Machine Learning Versus Traditional Scores in Predicting Erythrocyte Need

Comparative Analysis of Machine Learning Versus Conventional Models for Predicting Erythrocyte Need in Cardiovascular Surgery

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.

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.

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