Artificial intelligence-driven clustering for phenotyping life-threatening prehospital trauma

Area of Science:

• Emergency Medicine
• Artificial Intelligence in Healthcare
• Trauma Care

Background:

• Traumatic injuries present complex challenges for accurate risk stratification.
• Prehospital assessment of critically ill trauma patients requires advanced methodologies.

Purpose of the Study:

• To derive distinct phenotypes of prehospital acute life-threatening trauma.
• To apply unsupervised artificial intelligence (AI) clustering for trauma patient phenotyping.

Main Methods:

• Prospective, multicenter study involving 1474 adult trauma patients in Spain.
• Data collection included epidemiological variables, trauma details, vital signs, and blood tests.
• Unsupervised AI clustering was used to identify patient phenotypes.

Main Results:

• Three distinct trauma phenotypes (T-1, T-2, T-3) were identified.
• Mortality rates varied significantly across phenotypes: T-1 (93.1%), T-2 (68.1%), and T-3 (10.6%).
• Phenotypes were associated with specific injury patterns, including traumatic brain injury, thoracic trauma, burns, and orthopedic trauma.

Conclusions:

• AI-driven phenotyping offers a novel approach to characterizing trauma patients prehospital.
• Identified phenotypes have significant implications for guiding therapy and predicting outcomes.
• This method can enhance treatment and resource allocation in emergency medical services.