Ignite Creation Date:
2024-10-26 @ 3:40 PM
Last Modification Date:
2024-10-26 @ 3:40 PM
Study NCT ID:
NCT06594484
Status:
COMPLETED
Last Update Posted:
None
First Post:
2024-09-06
Brief Title:
Machine Learning Versus Traditional Scores in Predicting Erythrocyte Need
Sponsor:
None
Organization:
None
Study Overview
Official Title:
Comparative Analysis of Machine Learning Versus Conventional Models for Predicting Erythrocyte Need in Cardiovascular Surgery
Status:
COMPLETED
Status Verified Date:
2024-09
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
No
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
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
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 Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
None
Is a FDA Regulated Device?:
None
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None