Derivation of a Three Biomarker Panel to Improve Diagnosis in Patients with Mild Traumatic Brain Injury - Peacock, W. Frank; Van Meter, Timothy E.; Mirshahi, Nazanin; Ferber, Kyle; Gerwien, Robert; Rao, Vani; Sair, Haris Iqbal; Diaz-Arrastia, Ramon; Korley, Frederick K. (Frontiers in Neurology, 2017)
Background: Nearly 5 million emergency department (ED) visits for head injury occur each year in the United States, of which <10% of patients show abnormal CT findings. CT negative patients frequently suffer protracted somatic, behavioral, and neurocognitive dysfunction. Our goal was to evaluate biomarkers to identify mild TBI (mTBI) in patients with suspected head injury. Methods: An observational ED study of head injured and control patients was conducted at Johns Hopkins University (HeadSMART). Head CT was obtained (ACEP criteria) in patients with GCS scores of 13-15 and aged 18-80. Three candidate biomarker proteins, Neurogranin (NRGN), Neuron Specific Enolase (NSE), and Metallothionein 3 (MT3), were evaluated by immunoassay (samples < 24 hrs from injury). American Congress of Rehabilitation Medicine (ACRM) criteria were used for diagnosis of mTBI patients for model building. Univariate analysis, logistic regression and random forest algorithms were used for data analysis in R. Overall, 662 patients were studied. Statistical models were built using 328 healthy controls, and 179 mild TBI patients. Results: Median time from injury was 5.9 hours (IQR, 4.0; range 0.8-24 hours). MTBI patients had elevated NSE, but decreased MT3 vs. controls (p <0.01 for each). NRGN was also elevated but within 2-6 hours after injury. In the derivation set, the best model to distinguish mTBI from healthy controls used 3 markers, age, and sex as covariates (C-statistic = 0.91, sensitivity 98%, specificity 72%). Panel test accuracy was validated with the 155 remaining ACRM+ mTBI patients. Applying the random forest model to the ACRM+ mTBI validation set resulted in 78% correctly classified as mTBI (119/153). CT positive and CT negative validation subsets were 91% and 75% correctly classified. In samples taken < 2 hours from injury, 100 % (10/10) samples classified correctly, indicating that hyper-acute testing is possible with these biomarker assays. The model accuracy varied from 72-100% overall, and had greater accuracy with increasing severity, as shown by comparing CT+ with CT – (91% vs. 75%), and Injury Severity Score (ISS) ≥16 versus <16 (88% vs. 72%, respectively). Objective blood tests, detecting NRGN, NSE, and MT3, can be used to identify mTBI, irrespective of neuroimaging