Actively Recruiting
Digital Early Warning System for Acute Lung Injury in Liver Surgery
Led by Beijing Tsinghua Chang Gung Hospital · Updated on 2025-07-17
4000
Participants Needed
1
Research Sites
160 weeks
Total Duration
On this page
AI-Summary
What this Trial Is About
This study focuses on developing an explainable machine learning model based on cardiopulmonary interaction characteristics to achieve early prediction of acute lung injury (ALI) in patients undergoing major liver surgery. The research will establish a digital early-warning system for ALI to provide support for clinical diagnosis and treatment decisions, thereby reducing the incidence and fatality rate of ALI.
CONDITIONS
Official Title
Digital Early Warning System for Acute Lung Injury in Liver Surgery
Who Can Participate
Eligibility Criteria
You may qualify if you...
- Age �3e= 18 years
- Undergoing major liver surgery (including two-segment or more hepatectomy, liver transplantation, etc.)
- Voluntary participation with signed informed consent
You will not qualify if you...
History of severe allergic reactions to study medication Currently pregnant or breastfeeding Recent participation in another clinical trial within the last 30 days Presence of uncontrolled medical conditions that could affect safety
AI-Screening
AI-Powered Screening
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Trial Site Locations
Total: 1 location
1
Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua Medicine，Tsinghua University
Beijing, Beijing Municipality, China, 102218
Actively Recruiting
Research Team
G
Gao Zhifeng, MD
CONTACT
How is the study designed?
Study Type
OBSERVATIONAL
Masking
N/A
Allocation
N/A
Model
N/A
Primary Purpose
N/A
Number of Arms
1
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