Blood Transfusion, Surgery
Conditions
Keywords
Decision tree, eXtreme garadient boosting, k-nearest neighbors, Logistic regression, Machine learning, Prediction model, Random forest, Red blood cell transfusion, Surgery
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.
Interventions
Perioperative blood transfusion
Sponsors
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
Celal Bayar University
Osmaniye Government Hospital
Diskapi Teaching and Research Hospital
Study design
Observational model
OTHER
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 100 Years
Healthy volunteers
No
Inclusion criteria
* Adult * Underwent major elective surgery
Exclusion criteria
* Pediatric patients * Emergency cases
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Number of patients received Red blood cell transfusion | Perioperative period | Number of patients received Red blood cell transfusion |
| The area under the curve | Perioperative period | The the area under the curve of the receiver operating characteristics curves |
Countries
Turkey (Türkiye)
Outcome results
None listed