Digital Early Warning System for Acute Lung Injury in Liver Surgery

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.

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Key information

Conditions

Acute Lung Injury(ALI) ARDS, Human Acute Lung Injury Adenocarcinoma Carcinoma Carcinoma, Hepatocellular Digestive System Diseases Digestive System Neoplasms Fatty Liver Fibrosis Liver Cancer, Adult Liver Cirrhosis Liver Diseases Liver Neoplasms Lung Diseases Lung Injury MASLD MASLD/MASH (Metabolic Dysfunction-Associated Steatotic Liver Disease / Metabolic Dysfunction-Associated Steatohepatitis) NAFLD (Nonalcoholic Fatty Liver Disease) Neoplasms Neoplasms by Histologic Type Neoplasms by Site Neoplasms, Glandular and Epithelial Non-alcoholic Fatty Liver Disease Pathologic Processes Pathological Conditions, Signs and Symptoms Respiration Disorders Respiratory Distress Syndrome Respiratory Tract Diseases

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

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

About this study

This study will leverage 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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

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

Treatment and study plan

Primary outcomes

Occurrence of ALI within 7 Days after Surgery

Time frame: Perioperative period (Perioperative): Refers to the entire process from the determination of surgical treatment to postoperative rehabilitation (e.g., from 1 day before surgery to 7 days after surgery).
Berlin Definition:
- Onset: Acute exacerbation of known injury or new/worsening respiratory symptoms within 1 week.
- Chest Imaging (X-ray or CT): Bilateral pulmonary shadows not fully explained by exudation, atelectasis, or nodules.
- Pulmonary Edema Etiology: Respiratory failure not fully attributed to heart failure or fluid overload; if no related risk factors, objective tests (e.g., Doppler echocardiography) are needed to exclude hydrostatic pulmonary edema.
- Oxygenation Levels: Mild - With CPAP/PEEP >5 cmH2O, 200 mmHg < PaO2/FiO2 < 300 mmHg; Moderate - With CPAP/PEEP >5 cmH2O, 100 mmHg < PaO2/FiO2 < 200 mmHg; Severe - With CPAP/PEEP >5 cmH2O, PaO2/FiO2 < 100 mmHg.

Study contacts

Contact information is provided by the study sponsor or research team.

Gao Zhifeng, MD

CONTACT

[email protected]

+8615801249466

Sponsors and collaborators

Lead sponsor

Beijing Tsinghua Chang Gung Hospital

Other

Collaborators

Huangdao District People's Hospital of Qingdao
Peking University International Hospital
The First Affiliated Hospital of Army Medical University (Southwest Hospital)

Registry information

Official study title

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

Important dates

Study start: 2024
Primary completion: 2027
Study completion: 2027
First posted: Jul 17, 2025
Registry last updated: Aug 17, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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