Digital Early Warning System for Acute Lung Injury in Liver Surgery
About this study
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.
- Condition
- 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
- Sponsor
- Beijing Tsinghua Chang Gung Hospital
Who can join
- Age
- 18 years and older
- Sex
- All sexes
- Healthy volunteers
- Not accepted
Inclusion 5
- Minimum age: 18 years
- Study condition: 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
- Age ≥ 18 years
- Undergoing major liver surgery (including two-segment or more hepatectomy, liver transplantation, etc.)
- Voluntary participation with signed informed consent
Where
4 sites, 4 recruiting
Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua Medicine，Tsinghua University
Beijing, Beijing Municipality, China
Peking University International Hospital
Beijing, China
Southwest Hospital, The First Affiliated Hospital of Army Medical University
Chongqing, China
Huangdao District People's Hospital of Qingdao
Qingdao, China
Contact
-
Gao Zhifeng, MD
+8615801249466 btchgzf@hotmail.com
Potential match only. Final eligibility is determined by the study team.