The Construction of a Digital Intelligence Early Warning System for the Whole Process of Acute Lung Injury in Liver Surgery Based on Cardiopulmonary Interaction Characteristics
Status: Recruiting
Location: See location...
Intervention Type: Other
Study Type: Observational
SUMMARY
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.
Eligibility
Participation Requirements
Sex: All
Minimum Age: 18
Healthy Volunteers: f
View:
• Age ≥ 18 years
• Undergoing major liver surgery (including two-segment or more hepatectomy, liver transplantation, etc.)
• Voluntary participation with signed informed consent
Locations
Other Locations
China
Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua Medicine，Tsinghua University
RECRUITING
Beijing
Contact Information
Primary
Gao Zhifeng, MD
btchgzf@hotmail.com
+8615801249466
Time Frame
Start Date: 2024-11-01
Estimated Completion Date: 2027-11-30
Participants
Target number of participants: 4000
Treatments
patients undergoing major liver surgury
Population: 2,497 adult patients (≥18 years) who underwent major liver surgery (≥2 segments resection or transplantation) at Beijing Tsinghua Changgung Hospital, including retrospective (2019.06-2024.05) and prospective (from 2025.12) cohorts.~Inclusion Criteria: Aged ≥18, scheduled for major liver surgery, with informed consent.~Exclusion Criteria: Refusal to participate, comorbidities affecting ALI assessment, incomplete data, failed follow-up, or concurrent trials.~Interventions: None. Observational study; clinical management follows standard protocols without study-related interventions. Data collected from routine records and monitoring.
Related Therapeutic Areas
Sponsors
Leads: Beijing Tsinghua Chang Gung Hospital