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Digital Early Warning System for Acute Lung Injury in Liver Surgery

NCT07070362 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 4000

Last updated 2025-07-17

No results posted yet for this study

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.

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

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.

Sponsors & Collaborators

  • Beijing Tsinghua Chang Gung Hospital

    lead OTHER

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2024-11-01
Primary Completion
2027-06-01
Completion
2027-11-30

Countries

  • China

Study Locations

More Related Trials

Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07070362 on ClinicalTrials.gov