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Recruiting NCT07070362

Digital Early Warning System for Acute Lung Injury in Liver Surgery

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

Sex: All

Ages: 18 Years – N/A

Healthy volunteers: No

Enrollment: 3000

Sponsor: Beijing Tsinghua Chang Gung Hospital

Location: Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua Medicine，Tsinghua University Beijing Beijing Municipality

Summary

This study aims to develop an explainable machine learning model that takes into account the characteristics of cardiopulmonary interactions. This model will enable early prediction of acute lung injury (ALI) in patients undergoing major liver surgery. The research will create a digital early-warning system for ALI, thereby supporting clinical diagnosis and treatment decisions. This, in turn, should help reduce the incidence and mortality rates associated with ALI.

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

Interested in this study? View the official listing for contact and enrollment details.

View on ClinicalTrials.gov

Source: ClinicalTrials.gov (NCT07070362). StuddyBuddy aggregates publicly available trial information.