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 aims to develop an explainable machine learning model that takes into account the characteristics of cardiopulmonary interactions. This model will enable early prediction of acute lung injury (ALI) in patients undergoing major liver surgery. The research will create a digital early-warning system for ALI, thereby supporting clinical diagnosis and treatment decisions. This, in turn, should help reduce the incidence and mortality rates associated with 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

Not listed

Trial groups

3,000 Participants

are grouped into 2 trial groups

Sponsors and collaborators

Beijing Tsinghua Chang Gung Hospital

Lead sponsor

Huangdao District People's Hospital of Qingdao

Collaborator

Peking University International Hospital

Collaborator

The First Affiliated Hospital of Army Medical University (Southwest Hospital)

Collaborator

Source ClinicalTrials.gov