NCT05228548

Machine Learning Model for Perioperative Transfusion Prediction

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

• Status: Completed
• Phase: —
• Study type: Observational
• Enrollment: 6,121 (actual)
• Sponsor: Diskapi Teaching and Research Hospital · Academic / Other
• Sex: All
• Age: 18 Years – 100 Years
• Healthy volunteers: Not accepted

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.

Conditions

• Surgery
• Blood Transfusion

Interventions

Type	Name	Description
OTHER	Perioperative blood transfusion	Perioperative blood transfusion

Timeline

• Start date: 2022-01-13
• Primary completion: 2022-02-01
• Completion: 2022-02-01
• First posted: 2022-02-08
• Last updated: 2022-03-08

Locations

1 site across 1 country: Turkey (Türkiye)