Digital Early Warning System for Acute Lung Injury in Liver Surgery
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: None-placebo.
- Who it may be relevant to
- Registry conditions: Acute Lung Injury(ALI), Liver Cirrhosis, ARDS, Human, MASLD. Basic parameters: from 18 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- China
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
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
Overview
This study focuses on developing an explainable machine learning model based on cardiopulmonary interaction characteristics to achieve early prediction of acute lung injury (ALI) in patients undergoing major liver surgery. The research will establish a digital early-warning system for ALI to provide support for clinical diagnosis and treatment decisions, thereby reducing the incidence and fatality rate of ALI.
Detailed description
This study will leverage cardiopulmonary interaction parameters to predict ALI in patients undergoing major liver surgery. Specifically, the research will collect data from preoperative, intraoperative, and postoperative phases. Machine learning algorithms-including logistic regression, random forest, support vector machines (SVM), and neural networks-will be used to develop and validate the prediction model. Model performance will be evaluated using metrics such as accuracy, sensitivity, specificity, and the receiver operating characteristic (ROC) curve. The ultimate objective is to develop a highly accurate and interpretable model that can be integrated into a digital early-warning system for clinical application.
Interventions
- Other None-placebo
This observational cohort study is non-interventional. Perioperative treatment plans are made based on model - suggested results and anesthesiologists' thought processes, without adding new medicines for patients.
Primary outcome measures
- Occurrence of ALI within 7 Days after Surgery [Time frame: Perioperative period (Perioperative): Refers to the entire process from the determination of surgical treatment to postoperative rehabilitation (e.g., from 1 day before surgery to 7 days after surgery).]
Eligibility 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
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Cohort
Study locations
China · 1 center
- Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua Medicine，Tsingh — Beijing
Identifiers
NCT: NCT07070362 · BRWEP2024W032240113 · 25254-0-01