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
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
Sponsor: Beijing Tsinghua Chang Gung Hospital

Organization's
Unique Protocol
ID
NCT ID
BRWEP2024W
032240113
NCT ID not yet
assigned
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
2025-3-27

Digital Early Warning System for Acute Lung Injury in Liver Surgery.
BRWEP2024W032240113
1. Research Background
1.1 Research Objectives and Significance
Acute lung injury (ALI) and the more severe acute respiratory distress syndrome (ARDS) are
common postoperative complications of major liver surgery and liver transplantation, closely
associated with significantly increased patient morbidity and mortality. Early warning systems
based on interpretable machine learning models can identify abnormalities based on past
patterns and current observations, issuing warning signals before hazards occur. These systems
are considered highly valuable for precise prediction of clinical complications like ALI, which
have sudden onsets and lack specific predictive indicators. This study aims to develop
interpretable
machine
learning
models
using
digital
and
intelligent
methods,
with
cardiopulmonary interaction parameters as key features, to predict ALI in the perioperative
period of liver surgery. Further, by integrating expert systems and iteratively optimizing them
using information fusion technology, we aim to construct a digital and intelligent early warning
system for ALI throughout the entire liver surgery process, meeting specificity and accuracy
requirements and enabling early detection of high-risk ALI patients. Ultimately, we aim to
establish an integrated platform for prevention, diagnosis, and treatment of ALI throughout the
liver surgery process, centered on the ALI early warning system. This platform is designed to
provide robust clinical decision support for healthcare professionals, reducing the incidence
and mortality of ALI in the perioperative period of liver surgery.
1.2 Domestic and International Research Status
Domestic Research Status: While some progress has been made in ALI research for liver

surgery in China, large-scale clinical studies and effective predictive models remain
insufficient.
International Research Status: ALI research for liver surgery is relatively advanced abroad, but
challenges in predictive accuracy persist.
1.3 Research Conditions at Our Hospital
Data Resources: Beijing Tsinghua Changgung Hospital boasts rich clinical data resources,
including a medical data center, electronic medical record systems, laboratory information
systems, etc., providing ample data support for the research.
Technical Equipment: The hospital is equipped with advanced medical devices and
technologies to meet data collection and analysis needs.
Professional Team: The research team comprises members with extensive clinical and research
experience to ensure smooth research progress.

1. Research Objectives and Content
1.1Research Objectives
Acute lung injury (ALI) and the more severe acute respiratory distress syndrome (ARDS)
are common after major liver surgery and liver transplantation, and are closely linked to
significantly increased patient morbidity and mortality. Severe liver dysfunction can lead to
hemodynamic changes, coagulation abnormalities, and immune dysregulation, all of which can
increase the risk of perioperative pulmonary complications, particularly ALI. Given the
continuous and dynamic nature of ALI's pathophysiological process and its typically sudden
onset, early warning systems are crucial. Such systems identify abnormalities based on past
patterns and current observations, issuing warnings before hazards occur. A comprehensive
early warning system can provide more effective perioperative coverage and clinical protection.
This study aims to develop interpretable machine learning models using digital and
intelligent methods, focusing on ALI prediction in the perioperative period of liver surgery. It
also aims to construct an ALI early warning system for the entire liver surgery process and
establish an integrated ALI diagnosis and treatment platform for the entire liver surgery
process, centered on the ALI early warning system. This platform will provide strong clinical
decision support for medical professionals, with the goal of reducing the incidence and
mortality of ALI in the perioperative period of liver surgery.
1.2 Research Content
1.2.1 Construction and Validation of Interpretable Machine Learning Models
Data Collection and Preprocessing

Literature Review: Systematically review domestic and international literature to
understand the pathogenesis, risk factors, clinical manifestations, and pros and cons of existing
predictive models for acute lung injury (ALI). This will provide a theoretical basis for data
collection and model construction in this study.
Data Collection: Collect perioperative data from patients aged 18 and above who
underwent major liver surgery at Beijing Tsinghua Changgung Hospital between June 2019 and
May 2024. The data includes cardiopulmonary interaction parameters (e.g., pulse pressure
variation, systolic pressure variation, stroke volume variation), preoperative baseline
information (age, gender, weight, medical history), intraoperative information (surgery type,
duration, anesthesia method, blood transfusion status), and postoperative information
(complications, hospital stay, follow-up status).
Feature Selection: Use univariate and multivariate analyses to select features strongly
associated with ALI occurrence as input variables for the machine learning (ML) model.
Model Development
Algorithm Selection: Develop interpretable ML models using six algorithms, including
logistic regression, random forest, support vector machines, and neural networks, to enhance
model accuracy and generalization.
Model Training: Split the dataset into training and validation sets. Train the model using
the training set and optimize parameters through cross-validation to prevent overfitting.
Model Simplification and Optimization
Feature Importance Assessment: For the best-performing ML model, calculate the
Shapley Additive Explanations (SHAP) values of input features to evaluate their importance.
Model Simplification: Select the top 20 features based on SHAP values to create a

simplified risk prediction model, improving interpretability and practicality.
Feature Weight Determination: Determine the feature weights in the simplified model for
the entire major liver surgery process to support clinical decision-making.
Model Validation and Evaluation
Independent Test Set Validation: Validate the accuracy of the ML model using an
independent clinical test set and evaluate its predictive performance using metrics such as
sensitivity, specificity, accuracy, and ROC curve.
Interpretability Analysis: Conduct an interpretability analysis of the model's predictions,
explaining the decision-making process and prediction basis to enhance clinical acceptance.
1.2.2 Construction and Clinical Validation of the Early Warning System
Construction of the Early Warning System
Expert System Development: Develop an ALI treatment expert system for the entire liver
surgery process based on ML models. Integrate clinical guidelines, expert experience, and
model predictions to form an ALI early warning system.
Logical Reasoning and Decision-Making Functions: Implement automatic logical
reasoning and rapid clinical decision-making based on rules to provide real-time warnings and
decision support for clinicians.
System Iteration and Optimization
Application of Information Fusion Technology: Use information fusion technology to
integrate multi-source observational data, including vital signs, laboratory results, and imaging
data, to enhance the accuracy and reliability of the early warning system.
System Optimization: Refine the early warning system based on clinical feedback and

actual application scenarios to improve system performance and user experience.
Clinical Validation and Evaluation
Expert Concept Validation Tests: Conduct concept validation tests for the iterated early
warning system with clinical experts. Evaluate the system's probability predictions and
perioperative suggestions, assessing dimensions such as consistency and acceptability through
questionnaires.
Clinical Application Validation: Apply the early warning system in clinical practice,
collect feedback from clinicians and patients, and evaluate the system's clinical value and
effectiveness to further optimize its performance.
1.2.3 Establishment of an Integrated ALI Diagnosis and Treatment Platform
Theoretical Organization and Requirements Analysis
Theoretical Organization: Organize theories on the entire-process ALI early warning
system, perioperative ALI diagnosis and treatment guidelines, expert consensus, case quality
control, and knowledge retrieval needs in major liver surgery. Clarify the platform's functional
positioning and construction goals.
Requirements Analysis: Communicate with clinicians, nurses, and managers to
understand their actual needs and pain points in ALI diagnosis and treatment. This will provide
a basis for platform design and development.
Data Center Construction and System Integration
Data Center Establishment: Establish a comprehensive data center for liver surgery wards
to collect and integrate clinical, imaging, and laboratory data, providing data support for the
platform.

Integration Interface Development: Develop integration interfaces to connect the
entire-process ALI early warning platform and knowledge retrieval platform, enabling data
sharing and interaction within a unified information system.
User Interface Design and Development
Interface Design: Create an intuitive and user-friendly interface with a simple and clear
design style to facilitate quick adoption and operation by medical staff.
Function Implementation: Ensure medical staff can seamlessly access and operate all
functions of the integrated system, including patient information queries, early warning result
viewing, diagnosis guideline consultation, and knowledge retrieval.
System Testing and Optimization
Supervised Mode Testing: Conduct system testing and optimization in supervised mode
with participation from clinicians and nurses. Comprehensive evaluation and feedback will be
used to identify and resolve issues promptly.
Enhancing Safety and Effectiveness: Improve the safety and effectiveness of the
entire-process ALI diagnosis and treatment platform through testing and optimization. Refine
privacy protection mechanisms to ensure patient data security and privacy.
Training and Promotion
Comprehensive Training: Provide comprehensive training for medical staff, covering
platform functions, operation methods, and clinical application cases, to ensure proficient use
of the new system.
Application Promotion: Effectively implement the entire-process ALI diagnosis and
treatment platform in practice. Enhance its adoption through promotion and demonstration,

offering robust support for the clinical management of liver surgery patients.

2. Research Protocol
2.1Study Design
This single - center cohort study has two phases: retrospective and prospective.
The retrospective phase analyzed patient data from June 2019 to May 2024 to develop and
validate machine learning models.The prospective phase, starting December 2025, will enroll
eligible patients. It will collect preoperative, intraoperative, and postoperative data, including
cardiopulmonary interaction parameters and clinical blood samples. This data will assess the
model's and early warning system's accuracy and effectiveness.
2.2 Study Population
Inclusion criteria: Patients aged ≥18 years undergoing major liver surgery (e.g., resection of
two or more liver segments, liver transplantation). Exclusion criteria:Patients declining
participation, having comorbidities affecting outcome assessment, or having incomplete data
records.
2.3Sample Size and Calculation
This study will enroll 2,497 patients to ensure adequate statistical power for external
model validation. In major liver surgery, ALI incidence is approximately 4.1%.
Performance
Indicator
Hypothetical
Value
Target 95% Confidence
Interval Width
(Corresponding
Standard Error）
Sample Size: Number of
Study Participants
(Events) Required

Assuming the linear predictor follows a skewed normal distribution, with an outcome event rate
of 0.041.The required sample sizes, based on different model performance metrics, are shown
above.
In summary, the minimum clinical sample size required for external model validation is 2,497
participants.
2.4Inclusion Criteria:
a. Aged ≥18 years; b. Scheduled for major liver surgery (e.g., resection of two or more liver
segments, liver transplantation); c. Willing to participate and sign the informed consent form.
2.5Exclusion Criteria:
a. Patient refusal;
b. Presence of comorbidities that may affect outcome assessment; c
c. Incomplete recording of outcome measures;
d. Failure to follow up postoperatively;
e. Treating physician’s judgment that the patient is unsuitable for inclusion;
f. Participation in another study that may interfere with outcome assessment of this study.
O/E
1
1 (0.245a)
390 (16)
Calibration Slope
1
0.2 (0.051)
2497 (103)
C-index
0.8
0.1 (0.0255)
1912 (79)
Standardized Net
Benefit
0.53
0.2 (0.051)
2403 (99)

2.6Intervention Methods:
This study involves no therapeutic interventions for participants; only their clinical data and
samples will be collected.
2.7 Observation Indicators and Follow-up Plan
Observation indicators include cardiopulmonary interaction parameters and relevant
biomarkers in clinical blood samples. The follow-up plan involves assessing patients within
seven days after surgery to collect information on their recovery and test results.
2.8 Statistical Analysis Methods
Data will be analyzed using R or SPSS, with analyses including descriptive statistics,
correlation analysis, and regression analysis.

3. Flowchart
4. Evaluation Criteria
Primary outcome measure: Incidence of ALI within 7 postoperative days.
ALI diagnosis currently uses the Berlin definition of acute respiratory distress syndrome (ARDS):
a. Onset: Acute occurrence within one week of known injury or new/worsening respiratory symptoms;
b. Chest imaging (X-ray or CT): Bilateral pulmonary opacities not fully explained by effusion, atelectasis,
or nodules;
c. Pulmonary edema cause: Respiratory failure not fully attributed to heart failure or fluid overload; if no
related risk factors, objective tests (like Doppler echocardiography) are needed to exclude hydrostatic
pulmonary edema;
d. Oxygenation:
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 cmH ₂ O, PaO ₂ /FiO ₂
<100 mmHg.Opportunities for improved clinical trial
designs in acute respiratory distress syndrome Wick, Katherine D et al.
The Lancet Respiratory Medicine, Volume 10, Issue 9, 916 - 924
Secondary outcome indicators include postoperative recovery status, hospitalization duration,
and complication incidence, among others.
5. Safety Reporting and Adverse Event Management
5.1 Definitions of Adverse Events and Serious Adverse Events
An adverse event (AE) is any unfavorable event occurring during the study that may
impact the participant's well-being. A serious adverse event (SAE) refers to an event that
results in death, is life-threatening, requires hospitalization or prolongs hospital stay, causes
permanent or significant disability/damage, or leads to congenital anomalies/birth defects.
5.2 Management of AEs and SAEs
AEs will be promptly assessed and treated, with treatment plans adjusted if necessary. For
SAEs, immediate emergency measures will be taken to ensure participant safety, and they will
be reported to the ethics committee without delay.
5.3 Reporting Procedure
AEs and SAEs will be reported in accordance with hospital regulations and the
requirements of the ethics committee.
6.Medical Ethics Risk Analysis
Ethical Issues: This study involves privacy protection and informed consent of
participants.
Countermeasures: We will strictly adhere to ethical principles to ensure participant
privacy and informed consent rights. Personal information and samples will be anonymized to

safeguard privacy. Throughout the study, we will fully respect participants' autonomy and
voluntary participation.