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Digital Early Warning System for Acute Lung Injury in Liver Surgery

NCT07070362 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 3000

Last updated 2026-08-17

No results posted yet for this study

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.

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

Sponsors & Collaborators

  • Huangdao District People's Hospital of Qingdao

    collaborator UNKNOWN

  • Peking University International Hospital

    collaborator OTHER

  • The First Affiliated Hospital of Army Medical University (Southwest Hospital)

    collaborator UNKNOWN

  • 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