Articles: traumatic-brain-injuries.
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Traumatic brain injury (TBI) disproportionately affects low- and middle-income countries (LMICs). In these settings, accurate patient prognostication is both difficult and essential for high-quality patient care. With the ultimate goal of enhancing TBI triage in LMICs, we aim to develop the first deep learning model to predict outcomes after TBI and compare its performance with that of less complex algorithms. ⋯ We present the first use of deep learning for TBI prognostication, with an emphasis on LMICs, where there is great need for decision support to allocate limited resources. Optimal algorithm selection depends on the specific clinical setting; deep learning is not a panacea, though it may have a role in these efforts.
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Journal of neurosurgery · Aug 2022
A multicenter cohort study of early complications after cranioplasty: results of the German Cranial Reconstruction Registry.
Cranioplasty (CP) is a crucial procedure after decompressive craniectomy and has a significant impact on neurological improvement. Although CP is considered a standard neurosurgical procedure, inconsistent data on surgery-related complications after CP are available. To address this topic, the authors analyzed 502 patients in a prospective multicenter database (German Cranial Reconstruction Registry) with regard to early surgery-related complications. ⋯ The authors have presented class II evidence-based data on surgery-related complications after CP and have identified specific preexisting risk factors. These results may provide additional guidance for optimized treatment of these patients.
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Clinical Trial
Clustering identifies endotypes of traumatic brain injury in an intensive care cohort: a CENTER-TBI study.
While the Glasgow coma scale (GCS) is one of the strongest outcome predictors, the current classification of traumatic brain injury (TBI) as 'mild', 'moderate' or 'severe' based on this fails to capture enormous heterogeneity in pathophysiology and treatment response. We hypothesized that data-driven characterization of TBI could identify distinct endotypes and give mechanistic insights. ⋯ Six stable and clinically distinct TBI endotypes were identified by probabilistic unsupervised clustering. In addition to presenting neurology, a profile of biochemical derangement was found to be an important distinguishing feature that was both biologically plausible and associated with outcome. Our work motivates refining current TBI classifications with factors describing metabolic stress. Such data-driven clusters suggest TBI endotypes that merit investigation to identify bespoke treatment strategies to improve care. Trial registration The core study was registered with ClinicalTrials.gov, number NCT02210221 , registered on August 06, 2014, with Resource Identification Portal (RRID: SCR_015582).