Digital Early Warning System for Acute Lung Injury in Liver Surgery
Recruiting · NCT07070362 · Observational (researchers observe without assigning treatment) · Lead sponsor: Beijing Tsinghua Chang Gung Hospital
View the official record on ClinicalTrials.gov →What this trial is about
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.
Who can take part
Age range
18 Years and older
Sex
All (male and female)
Healthy volunteers
No - a diagnosis or condition is required
Phase
Not specified
Study type
Observational (researchers observe without assigning treatment)
Inclusion criteria
- Age ≥ 18 years
- Undergoing major liver surgery (including two-segment or more hepatectomy, liver transplantation, etc.)
- Voluntary participation with signed informed consent
Where it is running
8 locations listed.
- Peking University International Hospital - Beijing, China
- Peking University International Hospital - Beijing, China
- Southwest Hospital, The First Affiliated Hospital of Army Medical University - Chongqing, China
- Southwest Hospital, The First Affiliated Hospital of Army Medical University - Chongqing, China
- Huangdao District People's Hospital of Qingdao - Qingdao, China
- Huangdao District People's Hospital of Qingdao - Qingdao, China
- Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua Medicine，Tsinghua University - Beijing, Beijing Municipality, China
- Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua Medicine，Tsinghua University - Beijing, Beijing Municipality, China