Résumé
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.
Résumé
ABSTRACT Introduction: Transfusion of red blood cells is recurrent in cardiac surgery despite the well-established deleterious effects. Identifying patients with higher chances of requiring blood transfusion is essential to apply strategic preventive measures to reduce such chances, considering the restricted availability of this product. The most used risk scores to predict blood transfusion are the Transfusion Risk and Clinical Knowledge (TRACK) and Transfusion Risk Understanding Scoring Tool (TRUST). However, these scores were not validated for the Brazilian population. The objective of this study was to assess the accuracy of TRACK and TRUST scores in estimating the need for postoperative transfusion of red blood cell concentrates (TRBCC) after cardiac surgery. Methods: A clinical retrospective study was conducted using the database of a Brazilian reference service composed of patients operated between November 2019 and September 2021. Scores were compared using Mann-Whitney U test. Hosmer-Lemeshow goodness of fit test assessed calibration of the scores. Accuracy was assessed using the area under the receiver operating characteristic curve (AUC). All analyses considered a level of significance of 5%. The study was approved by the research ethics committee (CAAE 55577421.4.0000.5201). Results: This study assessed 498 patients. Only the TRACK score presented good calibration (P=0.238; TRUST P=0.034). AUC of TRACK was 0.678 (95% confidence interval 0.63 to 0.73; P<0.001), showing a significant accuracy. Conclusion: Between the scores analyzed, only the TRACK score showed a good calibration, but low accuracy, to predict postoperative TRBCC after cardiac surgery.
Résumé
Abstract Background: Right valve diseases are not benign, the tricuspid regurgitation has a significant impact on morbidity and mortality of patients. Objectives: This study aimed to report the short-term results of tricuspid annuloplasty using the De Vega technique modified by Manuel Antunes. Methods: A descriptive-analytical study was performed to evaluate the results of the tricuspid valvuloplasty performed at the Instituto de Medicina Integral Professor Fernando Figueira between 2012 and 2017. Data were collected by reviewing charts and databases of the Department of Cardiology and Cardiovascular Surgery of the institution. Those with rheumatic diseases or infective endocarditis with tricuspid valve involvement, or reoperation of the tricuspid valve were excluded. Student's t-test and McNemar's were used for statistical analysis. A p-value < 0.05 was considered statistically significant. Results: A total of 87 patients were studied, most of them were women (56.3%). The most associated heart valve diseases were mitral regurgitation (27.6%) and aortic regurgitation (20.7%). There was a significant decrease in the degree of tricuspid regurgitation in the postoperative period, with 83.3% of patients with none or mild regurgitation and only 1.1% with severe regurgitation (p = 0.0077). Conclusions: In the current study, tricuspid valve annuloplasty using the modified De Vega technique was shown to be effective in the short term. Further studies are needed to evaluate the long-term results.