI (N = 4,509) and US-based TRACK-TBI (N = 2,552) prospective observational studies. We assessed associations of the CBI-M Framework, its pillars, and pillar components with key clinical endpoints: diagnosis (traumatic intracranial injury on computed tomography); care pathways (hospital admission, intensive care admission, intracranial pressure monitoring, and major cranial surgery); and outcomes at 6 months post-injury (mortality, Glasgow Outcome Scale-Extended (GOSE), and health-related quality of life (HRQoL)). Discriminatory performance was calculated using the AUC and explained variability expressed with Nagelkerke’s pseudo-R2. Prognostic performance was compared to the established IMPACT-Lab and CRASH-CT prediction models in their respective target populations. For diagnostic and care path endpoints full CBI-M models achieved excellent discrimination (AUC CENTER-TBI: from 0.83 to 0.95; TRACK-TBI: from 0.88 to 0.98), and high proportions of explained variability (pseudo-R2 CENTER-TBI: from 0.41 to 0.72; TRACK-TBI: from 0.55 to 0.80). Contributions of individual pillars varied by endpoint, with the Biomarker pillar contributing most for CT-defined abnormalities and the Imaging pillar to major cranial surgery. For mortality and unfavorable outcome (GOSE ≤ 4) similarly strong performance was observed: CENTER-TBI: AUC 0.89 (95% Confidence intervals (CI) [0.88,0.90]) and 0.92 (95% CI [0.91,0.94]) respectively; pseudo-R2 0.53 (95% CI [0.50,0.57]) and 0.55 (95% CI [0.51,0.59]) respectively; TRACK-TBI: AUC 0.91 (95% CI [0.89,0.93]) and 0.95 (95% CI [0.93,0.97]) respectively; pseudo-R2 0.57 (95% CI [0.52,0.62] and 0.57 (95% CI [0.50, 0.64] respectively), whereas performance for HRQoL was modest. Compared to the IMPACT-Lab and CRASH-CT models in their respective target populations, pseudo-R2 values suggest that the full CBI-M model was more strongly associated with 6-month mortality and unfavorable outcomes. Methodological limitations include the restriction to analysis of data from high-income settings, limiting generalizability, the absence of three elements of the modifier pillar in the datasets accessed, and risk of possible reverse causality in some analyses. Conclusions: CBI-M provides a robust multimodal characterization of acute TBI that aligns closely with clinical decision-making and outcomes. Further prospective validation and optimization are justified and needed to inform implementation of the CBI-M Framework in clinical practice and research. Why was this study done?: What did the researchers do and find?: What do these findings mean?: Dana Pisică, Ana Mikolić, John K. Yue and colleagues find that the CBI-M Framework is associated with clinically relevant endpoints for patients with traumatic brain injury, including diagnosis, hospital care pathways, and outcomes at six months. Further optimisation and validation is needed before implementation in clinical practice and research.
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BibTeX
@article{pisica2026cbim,
title = {{The CBI-M framework for characterization of traumatic brain injury: Evaluation of performance using data from the CENTER-TBI and TRACK-TBI prospective cohort studies}},
author = {Dana Pisică and Ana Mikolić and John K Yue and Michael A McCrea and Kristen Dams-O'Connor and Véronique De Keyser and Sonia Jain and Amy J Markowitz and David K Menon and Virginia Newcombe and Ewout W Steyerberg and Xiaoying Sun and Hester F Lingsma and Geoffrey T Manley and Andrew I R Maas and CENTER-TBI and TRACK-TBI Investigators},
journal = {PLOS Medicine},
year = {2026},
month = oct,
doi = {10.1371/journal.pmed.1004945},
url = {https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1004945},
note = {CC BY},
}
RIS
TY - JOUR
TI - The CBI-M framework for characterization of traumatic brain injury: Evaluation of performance using data from the CENTER-TBI and TRACK-TBI prospective cohort studies
AU - Dana Pisică
AU - Ana Mikolić
AU - John K Yue
AU - Michael A McCrea
AU - Kristen Dams-O'Connor
AU - Véronique De Keyser
AU - Sonia Jain
AU - Amy J Markowitz
AU - David K Menon
AU - Virginia Newcombe
AU - Ewout W Steyerberg
AU - Xiaoying Sun
AU - Hester F Lingsma
AU - Geoffrey T Manley
AU - Andrew I R Maas
AU - CENTER-TBI and TRACK-TBI Investigators
PY - 2026
DA - 2026/10/20
JO - PLOS Medicine
DO - 10.1371/journal.pmed.1004945
UR - https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1004945
AB - Background: The CBI-M Framework, incorporating Clinical, Biomarker, Imaging, and Modifier pillars, was recently proposed to provide a comprehensive characterization of patients with acute traumatic brain injury (TBI). Such detailed characterization reflects the clinical and biological heterogeneity of TBI, and is considered essential to inform appropriate clinical management. Still unclear is the extent to which CBI-M captures clinically relevant heterogeneity in relation to diagnosis, clinical care pathways, and longer-term outcome. Prior to broad implementation into clinical practice and research, its diagnostic and prognostic performance needs to be ascertained. In this study, we aim to explore how CBI-M and its constituent pillars relate to diagnosis, clinical care pathways, and outcome in two large contemporary TBI cohorts. Methods and findings: We analyzed data of patients with acute TBI enrolled in the European-based CENTER-TBI (N = 4,509) and US-based TRACK-TBI (N = 2,552) prospective observational studies. We assessed associations of the CBI-M Framework, its pillars, and pillar components with key clinical endpoints: diagnosis (traumatic intracranial injury on computed tomography); care pathways (hospital admission, intensive care admission, intracranial pressure monitoring, and major cranial surgery); and outcomes at 6 months post-injury (mortality, Glasgow Outcome Scale-Extended (GOSE), and health-related quality of life (HRQoL)). Discriminatory performance was calculated using the AUC and explained variability expressed with Nagelkerke’s pseudo-R2. Prognostic performance was compared to the established IMPACT-Lab and CRASH-CT prediction models in their respective target populations. For diagnostic and care path endpoints full CBI-M models achieved excellent discrimination (AUC CENTER-TBI: from 0.83 to 0.95; TRACK-TBI: from 0.88 to 0.98), and high proportions of explained variability (pseudo-R2 CENTER-TBI: from 0.41 to 0.72; TRACK-TBI: from 0.55 to 0.80). Contributions of individual pillars varied by endpoint, with the Biomarker pillar contributing most for CT-defined abnormalities and the Imaging pillar to major cranial surgery. For mortality and unfavorable outcome (GOSE ≤ 4) similarly strong performance was observed: CENTER-TBI: AUC 0.89 (95% Confidence intervals (CI) [0.88,0.90]) and 0.92 (95% CI [0.91,0.94]) respectively; pseudo-R2 0.53 (95% CI [0.50,0.57]) and 0.55 (95% CI [0.51,0.59]) respectively; TRACK-TBI: AUC 0.91 (95% CI [0.89,0.93]) and 0.95 (95% CI [0.93,0.97]) respectively; pseudo-R2 0.57 (95% CI [0.52,0.62] and 0.57 (95% CI [0.50, 0.64] respectively), whereas performance for HRQoL was modest. Compared to the IMPACT-Lab and CRASH-CT models in their respective target populations, pseudo-R2 values suggest that the full CBI-M model was more strongly associated with 6-month mortality and unfavorable outcomes. Methodological limitations include the restriction to analysis of data from high-income settings, limiting generalizability, the absence of three elements of the modifier pillar in the datasets accessed, and risk of possible reverse causality in some analyses. Conclusions: CBI-M provides a robust multimodal characterization of acute TBI that aligns closely with clinical decision-making and outcomes. Further prospective validation and optimization are justified and needed to inform implementation of the CBI-M Framework in clinical practice and research. Why was this study done?: What did the researchers do and find?: What do these findings mean?: Dana Pisică, Ana Mikolić, John K. Yue and colleagues find that the CBI-M Framework is associated with clinically relevant endpoints for patients with traumatic brain injury, including diagnosis, hospital care pathways, and outcomes at six months. Further optimisation and validation is needed before implementation in clinical practice and research.
SN - 1549-1676
SN - 1549-1277
ER -