5 research outputs found
Assessing the accuracy of blood RNA profiles to identify patients with post-concussion syndrome: A pilot study in a military patient population
<div><p>Mild traumatic brain injury (mTBI) is a complex, neurophysiological condition that can have detrimental outcomes. Yet, to date, no objective method of diagnosis exists. Physical damage to the blood-brain-barrier and normal waste clearance via the lymphatic system may enable the detection of biomarkers of mTBI in peripheral circulation. Here we evaluate the accuracy of whole transcriptome analysis of blood to predict the clinical diagnosis of post-concussion syndrome (PCS) in a military cohort. Sixty patients with clinically diagnosed chronic concussion and controls (no history of concussion) were recruited (retrospective study design). Male patients (46) were split into a training set comprised of 20 long-term concussed (> 6 months and symptomatic) and 12 controls (no documented history of concussion). Models were validated in a testing set (control = 9, concussed = 5). RNA_Seq libraries were prepared from whole blood samples for sequencing using a SOLiD5500XL sequencer and aligned to hg19 reference genome. Patterns of differential exon expression were used for diagnostic modeling using support vector machine classification, and then validated in a second patient cohort. The accuracy of RNA profiles to predict the clinical diagnosis of post-concussion syndrome patients from controls was 86% (sensitivity 80%; specificity 89%). In addition, RNA profiles reveal duration of concussion. This pilot study shows the potential utility of whole transcriptome analysis to establish the clinical diagnosis of chronic concussion syndrome.</p></div
Exon expression pattern of concussion predicts clinical diagnosis.
<p>A. Hierarchical clustering shows pattern of separation between concussed and controls based on expression of 29 exons (x-axis) B. Principle component analysis plot shows the exon expression patterns separate due to concussion status. C Results of testing SVM generated model of 25 exons (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0183113#pone.0183113.t002" target="_blank">Table 2</a>) on testing data set, using a diagnosis of PCS/ concussion as the positive call. Note the accuracy was 86%, sensitivity 80% and specificity of 89%.</p
Exon expression values that give most accurate prediction of chronic concussion in testing data set.
<p>Note exon values give chromosomal location, based on RefSeq annotation guide.</p
Flow chart of study.
<p>Potential participants (62) were recruited from the Neuroscience and Rehabilitation Center–TBI Clinic at the Dwight D. Eisenhower Army Medical Hospital, Fort Gordon, Georgia. Two were found to be ineligible for the study due to moderate or severe TBI/tumor upon further review of medical chart. From the remaining 60 patients, we excluded 4 samples due to failed library QC, low RNA yield or abnormal mapping. Females were ultimately excluded from the study due to low recruitment (n = 11; one with failed library QC). Subjects (n = 46) were split into a modeling set and a second validation set. The modeling set was used to create an algorithm (Index test) for determining the clinical diagnosis of post-concussion syndrome (positive outcome). This was compared to the confirmation of cognitive symptoms consistent with post-concussion sequelae (Reference test). The algorithm was tested on the modeling data set (n = 32) and the test data set (n = 14). Performance on the modeling data was 94% accuracy, and 86% accuracy in the independent validation set.</p
Demographics of males subjects in the study.
<p>Demographics of males subjects in the study.</p