56 research outputs found
Sudden-onset disaster mass-casualty incident response: a modified delphi study on triage, prehospital life support, and processes
Get PDFhis study is supported by the NIGHTINGALE project ‘Novel InteGrated toolkit for enhanced prehospital life support and Triage IN challenging And Large Emergencies.’ This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 101021957.Cuthbertson J., Weinstein E., Franc J.M., Jones P., Lamine H., Magalini S., Gui D., Lennquist K., Marzi F., Borrello A., Fransvea P., Fidanzio A., Benítez C.Y., Achaz G., Dobson B., Malik N., Neeki M., Pirrallo R., Castro Delgado R., Strapazzon G., Farah Dell'Aringa M., Brugger H., Rafalowsky C., Marzoli M., Fresu G., Kolstadbraaten K.M., Lennquist S., Tilsed J., Claudius I., Cheeranont P., Callcut R., Bala M., Kerbage A., Vale L., Hecker N.P., Faccincani R., Ragazzoni L., Caviglia M
Brasses Design compendium
No full textContains disk in pocket at backSIGLEAvailable from British Library Document Supply Centre-DSC:7053.220V(117) / BLDSC - British Library Document Supply CentreGBUnited Kingdo
High conductivity coppers for electrical engineering
No full textCD-ROM in pocket at back; this replaces CDA TN29SIGLEAvailable from British Library Document Supply Centre-DSC:7053.220V(122) / BLDSC - British Library Document Supply CentreGBUnited Kingdo
Copper The vital metal
No full textSIGLEAvailable from British Library Document Supply Centre-DSC:7053.220(121) / BLDSC - British Library Document Supply CentreGBUnited Kingdo
Improving the outcomes from ruptured abdominal aortic aneurysm: interdisciplinary best practice guidelines
Get PDFCitrated kaolin thrombelastography (TEG) thresholds for goal-directed therapy in injured patients receiving massive transfusion
Get PDFIntroductionGoal-directed hemostatic resuscitation based on thrombelastography (TEG) has a survival benefit compared with conventional coagulation assays such as international normalized ratio, activated partial thromboplastin time, fibrinogen level, and platelet count. While TEG-based transfusion thresholds for patients at risk for massive transfusion (MT) have been defined using rapid TEG, cutoffs have not been defined for TEG using other activators such as kaolin. The purpose of this study was to develop thresholds for blood product transfusion using citrated kaolin TEG (CK-TEG) in patients at risk for MT.MethodsCK-TEG was assessed in trauma activation patients at two Level 1 trauma centers admitted between 2010 and 2017. Receiver operating characteristic (ROC) curve analyses were performed to test the predictive performance of CK-TEG measurements in patients requiring MT, defined as >10 units of red blood cells or death within the first 6 hours. The Youden Index defined optimal thresholds for CK-TEG-based resuscitation.ResultsOf the 825 trauma activations, 671 (81.3%) were men, 419 (50.8%) suffered a blunt injury, and 62 (7.5%) received a MT. Patients who had a MT were more severely injured, had signs of more pronounced shock, and more abnormal coagulation assays. CK-TEG R-time was longer (4.9 vs. 4.4 min, p = 0.0084), angle was lower (66.2 vs. 70.3 degrees, p < 0.0001), maximum amplitude was lower in MT (57 vs. 65.5 mm, p < 0.0001), and LY30 was greater (1.8% vs. 1.2%, p = 0.0012) in patients with MT compared with non-MT. To predict MT, R-time yielded an area under the ROC curve (AUROC) = 0.6002 and a cut point of >4.45 min. Angle had an AUROC = 0.6931 and a cut point of <67 degrees. CMA had an AUROC = 0.7425, and a cut point of <60 mm. LY30 had an AUROC = 0.623 with a cut point of >4.55%.ConclusionWe have identified CK-TEG thresholds that can guide MT in trauma. We propose plasma transfusion for R-time >4.45 min, fibrinogen products for an angle <67 degrees, platelet transfusion for MA <60 mm, and antifibrinolytics for LY30 >4.55%.Level of evidenceTherapeutic study, level V
Recommended from our members
Statistical machines for trauma hospital outcomes research: Application to the PRospective, Observational, Multi-center Major trauma Transfusion (PROMMTT) study
No full text© 2015 Moore et al.This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.I
All Massive Transfusion Criteria Are Not Created Equal: Defining the Predictive Value of Individual Transfusion Triggers to Better Determine Who Benefits From Blood
No full textArtificial neural networks can predict trauma volume and acuity regardless of center size and geography: A multicenter study
Get PDFBackgroundTrauma has long been considered unpredictable. Artificial neural networks (ANN) have recently shown the ability to predict admission volume, acuity, and operative needs at a single trauma center with very high reliability. This model has not been tested in a multicenter model with differing climate and geography. We hypothesize that an ANN can accurately predict trauma admission volume, penetrating trauma admissions, and mean Injury Severity Score (ISS) with a high degree of reliability across multiple trauma centers.MethodsThree years of admission data were collected from five geographically distinct US Level I trauma centers. Patients with incomplete data, pediatric patients, and primary thermal injuries were excluded. Daily number of traumas, number of penetrating cases, and mean ISS were tabulated from each center along with National Oceanic and Atmospheric Administration data from local airports. We trained a single two-layer feed-forward ANN on a random majority (70%) partitioning of data from all centers using Bayesian Regularization and minimizing mean squared error. Pearson's product-moment correlation coefficient was calculated for each partition, each trauma center, and for high- and low-volume days (>1 standard deviation above or below mean total number of traumas).ResultsThere were 5,410 days included. There were 43,380 traumas, including 4,982 penetrating traumas. The mean ISS was 11.78 (SD = 6.12). On the training partition, we achieved R = 0.8733. On the testing partition (new data to the model), we achieved R = 0.8732, with a combined R = 0.8732. For high- and low-volume days, we achieved R = 0.8934 and R = 0.7963, respectively.ConclusionAn ANN successfully predicted trauma volumes and acuity across multiple trauma centers with very high levels of reliability. The correlation was highest during periods of peak volume. This can potentially provide a framework for determining resource allocation at both the trauma system level and the individual hospital level.Level of evidenceCare Management, level IV
- …
