Blood Transfusion Management in Patients With Mitral Valve Replacement in China
Blood Transfusion Management in Patients With Mitral Valve Replacement in China
The evaluation uses ΔHb as an independent factor combined with artificial intelligence (AI)
 to predict its impact on the prognosis and blood transfusion of patients undergoing cardiac
 surgery, thereby guiding perioperative clinical blood use and improving patient prognosis.
1. Select 8 hospitals to form a multi-center team, and enter the keyword "mitral valve
 replacement" in the case system of 8 hospitals to collect information on 2000 heart
 surgery patients.

 2. Mainly collect information on preoperative, intraoperative and postoperative test
 indexes (blood routine, liver and kidney function, coagulation function, blood gas),
 cardiac color Doppler, blood transfusion and prognosis of patients undergoing cardiac
 surgery, using statistical methods for analysis. Identify key observations.

 3. Gradually incorporate and exclude data, and use statistical methods to conduct
 preliminary analysis on the collected data.

 The AI prediction model was established by machine learning algorithm to predict
 intraoperative blood transfusion, verify the specificity and sensitivity of the blood
 transfusion prediction model, and scientifically guide clinical blood use.
Inclusion Criteria:

 1. Patients undergoing "mitral valve replacement" surgery;

 2. Age 18-75 years old;

 3. 48h biochemical indicators before surgery, blood general indicators, coagulation
 indicators are complete.

 Exclusion Criteria:

 1. Lack of necessary tests and patient test information is not detailed, blood
 transfusion information is not detailed;

 2. The blood transfusion information is not detailed in the same period as other cardiac
 operations (such as aortic valve surgery, coronary artery bypass grafting, etc.), but
 the tricuspid valvuloplasty is not ruled out at the same time;

 3. Heart surgery or emergency surgery again.