Artificial Intelligence for Ulinastatin Responder Outcome & Risk Assessment in CPB
NCT07583290 · Status: ACTIVE_NOT_RECRUITING · Type: OBSERVATIONAL · Enrollment: 10000
Last updated 2026-05-13
Summary
This is a multicenter, retrospective, real-world observational study aimed at developing and validating an artificial intelligence-based tool for identifying ulinastatin treatment responders and risk stratification in cardiac surgery patients undergoing cardiopulmonary bypass (CPB).
Ulinastatin, a glycoprotein extracted from human urine, has shown potential benefits in reducing postoperative complications and inflammatory responses in cardiac surgery. However, evidence supporting its efficacy and optimal application in specific patient populations remains insufficient.
This study will collect clinical data from approximately 4 tertiary cardiac centers in China, including patients who underwent cardiac surgery with CPB. Using machine learning algorithms (such as weighted K-modes clustering and XGBoost), the study aims to: (1) construct a multicenter real-world database for cardiac surgery; (2) identify clinical characteristics associated with ulinastatin treatment response; (3) develop and validate an AI-based risk stratification tool to assist clinical decision-making. This study may provide evidence-based guidance for personalized perioperative anti-inflammatory treatment in cardiac surgery.
Conditions
- Cardiac Surgical Procedures
- Cardiopulmonary Bypass
- Systemic Inflammatory Response Syndrome
- Multiple Organ Dysfunction Syndrome
- Postoperative Complications
Interventions
- DRUG
-
ulinastatin
The patient used ulinastatin during the operation and while hospitalized in the ICU, and received mechanical ventilation.
Sponsors & Collaborators
-
Beijing Anzhen Hospital
lead OTHER
Eligibility
- Min Age
- 18 Years
- Sex
- ALL
- Healthy Volunteers
- No
Timeline & Regulatory
- Start
- 2018-01-01
- Primary Completion
- 2024-12-31
- Completion
- 2027-03-31
Countries
- China
Study Locations
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