Clinical trial NCT05228548: Machine Learning Model for Perioperative Transfusion Prediction
Official title: Development and Interpretation of a Machine Learning Model for Perioperative Transfusion Prediction
Study type: OBSERVATIONAL
Overall status: COMPLETED
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
Surgery
Lead sponsor: Diskapi Teaching and Research Hospital
Sponsor class: OTHER
Collaborators: name: Hacettepe University
name: Dokuz Eylul University
name: Saglik Bilimleri Universitesi
name: Bulent Ecevit University
name: Erzincan University
name: Kahramanmaras Sutcu Imam University
name: Ufuk University
name: Istanbul Medeniyet University
name: Marmara University
name: Eskisehir Osmangazi University
name: Inonu University
name: Mersin University
name: Istanbul University
name: Selcuk University
name: Balikesir University
name: Trakya University
name: Necmettin Erbakan University
name: Ankara University
name: Suleyman Demirel University
name: Tobb University of Economics and Technology
name: Akdeniz University
name: Uludag University
name: T.C. ORDU ÜNİVERSİTESİ
name: Gazi University
name: TC Erciyes University
name: Hitit University
name: Firat University
name: Karadeniz Technical University
name: Ondokuz Mayıs University
name: Yuzuncu Yıl University
name: Namik Kemal University
name: Baskent University
name: Celal Bayar University
name: Osmaniye Government Hospital
First posted: 2022-02-08
Enrollment: 6121
Sex: ALL
Minimum age (years): 18
Maximum age (years): 100
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: OTHER
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
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

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

Detailed description: 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.

Eligibility criteria: Inclusion Criteria:

* Adult
* Underwent major elective surgery

Exclusion Criteria:

* Pediatric patients
* Emergency cases

Primary outcomes:
measure: Number of patients received Red blood cell transfusion
timeFrame: Perioperative period
measure: The area under the curve
timeFrame: Perioperative period
Secondary outcomes:
Other outcomes:
Locations:
city: Ankara
country: Turkey
facility: Dilek D Unal
Study officials:
affiliation: UNIVERSITY OF HEALTH SCIENCES TURKEY DISKAPI YILDIRIM BEYAZIT TRAINING RESEARCH HOSPITAL ANKARA
name: Dilek D Unal, Prof
role: PRINCIPAL_INVESTIGATOR
References:
citation: 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.
citation: 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.
citation: 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.