Machine Learning Model for Perioperative Transfusion Prediction

Diskapi Teaching and Research Hospital

Status: Completed

Conditions: Surgery; Blood Transfusion

Treatments: Other: Perioperative blood transfusion

Study type: Observational

Funder types: Other

Identifiers

NCT05228548

Machine learning DiskapiTRH

Details and patient eligibility

About

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

Full 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.

Enrollment: 6,121 patients

Sex: All

Ages: 18 to 100 years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• Adult
• Underwent major elective surgery

Exclusion criteria

• Pediatric patients
• Emergency cases