UMIN-CTR Clinical Trial

Basic information
Public title	Evaluation of postoperative hemorrhage in cardiac surgery using image analysis system and Machine learning
Acronym	Evaluation of postoperative hemorrhage in cardiac surgery using image analysis system and Machine learning
Scientific Title	Evaluation of postoperative hemorrhage in cardiac surgery using image analysis system and Machine learning
Scientific Title:Acronym	Evaluation of postoperative hemorrhage in cardiac surgery using image analysis system and Machine learning
Region	Japan

Condition
Condition	Patients who have undergone open heart surgery
Classification by specialty	Cardiovascular surgery
Classification by malignancy	Others
Genomic information	NO

Objectives
Narrative objectives1	The purpose of this project is to create a classifier (system) that can discriminate post-operative blood loss and cases requiring reopening of the chest in open heart surgery from surgical images using an image analysis system and machine learning.
Basic objectives2	Efficacy
Basic objectives -Others
Trial characteristics_1
Trial characteristics_2
Developmental phase

Assessment
Primary outcomes	To create a classifier (system) that can identify cases that require reopening of the heart in open heart surgery. Then, create a receiver operating characteristic curve for the classifier and determine the area under the curve, sensitivity and specificity.
Key secondary outcomes

Base
Study type	Observational

Study design
Basic design
Randomization
Randomization unit
Blinding
Control
Stratification
Dynamic allocation
Institution consideration
Blocking
Concealment

Intervention
No. of arms
Purpose of intervention
Type of intervention
Interventions/Control_1
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Eligibility
Age-lower limit	20 years-old <=
Age-upper limit	Not applicable
Gender	Male and Female
Key inclusion criteria	Patients who underwent open heart surgery at Yamagata University Hospital between January 2010 and December 2020 will be included
Key exclusion criteria	Cases with missing image data Cases with missing blood loss data
Target sample size	500

Research contact person
Name of lead principal investigator	1st name Tatsuya Middle name Last name Hayasaka
Organization	Yamagata University Faculty of Medicine
Division name	Department of Anesthesia
Zip code	9909585
Address	2-2-2 Iida-nishi, Yamagata city, Yamagata 990-9585, Japan
TEL	023-628-5400
Email	hayasakatatsuya1101@gmail.com

Public contact
Name of contact person	1st name tatsuya Middle name Last name hayasaka
Organization	Yamagata University Faculty of Medicine
Division name	Department of Anesthesia
Zip code	9909585
Address	2-2-2 Iida-nishi, Yamagata city, Yamagata 990-9585, Japan
TEL	023-628-5400
Homepage URL
Email	hayasakatatsuya1101@gmail.com

Sponsor
Institute	Yamagata University Faculty of Medicine
Institute
Department

Funding Source
Organization	Department of Anesthesiology, Yamagata University Faculty of Medicine
Organization
Division
Category of Funding Organization	Self funding
Nationality of Funding Organization

Other related organizations
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IRB Contact (For public release)
Organization	The Ethical Review Committee of Yamagata University Faculty of Medicine
Address	2-2-2 Iida-nishi, Yamagata city, Yamagata 990-9585, Japan
Tel	0236285015
Email	ikekenkyu@jm.kj.yamagata-u.ac.jp

Secondary IDs
Secondary IDs	NO
Study ID_1
Org. issuing International ID_1
Study ID_2
Org. issuing International ID_2
IND to MHLW

Institutions
Institutions

Other administrative information
Date of disclosure of the study information	2020 Year 12 Month 01 Day

Related information
URL releasing protocol
Publication of results	Unpublished

Result
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Results
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Baseline Characteristics
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IPD sharing Plan description

Progress
Recruitment status	Preinitiation
Date of protocol fixation	2020 Year 11 Month 01 Day
Date of IRB
Anticipated trial start date	2020 Year 12 Month 01 Day
Last follow-up date	2030 Year 12 Month 31 Day
Date of closure to data entry
Date trial data considered complete
Date analysis concluded

Other

Other related information

Patients who had open heart surgery performed at Yamagata University Hospital between January 2010 and December 2020 will be included in the study. Videos of open heart surgery performed during the above period are reviewed, and the images just before chest closure are extracted. The images are subjected to machine learning using Scikit-Learn to create a system for supervised learning that predicts the blood loss in the 12 hours after the surgery from the images immediately before chest closure. We use images as input and bleeding volume as output, with 80% of the data as train data and 20% as test data. As a secondary evaluation, we will create a system for predicting postoperative blood loss using preoperative patient data (age, sex, height, weight, temperature, heart rate, blood pressure, oxygenation capacity, electrolytes, renal function, liver function, white blood cell count, red blood cell count, platelet count, fibrinogen concentration, cardiac function, etc.).

Management information
Registered date	2020 Year 11 Month 13 Day
Last modified on	2020 Year 11 Month 13 Day

Link to view the page
URL(English)	https://upload.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000048461

Research Plan
Registered date	File name

Research case data specifications
Registered date	File name

Research case data
Registered date	File name

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