AI Article Synopsis
- The systematic review analyzed 23 studies from 2003 to 2023 on biofluid biomarkers for predicting chronic subdural hematoma (cSDH) recurrence and patient outcomes, involving 3749 patients.
- Immune cell types and inflammatory cytokines were the most researched, with a recent trend towards focusing on circulating biomarkers.
- Several biomarkers were identified as independent predictors of cSDH recurrence and outcomes, and further multicenter studies are needed to solidify these findings and enhance clinical decision-making.
Article Abstract
The present systematic scoping review aimed at mapping and analyzing the available literature on biological fluid (biofluid) biomarkers showing promise in the prediction of chronic subdural hematoma (cSDH) recurrence and the prognosis of neurological/functional patient outcome. Twenty-three studies published between 2003 and 2023 investigating a diverse range of biomarkers in hematoma fluid and/or the circulation in 3749 patients were included. Immune cell populations and inflammatory/anti-inflammatory cytokines comprised the most studied category of biomarkers displaying significant findings. A notable time trend in biomarker studies was a recent shift in research focus towards the analysis of circulating biomarkers. Several biomarkers were indicated as independent predictors of cSDH recurrence and/or functional/neurological outcome, including circulating fibrinogen degradation products (FDP), brain natriuretic peptide (BNP-1) and high-density lipoprotein (HDL), as well as blood urea nitrogen (BUN) and the ratios of blood neutrophil to lymphocyte (NLR) or red blood cell distribution width to platelet count (RPR). While studies on cSDH prognostic biomarkers have gained, in recent years, momentum, additional multicenter prospective studies are warranted to confirm and extend their findings. The identification of prognostic biofluid biomarkers in cSDH is an active field of research that may provide future tools, guiding clinical decisions and allowing for the design of treatments based on risk stratification.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10378324 | PMC |
| http://dx.doi.org/10.3390/diagnostics13142449 | DOI Listing |
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