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Completed

NCT Number: NCT06594484

Machine Learning Versus Traditional Scores in Predicting Erythrocyte Need

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.

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Key information

Conditions

Erythrocyte Transfusion Machine Learning

Sex eligibility

All sexes

Study type

Observational

Primary location

Kocaeli City Hospital

Kocaeli, Izmıt, 41100, Turkey (Türkiye)

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

Ml Based Algorithm 1

Other

The values in the ML algorithm were selected according to logistic regression analysis and the values used in the other six scores tested. The success rate of the constructed networks correct predictions was considered as the success rate of the algorithm. The usefulness of the test was determined through AUROC analysis. Two algorithms were tested in our study. In the first algorithm (ML1), the dependent variable was erythrocyte suspension (ES) consumption, and the independent variables included patients; demographic data, laboratory data, and operational data

Ml Based Algroithm 2

Other

is an Ml algorithm created by combining commonly used bleeding scores

Bleeding Scores

Other

ACTION CRUSCADE TRACK WILL-BLEED PAPWORTH TRUST ACTAPORT skores used to predict ES need

Primary outcomes

  1. ML algorithm versus traditional scoring in predicting ES needs

    Time frame: During the intraoperative period Cardiac Surgery

    The success of the ML-based algorithm in correctly predicting the ES need will be calculated.

Secondary outcomes

  1. Deterdetermining the most effective method for predicting ES needs using traditional scores

    Time frame: 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.

Other outcomes

  1. ML algorithm of combination of scores

    Time frame: During the intraoperative period Cardiac Surgery

    Testing the success of the Ml algorithm based on ACTION CRUSCADE TRACK WILL-BLEED PAPWORTH TRUST ACTAPORT

Sponsors and collaborators

Lead sponsor

Kocaeli City Hospital

Other Gov

Registry information

Official study title

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

Important dates

Study start
2024
Primary completion
2024
Study completion
2024
First posted
Sep 19, 2024
Registry last updated
Sep 19, 2024

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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