Need for Emergent Intervention Within 6 Hours: A Novel Prediction Model for Hospital Trauma Triage

2021 ◽  
pp. 1-13
Author(s):  
Rachel Morris ◽  
Basil S Karam ◽  
Emily J Zolfaghari ◽  
Benjamin Chen ◽  
Thomas Kirsh ◽  
...  
2020 ◽  
Vol 37 (12) ◽  
pp. 838.1-838
Author(s):  
Thomas Shanahan ◽  
Carl Marincowitz ◽  
Gordon Fuller ◽  
Trevor Sheldon ◽  
Fionn Quilty ◽  
...  

Aims/Objectives/BackgroundThis is the first external validation of a European empirically derived prediction model for identifying major trauma in an unselected group of injured patients transported by ambulance in the United Kingdom.Methods/DesignThis study was an external validation of a Dutch prediction model for identifying major trauma using a retrospective cohort of injured patients who ambulance crews transported to hospitals in the South West region of England. Major trauma was defined as Injury Severity Score (ISS)>15.Participants were patients ≥16 years with a suspected injury and transported by ambulance from February 1, 2017 to February 1, 2018. This study had a census sample of cases available to us over a one year period.We tested the accuracy of the prediction model in terms of discrimination, calibration, clinical usefulness, sensitivity and specificity and under- and over triage rates compared to existing trauma triage practices in the South West region.Results/ConclusionsA total of 68 698 adult patients were included in the final external validation cohort. The median age of patients was 72 (i.q.r. 46–84); 55.5% were female; and 524 (0.8%) had an ISS>15. In comparison the Dutch cohort was younger (45 years), more were male (58.3%) and more patients had an ISS>15. (8.8%) The model achieved good discrimination with a C-Statistic 0.75 (95% CI, 0.73 – 0.78). At a maximal specificity of 50% the model resulted in a sensitivity of 86%. The model improved undertriage rates at the expense of increased overtriage rates compared with routine trauma triage methods in the South West of England.The Dutch prediction model for identifying major trauma can lower the undertriage rate to 17%, however it would increase the overtriage rate to 50% in this UK cohort. Further research is needed to determine whether the model can be practically implemented by paramedics and is cost-effective.


2005 ◽  
Vol 173 (4S) ◽  
pp. 427-427
Author(s):  
Sijo J. Parekattil ◽  
Udaya Kumar ◽  
Nicholas J. Hegarty ◽  
Clay Williams ◽  
Tara Allen ◽  
...  

Author(s):  
Vivek D. Bhise ◽  
Thomas F. Swigart ◽  
Eugene I. Farber
Keyword(s):  

2009 ◽  
Author(s):  
Christina Campbell ◽  
Eyitayo Onifade ◽  
William Davidson ◽  
Jodie Petersen

2019 ◽  
Author(s):  
Zool Hilmi Mohamed Ashari ◽  
Norzaini Azman ◽  
Mohamad Sattar Rasul

CICTP 2020 ◽  
2020 ◽  
Author(s):  
Qianqian Liang ◽  
Xiaodong Zhang ◽  
Jinliang Xu ◽  
Yang Zhang

Author(s):  
Karunesh Makker ◽  
Prince Patel ◽  
Hrishikesh Roy ◽  
Sonali Borse

Stock market is a very volatile in-deterministic system with vast number of factors influencing the direction of trend on varying scales and multiple layers. Efficient Market Hypothesis (EMH) states that the market is unbeatable. This makes predicting the uptrend or downtrend a very challenging task. This research aims to combine multiple existing techniques into a much more robust prediction model which can handle various scenarios in which investment can be beneficial. Existing techniques like sentiment analysis or neural network techniques can be too narrow in their approach and can lead to erroneous outcomes for varying scenarios. By combing both techniques, this prediction model can provide more accurate and flexible recommendations. Embedding Technical indicators will guide the investor to minimize the risk and reap better returns.


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