Digital Early Warning System for Lung Injury After Liver Surgery
Builds a computer model to predict acute lung injury in adults undergoing major liver surgery.
Plain-language title generated from the registry record
Registry title: Digital Early Warning System for Acute Lung Injury in Liver Surgery
Key facts
- Where
- Beijing, Chongqing, and Qingdao, ChinaSee the 4 sites
Where in China
- Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua Medicine，Tsinghua UniversityBeijing, ChinaTaking people
- Peking University International HospitalBeijing, ChinaTaking people
- Southwest Hospital, The First Affiliated Hospital of Army Medical UniversityChongqing, ChinaTaking people
- Huangdao District People's Hospital of QingdaoQingdao, ChinaTaking people
Questions to ask the study team
- How often do I need to come in, and for how long each time?
- Are travel costs paid?
- Can I keep my own doctor?
- What happens if I want to stop?
Trial details
- Who runs it
- Beijing Tsinghua Chang Gung Hospital
- Trial code
- NCT07070362
- Record updated
- 17 August 2026
- Original description
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.Read full original descriptionShow less
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
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.
Inclusion: Age ≥ 18 years; Undergoing major liver surgery (including two-segment or more hepatectomy, liver transplantation, etc.); Voluntary participation with signed informed consent.