Predicting the Need for Blood Transfusions in Cardiac Surgery: A Comparison between Machine Learning Algorithms and Established Risk Scores in the Brazilian Population.

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.

Predicting the Need for Blood Transfusions in Cardiac Surgery: A Comparison between Machine Learning Algorithms and Established Risk Scores in the Brazilian Population

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.

Validation of Blood Transfusion Risk Scores (TRACK and TRUST) in a Cardiac Surgery Service in Brazil.

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.

Validation of Blood Transfusion Risk Scores (TRACK and TRUST) in a Cardiac Surgery Service in Brazil

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.

Short-term functional changes after hospital discharge by COVID-19 through teleconsultation at a reference service in Northeast Brazil: A cross-sectional study.

Coronavirus infection disease 2019 (COVID-19) was associated with a physical-functional and emotional decline in patients with COVID-19 hospital internment. Objective: Evaluate the main functional changes after hospital discharge after COVID-19 by teleconsultation. A cross-sectional study was carried out between April and July 2020, the peak period of new cases, hospital admission, and deaths by COVID-19, in Recife-Brazil. We included patients (n = 89) over 18 years with positive COVID-19 RT-PCR tests and hospitalized for more than 7 days. Functional aspects such as muscle pain, shortness of breath, cough, weight loss >5 kg, weakness/fatigue, daily living activities, balance, walking, lying down, sensitivity, anxiety/sadness, altered memory, or understanding were assessed. Besides this, the impact of hospital admission on daily activities and the quality of information obtained by teleconsultation were quantified. The mean age was 63.5 years (±14), and in the hospital, the mean internment was 18 days (±16). The main findings of this study showed four predominant functional alterations: weight loss greater than 5 kg (60.7%), muscle fatigue/weakness (53.9%), muscle/joint pain (43.8%), and anxiety/sadness (46.1%). Of the functions analyzed, 59.6% of patients reported dysfunctions in at least three of the alterations evaluated. There were several short-term physical-functional and emotional changes in adults after hospital discharge.

Early Outcomes of Modified De Vega Annuloplasty for Functional Tricuspid Regurgitation at a Brazilian Hospital

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.