Events Calendar

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12:00 AM - TEDMED 2017
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TEDMED 2017
2017-11-01 - 2017-11-03    
All Day
A healthy society is everyone’s business. That’s why TEDMED speakers are thought leaders and accomplished individuals from every sector of society, both inside and outside [...]
AMIA 2017 Annual Symposium
2017-11-04 - 2017-11-08    
All Day
Call for Participation We invite you to contribute your best work for presentation at the AMIA Annual Symposium – the foremost symposium for the science [...]
Beverly Hills Health IT Summit
2017-11-09 - 2017-11-10    
All Day
About Health IT Summits U.S. healthcare is at an inflection point right now, as policy mandates and internal healthcare system reform begin to take hold, [...]
Forbes Healthcare Summit
2017-11-29 - 2017-11-30    
All Day
ForbesLive leverages unique access to the world’s most influential leaders, policy-makers, entrepreneurs, and artists—uniting these global forces to harness their collective knowledge, address today’s critical [...]
Events on 2017-11-01
TEDMED 2017
1 Nov 17
La Quinta
Events on 2017-11-04
AMIA 2017 Annual Symposium
4 Nov 17
WASHINGTON
Events on 2017-11-09
Beverly Hills Health IT Summit
9 Nov 17
Los Angeles
Events on 2017-11-29
Forbes Healthcare Summit
29 Nov 17
New York
Latest News

Leveraging AI to Improve ER Outcomes, Save Lives

Globally, about 4.5 million individuals succumb to traumatic injuries annually, often due to severe blood loss.

Administering tranexamic acid early can mitigate excessive bleeding by impeding blood clot breakdown. However, as this drug may induce adverse effects unnecessarily, precise patient selection based on objective criteria is crucial.

In a recent Critical Care publication, Osaka University researchers tackled this challenge by identifying trauma patient subgroups that could benefit most from tranexamic acid treatment. They discerned these subgroups by analyzing shared characteristics, termed phenotypes.

Lead author Jotaro Tachino elaborated, “We identified eight distinct trauma phenotypes and assessed the efficacy of tranexamic acid treatment across these groups.” They observed notably lower in-hospital mortality rates among certain patient subgroups receiving tranexamic acid, while others derived no advantage from the treatment.

Leveraging a machine learning model, the team categorized trauma patients into these subgroups. Analyzing data from over 50,000 patients in the Japan Trauma Data Bank, they discerned patterns correlating trauma, treatment, and survival.

The study revealed a correlation between trauma phenotypes and in-hospital mortality, suggesting that tranexamic acid treatment could influence this relationship.

The researchers emphasized the heterogeneous nature of trauma patients, whose injuries vary widely in type and severity, making individual treatment efficacy prediction challenging. They anticipate their findings will facilitate personalized care for trauma patients and enhance overall treatment quality.

Given the significant toll of traumatic injuries, strategies enhancing survival are paramount. This research represents a pivotal advancement in optimizing tranexamic acid utilization among trauma patients.