Tignanelli, Christopher J.

Background The optimal timing and type of venous thromboembolism chemoprophylaxis (VTEp) for adults with acute traumatic brain injury (TBI) remains unknown. This systematic review synthesized evidence on the timing and type of VTEp in adults with TBI and highlights evidence gaps. Methods We searched Ovid MEDLINE, Embase, and the Cochrane Library through November 4, 2024 for English language, randomized or non-randomized studies with a comparator that reported VTEp timing or agent in adults with acute TBI, and reported intracranial hemorrhage progression (ICHP), VTE, pulmonary embolism (PE), deep vein thrombosis (DVT), neurosurgical intervention (NSI), adverse events (AEs), length of stay (LOS), function, or mortality. Risk of bias (ROB) was assessed with ROBINS-I as low, moderate, serious, or critical risk in studies that used advanced analytic methods (AAMs) to control selection bias; all other studies were deemed critical risk. We qualitatively synthesized evidence and focused text reporting on studies with AAM. Results From 738 citations, 21 non-randomized studies met criteria (1 moderate, 20 critical ROB): 16 on VTEp timing, 7 on agents. Seven studies used AAM. For VTEp timing (five AAM studies), one study of adults with mostly mild TBI found no significant difference between Early and Late VTEp on ICHP (moderate ROB); no timing studies with AAM reported ICHP for adults with moderate or severe TBI. Findings were mixed for VTE timing on NSI and PE. Early VTEp was associated with fewer DVTs. There was no difference in mortality or serious AEs by VTEp timing. For VTEp agents, three studies with AAM found no significant difference between low molecular weight heparin (LMWH) and unfractionated heparin (UH) on ICHP, PE, DVT, and serious or overall AEs. Results were mixed for LMWH versus UH effects on NSI after VTEp, VTE, LOS and mortality. TBI severity labeling varied across studies and within measures, reducing comparability. Conclusions Clinical evidence on the timing and type of VTEp for adults with acute TBI is of insufficient quality for clinical decision-making. Prospective research designs, standardization of TBI severity labeling, and improved reporting of interventions and outcomes would advance the field. PROSPERO registration number CRD42023421534 Level of evidence Systematic Review, Level IV

AbstractObjectiveWith COVID-19, there was a need for a rapidly scalable annotation system that facilitated real-time integration with clinical decision support systems (CDS). Current annotation systems suffer from a high-resource utilization and poor scalability limiting real-world integration with CDS. A potential solution to mitigate these issues is to use the rule-based gazetteer developed at our institution.Materials and MethodsPerformance, resource utilization, and runtime of the rule-based gazetteer were compared with five annotation systems: BioMedICUS, cTAKES, MetaMap, CLAMP, and MedTagger.ResultsThis rule-based gazetteer was the fastest, had a low resource footprint, and similar performance for weighted microaverage and macroaverage measures of precision, recall, and f1-score compared to other annotation systems.DiscussionOpportunities to increase its performance include fine-tuning lexical rules for symptom identification. Additionally, it could run on multiple compute nodes for faster runtime.ConclusionThis rule-based gazetteer overcame key technical limitations facilitating real-time symptomatology identification for COVID-19 and integration of unstructured data elements into our CDS. It is ideal for large-scale deployment across a wide variety of healthcare settings for surveillance of acute COVID-19 symptoms for integration into prognostic modeling. Such a system is currently being leveraged for monitoring of postacute sequelae of COVID-19 (PASC) progression in COVID-19 survivors. This study conducted the first in-depth analysis and developed a rule-based gazetteer for COVID-19 symptom extraction with the following key features: low processor and memory utilization, faster runtime, and similar weighted microaverage and macroaverage measures for precision, recall, and f1-score compared to industry-standard annotation systems.