Digital Early Warning System for Acute Lung Injury in Liver Surgery

ConditionsAcute 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

Trial statusRecruiting

Trial phaseNot listed

Trial typeObservational

Biological sexAll

Age18+

SponsorBeijing Tsinghua Chang Gung Hospital

About this trial

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 criteria

Qualifiers

Age ≥ 18 years

Undergoing major liver surgery (including two-segment or more hepatectomy, liver transplantation, etc.)

Voluntary participation with signed informed consent

Disqualifiers

None

Trial design

Treatments tested in this trial

None-placebo

Treatment groups

4,000 Participants

are divided into 1 treatment group

Locations

Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua Medicine，Tsinghua University102218, BeijingBeijing Municipality, China

Sponsors and collaborators

Beijing Tsinghua Chang Gung Hospital

Lead sponsor

Source ClinicalTrials.gov