Digital early-warning system to predict acute lung injury after major liver surgery
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 will try a machine-learning based warning tool to predict early acute lung injury in adults having major liver surgery.
Quick facts
| Study type | Observational |
|---|---|
| Enrollment | 4000 (estimated) |
| Ages | 18 Years and up |
| Sex | All |
| Sponsor | Beijing Tsinghua Chang Gung Hospital Academic / other |
| Locations | 1 site (Beijing, Beijing Municipality) |
| Trial ID | NCT07070362 on ClinicalTrials.gov |
What this trial studies
Researchers will collect preoperative, intraoperative, and postoperative cardiopulmonary interaction data from adults undergoing major liver operations at a single center. They will train and validate explainable machine-learning models — including logistic regression, random forest, support vector machines, and neural networks — to predict acute lung injury. Model performance will be measured with accuracy, sensitivity, specificity, and ROC curves, and interpretability methods will be used to explain key contributing features. The goal is to create a reliable digital early-warning system that can support clinical diagnosis and treatment decisions to reduce ALI incidence and mortality.
Who should consider this trial
Good fit: Adults aged 18 or older who are scheduled for major liver surgery (such as two-segment-or-more hepatectomy or liver transplantation) and can give informed consent are the intended participants.
Not a fit: Patients under 18, those not undergoing major liver surgery, or those lacking the required perioperative monitoring data are unlikely to benefit from this model.
Why it matters
Potential benefit: If successful, the system could alert clinicians earlier to impending acute lung injury and help reduce its incidence and related deaths after major liver surgery.
How similar studies have performed: Machine-learning approaches have shown promise for predicting ARDS/ALI in critical care settings, but applying an explainable cardiopulmonary interaction model specifically for major liver surgery is relatively novel.
Eligibility criteria
Show full inclusion / exclusion criteria
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 this trial is running
Beijing, Beijing Municipality
- Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua Medicine，Tsinghua University — Beijing, Beijing Municipality, China (Recruiting)
Study contacts
- Study coordinator: Gao Zhifeng, MD
- Email: btchgzf@hotmail.com
- Phone: +8615801249466
How to participate
- Review the eligibility criteria above with your treating physician.
- Visit the official trial page on ClinicalTrials.gov for the most current contact information and recruitment status.
- Contact the listed study coordinator or principal investigator to request pre-screening. Pre-screening is free and never obligates you to enroll.