Machine Learning Versus Traditional Scores in Predicting Erythrocyte Need

Completed

• Conditions: Erythrocyte Transfusion; Machine Learning
• Interventions: Other: Ml Based Algorithm 1; Other: Ml Based Algroithm 2; Other: Bleeding Scores

• Registration Number: NCT06594484
• Lead Sponsor: Kocaeli City Hospital

Brief Summary

In this study, we compared perioperative bleeding prediction scores with our machine learning-based prediction system in predicting the need for erythrocyte suspension during cardiovascular surgery.

Detailed Description

The success of ML algorithms in predicting perioperative blood product use in CABG remains an under-tested topic. Unnecessary preparation of blood products or not being able to supply them when necessary is critical for both patient safety and the effective use of hospital resources \[8\]. Bleeding amounts and blood product use strategies can vary with institute protocols. Scoring systems that determine the general framework may not perform well due to local factors. ML algorithms can be created locally according to previous patient data of each clinic and can improve themselves with learning mechanisms, suggesting significant potential in this field.

In the current study, a new estimation system created with the ML algorithm was compared with the known estimation systems. Comparing the ML algorithm with 6 different classical scoring systems is important in terms of demonstrating the potential of this technology.

The aim of this study is to investigate whether the model created with ML in predicting perioperative blood product consumption in cardiovascular surgeries is superior to predictive scoring systems that have proven themselves in the literature. Secondary aim is to compare the predictive value of using more than one scoring system in combination.

Study Timeline

• Study Start: 2024-02-22
• Primary Completion: 2024-05-22
• Study Completion: 2024-05-30
• Status Verified: 2024-09
• Study First Submitted: 2024-09-06
• Study First Submitted QC: 2024-09-10
• Last Update Submitted: 2024-09-10
• Study First Posted: 2024-09-13
• Last Update Posted: 2024-09-13

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 430

Inclusion Criteria

• Data from patients who underwent isolated CABG surgeries in the cardiac and vascular surgery operating rooms between 01.01.2023 and 01.01.2024 were evaluated.

Exclusion Criteria

• Missing Data
• Emergency surgery
• İntraoperative mortality

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
General Anesthesia Group	Ml Based Algorithm 1	The need for ES was recorded in patients undergoing cardiovascular surgery.
General Anesthesia Group	Ml Based Algroithm 2	The need for ES was recorded in patients undergoing cardiovascular surgery.
General Anesthesia Group	Bleeding Scores	The need for ES was recorded in patients undergoing cardiovascular surgery.

Primary Outcome Measures

Name	Time	Method
ML algorithm versus traditional scoring in predicting ES needs	During the intraoperative period Cardiac Surgery	The success of the ML-based algorithm in correctly predicting the ES need will be calculated.

Secondary Outcome Measures

Name	Time	Method
Deterdetermining the most effective method for predicting ES needs using traditional scores	During the intraoperative period Cardiac Surgery	After comparing the ACTION CRUSCADE TRACK WILL-BLEED PAPWORTH TRUST ACTAPORT scores, the most successful one scoring system will be revealed. The results will be shown numerically with the percentages of predicting the need for ES.

Trial Locations

Locations (1)

Kocaeli City Hospital; 🇹🇷 Kocaeli, Izmıt, Turkey