Digital early-warning system to predict acute lung injury after major liver surgery

Is this trial a fit for you?

A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.

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.

Why it matters

Who can join: the full rules

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's running

• Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua Medicine，Tsinghua University
  Beijing, Beijing Municipality, China Recruiting
• Peking University International Hospital
  Beijing, China Recruiting
• Southwest Hospital, The First Affiliated Hospital of Army Medical University
  Chongqing, China Recruiting
• Huangdao District People's Hospital of Qingdao
  Qingdao, China Recruiting

Study contacts

• Study coordinator: Gao Zhifeng, MD
• Email: btchgzf@hotmail.com
• Phone: +8615801249466

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How to take part

1. Review the eligibility criteria above with your treating physician.
2. Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
3. Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.