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Updated: Oct 3, 2026

Systems Analysis of the Neuroinflammatory and Hemodynamic Response to Traumatic Brain Injury
Published on: May 27, 2022
Quantification of blood-based neurovascular unit biomarkers: an analytical platform comparison study
Chloe P Allen1, Dorothée J Goulet1, Sophie Stukas1,2
1Division of Critical Care Medicine, Department of Medicine, Vancouver General Hospital, West 12th Avenue, University of British Columbia, Vancouver, BC, Canada.
Background:
Hypoxic ischemic brain injury (HIBI) remains a significant cause of morbidity and mortality in patients following cardiac arrest. Many international guidelines for HIBI prognostication advocate for a multimodal approach with inclusion of blood-based biomarkers. However, advancements in analytical technology have increased uncertainty in selecting the optimal quantification tool and impeded application of these results across platforms in accordance with amalgamated prognostication thresholds.
Methods:
We conducted a post hoc analysis of a prospective observational cohort study in 32 critically ill patients undergoing withdrawal of life-sustaining therapies. Serum and plasma glial fibrillary acidic protein (GFAP), neurofilament-light (Nf-L), tau, and ubiquitin carboxyl hydrolase L-1 (UCH-L1) were quantified on the Simoa HD-X and ARGO HT analytical platforms respectively. Plasma GFAP and UCH-L1 samples were additionally quantified using the point-of-care i-STAT Alinity.
Results:
GFAP, Nf-L, Tau, and UCH-L1 were highly correlated between the ARGO HT and Simoa HD-X analytical platforms (all Pearson's r ≥ 0.97, P ≤ 4.5e-13). In contrast, absolute agreement between the i-STAT Alinity and Simoa HD-X platforms was poor-to-moderate for GFAP (Intraclass correlation coefficient; ICC: 0.25 [-0.14, 0.59], P = 0.11) and poor-to-good for UCH-L1 (ICC: 0.60 [0.20, 0.82], P = 0.0026). The ARGO HT demonstrated strong inter-analyzer correlation with the i-STAT Alinity for GFAP (r = 0.98, P = 6.2e-18) and moderate correlation for UCH-L1 (r = 0.66, P = 0.00022).
Conclusion:
Collectively, this cross-platform validation highlights the importance of device-specific analytical methods and prognostication thresholds for each analytical platform considered for use in clinical practice.