RecruitingNCT07070362

Digital Early Warning System for Acute Lung Injury in 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

Sponsor

Beijing Tsinghua Chang Gung Hospital

Enrollment

4,000 participants

Start Date

Nov 1, 2024

Study Type

OBSERVATIONAL

Conditions

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

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.

Eligibility

Min Age: 18 Years

Inclusion Criteria3

Age ≥ 18 years
Undergoing major liver surgery (including two-segment or more hepatectomy, liver transplantation, etc.)
Voluntary participation with signed informed consent

Interventions

OTHERNone-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.

Locations(1)

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

Beijing, Beijing Municipality, China

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NCT07070362

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