Utility of Acute and Subacute Blood Biomarkers to Assist Diagnosis in CT-Negative Isolated Mild Traumatic Brain Injury
Bibliographic record
Abstract
Objectives : Blood biomarkers GFAP and UCH-L1 have recently been FDA approved as predictors of intracranial lesions on CT after mild traumatic brain injury (mTBI). However, the vast majority of mTBI cases are CT negative, and no biomarkers are approved to assist diagnosis in these individuals. Here we aimed to determine the optimal combination of blood biomarkers to assist mTBI diagnosis in otherwise healthy adults aged under 50 presenting to an ED within 6h of injury. To further understand the utility of biomarkers, we assessed how biological sex, presence or absence of loss of consciousness and/or post traumatic amnesia (LOC/PTA), and delayed presentation, affected classification performance. Methods : Blood samples, symptom questionnaires and cognitive tests were conducted prospectively for mTBI participants recruited from The Alfred Hospital Level 1 Emergency & Trauma Centre and uninjured controls. Follow-up testing was conducted at 7 days. Simoa® quantified plasma GFAP, UCH-L1, Tau, NfL, IL-6 and IL-1β. AUC analysis assessed classification accuracy for diagnosed mTBI and logistic regression models identified optimal biomarker combinations. Results : Plasma IL-6 (AUC=0.91, 95%CI=0.86-0.96), GFAP (AUC=0.85, 95%CI=0.78-0.93) and UCH-L1 (AUC=0.79, 95%CI=0.70-0.88) best differentiated mTBI (n=74) from controls (n=44) acutely (<6h), with NfL (AUC=0.81, 95%CI=0.72-0.90) the only marker to have such utility sub-acutely (7 days). Biomarker performance was similar between sexes and for participants with and without LOC/PTA, with the exception at 7 days, where GFAP and IL-6 retained some utility in female participants (GFAP AUC=0.71, 95%CI=0.55-0.88; IL-6 AUC=0.71, 95%CI=0.55-0.87) and those with LOC/PTA (GFAP AUC=0.73, 95%CI=0.59-0.86; IL-6 AUC=0.71, 95%CI=0.57-0.84). Acute IL-6 (R2=0.50, 95%CI=0.34-0.64) outperformed GFAP and UCH-L1 combined (R2=0.35, 95%CI=0.17-0.50), with the best acute model featuring GFAP and IL-6 (R2=0.54, 95%CI=0.34-0.68). Discussion : These findings indicate that adding IL-6 to a panel of brain-specific proteins such as GFAP and UCH-L1 might assist in the acute diagnosis of mTBI in adults under 50. Multiple markers had high classification accuracy in participants without LOC/PTA. When compared with the best performing acute markers, sub-acute measures of plasma NfL resulted in minimal reduction in classification accuracy. Future studies will investigate the optimal time frame over which plasma IL-6 might assist diagnostic decisions and how extracranial trauma affects utility.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".