Machine Learning Model for Perioperative Transfusion Prediction

NCT ID: NCT05228548

Last Updated: 2022-03-08

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Total Enrollment

6121 participants

Study Classification

OBSERVATIONAL

Study Start Date

2022-01-13

Study Completion Date

2022-02-01

Brief Summary

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This study aimed to develop and interpret a machine learning model to predict red blood cell (RBC) transfusion.

Detailed Description

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A dataset from a multicenter study involving 6121 patients underwent elective major surgery was analysed. Data concerning patients who received inappropriate RBC transfusion were excluded. Twenty one perioperative features were used to predict RBC transfusion. The data set was randomly split into train and validation sets (70-30). Decision tree, random forest, k-nearest neighbors, logistic regression, and eXtreme garadient boosting (XGBoost) methods were used for prediction. The area under the curves (AUC) of the receiver operating characteristics curves for the machine learning models used for RBC transfusion prediction were compared.

Conditions

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Surgery Blood Transfusion

Study Design

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Observational Model Type

OTHER

Study Time Perspective

PROSPECTIVE

Interventions

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Perioperative blood transfusion

Perioperative blood transfusion

Intervention Type OTHER

Eligibility Criteria

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Inclusion Criteria

* Adult
* Underwent major elective surgery

Exclusion Criteria

* Pediatric patients
* Emergency cases
Minimum Eligible Age

18 Years

Maximum Eligible Age

100 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Hacettepe University

OTHER

Sponsor Role collaborator

Dokuz Eylul University

OTHER

Sponsor Role collaborator

Saglik Bilimleri Universitesi

OTHER

Sponsor Role collaborator

Bulent Ecevit University

OTHER

Sponsor Role collaborator

Erzincan University

OTHER

Sponsor Role collaborator

Kahramanmaras Sutcu Imam University

OTHER

Sponsor Role collaborator

Ufuk University

OTHER

Sponsor Role collaborator

Istanbul Medeniyet University

OTHER

Sponsor Role collaborator

Marmara University

OTHER

Sponsor Role collaborator

Eskisehir Osmangazi University

OTHER

Sponsor Role collaborator

Inonu University

OTHER

Sponsor Role collaborator

Mersin University

OTHER

Sponsor Role collaborator

Istanbul University

OTHER

Sponsor Role collaborator

Selcuk University

OTHER

Sponsor Role collaborator

Balikesir University

OTHER

Sponsor Role collaborator

Trakya University

OTHER

Sponsor Role collaborator

Necmettin Erbakan University

OTHER

Sponsor Role collaborator

Ankara University

OTHER

Sponsor Role collaborator

Suleyman Demirel University

OTHER

Sponsor Role collaborator

Tobb University of Economics and Technology

OTHER

Sponsor Role collaborator

Akdeniz University

OTHER

Sponsor Role collaborator

Uludag University

OTHER

Sponsor Role collaborator

Ordu University

OTHER

Sponsor Role collaborator

Gazi University

OTHER

Sponsor Role collaborator

TC Erciyes University

OTHER

Sponsor Role collaborator

Hitit University

OTHER

Sponsor Role collaborator

Firat University

OTHER

Sponsor Role collaborator

Karadeniz Technical University

OTHER

Sponsor Role collaborator

Ondokuz Mayıs University

OTHER

Sponsor Role collaborator

Yuzuncu Yıl University

OTHER

Sponsor Role collaborator

Namik Kemal University

OTHER

Sponsor Role collaborator

Baskent University

OTHER

Sponsor Role collaborator

Celal Bayar University

OTHER

Sponsor Role collaborator

Osmaniye Government Hospital

OTHER_GOV

Sponsor Role collaborator

Diskapi Teaching and Research Hospital

OTHER

Sponsor Role lead

Responsible Party

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DILEK YAZICIOGLU

Prof. Dr. Dilek Unal

Responsibility Role PRINCIPAL_INVESTIGATOR

Principal Investigators

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Dilek D Unal, Prof

Role: PRINCIPAL_INVESTIGATOR

UNIVERSITY OF HEALTH SCIENCES TURKEY DISKAPI YILDIRIM BEYAZIT TRAINING RESEARCH HOSPITAL ANKARA

Locations

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Dilek D Unal

Ankara, , Turkey (Türkiye)

Site Status

Countries

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Turkey (Türkiye)

References

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Murphy GJ, Reeves BC, Rogers CA, Rizvi SI, Culliford L, Angelini GD. Increased mortality, postoperative morbidity, and cost after red blood cell transfusion in patients having cardiac surgery. Circulation. 2007 Nov 27;116(22):2544-52. doi: 10.1161/CIRCULATIONAHA.107.698977. Epub 2007 Nov 12.

Reference Type RESULT
PMID: 17998460 (View on PubMed)

Bernard AC, Davenport DL, Chang PK, Vaughan TB, Zwischenberger JB. Intraoperative transfusion of 1 U to 2 U packed red blood cells is associated with increased 30-day mortality, surgical-site infection, pneumonia, and sepsis in general surgery patients. J Am Coll Surg. 2009 May;208(5):931-7, 937.e1-2; discussion 938-9. doi: 10.1016/j.jamcollsurg.2008.11.019. Epub 2009 Mar 26.

Reference Type RESULT
PMID: 19476865 (View on PubMed)

Walczak S, Velanovich V. Prediction of perioperative transfusions using an artificial neural network. PLoS One. 2020 Feb 24;15(2):e0229450. doi: 10.1371/journal.pone.0229450. eCollection 2020.

Reference Type RESULT
PMID: 32092108 (View on PubMed)

Other Identifiers

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Machine learning DiskapiTRH

Identifier Type: -

Identifier Source: org_study_id

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