Clinical trial NCT03885570: Blood Transfusion Management in Patients With Mitral Valve Replacement in China
Official title: Establishment of AI Prediction Model of Blood Transfusion Management in Patients With Mitral Valve Replacement
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Mitral Valve Replacement
Transfusion Reaction
Blood Transfusion Complication
Keywords: mitral valve replacement
Predictive Model
Blood Transfusion
Perioperative period
Lead sponsor: The Third Xiangya Hospital of Central South University
Sponsor class: OTHER
Collaborators: name: Second Xiangya Hospital of Central South University
name: Chinese Academy of Medical Sciences, Fuwai Hospital
name: The Affiliated Hospital Of Southwest Medical University
name: Qilu Hospital of Shandong University
name: Zhejiang Provincial People's Hospital
name: Beijing Aerospace General Hospital
First posted: 2019-03-21
Enrollment: 2000
Sex: ALL
Minimum age (years): 18
Maximum age (years): 75
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: OTHER
Time perspective: CROSS_SECTIONAL
Sampling method: PROBABILITY_SAMPLE
Study population: Patients with "mitral valve replacement" surgery who meet the standard in China, 18\<Age\<75.

Brief summary: 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.

Detailed description: 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.

Eligibility criteria: 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.

Primary outcomes:
measure: Mortality
timeFrame: through study completion, an average of 2 year
measure: Intraoperative blood transfusion
timeFrame: through study completion, an average of 1 year
Secondary outcomes:
Other outcomes:
Locations:
city: Changsha
country: China
facility: Third Xiangya Hospital
state: Hunan
Study officials:
affiliation: The Third Xiangya Hospital of Central South University
name: Rong Gui
role: PRINCIPAL_INVESTIGATOR
References:
citation: de Schipper LJ, Baharoglu MI, Roos YBWEM, de Beer F. Medical Treatment for Spontaneous Anticoagulation-Related Intracerebral Hemorrhage in the Netherlands. J Stroke Cerebrovasc Dis. 2017 Jul;26(7):1427-1432. doi: 10.1016/j.jstrokecerebrovasdis.2017.03.019. Epub 2017 Apr 12.
citation: Spolverato G, Bagante F, Weiss M, He J, Wolfgang CL, Johnston F, Makary MA, Yang W, Frank SM, Pawlik TM. Impact of Delta Hemoglobin on Provider Transfusion Practices and Post-operative Morbidity Among Patients Undergoing Liver and Pancreatic Surgery. J Gastrointest Surg. 2016 Dec;20(12):2010-2020. doi: 10.1007/s11605-016-3279-8. Epub 2016 Sep 30.
citation: Spolverato G, Kim Y, Ejaz A, Frank SM, Pawlik TM. Effect of Relative Decrease in Blood Hemoglobin Concentrations on Postoperative Morbidity in Patients Who Undergo Major Gastrointestinal Surgery. JAMA Surg. 2015 Oct;150(10):949-56. doi: 10.1001/jamasurg.2015.1704.
citation: Hogervorst E, Rosseel P, van der Bom J, Bentala M, Brand A, van der Meer N, van de Watering L. Tolerance of intraoperative hemoglobin decrease during cardiac surgery. Transfusion. 2014 Oct;54(10 Pt 2):2696-704. doi: 10.1111/trf.12654. Epub 2014 Apr 14.
citation: Karkouti K, Wijeysundera DN, Yau TM, McCluskey SA, van Rensburg A, Beattie WS. The influence of baseline hemoglobin concentration on tolerance of anemia in cardiac surgery. Transfusion. 2008 Apr;48(4):666-72. doi: 10.1111/j.1537-2995.2007.01590.x. Epub 2008 Jan 10.