Machine Learning Model for Perioperative Transfusion Prediction

Completed

• Conditions: Blood Transfusion; Surgery

• Registration Number: NCT05228548
• Lead Sponsor: Diskapi Teaching and Research Hospital

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.

Study Timeline

• Status Verified: 2022-01
• Study Start: 2022-01-13
• Study First Submitted: 2022-01-13
• Study First Submitted QC: 2022-01-27
• Primary Completion: 2022-02-01
• Study Completion: 2022-02-01
• Study First Posted: 2022-02-08
• Last Update Submitted: 2022-03-07
• Last Update Posted: 2022-03-08

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 6121

Inclusion Criteria

• Adult
• Underwent major elective surgery

Exclusion Criteria

• Pediatric patients
• Emergency cases

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
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

Trial Locations

Locations (1)

Dilek D Unal; 🇹🇷 Ankara, Turkey