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Machine Learning Versus Traditional Scores in Predicting Erythrocyte Need

NCT06594484 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 430

Last updated 2024-09-19

No results posted yet for this study

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.

Conditions

  • Erythrocyte Transfusion
  • Machine Learning

Interventions

OTHER

Ml Based Algorithm 1

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

OTHER

Ml Based Algroithm 2

is an Ml algorithm created by combining commonly used bleeding scores

OTHER

Bleeding Scores

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

Sponsors & Collaborators

  • Kocaeli City Hospital

    lead OTHER_GOV

Eligibility

Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2024-02-22
Primary Completion
2024-05-22
Completion
2024-05-30

Countries

  • Turkey (Türkiye)

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06594484 on ClinicalTrials.gov