Digital Early Warning System for Acute Lung Injury in Liver Surgery

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.

NCT Number	NCT07070362
Lead Sponsor	Beijing Tsinghua Chang Gung Hospital
Conditions	Acute Lung Injury(ALI), Liver Cirrhosis, ARDS, Human, MASLD, MASLD/MASH (Metabolic Dysfunction-Associated Steatotic Liver Disease / Metabolic Dysfunction-Associated Steatohepatitis), NAFLD (Nonalcoholic Fatty Liver Disease), Liver Cancer, Adult
Enrollment	4,000 participants
Start Date	2024-11-01
Primary Completion	2027-06-01 (estimated)
Study Completion	2027-11-30 (estimated)
Updated on ClinicalTrials.gov	2025-07-17