Predicting the Need for Blood Transfusions in Cardiac Surgery: A Comparison between Machine Learning Algorithms and Established Risk Scores in the Brazilian Population
Rev. bras. cir. cardiovasc
;
39(2): e20230212, 2024. tab, graf
Article
Dans Anglais
|
LILACS-Express
| LILACS
| ID: biblio-1535540
ABSTRACT
ABSTRACT Introduction: Blood transfusion is a common practice in cardiac surgery, despite its well-known negative effects. To mitigate blood transfusion-associated risks, identifying patients who are at higher risk of needing this procedure is crucial. Widely used risk scores to predict the need for blood transfusions have yielded unsatisfactory results when validated for the Brazilian population. Methods: In this retrospective study, machine learning (ML) algorithms were compared to predict the need for blood transfusions in a cohort of 495 cardiac surgery patients treated at a Brazilian reference service between 2019 and 2021. The performance of the models was evaluated using various metrics, including the area under the curve (AUC), and compared to the commonly used Transfusion Risk and Clinical Knowledge (TRACK) and Transfusion Risk Understanding Scoring Tool (TRUST) scoring systems. Results: The study found that the model had the highest performance, achieving an AUC of 0.7350 (confidence interval [CI]: 0.7203 to 0.7497). Importantly, all ML algorithms performed significantly better than the commonly used TRACK and TRUST scoring systems. TRACK had an AUC of 0.6757 (CI: 0.6609 to 0.6906), while TRUST had an AUC of 0.6622 (CI: 0.6473 to 0.6906). Conclusion: The findings of this study suggest that ML algorithms may offer a more accurate prediction of the need for blood transfusions than the traditional scoring systems and could enhance the accuracy of predicting blood transfusion requirements in cardiac surgery patients. Further research could focus on optimizing and refining ML algorithms to improve their accuracy and make them more suitable for clinical use.
Texte intégral:
Disponible
Indice:
LILAS (Amériques)
Pays comme sujet:
Amérique du Sud
/
Brésil
langue:
Anglais
Texte intégral:
Rev. bras. cir. cardiovasc
Thème du journal:
Cardiologie
/
Chirurgie générale
Année:
2024
Type:
Article
Pays d'affiliation:
Brésil
/
États-Unis d'Amérique
Institution/Pays d'affiliation:
Faculdade Pernambucana de Saúde/BR
/
Harvard Medical School/US
/
Instituto Federal de Pernambuco/BR
/
Instituto de Medicina Integral Professor Fernando Figueira/BR
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