外傷性脳損傷におけるDTIの20年 パート2：重度および中等度のTBIの系統的レビュー

Traumatic Brain Injury (TBI) is a pervasive and important public health concern. TBI can range from mild, resulting in headaches and other neurologic symptoms, to severe resulting in coma and death. Diffusion tensor imaging (DTI) offers the ability to assess tissue microstructure at a level inaccessible to classical neuroimaging methods, such as CT and structural MRI. This systematic review aims to explore studies using DTI in moderate-severe TBI (msTBI) during the 2012-2022 decade, which is the second decade of reported use. The use of DTI in mild TBI during this time period is discussed in our companion systematic review.

A systematic literature review was conducted by a medical librarian in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched the electronic databases PubMed/MEDLINE, Embase, Cochrane Library, and Web of Science from 2012 through September 28, 2022.

One hundred twenty-nine studies of moderate to severe TBI were included, which accounts for 9,609 patients. There were more longitudinal studies in 2012-2022 compared to the prior decade (25.6% vs. 13%). Fractional anisotropy (FA) and mean diffusivity (MD) were the most commonly used DTI measures. Regardless of acquisition techniques and analysis methods, the majority of studies that compared FA between those with msTBI and controls, found lower FA in msTBI patients. Lower FA was associated with worse cognitive outcomes and greater severity of TBI.

Since its first decade (2002-2012) of reported use, DTI applications to msTBI have continued to expand in both quantity and scope, including notable increases in longitudinal studies, those employing whole brain analyses, and those addressing clinical and cognitive outcomes. The most salient finding across studies remains similar to 2002-2012, that despite heterogeneity of clinical and technical features of the individual studies, lower FA is consistently identified in msTBI patients compared to controls.

https://www.crd.york.ac.uk/prospero, identifier CRD42022361318.