Dataset Information

Early measurement of interleukin-10 predicts the absence of CT scan lesions in mild traumatic brain injury.

ABSTRACT: Traumatic brain injury is a common event where 70%-90% will be classified as mild TBI (mTBI). Among these, only 10% will have a brain lesion visible via CT scan. A triage biomarker would help clinicians to identify patients with mTBI who are at risk of developing a brain lesion and require a CT scan. The brain cells damaged by the shearing, tearing and stretching of a TBI event set off inflammation cascades. These cause altered concentrations of a high number of both pro-inflammatory and anti-inflammatory proteins. This study aimed to discover a novel diagnostic biomarker of mTBI by investigating a broad panel of inflammation biomarkers and their capacity to correctly identify CT-positive and CT-negative patients. Patients enrolled in this study had been diagnosed with mTBI, had a GCS score of 15 and suffered from at least one clinical symptom. There were nine patients in the discovery group, 45 for verification, and 133 mTBI patients from two different European sites in the validation cohort. All patients gave blood samples, underwent a CT scan and were dichotomised into CT-positive and CT-negative groups for statistical analyses. The ability of each protein to classify patients was evaluated with sensitivity set at 100%. Three of the 92 inflammation proteins screened-MCP-1, MIP-1alpha and IL-10 -were further investigated in the verification group, and at 100% sensitivity their specificities reached 7%, 0% and 31%, respectively. IL-10 was validated on a larger cohort in comparison to the most studied mTBI diagnostic triage protein to date, S100B. Levels of both proteins were significantly higher in CT-positive than in CT-negative patients (p < 0.001). S100B's specificity at 100% sensitivity was 18% (95% CI 10.8-25.2), whereas IL-10 reached a specificity of 27% (95% CI 18.9-35.1). These results showed that IL-10 might be an interesting and clinically useful diagnostic tool, capable of differentiating between CT-positive and CT-negative mTBI patients.

SUBMITTER: Lagerstedt L

PROVIDER: S-EPMC5821397 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Early measurement of interleukin-10 predicts the absence of CT scan lesions in mild traumatic brain injury.

Lagerstedt Linnéa L Egea-Guerrero Juan José JJ Rodríguez-Rodríguez Ana A Bustamante Alejandro A Montaner Joan J El Rahal Amir A Andereggen Elisabeth E Rinaldi Lara L Sarrafzadeh Asita A Schaller Karl K Sanchez Jean-Charles JC

PloS one 20180221 2

Traumatic brain injury is a common event where 70%-90% will be classified as mild TBI (mTBI). Among these, only 10% will have a brain lesion visible via CT scan. A triage biomarker would help clinicians to identify patients with mTBI who are at risk of developing a brain lesion and require a CT scan. The brain cells damaged by the shearing, tearing and stretching of a TBI event set off inflammation cascades. These cause altered concentrations of a high number of both pro-inflammatory and anti-in ...[more]

PMID: 29466474

Similar Datasets

Project description:The purpose of this study was to explore the effects of CT slice thickness, reconstruction algorithm, and radiation dose on quantification of CT features to characterize lung nodules using a chest phantom. Spherical lung nodule phantoms of known densities (-630 and + 100 HU) were inserted into an anthropomorphic thorax phantom. CT scan was performed ten times with relocations. CT data were reconstructed using 12 different imaging settings; three different slice thicknesses of 1.25, 2.5, and 5.0 mm, two reconstruction kernels of sharp and standard, and two radiation dose of 30 mAs and 12 mAs. Lesions were segmented using a semiautomated method. Twenty representative CT quantitative features representing CT density and texture were compared using multiple regression analysis. In 100 HU nodule phantoms, 18 and 19 among 20 computer features showed significant difference between different mAs and reconstruction algorithms, respectively (p ≤ 0.05). 20, 19, and 19 computer features showed difference between slice thickness of 5.0 vs 1.25, 5.0 vs 2.5, and 2.5 vs 1.25 mm, respectively (p ≤ 0.05). In -630 HU nodule phantoms, 18 and 19 showed significant difference between different mAs and reconstruction algorithms, respectively (p ≤ 0.05). 18, 11, and 17 computer features showed difference between slice thickness of 5.0 vs 1.25, 5.0 vs 2.5, and 2.5 vs 1.25 mm, respectively (p ≤ 0.05). When comparing the absolute value of regression coefficient, the effect of slice thickness in 100 HU nodule and reconstruction algorithm in -630 HU nodule was greater than the effect of remaining scan parameters. The slice thickness, mAs, and reconstruction algorithm had a significant impact on the quantitative image features. In clinical studies involving deep learning or radiomics, it should be noted that differences in values can occur when using computer features obtained from different CT scan parameters in combination. Therefore, when interpreting the statistical analysis results, it is necessary to reflect the difference in the computer features depending on the scan parameters.

Dataset Information

Early measurement of interleukin-10 predicts the absence of CT scan lesions in mild traumatic brain injury.

Publications

Early measurement of interleukin-10 predicts the absence of CT scan lesions in mild traumatic brain injury.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets