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
Conditions
Keywords
Digital Intelligence, Acute Lung Injury, PPCs, Liver Surgery, Cardiopulmonary Interaction;
Brief 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.
Detailed description
This study will leverage cardiopulmonary interaction 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.
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.
Sponsors
Beijing Tsinghua Chang Gung Hospital
Study design
Observational model
COHORT
Time perspective
OTHER
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* Age ≥ 18 years * Undergoing major liver surgery (including two-segment or more hepatectomy, liver transplantation, etc.) * Voluntary participation with signed informed consent
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Occurrence of ALI within 7 Days after Surgery | 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: 1. Onset: Acute exacerbation of known injury or new/worsening respiratory symptoms within 1 week. 2. Chest Imaging (X-ray or CT): Bilateral pulmonary shadows not fully explained by exudation, atelectasis, or nodules. 3. 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. 4. 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. |
Countries
China
Contacts
Primary ContactGao Zhifeng, MD
Outcome results
None listed