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1. Temporal validation of the MMCD score to predict kidney replacement therapy and in-hospital mortality in COVID-19 patients

Author

Vanessa das Graças José Ventura, Polianna Delfino Pereira, Magda Carvalho Pires, Alisson Alves Asevedo, Alzira de Oliveira Jorge, Ana Carolina Pitanga dos Santos, André Soares de Moura Costa, Angélica Gomides dos Reis Gomes, Beatriz Figueiredo Lima, Bruno Porto Pessoa, Christiane Corrêa Rodrigues Cimini, Claudio Moisés Valiense de Andrade, Daniela Ponce, Danyelle Romana Alves Rios, Elayne Crestani Pereira, Euler Roberto Fernandes Manenti, Evelin Paola de Almeida Cenci, Felício Roberto Costa, Fernando Anschau, Fernando Graça Aranha, Flavia Maria Borges Vigil, Frederico Bartolazzi, Gabriella Genta Aguiar, Genna Maira Santos Grizende, Joanna d’Arc Lyra Batista, João Victor Baroni Neves, Karen Brasil Ruschel, Letícia do Nascimento, Lucas Moyses Carvalho de Oliveira, Luciane Kopittke, Luís César de Castro, Manuela Furtado Sacioto, Marcelo Carneiro, Marcos André Gonçalves, Maria Aparecida Camargos Bicalho, Mônica Aparecida da Paula Sordi, Natália da Cunha Severino Sampaio, Pedro Gibson Paraíso, Rochele Mosmann Menezes, Silvia Ferreira Araújo, Vivian Costa Morais de Assis, Katia de Paula Farah, and Milena Soriano Marcolino

Subjects
COVID-19, Acute kidney injury, Kidney replacement therapy, Score predictive, Risk prediction, Mortality, Diseases of the genitourinary system. Urology, RC870-923
Abstract

Abstract Background Acute kidney injury has been described as a common complication in patients hospitalized with COVID-19, which may lead to the need for kidney replacement therapy (KRT) in its most severe forms. Our group developed and validated the MMCD score in Brazilian COVID-19 patients to predict KRT, which showed excellent performance using data from 2020. This study aimed to validate the MMCD score in a large cohort of patients hospitalized with COVID-19 in a different pandemic phase and assess its performance to predict in-hospital mortality. Methods This study is part of the “Brazilian COVID-19 Registry”, a retrospective observational cohort of consecutive patients hospitalized for laboratory-confirmed COVID-19 in 25 Brazilian hospitals between March 2021 and August 2022. The primary outcome was KRT during hospitalization and the secondary was in-hospital mortality. We also searched literature for other prediction models for KRT, to assess the results in our database. Performance was assessed using area under the receiving operator characteristic curve (AUROC) and the Brier score. Results A total of 9422 patients were included, 53.8% were men, with a median age of 59 (IQR 48–70) years old. The incidence of KRT was 8.8% and in-hospital mortality was 18.1%. The MMCD score had excellent discrimination and overall performance to predict KRT (AUROC: 0.916 [95% CI 0.909–0.924]; Brier score = 0.057). Despite the excellent discrimination and overall performance (AUROC: 0.922 [95% CI 0.914–0.929]; Brier score = 0.100), the calibration was not satisfactory concerning in-hospital mortality. A random forest model was applied in the database, with inferior performance to predict KRT requirement (AUROC: 0.71 [95% CI 0.69–0.73]). Conclusion The MMCD score is not appropriate for in-hospital mortality but demonstrates an excellent predictive ability to predict KRT in COVID-19 patients. The instrument is low cost, objective, fast and accurate, and can contribute to supporting clinical decisions in the efficient allocation of assistance resources in patients with COVID-19.

Published
2023
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2. Clinical characteristics and outcomes of hospital-manifested COVID-19 among Brazilians

Author

Polianna Delfino-Pereira, Magda Carvalho Pires, Virginia Mara Reis Gomes, Matheus Carvalho Alves Nogueira, Maria Clara Pontello Barbosa Lima, Alexandre Vargas Schwarzbold, Amanda de Oliveira Maurílio, Ana Luiza Bahia Alves Scotton, André Soares de Moura Costa, Barbara Lopes Farace, Bruno Mateus de Castro, Christiane Corrêa Rodrigues Cimini, Daniel Vitório Silveira, Daniela Ponce, Elayne Crestani Pereira, Eliane Würdig Roesch, Euler Roberto Fernandes Manenti, Evelin Paola de Almeida Cenci, Fernanda Costa dos Santos, Fernando Anschau, Fernando Graça Aranha, Frederico Bartolazzi, Guilherme Fagundes Nascimento, Heloisa Reniers Vianna, Joanna d'Arc Lyra Batista, Joice Coutinho de Alvarenga, Juliana da Silva Nogueira Carvalho, Juliana Machado-Rugolo, Karen Brasil Ruschel, Luanna Silva Monteiro Menezes, Luís César de Castro, Luiz Antônio Nasi, Maiara Anschau Floriani, Maíra Dias Souza, Maíra Viana Rego Souza-Silva, Marcelo Carneiro, Maria Aparecida Camargos Bicalho, Mariana Frizzo de Godoy, Milton Henriques Guimarães-Júnior, Patricia Klarmann Ziegelmann, Pedro Ledic Assaf, Petrônio José de Lima Martelli, Renan Goulart Finger, Saionara Cristina Francisco, Silvia Ferreira Araújo, Talita Fischer Oliveira, Thainara Conceição de Oliveira, Thalita Martins Lage, Vanessa Muller, Yuri Carlotto Ramires, Teresa Cristina de Abreu Ferrari, and Milena Soriano Marcolino

Subjects
COVID-19, Hospital-manifested infection, Clinical characteristics, Outcomes, Risk factors, In-hospital mortality, Infectious and parasitic diseases, RC109-216
Abstract

ABSTRACT: Objectives: To analyze the clinical characteristics and outcomes of admitted patients with the hospital- versus community-manifested COVID-19 and to evaluate the risk factors related to mortality in the first population. Methods: This retrospective cohort included consecutive adult patients with COVID-19, hospitalized between March and September 2020. The demographic data, clinical characteristics, and outcomes were extracted from medical records. Patients with hospital-manifested COVID-19 (study group) and those with community-manifested COVID-19 (control group) were matched by the propensity score model. Logistic regression models were used to verify the risk factors for mortality in the study group. Results: Among 7,710 hospitalized patients who had COVID-19, 7.2% developed symptoms while admitted for other reasons. Patients with hospital-manifested COVID-19 had a higher prevalence of cancer (19.2% vs 10.8%) and alcoholism (8.8% vs 2.8%) than patients with community-manifested COVID-19 and also had a higher rate of intensive care unit requirement (45.1% vs 35.2%), sepsis (23.8% vs 14.5%), and death (35.8% vs 22.5%) (P

Published
2023
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3. Potential and limitations of machine meta-learning (ensemble) methods for predicting COVID-19 mortality in a large inhospital Brazilian dataset

Author

Bruno Barbosa Miranda de Paiva, Polianna Delfino Pereira, Claudio Moisés Valiense de Andrade, Virginia Mara Reis Gomes, Maira Viana Rego Souza-Silva, Karina Paula Medeiros Prado Martins, Thaís Lorenna Souza Sales, Rafael Lima Rodrigues de Carvalho, Magda Carvalho Pires, Lucas Emanuel Ferreira Ramos, Rafael Tavares Silva, Alessandra de Freitas Martins Vieira, Aline Gabrielle Sousa Nunes, Alzira de Oliveira Jorge, Amanda de Oliveira Maurílio, Ana Luiza Bahia Alves Scotton, Carla Thais Candida Alves da Silva, Christiane Corrêa Rodrigues Cimini, Daniela Ponce, Elayne Crestani Pereira, Euler Roberto Fernandes Manenti, Fernanda d’Athayde Rodrigues, Fernando Anschau, Fernando Antônio Botoni, Frederico Bartolazzi, Genna Maira Santos Grizende, Helena Carolina Noal, Helena Duani, Isabela Moraes Gomes, Jamille Hemétrio Salles Martins Costa, Júlia di Sabatino Santos Guimarães, Julia Teixeira Tupinambás, Juliana Machado Rugolo, Joanna d’Arc Lyra Batista, Joice Coutinho de Alvarenga, José Miguel Chatkin, Karen Brasil Ruschel, Liege Barella Zandoná, Lílian Santos Pinheiro, Luanna Silva Monteiro Menezes, Lucas Moyses Carvalho de Oliveira, Luciane Kopittke, Luisa Argolo Assis, Luiza Margoto Marques, Magda Cesar Raposo, Maiara Anschau Floriani, Maria Aparecida Camargos Bicalho, Matheus Carvalho Alves Nogueira, Neimy Ramos de Oliveira, Patricia Klarmann Ziegelmann, Pedro Gibson Paraiso, Petrônio José de Lima Martelli, Roberta Senger, Rochele Mosmann Menezes, Saionara Cristina Francisco, Silvia Ferreira Araújo, Tatiana Kurtz, Tatiani Oliveira Fereguetti, Thainara Conceição de Oliveira, Yara Cristina Neves Marques Barbosa Ribeiro, Yuri Carlotto Ramires, Maria Clara Pontello Barbosa Lima, Marcelo Carneiro, Adriana Falangola Benjamin Bezerra, Alexandre Vargas Schwarzbold, André Soares de Moura Costa, Barbara Lopes Farace, Daniel Vitorio Silveira, Evelin Paola de Almeida Cenci, Fernanda Barbosa Lucas, Fernando Graça Aranha, Gisele Alsina Nader Bastos, Giovanna Grunewald Vietta, Guilherme Fagundes Nascimento, Heloisa Reniers Vianna, Henrique Cerqueira Guimarães, Julia Drumond Parreiras de Morais, Leila Beltrami Moreira, Leonardo Seixas de Oliveira, Lucas de Deus Sousa, Luciano de Souza Viana, Máderson Alvares de Souza Cabral, Maria Angélica Pires Ferreira, Mariana Frizzo de Godoy, Meire Pereira de Figueiredo, Milton Henriques Guimarães-Junior, Mônica Aparecida de Paula de Sordi, Natália da Cunha Severino Sampaio, Pedro Ledic Assaf, Raquel Lutkmeier, Reginaldo Aparecido Valacio, Renan Goulart Finger, Rufino de Freitas, Silvana Mangeon Meirelles Guimarães, Talita Fischer Oliveira, Thulio Henrique Oliveira Diniz, Marcos André Gonçalves, and Milena Soriano Marcolino

Subjects
Medicine, Science
Abstract

Abstract The majority of early prediction scores and methods to predict COVID-19 mortality are bound by methodological flaws and technological limitations (e.g., the use of a single prediction model). Our aim is to provide a thorough comparative study that tackles those methodological issues, considering multiple techniques to build mortality prediction models, including modern machine learning (neural) algorithms and traditional statistical techniques, as well as meta-learning (ensemble) approaches. This study used a dataset from a multicenter cohort of 10,897 adult Brazilian COVID-19 patients, admitted from March/2020 to November/2021, including patients [median age 60 (interquartile range 48–71), 46% women]. We also proposed new original population-based meta-features that have not been devised in the literature. Stacking has shown to achieve the best results reported in the literature for the death prediction task, improving over previous state-of-the-art by more than 46% in Recall for predicting death, with AUROC 0.826 and MacroF1 of 65.4%. The newly proposed meta-features were highly discriminative of death, but fell short in producing large improvements in final prediction performance, demonstrating that we are possibly on the limits of the prediction capabilities that can be achieved with the current set of ML techniques and (meta-)features. Finally, we investigated how the trained models perform on different hospitals, showing that there are indeed large differences in classifier performance between different hospitals, further making the case that errors are produced by factors that cannot be modeled with the current predictors.

Published
2023
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4. Hypothyroidism does not lead to worse prognosis in COVID-19: findings from the Brazilian COVID-19 registry

Author

Daniella Nunes Pereira, Leticia Ferreira Gontijo Silveira, MD, PhD, Milena Maria Moreira Guimarães, Carísi Anne Polanczyk, Aline Gabrielle Sousa Nunes, André Soares de Moura Costa, Barbara Lopes Farace, Christiane Corrêa Rodrigues Cimini, Cíntia Alcantara de Carvalho, Daniela Ponce, Eliane Würdig Roesch, Euler Roberto Fernandes Manenti, Fernanda Barbosa Lucas, Fernanda d'Athayde Rodrigues, Fernando Anschau, Fernando Graça Aranha, Frederico Bartolazzi, Giovanna Grunewald Vietta, Guilherme Fagundes Nascimento, Helena Duani, Heloisa Reniers Vianna, Henrique Cerqueira Guimarães, Jamille Hemétrio Salles Martins Costa, Joanna d'Arc Lyra Batista, Joice Coutinho de Alvarenga, José Miguel Chatkin, Júlia Drumond Parreiras de Morais, Juliana Machado-Rugolo, Karen Brasil Ruschel, Lílian Santos Pinheiro, Luanna Silva Monteiro Menezes, Luciana Siuves Ferreira Couto, Luciane Kopittke, Luís César de Castro, Luiz Antônio Nasi, Máderson Alvares de Souza Cabral, Maiara Anschau Floriani, Maíra Dias Souza, Marcelo Carneiro, Maria Aparecida Camargos Bicalho, Mariana Frizzo de Godoy, Matheus Carvalho Alves Nogueira, Milton Henriques Guimarães Júnior, Natália da Cunha Severino Sampaio, Neimy Ramos de Oliveira, Pedro Ledic Assaf, Renan Goulart Finger, Roberta Xavier Campos, Rochele Mosmann Menezes, Saionara Cristina Francisco, Samuel Penchel Alvarenga, Silvana Mangeon Mereilles Guimarães, Silvia Ferreira Araújo, Talita Fischer Oliveira, Thulio Henrique Oliveira Diniz, Yuri Carlotto Ramires, Evelin Paola de Almeida Cenci, Thainara Conceição de Oliveira, Alexandre Vargas Schwarzbold, Patricia Klarmann Ziegelmann, Roberta Pozza, Caroline Scherer Carvalho, Magda Carvalho Pires, and Milena Soriano Marcolino

Subjects
Hypothyroidism, COVID-19, Mortality, Prognosis, Epidemiology, Infectious and parasitic diseases, RC109-216
Abstract

Background: It is not clear whether previous thyroid diseases influence the course and outcomes of COVID-19. Methods: The study is a part of a multicentric cohort of patients with confirmed COVID-19 diagnosis from 37 hospitals. Matching for age, sex, number of comorbidities, and hospital was performed for the paired analysis. Results: Of 7,762 patients with COVID-19, 526 had previously diagnosed hypothyroidism and 526 were matched controls. The median age was 70 years, and 68.3% were females. The prevalence of comorbidities was similar, except for coronary and chronic kidney diseases that were higher in the hypothyroidism group (p=0.015 and p=0.001). D-dimer levels were lower in patients with hypothyroid (p=0.037). In-hospital management was similar, but hospital length-of-stay (p=0.029) and mechanical ventilation requirement (p=0.006) were lower for patients with hypothyroidism. There was a trend of lower in-hospital mortality in patients with hypothyroidism (22.1% vs 27.0%; p=0.062). Conclusion: Patients with hypothyroidism had a lower requirement of mechanical ventilation and showed a trend of lower in-hospital mortality. Therefore, hypothyroidism does not seem to be associated with a worse prognosis.

Published
2022
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6. Extracorporeal membrane oxygenation outcomes in COVID‐19 patients: Case series from the Brazilian COVID‐19 Registry

Author

Daniela Ponce, Rafael Lima Rodrigues Carvalho, Magda Carvalho Pires, Heloisa Reniers Vianna, Matheus Carvalho Alves Nogueira, Fernando Antonio Botoni, Fernando Graça Aranha, André Soares de Moura Costa, Giovanna Grunewald Vietta, Felipe Ferraz Martins Graça Aranha, Virginia Mara Reis Gomes, Ana Paula Beck da Silva Etges, Antonio Tolentino Nogueira Sá, Polianna Delfino Pereira, Milena Soriano Marcolino, Universidade Estadual Paulista (UNESP), Institute for Health Technology Assessment (IATS/CNPq), Universidade Federal de Minas Gerais (UFMG), Hospital Universitário Ciências Médicas, Hospitais da Rede Mater Dei, Rede Mater Dei de Saúde, Hospital SOS Cárdio, Centro Universitário de Belo Horizonte (UniBH), and Universidade Federal do Rio Grande do Sul

Subjects
Male, Respiratory Distress Syndrome, Biomedical Engineering, COVID-19, Medicine (miscellaneous), Bioengineering, General Medicine, extracorporeal membrane oxygenation, Middle Aged, mortality, Biomaterials, Extracorporeal Membrane Oxygenation, Humans, Female, Registries, propensity score, Brazil
Abstract

Made available in DSpace on 2022-04-28T19:48:25Z (GMT). No. of bitstreams: 0 Previous issue date: 2022-05-01 Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG) Around 5% of coronavirus disease 2019 (COVID-19) patients develop critical disease, with severe pneumonia and acute respiratory distress syndrome (ARDS). In these cases, extracorporeal membrane oxygenation (ECMO) may be considered when conventional therapy fails. This study aimed to describe the clinical characteristics and in-hospital outcomes of COVID-19 patients with ARDS refractory to lung-protective ventilation and prone positioning on ECMO support, as well as to review the available literature on ECMO use and COVID-19 patients’ outcome. Patients from this case series were selected from the Brazilian COVID-19 Registry. From the 7646 patients included in the registry, only eight received ECMO support (0.1%), in four hospitals. The median age of the entire sample was 59 (interquartile range 54.2–64.4) years old and 87.5% were male. Hypertension (50.0%), diabetes mellitus (50.0%) and obesity (37.5%) were the most frequent comorbidities. The indications for ECMO were PaO2/FiO2 ratio

Published
2021

7. Hospital characteristics associated with COVID-19 mortality: data from the multicenter cohort Brazilian Registry

Author

Maira Viana Rego Souza-Silva, Patricia Klarmann Ziegelmann, Vandack Nobre, Virginia Mara Reis Gomes, Ana Paula Beck da Silva Etges, Alexandre Vargas Schwarzbold, Aline Gabrielle Sousa Nunes, Amanda de Oliveira Maurílio, Ana Luiza Bahia Alves Scotton, André Soares de Moura Costa, Andressa Barreto Glaeser, Bárbara Lopes Farace, Bruno Nunes Ribeiro, Carolina Marques Ramos, Christiane Corrêa Rodrigues Cimini, Cíntia Alcantara de Carvalho, Claudete Rempel, Daniel Vitório Silveira, Daniela dos Reis Carazai, Daniela Ponce, Elayne Crestani Pereira, Emanuele Marianne Souza Kroger, Euler Roberto Fernandes Manenti, Evelin Paola de Almeida Cenci, Fernanda Barbosa Lucas, Fernanda Costa dos Santos, Fernando Anschau, Fernando Antonio Botoni, Fernando Graça Aranha, Filipe Carrilho de Aguiar, Frederico Bartolazzi, Gabriela Petry Crestani, Giovanna Grunewald Vietta, Guilherme Fagundes Nascimento, Helena Carolina Noal, Helena Duani, Heloisa Reniers Vianna, Henrique Cerqueira Guimarães, Joice Coutinho de Alvarenga, José Miguel Chatkin, Júlia Drumond Parreiras de Morais, Juliana da Silva Nogueira Carvalho, Juliana Machado Rugolo, Karen Brasil Ruschel, Lara de Barros Wanderley Gomes, Leonardo Seixas de Oliveira, Liege Barella Zandoná, Lílian Santos Pinheiro, Liliane Souto Pacheco, Luanna da Silva Monteiro Menezes, Lucas de Deus Sousa, Luis Cesar Souto de Moura, Luisa Elem Almeida Santos, Luiz Antonio Nasi, Máderson Alvares de Souza Cabral, Maiara Anschau Floriani, Maíra Dias Souza, Marcelo Carneiro, Mariana Frizzo de Godoy, Marilia Mastrocolla de Almeida Cardoso, Matheus Carvalho Alves Nogueira, Mauro Oscar Soares de Souza Lima, Meire Pereira de Figueiredo, Milton Henriques Guimarães-Júnior, Natália da Cunha Severino Sampaio, Neimy Ramos de Oliveira, Pedro Guido Soares Andrade, Pedro Ledic Assaf, Petrônio José de Lima Martelli, Raphael Castro Martins, Reginaldo Aparecido Valacio, Roberta Pozza, Rochele Mosmann Menezes, Rodolfo Lucas Silva Mourato, Roger Mendes de Abreu, Rufino de Freitas Silva, Saionara Cristina Francisco, Silvana Mangeon Mereilles Guimarães, Silvia Ferreira Araújo, Talita Fischer Oliveira, Tatiana Kurtz, Tatiani Oliveira Fereguetti, Thainara Conceição de Oliveira, Yara Cristina Neves Marques Barbosa Ribeiro, Yuri Carlotto Ramires, Carísi Anne Polanczyk, and Milena Soriano Marcolino

Subjects
Cohort Studies, Intensive Care Units, Adolescent, Emergency Medicine, Internal Medicine, Humans, COVID-19, Hospital Mortality, Registries, Hospitals, General, Pandemics, Brazil, Retrospective Studies
Abstract

The COVID-19 pandemic caused unprecedented pressure over health care systems worldwide. Hospital-level data that may influence the prognosis in COVID-19 patients still needs to be better investigated. Therefore, this study analyzed regional socioeconomic, hospital, and intensive care units (ICU) characteristics associated with in-hospital mortality in COVID-19 patients admitted to Brazilian institutions. This multicenter retrospective cohort study is part of the Brazilian COVID-19 Registry. We enrolled patients ≥ 18 years old with laboratory-confirmed COVID-19 admitted to the participating hospitals from March to September 2020. Patients' data were obtained through hospital records. Hospitals' data were collected through forms filled in loco and through open national databases. Generalized linear mixed models with logit link function were used for pooling mortality and to assess the association between hospital characteristics and mortality estimates. We built two models, one tested general hospital characteristics while the other tested ICU characteristics. All analyses were adjusted for the proportion of high-risk patients at admission. Thirty-one hospitals were included. The mean number of beds was 320.4 ± 186.6. These hospitals had eligible 6556 COVID-19 admissions during the study period. Estimated in-hospital mortality ranged from 9.0 to 48.0%. The first model included all 31 hospitals and showed that a private source of funding (β = - 0.37; 95% CI - 0.71 to - 0.04; p = 0.029) and location in areas with a high gross domestic product (GDP) per capita (β = - 0.40; 95% CI - 0.72 to - 0.08; p = 0.014) were independently associated with a lower mortality. The second model included 23 hospitals and showed that hospitals with an ICU work shift composed of more than 50% of intensivists (β = - 0.59; 95% CI - 0.98 to - 0.20; p = 0.003) had lower mortality while hospitals with a higher proportion of less experienced medical professionals had higher mortality (β = 0.40; 95% CI 0.11-0.68; p = 0.006). The impact of those association increased according to the proportion of high-risk patients at admission. In-hospital mortality varied significantly among Brazilian hospitals. Private-funded hospitals and those located in municipalities with a high GDP had a lower mortality. When analyzing ICU-specific characteristics, hospitals with more experienced ICU teams had a reduced mortality.

Published
2022

8. Frequency and burden of neurological manifestations upon hospital presentation in COVID-19 patients: Findings from a large Brazilian cohort

Author

Milena Soriano Marcolino, Fernando Anschau, Luciane Kopittke, Magda Carvalho Pires, Izabela Guimarães Barbosa, Daniella Nunes Pereira, Lucas Emanuel Ferreira Ramos, Luís Fernando Israel Assunção, André Soares de Moura Costa, Matheus Carvalho Alves Nogueira, Helena Duani, Karina Paula Medeiros Prado Martins, Leila Beltrami Moreira, Carla Thais Cândida Alves da Silva, Neimy Ramos de Oliveira, Patricia Klarmann Ziegelmann, Milton Henriques Guimarães-Júnior, Mauro Oscar Soares de Souza Lima, Rubia Laura Oliveira Aguiar, Luanna Silva Monteiro Menezes, Talita Fischer Oliveira, Maíra Dias Souza, Bárbara Lopes Farace, Christiane Corrêa Rodrigues Cimini, Amanda de Oliveira Maurílio, Silvana Mangeon Mereilles Guimarães, Silvia Ferreira Araújo, Guilherme Fagundes Nascimento, Daniel Vitório Silveira, Karen Brasil Ruschel, Thainara Conceição de Oliveira, Alexandre Vargas Schwarzbold, Luiz Antônio Nasi, Maiara Anschau Floriani, Veridiana Baldon dos Santos, Carolina Marques Ramos, Joice Coutinho de Alvarenga, Ana Luiza Bahia Alves Scotton, Euler Roberto Fernandes Manenti, Gabriela Petry Crestani, Joanna d'Arc Lyra Batista, Daniela Ponce, Juliana Machado-Rugolo, Adriana Falangola Benjamin Bezerra, Petrônio José de Lima Martelli, Heloisa Reniers Vianna, Luís César de Castro, Cássia Regina Gotler Medeiros, Giovanna Grunewald Vietta, Elayne Crestani Pereira, José Miguel Chatkin, Mariana Frizzo de Godoy, Polianna Delfino-Pereira, and Antonio Lucio Teixeira

Subjects
Neurology, SARS-CoV-2, Anosmia, Headache, Humans, COVID-19, Neurology (clinical), Ageusia, Shock, Septic, Brazil, Hospitals
Abstract

Scientific data regarding the prevalence of COVID-19 neurological manifestations and prognosis in Latin America countries is still lacking. Therefore, the study aims to understand neurological manifestations of SARS-CoV 2 infection and outcomes in the Brazilian population.This study is part of the Brazilian COVID-19 Registry, a multicentric cohort, including data from 37 hospitals. For the present analysis, patients were grouped according to the presence of reported symptoms (i.e., headache; anosmia and ageusia; syncope and dizziness) vs. clinically-diagnosed neurological manifestations (clinically-defined neurological syndrome: neurological signs or diagnoses captured by clinical evaluation) and matched with patients without neurological manifestations by age, sex, number of comorbidities, hospital of admission, and whether or not patients had underlying neurological disease.From 6,635 hospitalized patients with COVID-19, 30.8% presented reported neurological manifestations, 10.3% were diagnosed with a neurological syndrome and 60.1% did not show any neurological manifestations. In patients with reported symptoms, the most common ones were headache (20.7%), ageusia (11.1%) and anosmia (8.0%). In patients with neurological syndromes, acute encephalopathy was the most common diagnosis (9.7%). In the matched analysis, patients with neurological syndromes presented more cases of septic shock (17.0 vs. 13.0%, p = 0.045), intensive care unit admission (45.3 vs. 38.9%, p = 0.023), and mortality (38.7 vs. 32.6%, p = 0.026; and 39.2 vs. 30.3%, p 0.001) when compared to controls.COVID-19 in-hospital patients with clinically defined neurological syndromes presented a higher incidence of septic shock, ICU admission and death when compared to controls.

Published
2022

9. Effectiveness, Explainability and Reliability of Machine Meta-Learning Methods for Predicting Mortality in Patients with COVID-19: Results of the Brazilian COVID-19 Registry

Author

Polianna Delfino-Pereira, Cláudio Moisés Valiense De Andrade, Virginia Mara Reis Gomes, Maria Clara Pontello Barbosa Lima, Maira Viana Rego Souza-Silva, Marcelo Carneiro, Karina Paula Medeiros Prado Martins, Thaís Lorenna Souza Sales, Rafael Lima Rodrigues De Carvalho, Magda Carvalho Pires, Lucas Emanuel Ferreira Ramos, Rafael T Silva, Adriana Falangola Benjamin Bezerra, Alexandre Vargas Schwarzbold, Aline Gabrielle Sousa Nunes, Amanda de Oliveira Maurilio, Ana Luiza Bahia Alves Scotton, André Soares de Moura Costa, Andriele Abreu Castro, Bárbara Lopes Farace, Christiane Corrêa Rodrigues Cimini, Cíntia Alcântara De Carvalho, Daniel Vitorio Silveira, Daniela Ponce, Elayne Crestani Pereira, Euler Roberto Fernandes Manenti, Evelin Paola de Almeida Cenci, Fernanda Barbosa Lucas, Fernanda d’Athayde Rodrigues, Fernando Anschau, Fernando Antônio Botoni, Fernando Graça Aranha, Frederico Bartolazzi, Gisele Alsina Nader Bastos, Giovanna Grunewald Vietta, Guilherme Fagundes Nascimento, Helena Carolina Noal, Helena Duani, Heloísa Reniers Vianna, Henrique Cerqueira Guimarães, Isabela Moraes Gomes, Jamille Hemerito Salles Martins Costa, Jessica Rayane Corrêa Silva Da Fonseca, Júlia Di Sabatino Santos Guimarães, Júlia Drumond Parreiras De Morais, Juliana Machado Rugolo, Joanna d’Arc Lyra Batista, Joice Coutinho De Alvarenga, José Miguel Chatkin, Karen Brasil Ruschel, Leila Beltrami Moreira, Leonardo Seixas De Oliveira, Liege Barella Zandona, Lilian Santos Pinheiro, Luanna da Silva Monteiro, Lucas de Deus Sousa, Luciane Kopittke, Luciano de Souza Viana, Luís César De Castro, Luísa Argolo Assis, Luísa Elem Almeida Santos, Maderson Álvares de Souza Cabral, Magda Cesar Raposo, Maiara Anschau Floriani, Maria Angélica Pires Ferreira, Maria Aparecida Camargos Bicalho, Mariana Frizzo De Godoy, Matheus Carvalho Alves Nogueira, Meire Pereira De Figueiredo, Milton Henriques Guimarães Júnior, Monica Aparecida de Paula De Sordi, Natália da Cunha Severino Sampaio, Neimy Ramos De Oliveira, Pedro Ledic Assaf, Raquel Lutkmeier, Reginaldo Aparecido Valacio, Renan Goulart Finger, Rochele Mosmann Menezes, Rufino de Freitas Silva, Saionara Cristina Francisco, Silvana Mangeon Meireles Guimaraes, Silvia Ferreira Araujo, Talita Fischer Oliveira, Tatiana Kurtz, Tatiana Oliveira Fereguetti, Thainara Conceição De Oliveira, Túlio Henrique Oliveira Diniz, Yara Cristina Neves Marques Barbosa Ribeiro, Yuri Carlotto Ramires, Marcos André Gonçalves, Milena Soriano Marcolino, and Bruno Barbosa Miranda de Paiva

Abstract

The majority prognostic scores proposed for early assessment of coronavirus disease 19 (COVID-19) patients are bounded by methodological flaws. Our group recently developed a new risk score - ABC2SPH - using traditional statistical methods (least absolute shrinkage and selection operator logistic regression - LASSO). In this article, we provide a thorough comparative study between modern machine learning (ML) methods and state-of-the-art statistical methods, represented by ABC2SPH, in the task of predicting in-hospital mortality in COVID-19 patients using data upon hospital admission. We overcome methodological and technological issues found in previous similar studies, while exploring a large sample (5,032 patients). Additionally, we take advantage of a large and diverse set of methods and investigate the effectiveness of applying meta-learning, more specifically Stacking, in order to combine the methods' strengths and overcome their limitations. In our experiments, our Stacking solutions improved over previous state-of-the-art by more than 26% in predicting death, achieving 87.1% of AUROC and MacroF1 of 73.9%. We also investigated issues related to the interpretability and reliability of the predictions produced by the most effective ML methods. Finally, we discuss the adequacy of AUROC as an evaluation metric for highly imbalanced and skewed datasets commonly found in health-related problems.

Published
2022

10. Frequency and burden of neurological manifestations in hospitalized patients with COVID-19: findings from a large Brazilian cohort

Author

Milena Soriano Marcolino, Fernando Anschau, Luciane Kopittke, Magda Carvalho Pires, Izabela Guimarães Barbosa, Daniella Nunes Pereira, Renata Brant de Souza Melo, Luís Fernando Israel Assunção, André Soares de Moura Costa, Matheus Carvalho Alves Nogueira, Helena Duani, Karina Paula Medeiros Prado Martins, Leila Beltrami Moreira, Carla Thais Cândida Alves da Silva, Neimy Ramos de Oliveira, Patricia Klarmann Ziegelmann, Milton Henriques Guimarães Júnior, Mauro Oscar Soares de Souza Lima, Saionara Cristina Francisco, Luanna Silva Monteiro Menezes, Talita Fischer Oliveira, Maíra Dias Souza, Bárbara Lopes Farace, Christiane Corrêa Rodrigues Cimini, Amanda de Oliveira Maurílio, Silvana Mangeon Mereilles Guimarães, Silvia Ferreira Araújo, Guilherme Fagundes Nascimento, Daniel Vitório Silveira, Karen Brasil Ruschel, Thainara Conceição de Oliveira, Alexandre Vargas Schwarzbold, Luiz Antonio Nasi, Maiara Anschau Floriani, Veridiana Baldon dos Santos, Carolina Marques Ramos, Joice Coutinho de Alvarenga, Ana Luiza Bahia Alves Scotton, Euler Roberto Fernandes Manenti, Gabriela Petry Crestani, Joana d’Arc Lyra Batista, Daniela Ponce, Juliana Machado-Rugolo, Adriana Falangola Benjamin Bezerra, Petrônio José de Lima Martelli, Heloisa Reniers Vianna, Luís César de Castro, Cássia Regina Gotler Medeiros, Giovanna Grunewald Vietta, Elayne Crestani Pereira, José Miguel Chatkin, Mariana Frizzo de Godoy, and Antonio Lucio Teixeira

Abstract

Background Scientific data regarding the prevalence of COVID-19 neurological manifestations and prognosis in Latin America countries is still lacking. Therefore, the study aims to understand neurological manifestations of SARS-CoV 2 infection in the Brazilian population and its association with patient outcomes, such as in-hospital mortality. Methods This study is part of the Brazilian COVID-19 Registry, a multicentric COVID-19 cohort, including data from 37 Brazilian hospitals. For the analysis, patients were grouped according to the presence of self-reported vs. clinically-diagnosed neurological manifestations and matched with patients without neurological manifestations by age, sex, number of comorbidities, hospital, and whether or not patients ha neurological underlying disease. Results From 7,232 hospitalized patients with COVID-19, 27.8% presented self-reported neurological manifestations, 9.9% were diagnosed with a clinically-defined neurological syndrome and 1.2% did not show any neurological symptoms. In patients with self-reported symptoms, the most common ones were headache (19.3%), ageusia (10.4%) and anosmia (7.4%). Meanwhile, in the group with clinically-defined neurological syndromes, acute encephalopathy was the most common diagnosis (10.5%), followed by coma (0.6%1) and seizures (0.4%). Men and younger patients were more likely to self-report neurological symptoms, while women and older patients were more likely to develop a neurological syndrome. Patients with clinically-defined neurological syndromes presented a higher prevalence of comorbidities, as well as lower oxygen saturation and blood pressure at hospital admission. In the paired analysis, it was observed that patients with clinically-defined neurological syndromes were more likely to require ICU admission (46.9 vs. 37.9%), mechanical ventilation (33.4 vs. 28.2%), to develop acute heart failure (5.1 vs. 3.0%, p=0.037) and to die (40.7 vs. 32.3%, p

Published
2022

11. Hospital-Manifested SARS-CoV-2 Infection in Brazil: Clinical Characteristics, Outcomes and Risk Factors for Mortality

Author

Polianna Delfino Pereira, Magda Carvalho Pires, Virginia Mara Reis Gomes, Matheus Carvalho Alves Nogueira, Maria Clara Pontello Barbosa Lima, Alexandre Vargas Schwarzbold, Amanda de Oliveira Maurílio, Ana Luiza Bahia Alves Scotton, André Soares de Moura Costa, Bárbara Lopes Farace, Bruno Mateus de Castro, Christiane Corrêa Rodrigues Cimini, Daniel Vitório Silveira, Daniela Ponce, Elayne Crestani Pereira, Eliane Würdig Roesch, Euler Roberto Fernandes Manenti, Evelin Paola de Almeida Cenci, Fernanda Costa dos Santos, Fernando Anschau, Fernando Graça Aranha, Frederico Bartolazzi, Guilherme Fagundes Nascimento, Heloisa Reniers Vianna, Joanna Batista, Joice Coutinho de Alvarenga, Juliana da Silva Nogueira Carvalho, Juliana Machado Rugolo, Karen Brasil Ruschel, Luanna da Silva Monteiro, Luis C. Castro, Luiz Antonio Nasi, Maiara Anschau Floriani, Maira Dias Souza, Maíra Viana Rego Souza-Silva, Marcelo Carneiro, Maria Aparecida Camargos Bicalho, Mariana Frizzo de Godoy, Milton Henriques Guimarães Júnior, Patricia K. Ziegelmann, Pedro Ledic Assaf, Petrônio José de Lima Martelli, Renan Goulart Finger, Saionara Cristina Francisco, Silvia Ferreira Araújo, Talita Fischer Oliveira, Thainara Conceição de Oliveira, Thalita Martins Lage, Vanessa Muller, Yuri Carlotto Ramires, Teresa Cristina de Abreu Ferrari, and Milena Soriano Marcolino

Published
2022

12. Hospital Characteristics Associated With COVID-19 Mortality: Data From the Multicenter Cohort Brazilian COVID-19 Registry

Author

Maíra Viana Rego Souza-Silva, Patrícia Klarmann Ziegelmann, Vandack Nobre, Virginia Mara Reis Gomes, Ana Paula Beck da Silva Etges, Alexandre Vargas Schwarzbold, Aline Gabrielle Sousa Nunes, Amanda de Oliveira Maurílio, Ana Luiza Bahia Alves Scotton, André Soares de Moura Costa, Andressa Barreto Glaeser, Bárbara Lopes Farace, Bruno Nunes Ribeiro, Carolina Marques Ramos, Christiane Corrêa Rodrigues Cimini, Cíntia Alcantara de Carvalho, Claudete Rempel, Daniel Vitório Silveira, Daniela dos Reis Carazai, Daniela Ponce, Elayne Crestani Pereira, Emanuele Marianne Souza Kroger, Euler Roberto Fernandes Manenti, Evelin Paola de Almeida Cenci, Fernanda Barbosa Lucas, Fernanda Costa dos Santos, Fernando Anschau, Fernando Antonio Botoni, Fernando Graça Aranha, Filipe Carrilho de Aguiar, Frederico Bartolazzi, Gabriela Petry Crestani, Giovanna Grunewald Vietta, Guilherme Fagundes Nascimento, Helena Carolina Noal, Helena Duani, Heloisa Reniers Vianna, Henrique Cerqueira Guimarães, Joice Coutinho de Alvarenga, José Miguel Chatkin, Júlia Drumond Parreiras de Morais, Juliana da Silva Nogueira Carvalho, Juliana Machado Rugolo, Karen Brasil Ruschel, Lara de Barros Wanderley Gomes, Leonardo Seixas de Oliveira, Liege Barella Zandoná, Lilian Santos Pinheiro, Liliane Souto Pacheco, Luanna da Silva Monteiro, Lucas de Deus Sousa, Luis Cesar Souto de Moura, Luisa Elem Almeida Santos, Luiz Antonio Nasi, Máderson Alvares de Souza Cabral, Maiara Anschau Floriani, Maira Dias Souza, Marcelo Carneiro, Mariana Frizzo de Godoy, Marilia Mastrocolla de Almeida Cardoso, Matheus Carvalho Alves Nogueira, Mauro Oscar Soares de Souza Lima, Meire Pereira de Figueiredo, Milton Henriques Guimarães Júnior, Natália da Cunha Severino Sampaio, Neimy Ramos de Oliveira, Pedro Guido Soares Andrade, Pedro Ledic Assaf, Petrônio José de Lima Martelli, Raphael Castro Martins, Reginaldo Aparecido Valacio, Roberta Pozza, Rochele Mosmann Menezes, Rodolfo Lucas Silva Mourato, Roger Mendes de Abreu, Rufino de Freitas Silva, Saionara Cristina Francisco, Silvana Mangeon Mereilles Guimarães, Silvia Ferreira Araújo, Talita Fischer Oliveira, Tatiana Kurtz, Tatiani Oliveira Fereguetti, Thainara Conceição de Oliveira, Yara Cristina Neves Marques Barbosa Ribeiro, Yuri Carlotto Ramires, Carísi Anne Polanczyk, and Milena Soriano Marcolino

Published
2021

13. Mobile right heart thrombus and pulmonary thromboembolism

Author

Carolina Martins Vieira, Juliana Bernardes Costa, Fernando Antônio Bottoni, Ricardo de Amorim Corrêa, Maria do Carmo Pereira Nunes, André Soares de Moura Costa, Tarciane Aline Prata, and Thauana Luiza de Oliveira

Subjects
Pulmonary and Respiratory Medicine, medicine.medical_specialty, business.industry, Internal medicine, medicine, Cardiology, business
Published
2012

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