Machine Learning Model for Perioperative Transfusion Prediction

Study at a glance

From the registry record, last updated by the sponsor on 8 Mar 2022. Recruitment status is as the sponsor reported it: confirm with the study team.

Official title

Development and Interpretation of a Machine Learning Model for Perioperative Transfusion Prediction

ClinicalTrials.gov ID

NCT05228548

Acronym / sponsor ID

Machine learning DiskapiTRH

Conditions

Surgery, Blood Transfusion

Keywords

Decision tree, eXtreme garadient boosting, k-nearest neighbors, Logistic regression, Machine learning, Prediction model, Random forest, Red blood cell transfusion

Study type

Observational

Recruitment status

Completed

Enrolment

6,121 participants (actual)

Lead sponsor

Diskapi Teaching and Research HospitalUniversity, hospital or other

Collaborators

Hacettepe University, Dokuz Eylul University, Saglik Bilimleri Universitesi, Bulent Ecevit University, Erzincan University, Kahramanmaras Sutcu Imam University, Ufuk University, Istanbul Medeniyet University, Marmara University, Eskisehir Osmangazi University, Inonu University, Mersin University, Istanbul University, Selcuk University, Balikesir University, Trakya University, Necmettin Erbakan University, Ankara University, Suleyman Demirel University, Tobb University of Economics and Technology, Akdeniz University, Uludag University, Ordu University, Gazi University, TC Erciyes University, Hitit University, Firat University, Karadeniz Technical University, Ondokuz Mayıs University, Yuzuncu Yil University, Namik Kemal University, Baskent University, Manisa Celal Bayar University, Osmaniye Government Hospital

Study start

13 Jan 2022 (actual)

Primary completion

1 Feb 2022 (actual)

Study completion

1 Feb 2022 (actual)

First posted

8 Feb 2022 (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.

Study design

Observational model

Other

Time perspective

Prospective

Who may qualify

Ages

18 to 100 years

Sex

All

Healthy volunteers

Not accepted

Age groups

Adult, Older adult

Study population

Patients undergoing major elective surgical procedures were considered eligible. All age groups and both genders were included. The surgical procedures were predefined according to the Classification of Diagnostic, Therapeutic, and Surgical Procedures of the National Social Insurance Institution, in which every procedure has a unique cod

Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.

Interventions

Primary outcome

1. Number of patients received Red blood cell transfusion

   Time frame: Perioperative period

   Number of patients received Red blood cell transfusion

2. The area under the curve

   Time frame: Perioperative period

   The the area under the curve of the receiver operating characteristics curves

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 8 Mar 2022.
• Results publication: PubMed 17998460Murphy 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.
• Results publication: PubMed 19476865Bernard 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.
• Results publication: PubMed 32092108Walczak 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.
• Individual participant dataSharing planned: No

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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.

1 site in 1 country: see every site and its status.