Prior traumatic brain injury is a risk factor for in-hospital mortality in moderate to severe traumatic brain injury: a TRACK-TBI cohort study.

OBJECTIVES: An estimated 14-23% of patients with traumatic brain injury (TBI) incur multiple lifetime TBIs. The relationship between prior TBI and outcomes in patients with moderate to severe TBI (msTBI) is not well delineated. We examined the associations between prior TBI, in-hospital mortality, and outcomes up to 12 months after injury in a prospective US msTBI cohort. METHODS: Data from hospitalized subjects with Glasgow Coma Scale score of 3-12 were extracted from the Transforming Research and Clinical Knowledge in Traumatic Brain Injury Study (enrollment period: 2014-2019). Prior TBI with amnesia or alteration of consciousness was assessed using the Ohio State University TBI Identification Method. Competing risk regressions adjusting for age, sex, psychiatric history, cranial injury and extracranial injury severity examined the associations between prior TBI and in-hospital mortality, with hospital discharged alive as the competing risk. Adjusted HRs (aHR (95% CI)) were reported. Multivariable logistic regressions assessed the associations between prior TBI, mortality, and unfavorable outcome (Glasgow Outcome Scale-Extended score 1-3 (vs. 4-8)) at 3, 6, and 12 months after injury. RESULTS: Of 405 acute msTBI subjects, 21.5% had prior TBI, which was associated with male sex (87.4% vs. 77.0%, p=0.037) and psychiatric history (34.5% vs. 20.7%, p=0.010). In-hospital mortality was 10.1% (prior TBI: 17.2%, no prior TBI: 8.2%, p=0.025). Competing risk regressions indicated that prior TBI was associated with likelihood of in-hospital mortality (aHR=2.06 (1.01-4.22)), but not with hospital discharged alive. Prior TBI was not associated with mortality or unfavorable outcomes at 3, 6, and 12 months. CONCLUSIONS: After acute msTBI, prior TBI history is independently associated with in-hospital mortality but not with mortality or unfavorable outcomes within 12 months after injury. This selective association underscores the importance of collecting standardized prior TBI history data early after acute hospitalization to inform risk stratification. Prospective validation studies are needed. LEVEL OF EVIDENCE: IV. TRIAL REGISTRATION NUMBER: NCT02119182

Phenotypic spectrum and transcriptomic profile associated with germline variants in TRAF7

PURPOSE: Somatic variants in tumor necrosis factor receptor-associated factor 7 (TRAF7) cause meningioma, while germline variants have recently been identified in seven patients with developmental delay and cardiac, facial, and digital anomalies. We aimed to define the clinical and mutational spectrum associated with TRAF7 germline variants in a large series of patients, and to determine the molecular effects of the variants through transcriptomic analysis of patient fibroblasts. METHODS: We performed exome, targeted capture, and Sanger sequencing of patients with undiagnosed developmental disorders, in multiple independent diagnostic or research centers. Phenotypic and mutational comparisons were facilitated through data exchange platforms. Whole-transcriptome sequencing was performed on RNA from patient- and control-derived fibroblasts. RESULTS: We identified heterozygous missense variants in TRAF7 as the cause of a developmental delay-malformation syndrome in 45 patients. Major features include a recognizable facial gestalt (characterized in particular by blepharophimosis), short neck, pectus carinatum, digital deviations, and patent ductus arteriosus. Almost all variants occur in the WD40 repeats and most are recurrent. Several differentially expressed genes were identified in patient fibroblasts. CONCLUSION: We provide the first large-scale analysis of the clinical and mutational spectrum associated with the TRAF7 developmental syndrome, and we shed light on its molecular etiology through transcriptome studies

Traumatic brain injury: integrated approaches to improve prevention, clinical care, and research

No abstract available

Accelerating a random forest classifier: multi-core, GP-GPU, or FPGA? Accelerating a random forest classifier: multi-core, GP-GPU, or FPGA?

Abstract-Random forest classification is a well known machine learning technique that generates classifiers in the form of an ensemble ("forest") of decision trees. The classification of an input sample is determined by the majority classification by the ensemble. Traditional random forest classifiers can be highly effective, but classification using a random forest is memory bound and not typically suitable for acceleration using FPGAs or GP-GPUs due to the need to traverse large, possibly irregular decision trees. Recent work at Lawrence Livermore National Laboratory has developed several variants of random forest classifiers, including the Compact Random Forest (CRF), that can generate decision trees more suitable for acceleration than traditional decision trees. Our paper compares and contrasts the effectiveness of FPGAs, GP-GPUs, and multi-core CPUs for accelerating classification using models generated by compact random forest machine learning classifiers. Taking advantage of training algorithms that can produce compact random forests composed of many, small trees rather than fewer, deep trees, we are able to regularize the forest such that the classification of any sample takes a deterministic amount of time. This optimization then allows us to execute the classifier in a pipelined or single-instruction multiple thread (SIMT) fashion. We show that FPGAs provide the highest performance solution, but require a multi-chip / multi-board system to execute even modest sized forests. GP-GPUs offer a more flexible solution with reasonably high performance that scales with forest size. Finally, multi-threading via OpenMP on a shared memory system was the simplest solution and provided near linear performance that scaled with core count, but was still significantly slower than the GP-GPU and FPGA