Digital Early Warning System for Acute Lung Injury in Liver Surgery

NCT ID: NCT07070362

Last Updated: 2025-07-17

Basic Information

• Recruitment Status: RECRUITING
• Total Enrollment: 4000 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2024-11-01
• Study Completion Date: 2027-11-30

Brief Summary

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.

Conditions

Acute Lung Injury(ALI); Liver Cirrhosis; ARDS, Human; MASLD; MASLD/MASH (Metabolic Dysfunction-Associated Steatotic Liver Disease / Metabolic Dysfunction-Associated Steatohepatitis); NAFLD (Nonalcoholic Fatty Liver Disease); Liver Cancer, Adult

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: OTHER

Study Groups

patients undergoing major liver surgury

Population: 2,497 adult patients (≥18 years) who underwent major liver surgery (≥2 segments resection or transplantation) at Beijing Tsinghua Changgung Hospital, including retrospective (2019.06-2024.05) and prospective (from 2025.12) cohorts.

Inclusion Criteria: Aged ≥18, scheduled for major liver surgery, with informed consent.

Exclusion Criteria: Refusal to participate, comorbidities affecting ALI assessment, incomplete data, failed follow-up, or concurrent trials.

Interventions: None. Observational study; clinical management follows standard protocols without study-related interventions. Data collected from routine records and monitoring.

None-placebo

Intervention Type: OTHER

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.

Interventions

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.

Intervention Type: OTHER

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

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Beijing Tsinghua Chang Gung Hospital

OTHER

Sponsor Role: lead

Responsible Party

Zhifeng Gao

Chief physician

Responsibility Role: PRINCIPAL_INVESTIGATOR

Locations

Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua Medicine，Tsinghua University

Beijing, Beijing Municipality, China

Site Status: RECRUITING

Countries

China

Central Contacts

Gao Zhifeng, MD

Role: CONTACT

btchgzf@hotmail.com

+8615801249466

Provided Documents

Document Type: Study Protocol

View Document

Other Identifiers

25254-0-01

Identifier Type: OTHER

Identifier Source: secondary_id

BRWEP2024W032240113

Identifier Type: -

Identifier Source: org_study_id