TY - JOUR
T1 - Derivation of a three biomarker panel to improve diagnosis in patients with mild traumatic brain injury
AU - Peacock, W. Frank
AU - Van Meter, Timothy E.
AU - Mirshahi, Nazanin
AU - Ferber, Kyle
AU - Gerwien, Robert
AU - Rao, Vani
AU - Sair, Haris Iqbal
AU - Diaz-Arrastia, Ramon
AU - Korley, Frederick K.
N1 - Publisher Copyright:
© 2017 Peacock, Van Meter, Mirshahi, Ferber, Gerwien, Rao, Sair, Diaz-Arrastia and Korley.
PY - 2017/11/30
Y1 - 2017/11/30
N2 - 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 computed tomography (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 Glasgow Coma Scale 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 h 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 (RF) algorithms were used for data analysis in R. Overall, 662 patients were studied. Statistical models were built using 328 healthy controls and 179 mTBI patients. Results: Median time from injury was 5.9 h (IQR, 4.0; range 0.8-24 h). mTBI patients had elevated NSE, but decreased MT3 versus controls (p < 0.01 for each). NRGN was also elevated but within 2-6 h after injury. In the derivation set, the best model to distinguish mTBI from healthy controls used three 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 RF 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 h from injury, 100% (10/10) samples classified correctly, indicating that hyperacute 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% versus 75%), and Injury Severity Score ≥16 versus < 16 (88% versus 72%, respectively). Objective blood tests, detecting NRGN, NSE, and MT3, can be used to identify mTBI, irrespective of neuroimaging findings.
AB - 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 computed tomography (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 Glasgow Coma Scale 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 h 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 (RF) algorithms were used for data analysis in R. Overall, 662 patients were studied. Statistical models were built using 328 healthy controls and 179 mTBI patients. Results: Median time from injury was 5.9 h (IQR, 4.0; range 0.8-24 h). mTBI patients had elevated NSE, but decreased MT3 versus controls (p < 0.01 for each). NRGN was also elevated but within 2-6 h after injury. In the derivation set, the best model to distinguish mTBI from healthy controls used three 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 RF 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 h from injury, 100% (10/10) samples classified correctly, indicating that hyperacute 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% versus 75%), and Injury Severity Score ≥16 versus < 16 (88% versus 72%, respectively). Objective blood tests, detecting NRGN, NSE, and MT3, can be used to identify mTBI, irrespective of neuroimaging findings.
KW - Biomarker
KW - Machine learning
KW - Metallothionein 3
KW - Mild TBI
KW - Mild brain injury
KW - Neurogranin
KW - Neuron-specific enolase
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U2 - 10.3389/fneur.2017.00641
DO - 10.3389/fneur.2017.00641
M3 - Article
C2 - 29250027
AN - SCOPUS:85036647495
SN - 1664-2295
VL - 8
JO - Frontiers in Neurology
JF - Frontiers in Neurology
IS - NOV
M1 - 641
ER -