Clinical trial NCT06594484: Machine Learning Versus Traditional Scores in Predicting Erythrocyte Need
Official title: Comparative Analysis of Machine Learning Versus Conventional Models for Predicting Erythrocyte Need in Cardiovascular Surgery
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Erythrocyte Transfusion
Machine Learning
Keywords: Machine Learning
Cardiovascular Surgery
Perioperative Bleeding
Erythrocyte Transfusion
Predictive Modeling
Lead sponsor: Kocaeli City Hospital
Sponsor class: OTHER_GOV
First posted: 2024-09-19
Enrollment: 430
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Complete files from the data of patients undergoing elective cardiac surgery in Kocaeli City Hospital operating rooms will constitute the study data.

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.

Eligibility criteria: 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

Primary outcomes:
measure: ML algorithm versus traditional scoring in predicting ES needs
timeFrame: During the intraoperative period Cardiac Surgery
Secondary outcomes:
measure: Deterdetermining the most effective method for predicting ES needs using traditional scores
timeFrame: During the intraoperative period Cardiac Surgery
Other outcomes:
measure: ML algorithm of combination of scores
timeFrame: During the intraoperative period Cardiac Surgery
Locations:
city: Kocaeli
country: Turkey
facility: Kocaeli City Hospital
state: Izmıt
Study officials:
References:
citation: Park J, Bonde PN. Machine Learning in Cardiac Surgery: Predicting Mortality and Readmission. ASAIO J. 2022 Dec 1;68(12):1490-1500. doi: 10.1097/MAT.0000000000001696. Epub 2022 May 9.
citation: El-Sherbini AH, Shah A, Cheng R, Elsebaie A, Harby AA, Redfearn D, El-Diasty M. Machine Learning for Predicting Postoperative Atrial Fibrillation After Cardiac Surgery: A Scoping Review of Current Literature. Am J Cardiol. 2023 Dec 15;209:66-75. doi: 10.1016/j.amjcard.2023.09.079. Epub 2023 Oct 21.
citation: Shahian DM, Lippmann RP. Commentary: Machine learning and cardiac surgery risk prediction. J Thorac Cardiovasc Surg. 2022 Jun;163(6):2090-2092. doi: 10.1016/j.jtcvs.2020.08.058. Epub 2020 Aug 24. No abstract available.
citation: Miles TJ, Ghanta RK. Machine learning in cardiac surgery: a narrative review. J Thorac Dis. 2024 Apr 30;16(4):2644-2653. doi: 10.21037/jtd-23-1659. Epub 2024 Apr 24.
citation: Tseng PY, Chen YT, Wang CH, Chiu KM, Peng YS, Hsu SP, Chen KL, Yang CY, Lee OK. Prediction of the development of acute kidney injury following cardiac surgery by machine learning. Crit Care. 2020 Jul 31;24(1):478. doi: 10.1186/s13054-020-03179-9.