Erythrocyte TransfusionNCT06594484
Machine Learning Versus Traditional Scores in Predicting Erythrocyte Need
An observational study of Erythrocyte Transfusion and Machine Learning, run by Kocaeli City Hospital at 1 site in Turkey (Türkiye).
Trial summary
In this study, we compared perioperative bleeding prediction scores with our machine learning-based prediction system in predicting the need for erythrocyte suspension during cardiovascular surgery. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Erythrocyte Transfusion, Machine Learning
- What is being tested?
- Ml Based Algorithm 1, Ml Based Algroithm 2 and Bleeding Scores
- How many people
- 430 participants
- Where
- 1 siteTurkey (Türkiye)
- Study dates
- 22 Feb 2024to 30 May 2024
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: Complete files from the data of patients undergoing elective cardiac surgery in Kocaeli City Hospital operating rooms will constitute the study data.
Who may be able to join
- Data from patients who underwent isolated CABG surgeries in the cardiac and vascular surgery operating rooms between 01.01.2023 and 01.01.2024 were evaluated.
Who cannot join
- Missing Data
- Emergency surgery
- İntraoperative mortality
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Comparative Analysis of Machine Learning Versus Conventional Models for Predicting Erythrocyte Need in Cardiovascular Surgery
In this study, we compared perioperative bleeding prediction scores with our machine learning-based prediction system in predicting the need for erythrocyte suspension during cardiovascular surgery.
Read the detailed description
The success of ML algorithms in predicting perioperative blood product use in CABG remains an under-tested topic. Unnecessary preparation of blood products or not being able to supply them when necessary is critical for both patient safety and the effective use of hospital resources [8]. Bleeding amounts and blood product use strategies can vary with institute protocols. Scoring systems that determine the general framework may not perform well due to local factors. ML algorithms can be created locally according to previous patient data of each clinic and can improve themselves with learning mechanisms, suggesting significant potential in this field.
In the current study, a new estimation system created with the ML algorithm was compared with the known estimation systems. Comparing the ML algorithm with 6 different classical scoring systems is important in terms of demonstrating the potential of this technology.
The aim of this study is to investigate whether the model created with ML in predicting perioperative blood product consumption in cardiovascular surgeries is superior to predictive scoring systems that have proven themselves in the literature. Secondary aim is to compare the predictive value of using more than one scoring system in combination.
- Conditions
- Erythrocyte Transfusion, Machine Learning
- Being tested
- Ml Based Algorithm 1, Ml Based Algroithm 2, Bleeding Scores
- Keywords
- Machine Learning, Cardiovascular Surgery, Perioperative Bleeding, Erythrocyte Transfusion, Predictive Modeling
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Ml Based Algorithm 1Other
The values in the ML algorithm were selected according to logistic regression analysis and the values used in the other six scores tested. The success rate of the constructed networks correct predictions was considered as the success rate of the algorithm. The usefulness of the test was determined through AUROC analysis. Two algorithms were tested in our study. In the first algorithm (ML1), the dependent variable was erythrocyte suspension (ES) consumption, and the independent variables included patients; demographic data, laboratory data, and operational data
Ml Based Algroithm 2Other
is an Ml algorithm created by combining commonly used bleeding scores
Bleeding ScoresOther
ACTION CRUSCADE TRACK WILL-BLEED PAPWORTH TRUST ACTAPORT skores used to predict ES need
| Study group | Role | What they receive |
|---|---|---|
| General Anesthesia Group | The need for ES was recorded in patients undergoing cardiovascular surgery. Receives: Other: Ml Based Algorithm 1; Other: Ml Based Algroithm 2; Other: Bleeding Scores |
What is being measured
The primary outcome is the main question the study is designed to answer.
ML algorithm versus traditional scoring in predicting ES needs
The success of the ML-based algorithm in correctly predicting the ES need will be calculated.
Measured: During the intraoperative period Cardiac Surgery
Secondary outcomes (1)
Deterdetermining the most effective method for predicting ES needs using traditional scores
After comparing the ACTION CRUSCADE TRACK WILL-BLEED PAPWORTH TRUST ACTAPORT scores, the most successful one scoring system will be revealed. The results will be shown numerically with the percentages of predicting the need for ES.
Measured: During the intraoperative period Cardiac Surgery
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted19 Sep 2024
- Study start22 Feb 2024
- Primary completion22 May 2024
- Study completion30 May 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (5)Papers the sponsor has linked to this study.
- Other trials for Erythrocyte TransfusionStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 430 participants (actual)
Sponsor and investigators
- Lead sponsor
- Kocaeli City Hospital
- Sponsor type
- Other government
- Responsible party
- Principal investigator, Ahmet Yuksek, Kocaeli City Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06594484
- Sponsor's protocol ID
- 2024-6
- Status last verified
- Sep 2024
- First submitted
- 6 Sep 2024
- Last updated
- 19 Sep 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Park J, Bonde PN. Machine Learning in Cardiac Surgery: Predicting Mortality and Readmission. ASAIO J. 2022 Dec 1;68(12):1490-1500. doi: 10.1097/MAT.0000000000001696. Epub 2022 May 9.
PubMed 35544455El-Sherbini AH, Shah A, Cheng R, Elsebaie A, Harby AA, Redfearn D, El-Diasty M. Machine Learning for Predicting Postoperative Atrial Fibrillation After Cardiac Surgery: A Scoping Review of Current Literature. Am J Cardiol. 2023 Dec 15;209:66-75. doi: 10.1016/j.amjcard.2023.09.079. Epub 2023 Oct 21.
PubMed 37871512Shahian DM, Lippmann RP. Commentary: Machine learning and cardiac surgery risk prediction. J Thorac Cardiovasc Surg. 2022 Jun;163(6):2090-2092. doi: 10.1016/j.jtcvs.2020.08.058. Epub 2020 Aug 24. No abstract available.
PubMed 32951875Miles TJ, Ghanta RK. Machine learning in cardiac surgery: a narrative review. J Thorac Dis. 2024 Apr 30;16(4):2644-2653. doi: 10.21037/jtd-23-1659. Epub 2024 Apr 24.
PubMed 38738250Tseng PY, Chen YT, Wang CH, Chiu KM, Peng YS, Hsu SP, Chen KL, Yang CY, Lee OK. Prediction of the development of acute kidney injury following cardiac surgery by machine learning. Crit Care. 2020 Jul 31;24(1):478. doi: 10.1186/s13054-020-03179-9.
PubMed 32736589
Study at a glance
From the registry record, last updated by the sponsor on 19 Sep 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Comparative Analysis of Machine Learning Versus Conventional Models for Predicting Erythrocyte Need in Cardiovascular Surgery
- ClinicalTrials.gov ID
- NCT06594484
- Acronym / sponsor ID
- 2024-6
- Conditions
- Erythrocyte Transfusion, Machine Learning
- Keywords
- Machine Learning, Cardiovascular Surgery, Perioperative Bleeding, Erythrocyte Transfusion, Predictive Modeling
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 430 participants (actual)
- Lead sponsor
- Kocaeli City HospitalOther government
- Study start
- 22 Feb 2024 (actual)
- Primary completion
- 22 May 2024 (actual)
- Study completion
- 30 May 2024 (actual)
- First posted
- 19 Sep 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
In this study, we compared perioperative bleeding prediction scores with our machine learning-based prediction system in predicting the need for erythrocyte suspension during cardiovascular surgery.
Detailed description
The success of ML algorithms in predicting perioperative blood product use in CABG remains an under-tested topic. Unnecessary preparation of blood products or not being able to supply them when necessary is critical for both patient safety and the effective use of hospital resources [8]. Bleeding amounts and blood product use strategies can vary with institute protocols. Scoring systems that determine the general framework may not perform well due to local factors. ML algorithms can be created locally according to previous patient data of each clinic and can improve themselves with learning mechanisms, suggesting significant potential in this field.
In the current study, a new estimation system created with the ML algorithm was compared with the known estimation systems. Comparing the ML algorithm with 6 different classical scoring systems is important in terms of demonstrating the potential of this technology.
The aim of this study is to investigate whether the model created with ML in predicting perioperative blood product consumption in cardiovascular surgeries is superior to predictive scoring systems that have proven themselves in the literature. Secondary aim is to compare the predictive value of using more than one scoring system in combination.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| General Anesthesia Group | – | Other: Ml Based Algorithm 1, Other: Ml Based Algroithm 2, Other: Bleeding ScoresThe need for ES was recorded in patients undergoing cardiovascular surgery. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Complete files from the data of patients undergoing elective cardiac surgery in Kocaeli City Hospital operating rooms will constitute the study data.
Key inclusion criteria (1 listed)
- Data from patients who underwent isolated CABG surgeries in the cardiac and vascular surgery operating rooms between 01.01.2023 and 01.01.2024 were evaluated.
Key exclusion criteria (3 listed)
- Missing Data
- Emergency surgery
- İntraoperative mortality
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Ml Based Algorithm 1The values in the ML algorithm were selected according to logistic regression analysis and the values used in the other six scores tested. The success rate of the constructed networks correct predictions was considered as the success rate of the algorithm. The usefulness of the test was determined through AUROC analysis. Two algorithms were tested in our study. In the first algorithm (ML1), the dependent variable was erythrocyte suspension (ES) consumption, and the independent variables included patients; demographic data, laboratory data, and operational data | Other | General Anesthesia Group |
| Ml Based Algroithm 2is an Ml algorithm created by combining commonly used bleeding scores | Other | General Anesthesia Group |
| Bleeding ScoresACTION CRUSCADE TRACK WILL-BLEED PAPWORTH TRUST ACTAPORT skores used to predict ES need | Other | General Anesthesia Group |
Primary outcome
ML algorithm versus traditional scoring in predicting ES needs
Time frame: During the intraoperative period Cardiac Surgery
The success of the ML-based algorithm in correctly predicting the ES need will be calculated.
1 secondary outcome
Deterdetermining the most effective method for predicting ES needs using traditional scores
Time frame: During the intraoperative period Cardiac Surgery
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 19 Sep 2024.
- Background: PubMed 35544455Park J, Bonde PN. Machine Learning in Cardiac Surgery: Predicting Mortality and Readmission. ASAIO J. 2022 Dec 1;68(12):1490-1500. doi: 10.1097/MAT.0000000000001696. Epub 2022 May 9.
- Background: PubMed 37871512El-Sherbini AH, Shah A, Cheng R, Elsebaie A, Harby AA, Redfearn D, El-Diasty M. Machine Learning for Predicting Postoperative Atrial Fibrillation After Cardiac Surgery: A Scoping Review of Current Literature. Am J Cardiol. 2023 Dec 15;209:66-75. doi: 10.1016/j.amjcard.2023.09.079. Epub 2023 Oct 21.
- Background: PubMed 32951875Shahian DM, Lippmann RP. Commentary: Machine learning and cardiac surgery risk prediction. J Thorac Cardiovasc Surg. 2022 Jun;163(6):2090-2092. doi: 10.1016/j.jtcvs.2020.08.058. Epub 2020 Aug 24. No abstract available.
- Background: PubMed 38738250Miles TJ, Ghanta RK. Machine learning in cardiac surgery: a narrative review. J Thorac Dis. 2024 Apr 30;16(4):2644-2653. doi: 10.21037/jtd-23-1659. Epub 2024 Apr 24.
- Background: PubMed 32736589Tseng PY, Chen YT, Wang CH, Chiu KM, Peng YS, Hsu SP, Chen KL, Yang CY, Lee OK. Prediction of the development of acute kidney injury following cardiac surgery by machine learning. Crit Care. 2020 Jul 31;24(1):478. doi: 10.1186/s13054-020-03179-9.
- Individual participant dataSharing planned: No
Related trials for Erythrocyte Transfusion
RecruitingObservational study
Transfusion-Related Changes in Oxidative Stress Biomarkers in Neonates
Oxidative Stress in Neonates, Near Infrared Spectroscopy
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Erythrocyte Transfusion and Machine Learning, run by Kocaeli City Hospital at 1 site in Turkey (Türkiye).
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 19 Sep 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Turkey (Türkiye). Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
ML algorithm versus traditional scoring in predicting ES needs (time frame: During the intraoperative period Cardiac Surgery). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06594484, which also lists 5 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 19 Sep 2024; AzTrials last refreshed its copy on 1 Nov 2026.