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.
ABSTRACT
Abstract Objectives: to analyze the lethality and clinical characteristics in Pernambuco women with neoplasia that were infected by SARS-CoV-2. Methods: a cross-sectional, retrospective study with female patients with neoplasm sin the state of Pernambuco registered and made available by the Secretariat of Planning and Management of the State of Pernambuco (SEPLAG PE). Secondary data from public domain notifications and the independent factors associated with death were analyzed through logistic regression. The value ofp<0.25 was considered significant in the bivariate analysis and for a multivariate analysis, the value ofp<0.05 was considered significant. Results: forty-nine women died. The mean age and standard deviation were 58.75 ± 20.93 years. 55.86% of the patients were 60 years old or more. The overall lethality rate was 72.06% (CI95%=59.8 - 82.2). The most prevalent symptoms were fever (70.59%), cough (58.82%), dyspnea (57.35%) and O2 saturation less than 95% (48.53%). Conclusions: female patients, with cancer and infected by SARS-CoV-2 are particularly susceptible to death, regardless of the presence of comorbidities or age, with peripheral O2 saturation <95% being the only independent factor associated with death in this group.
Resumo Objetivos: analisar a letalidade e características clínicas em mulheres pernambucanas portadoras de neoplasia que apresentaram infecção por SARS-CoV-2. Métodos: estudo de corte transversal, retrospectivo com pacientes do sexo feminino, portadoras de neoplasias no estado de Pernambuco com registros disponibilizados pela Secretaria de Planejamento e Gestão do Estado de Pernambuco. Analisou-se dados secundários de notificações de domínio público e os fatores independentes associados ao óbito através de regressão logística. Foi considerado significativo o valor de p<0,25 na análise bivariada e para a análise multivariada foi considerado significativo o valor de p<0,05. Resultados: quarenta e nove mulheres vieram a óbito. A média da idade e desvio padrão foram 58, 75 ± 20,93 anos. 55,86% das pacientes tinham 60 anos ou mais. A taxa de letalidade global foi de 72,06% (IC95%= 59,8 - 82,2). Os sintomas mais prevalentes foram febre (70,59%), tosse (58,82%), dispneia (57,35%) e saturação de O2 <95% (48,53%). Conclusão: pacientes do sexo feminino, com câncer e infectadas pelo SARS-CoV-2 são particularmente suscetíveis a óbito, independentemente da presença de comorbidades ou da idade, sendo a saturação periférica de O2 <95% o único fator independente associado ao óbito nesse grupo.
Subject(s)
Humans , Female , Comorbidity , Risk Factors , SARS-CoV-2 , COVID-19/epidemiology , Neoplasms/diagnosis , Neoplasms/mortality , Brazil/epidemiology , Logistic Models , Indicators of Morbidity and Mortality , Multivariate Analysis , MortalityABSTRACT
Abstract Objectives: train a Random Forest (RF) classifier to estimate death risk in elderly people (over 60 years old) diagnosed with COVID-19 in Pernambuco. A "feature" of this classifier, called feature importance, was used to identify the attributes (main risk factors) related to the outcome (cure or death) through gaining information. Methods: data from confirmed cases of COVID-19 was obtained between February 13 and June 19, 2020, in Pernambuco, Brazil. The K-fold Cross Validation algorithm (K=10) assessed RF performance and the importance of clinical features. Results: the RF algorithm correctly classified 78.33% of the elderly people, with AUC of 0.839. Advanced age was the factor representing the highest risk of death. The main comorbidity and symptom were cardiovascular disease and oxygen saturation ≤ 95%, respectively. Conclusion: this study applied the RF classifier to predict risk of death and identified the main clinical features related to this outcome in elderly people with COVID-19 in the state of Pernambuco.
Resumo Objetivos: treinar um classificador do tipo Random Forest (RF) para estimar o risco de óbito em idosos (com mais de 60 anos) diagnosticados com COVID-19 em Pernambuco. Uma "feature" deste classificador, chamada feature_importance, foi usada para identificar os atributos (principais fatores de risco) relacionados com o desfecho final (cura ou óbito) através do ganho de informação. Métodos: dados dos casos confirmados de COVID-19foram obtidos entre os dias 13 de fevereiro e 19 de junho de 2020, em Pernambuco, Brasil. O algoritmo K-fold Cross Validation, com K=10, foi usado para avaliar tanto o desempenho do RF quanto a importância das características clínicas. Resultados: o algoritmo RF classificou corretamente 78,33% dos idosos, com AUC de 0,839. A idade avançada é o fator que representa maior risco de evolução para óbito. Além disso, a principal comorbidade e sintoma também identificados, foram, respectivamente, doença cardiovascular e saturação de oxigênio ≤95%. Conclusão: este trabalho se dedicou à aplicação do classificador RF para previsão de óbito e identificou as principais características clínicas relacionadas com este desfecho em idosos com COVID-19 no estado de Pernambuco.