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1. Dados de Vida Real sobre o Uso da Hidroxicloroquina ou da Cloroquina Combinadas ou Não à Azitromicina em Pacientes com Covid-19: Uma Análise Retrospectiva no Brasil

Author

Maíra Viana Rego Souza-Silva, Daniella Nunes Pereira, Magda Carvalho Pires, Isabela Muzzi Vasconcelos, Alexandre Vargas Schwarzbold, Diego Henrique de Vasconcelos, Elayne Crestani Pereira, Euler Roberto Fernandes Manenti, Felício Roberto Costa, Filipe Carrilho de Aguiar, Fernando Anschau, Frederico Bartolazzi, Guilherme Fagundes Nascimento, Heloisa Reniers Vianna, Joanna d’Arc Lyra Batista, Juliana Machado-Rugolo, Karen Brasil Ruschel, Maria Angélica Pires Ferreira, Leonardo Seixas de Oliveira, Luanna Silva Monteiro Menezes, Patricia Klarmann Ziegelmann, Marcela Gonçalves Trindade Tofani, Maria Aparecida Camargos Bicalho, Matheus Carvalho Alves Nogueira, Milton Henriques Guimarães-Júnior, Rúbia Laura Oliveira Aguiar, Danyelle Romana Alves Rios, Carisi Anne Polanczyk, and Milena Soriano Marcolino

Subjects
Cloroquina, Hidroxicloroquina, azitromicina, covid-19, Diseases of the circulatory (Cardiovascular) system, RC666-701
Abstract

Resumo Fundamento Apesar da ausência de evidência mostrando benefícios da hidroxicloroquina e da cloroquina combinadas ou não à azitromicina no tratamento da covid-19, esses medicamentos têm sido amplamente prescritos no Brasil. Objetivos Avaliar desfechos, incluindo moralidade hospitalar, alterações eletrocardiográficas, tempo de internação, admissão na unidade de terapia intensiva, e necessidade de diálise e de ventilação mecânica em pacientes hospitalizados com covid-19 que receberam cloroquina ou hidroxicloroquina, e comparar os desfechos entre aqueles pacientes e seus controles pareados. Métodos Estudo multicêntrico retrospectivo do tipo coorte que incluiu pacientes com diagnóstico laboratorial de covid-19 de 37 hospitais no Brasil de março a setembro de 2020. Escore de propensão foi usado para selecionar controles pareados quanto a idade, sexo, comorbidades cardiovasculares, e uso de corticosteroides durante a internação. Um valor de p

Published
2023
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2. 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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3. Clinical characteristics and outcomes of patients hospitalized with COVID-19 in Brazil: Results from the Brazilian COVID-19 registry

Author

Milena S. Marcolino, Patricia K. Ziegelmann, Maira V.R. Souza-Silva, I.J.B. Nascimento, Luana M. Oliveira, Luanna S. Monteiro, Thaís L.S. Sales, Karen B. Ruschel, Karina P.M.P. Martins, Ana Paula B.S. Etges, Israel Molina, Carisi A. Polanczyk, Alexandre Vargas Schwarzbold, Amanda de Oliveira Maurílio, Ana Lara Rodrigues Monteiro de Barros, Ana Luiza Bahia Alves Scotton, Alfonso J. Rodríguez-Morales, Anderson Lacerda dos Reis, André Soares Moura Costa, Argenil José Assis de Oliveira, Bárbara Lopes Farace, Carla Thais Cândida Alves da Silva, Carolina Marques Ramos, Christiane Corrêa Rodrigues Cimini, Cíntia Alcantara de Carvalho, Daniel Vitório Silveira, Daniela Ponce, Emanuele Marianne Souza Kroger, Euler Roberto Fernandes Manenti, Fernanda Barbosa Lucas, Fernanda d'Athayde Rodrigues, Fernando Anschau, Fernando Antonio Botoni, Frederico Bartolazzi, Gabriela Petry Crestani, Guilherme Fagundes Nascimento, Helena Carolina Noal, Helena Duani, Heloisa Reniers Vianna, Henrique Cerqueira Guimarães, Joice Coutinho de Alvarenga, Júlia Drumond Parreiras de Morais, Juliana Machado Rugolo, Lara Monalyza Gonçalves Franco, Leila Beltrami Moreira, Leonardo Seixas de Oliveira, Lílian Santos Pinheiro, Liliane Souto Pacheco, Luciane Kopittke, Luciano de Souza Viana, Luis Cesar Souto de Moura, Luisa Elem Almeida Santos, Máderson Alvares de Souza Cabral, Maíra Dias Souza, Marcela Gonçalves Trindade Tofani, Marconi Franco da Silveira, Marcus Vinicius de Melo Andrade, Maria Angélica Pires Ferreira, Maria Aparecida Camargos Bicalho, Maria Auxiliadora Parreiras Martins, Maria Clara Pontello Barbosa Lima, Mariana Balbinot Borges, Mariana de Braga Lima Carvalho Canesso, Matheus Carvalho Alves Nogueira, Meire Pereira de Figueiredo, Milton Henriques Guimarães Junior, Mychelle Stefany Santos Almeida, Mônica Aparecida de Paula de Sordi, Natália da Cunha Severino Sampaio, Neimy Ramos de Oliveira, Paulo Tarso Lima Vianna, Pedro Guido Soares Andrade, Pedro Ledic Assaf, Rafael Fusaro Aguiar Oliveira, Rafael Lima Rodrigues de Carvalho, Rafaela dos Santos Charão de Almeida, Raphael Castro Martins, Reginaldo Aparecido Valacio, Ricardo Bertoglio Cardoso, Ricardo Braga Coelho, Roberta Pozza, Rodolfo Lucas Silva Mourato, Rodrigo Costa Pereira Vieira, Roger Mendes de Abreu, Rufino de Freitas Silva, Saionara Cristina Francisco, Silvana Mangeon Mereilles Guimarães, Silvia Ferreira Araújo, Talita Fischer Oliveira, Tatiana de Vargas, Tatiani Oliveira Fereguetti, Thalita Martins Lage, Thulio Henrique Oliveira Diniz, and Veridiana Baldon dos Santos

Subjects
COVID-19, SARS-CoV-2, hospitalizations, pandemic, Brazil, mortality, Infectious and parasitic diseases, RC109-216
Abstract

Objectives: To describe the clinical characteristics, laboratory results, imaging findings, and in-hospital outcomes of COVID-19 patients admitted to Brazilian hospitals. Methods: A cohort study of laboratory-confirmed COVID-19 patients who were hospitalized from March 2020 to September 2020 in 25 hospitals. Data were collected from medical records using Research Electronic Data Capture (REDCap) tools. A multivariate Poisson regression model was used to assess the risk factors for in-hospital mortality. Results: For a total of 2,054 patients (52.6% male; median age of 58 years), the in-hospital mortality was 22.0%; this rose to 47.6% for those treated in the intensive care unit (ICU). Hypertension (52.9%), diabetes (29.2%), and obesity (17.2%) were the most prevalent comorbidities. Overall, 32.5% required invasive mechanical ventilation, and 12.1% required kidney replacement therapy. Septic shock was observed in 15.0%, nosocomial infection in 13.1%, thromboembolism in 4.1%, and acute heart failure in 3.6%. Age >= 65 years, chronic kidney disease, hypertension, C-reactive protein ≥ 100 mg/dL, platelet count < 100 × 109/L, oxygen saturation < 90%, the need for supplemental oxygen, and invasive mechanical ventilation at admission were independently associated with a higher risk of in-hospital mortality. The overall use of antimicrobials was 87.9%. Conclusions: This study reveals the characteristics and in-hospital outcomes of hospitalized patients with confirmed COVID-19 in Brazil. Certain easily assessed parameters at hospital admission were independently associated with a higher risk of death. The high frequency of antibiotic use points to an over-use of antimicrobials in COVID-19 patients.

Published
2021
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4. ABC2-SPH risk score for in-hospital mortality in COVID-19 patients: development, external validation and comparison with other available scores

Author

Mariana Frizzo de Godoy, Luana Martins Oliveira, Christiane Correa Rodrigues cimini, Fernando Antônio Botoni, Raquel Lutkmeier, Neimy Ramos de Oliveira, Andre Pinheiro Weber, Roberta Xavier Campos, Andressa Barreto Glaeser, Cintia Alcantara de Carvalho, Renan Goulart Finger, Israel Júnior Borges do Nascimento, Yuri Carlotto Ramires, Carolina Marques Ramos, Angelinda Rezende Bhering, Karina Paula Medeiros Prado Martins, Julia Drumond Parreiras de Morais, Rufino de Freitas Silva, Heloisa Reniers Vianna, Amanda de Oliveira Maurilio, Luis Cesar Souto de Moura, Giovanna Grunewald Vietta, Alexandre Vargas Schwarzbold, Daniel Taiar Marinho Oliveira Ferrara, Maíra Viana Rego Souza-Silva, Milton Henriques Guimarães-Júnior, Luís César de Castro, Thaiza Simonia Marinho Albino de Araujo, Silvia Ferreira Araujo, Caroline Danubia Gomes, Marilia Mastrocolla de Almeida Cardoso, Berta Raventós, Milena Soriano Marcolino, Saionara Cristina Francisco, Rafael Guimarães Tavares da Silva, José Miguel Chatkin, Carisi Anne Polanczyk, Raphael Castro Martins, Lucas de Deus Sousa, Susany Anastacia Pereira, Eric Boersma, Pedro Ledic Assaf, Patricia Klarmann Ziegelmann, Karen Cristina Jung Rech Pontes, Tatiana Kurtz, Roger Mendes de Abreu, Petrônio José de Lima Martelli, Angelica Aparecida Coelho Madureira, Carla Thais Candida Alves da Silva, Lilian Santos Pinheiro, Luanna da Silva Monteiro, Frederico Bartolazzi, Kauane Aline Maciel dos Santos, Natalia Lima Rangel, Marcela Goncalves Trindade Tofani, Maria Aparecida Camargos Bicalho, Natalia da Cunha Severino Sampaio, Virginia Mara Reis Gomes, Maria Angelica Pires Ferreira, Luisa Elem Almeida Santos, Bruno Mateus de Castro, Thaís Lorenna Souza Sales, Ana Luiza Bahia Alves Scotton, Joanna d'Arc L. Batista, Fernando Graca Aranha, Thainara Conceicao de Oliveira, Fernando Anschau, Felipe Barbosa Vallt, Thulio Henrique Oliveira Diniz, Rafael Lima Rodrigues de Carvalho, Guilherme Fagundes Nascimento, Roberta Pozza, Elayne Crestani Pereira, Máderson Alvares de Souza Cabral, Rodolfo Lucas Silva Mourato, Isabela Moraes Gomes, Julia Di Sabatino Santos Guimaraes, Ana Paula Beck da Silva Etges, Luciana Siuves Ferreira Couto, Gisele Alsina Nader Bastos, Juliana Machado Rugolo, Rochele Mosmann Menezes, L. E. F. Ramos, Liliane Souto Pacheco, Helena Carolina Noal, Veridiana Baldon dos Santos Santos, Henrique Cerqueira Guimaraes, Matheus Carvalho Alves Nogueira, Ricardo Bertoglio Cardoso, Glicia Cristina de Castro Madeira, Daniela Ponce, Helena Duani, Vitor Augusto Lima do Vale, Marcelo Carneiro, Leonardo Seixas de Oliveira, Talita Fischer Oliveira, Emanuele Marianne Souza Kroger, Israel Molina, Natalia Trifiletti Crespo, Edilson Cezar, Karen Brasil Ruschel, Tatiani Oliveira Fereguetti, Rafaela dos Santos Charao de Almeida, Joice Coutinho de Alvarenga, Maiara Anschau Floriani, Maira Dias Souza, Adrián Sánchez-Montalvá, Barbara Lopes Farace, Maria Clara Pontello Barbosa Lima, Meire Pereira de Figueiredo, Luciane Kopittke, Gabriela Petry Crestani, Andre Soares de Moura Costa, Silvana Mangeon Meirelles Guimarães, Fernanda Barbosa Lucas, Reginaldo Aparecido Valacio, Daniel Vitorio Silveira, Magda Carvalho Pires, Cardiology, Universidade Federal de Minas Gerais (UFMG), Institute for Health Technology Assessment IATS/ CNPq)., Universidade Federal de São João del-Rei, Universitat Autònoma de Barcelona, Grupo Hospitalar Conceição, Pontifícia Universidade Católica do Rio Grande do Sul RGS), Hospital São Lucas PUCRS, Rede Mater Dei de Saúde, Hospital Márcio Cunha, Universidade do Sul de Santa Catarina UNISUL, Dissertare Scientific Advice, SOS Cardio Hospital, Universidade Estadual Paulista (UNESP), Universidade Federal do Rio Grande do Sul, Hospital Bruno Born, Research Center of Vale do Taquari., Hospital Mãe de Deus, Hospital Universitário de Canoas, Universidade Federal de Viçosa (UFV), Hospital Santa Rosalia, Hospital Metropolitano Doutor Célio de Castro, Hospital Moinhos de Vento, Hospital Unimed BH, Hospital Risoleta Tolentino Neves, Hospital Metropolitano Odilon Behrens, Hospital Eduardo de Menezes, Universidade FUMEC, Hospital Julia Kubitschek, Hospital Universitário de Santa Maria, Universidade Federal de Santa Maria, Hospital São João de Deus, Hospital Regional Antônio Dias, Faculdade Ciências Médicas de Minas Gerais, Faculdade de Ciências Humanas de Curvelo, Av. Professor Alfredo Balena, Hospital Santo Antônio, Hospital Universitário Ciências Médicas, PROSICS Barcelona, Instituto René Rachou-FIOCRUZ Minas., Universidade Federal da Fronteira Sul, Hospital Regional do Oeste, Pontifícia Universidade Católica de Minas Gerais, Hospital Tacchini, Centro Universitário de Patos de Minas. RPatos de Minas, Hospital Semper, Hospital Santa Cruz, Universidade de Santa Cruz, Fundação Hospitalar do Estado de Minas Gerais – FHEMIG., Universidade Federal de Ouro Preto, Universidade Federal de Pernambuco (UFPE), Centro Universitário de Belo Horizonte UniBH), University Medical Center Rotterdam, Institut Català de la Salut, [Marcolino MS] Department of Internal Medicine, Medical School, Universidade Federal de Minas Gerais. Belo Horizonte, Brazil. Telehealth Center, University Hospital, Universidade Federal de Minas Gerais. Belo Horizonte, Brazil. Institute for Health Technology Assessment (IATS/ CNPq). Rua Ramiro Barcelos, 2359. Prédio 21 | Sala 507, Porto Alegre, Brazil. [Pires MC] Institute for Health Technology Assessment (IATS/ CNPq). Rua Ramiro Barcelos, 2359. Prédio 21 | Sala 507, Porto Alegre, Brazil. Department of Statistics, Universidade Federal de Minas Gerais. Belo Horizonte, Brazil. [Ramos LEF, Silva RT] Department of Statistics, Universidade Federal de Minas Gerais. Belo Horizonte, Brazil. [Oliveira LM] Institute for Health Technology Assessment (IATS/ CNPq). Rua Ramiro Barcelos, 2359. Prédio 21 | Sala 507, Porto Alegre, Brazil. Center for Research and Graduate Studies in Business Administration, Universidade Federal de Minas Gerais. Belo Horizonte, Brazil. [Carvalho RLR] Institute for Health Technology Assessment (IATS/ CNPq). Rua Ramiro Barcelos, 2359. Prédio 21 | Sala 507, Porto Alegre, Brazil. [Sánchez-Montalvá A, Raventós B] Servei de Malalties Infeccioses, Vall d’Hebron Hospital Universitari, Barcelona, Spain. Universitat Autònoma de Barcelona, Bellaterra, Spain. PROSICS, Barcelona, Spain. [Molina I] Vall d’Hebron Hospital Universitari, Barcelona, Spain. PROSICS Barcelona. Barcelona, Spain. Instituto René Rachou-FIOCRUZ Minas. Belo Horizonte, Brazil, and Vall d'Hebron Barcelona Hospital Campus

Subjects
Microbiology (medical), Percentile, medicine.medical_specialty, Coronavirus disease 2019 (COVID-19), COVID-19 (Malaltia) - Mortalitat, Infectious and parasitic diseases, RC109-216, Logistic regression, Article, SDG 3 - Good Health and Well-being, Mortalitat - Estadístiques, Other subheadings::Other subheadings::Other subheadings::/mortality [Other subheadings], Internal medicine, Heart rate, virosis::infecciones por virus ARN::infecciones por Nidovirales::infecciones por Coronaviridae::infecciones por Coronavirus [ENFERMEDADES], técnicas de investigación::métodos epidemiológicos::recopilación de datos::estadísticas vitales::mortalidad::mortalidad hospitalaria [TÉCNICAS Y EQUIPOS ANALÍTICOS, DIAGNÓSTICOS Y TERAPÉUTICOS], medicine, score, Humans, Hospital Mortality, Mortality, Diagnosis::Prognosis [ANALYTICAL, DIAGNOSTIC AND THERAPEUTIC TECHNIQUES, AND EQUIPMENT], Investigative Techniques::Epidemiologic Methods::Data Collection::Vital Statistics::Mortality::Hospital Mortality [ANALYTICAL, DIAGNOSTIC AND THERAPEUTIC TECHNIQUES, AND EQUIPMENT], diagnóstico::pronóstico [TÉCNICAS Y EQUIPOS ANALÍTICOS, DIAGNÓSTICOS Y TERAPÉUTICOS], Blood urea nitrogen, Aged, Retrospective Studies, Hospitalizations, COVID-19 (Malaltia) - Prognosi, Otros calificadores::Otros calificadores::Otros calificadores::/mortalidad [Otros calificadores], Framingham Risk Score, SARS-CoV-2, business.industry, Score, COVID-19, Virus Diseases::RNA Virus Infections::Nidovirales Infections::Coronaviridae Infections::Coronavirus Infections [DISEASES], General Medicine, Emergency department, Middle Aged, Prognosis, Hospitalization, Infectious Diseases, Risk factors, Cohort, business
Abstract

Made available in DSpace on 2022-04-29T08:31:56Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-09-01 Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Objectives: The majority of available scores to assess mortality risk of coronavirus disease 2019 (COVID-19) patients in the emergency department have high risk of bias. Therefore, this cohort aimed to develop and validate a score at hospital admission for predicting in-hospital mortality in COVID-19 patients and to compare this score with other existing ones. Methods: Consecutive patients (≥ 18 years) with confirmed COVID-19 admitted to the participating hospitals were included. Logistic regression analysis was performed to develop a prediction model for in-hospital mortality, based on the 3978 patients admitted between March–July, 2020. The model was validated in the 1054 patients admitted during August–September, as well as in an external cohort of 474 Spanish patients. Results: Median (25–75th percentile) age of the model-derivation cohort was 60 (48–72) years, and in-hospital mortality was 20.3%. The validation cohorts had similar age distribution and in-hospital mortality. Seven significant variables were included in the risk score: age, blood urea nitrogen, number of comorbidities, C-reactive protein, SpO2/FiO2 ratio, platelet count, and heart rate. The model had high discriminatory value (AUROC 0.844, 95% CI 0.829–0.859), which was confirmed in the Brazilian (0.859 [95% CI 0.833–0.885]) and Spanish (0.894 [95% CI 0.870–0.919]) validation cohorts, and displayed better discrimination ability than other existing scores. It is implemented in a freely available online risk calculator (https://abc2sph.com/). Conclusions: An easy-to-use rapid scoring system based on characteristics of COVID-19 patients commonly available at hospital presentation was designed and validated for early stratification of in-hospital mortality risk of patients with COVID-19. Department of Internal Medicine Medical School Universidade Federal de Minas Gerais Telehealth Center University Hospital Universidade Federal de Minas Gerais Institute for Health Technology Assessment IATS/ CNPq)., Rua Ramiro Barcelos, 2359. Prédio 21 | Sala 507 Department of Statistics Universidade Federal de Minas Gerais Center for Research and Graduate Studies in Business Administration Universidade Federal de Minas Gerais Universidade Federal de São João del-Rei Infectious Diseases Department Vall d'Hebron University Hospita Universitat Autònoma de Barcelona Hospital Nossa Senhora da Conceição and Hospital Cristo Redentor Grupo Hospitalar Conceição Pontifícia Universidade Católica do Rio Grande do Sul RGS) Hospital São Lucas PUCRS Rede Mater Dei de Saúde Hospital Márcio Cunha Universidade do Sul de Santa Catarina UNISUL Dissertare Scientific Advice SOS Cardio Hospital Internal Medicine Department University Hospital Universidade Federal de Minas Gerais Faculdade de Medicina de Botucatu Universidade Estadual Paulista Júlio de Mesquita Filho Hospital das Clínicas da Faculdade de Medicina de Botucatu Universidade Federal do Rio Grande do Sul Hospital Bruno Born Research Center of Vale do Taquari. Hospital Mãe de Deus Hospital Universitário de Canoas Mucuri Medical School FAMMUC Universidade Federal dos Vales do Jequitinhonha e Mucuri – UFVJM Hospital Santa Rosalia Hospital Metropolitano Doutor Célio de Castro Hospital Moinhos de Vento Hospital Unimed BH Hospital Risoleta Tolentino Neves Post-graduation Center Medical School Universidade Federal de Minas Gerais Hospital Metropolitano Odilon Behrens Hospital Eduardo de Menezes Universidade FUMEC Hospital Julia Kubitschek Hospital Universitário de Santa Maria Departamento de Medicina Interna Universidade Federal de Santa Maria Hospital São João de Deus Hospital Regional Antônio Dias Faculdade Ciências Médicas de Minas Gerais Hospital de Clínicas de Porto Alegre Universidade Federal do Rio Grande do Sul Faculdade de Ciências Humanas de Curvelo Hospital João XXIII Av. Professor Alfredo Balena Hospital Santo Antônio Hospital Universitário Ciências Médicas Vall d'Hebron University Hospital PROSICS Barcelona Instituto René Rachou-FIOCRUZ Minas. Universidade Federal da Fronteira Sul Hospital Regional do Oeste Pontifícia Universidade Católica de Minas Gerais Hospital Tacchini Centro Universitário de Patos de Minas. RPatos de Minas Hospital Semper Hospital Santa Cruz Universidade de Santa Cruz Fundação Hospitalar do Estado de Minas Gerais – FHEMIG. Universidade Federal de Ouro Preto Hospital das Clínicas da Universidade Federal de Pernambuco Universidade Federal de Pernambuco Centro de Ciências Médicas Centro Universitário de Belo Horizonte UniBH) Erasmus MC University Medical Center Rotterdam Department of Cardiology Faculdade de Medicina de Botucatu Universidade Estadual Paulista Júlio de Mesquita Filho Hospital das Clínicas da Faculdade de Medicina de Botucatu

Published
2021
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5. 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

6. 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
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7. 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

Daniela Ponce, Roberta Senger, Rufino de Freitas Silva, Elayne Crestani Pereira, Pedro Ledic Assaf, Maria Angelica Pires Ferreira, Adriana Falangola Benjamin Bezerra, Heloisa Reniers Vianna, Milena Soriano Marcolino, Saionara Cristina Francisco, Natalia da Cunha Severino Sampaio, Fernanda D'Athayde Rodrigues, Maiara Anschau Floriani, Ana Luiza Bahia Alves Scotton, Fernando Graca Aranha, Neimy Ramos de Oliveira, Yara Neves Marques Barbosa Ribeiro, Máderson Alvares de Souza Cabral, Maria Aparecida Camargos Bicalho, Giovanna Grunewald Vietta, José Miguel Chatkin, Maria Clara Pontello Barbosa Lima, Thaís Lorenna Souza Sales, Thainara Conceicao de Oliveira, Amanda de Oliveira Maurilio, Barbara Lopes Farace, Cláudio Moisés Valiense de Andrade, Rochele Mosmann Menezes, Milton Henriques Guimaraes Junior, Raquel Lutkmeier, Magda Cesar Raposo, Renan Goulart Finger, Marcelo Carneiro, Tatiana Kurtz, Andriele Abreu Castro, Fernando Anschau, Lucas de Deus Sousa, Mariana Frizzo de Godoy, Luisa Elem Almeida Santos, Meire Pereira de Figueiredo, Luciane Kopittke, Monica Aparecida de Paula De Sordi, Leonardo Seixas de Oliveira, Christiane Correa Rodrigues cimini, Luciano de Souza Viana, Talita Fischer Oliveira, Yuri Carlotto Ramires, Fernando Antônio Botoni, Luisa Argolo Assis, Polianna Delfino Pereira, Cintia Alcantara de Carvalho, Rafael Guimarães Tavares da Silva, Bruno Barbosa Miranda de Paiva, Andre Soares de Moura Costa, Guilherme Fagundes Nascimento, Joice Coutinho de Alvarenga, Gisele Alsina Nader Bastos, Rafael Lima Rodrigues de Carvalho, Karina Paula Medeiros Prado Martins, Julia Drumond Parreiras de Morais, Matheus Carvalho Alves Nogueira, Luís César de Castro, Helena Duani, Julia Di Sabatino Santos Guimaraes, Alexandre Vargas Schwarzbold, Helena Carolina Noal, Virginia Mara Reis Gomes, Thulio Henrique Oliveira Diniz, Evelin Paola de Almeida Cenci, Marcos André Gonçalves, Jamille Hemetrio Salles Martins Costa, Lilian Santos Pinheiro, Maíra Viana Rego Souza e Silva, Silvia Ferreira Araujo, Aline Gabrielle Sousa Nunes, Luanna da Silva Monteiro, Frederico Bartolazzi, Leila Beltrami Moreira, Fernanda Barbosa Lucas, Reginaldo Aparecido Valacio, Daniel Vitorio Silveira, Magda Carvalho Pires, L. E. F. Ramos, Joanna d'Arc Lyra Batista, Henrique Cerqueira Guimaraes, Euler Roberto Fernandes Manenti, Isabela Moraes Gomes, Karen Brasil Ruschel, Tatiani Oliveira Fereguetti, Silvana Mangeon Mereilles Guimaraes, Liege Barella Zandona, Jessica Rayane Correa Silva da Fonseca, and Juliana Machado Rugolo

Subjects
Ensemble forecasting, Meta learning (computer science), Computer science, business.industry, Machine learning, computer.software_genre, Class (biology), Outcome (probability), Cross-validation, Task (project management), Artificial intelligence, business, computer, Reliability (statistics), Interpretability
Abstract

ObjectiveTo provide a thorough comparative study among state-of-the-art machine learning methods and statistical methods for determining in-hospital mortality in COVID-19 patients using data upon hospital admission; to study the reliability of the predictions of the most effective methods by correlating the probability of the outcome and the accuracy of the methods; to investigate how explainable are the predictions produced by the most effective methods.Materials and MethodsDe-identified data were obtained from COVID-19 positive patients in 36 participating hospitals, from March 1 to September 30, 2020. Demographic, comorbidity, clinical presentation and laboratory data were used as training data to develop COVID-19 mortality prediction models. Multiple machine learning and traditional statistics models were trained on this prediction task using a folded cross-validation procedure, from which we assessed performance and interpretability metrics.ResultsThe Stacking of machine learning models improved over the previous state-of-the-art results by more than 26% in predicting the class of interest (death), achieving 87.1% of AUROC and macro F1 of 73.9%. We also show that some machine learning models can be very interpretable and reliable, yielding more accurate predictions while providing a good explanation for the ‘why’.ConclusionThe best results were obtained using the meta-learning ensemble model – Stacking. State-of the art explainability techniques such as SHAP-values can be used to draw useful insights into the patterns learned by machine-learning algorithms. Machine-learning models can be more explainable than traditional statistics models while also yielding highly reliable predictions.

Published
2021
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8. ABC2-SPH risk score for in-hospital mortality in COVID-19 patients: development, external validation and comparison with other available scores

Author

Natalia Lima Rangel, Daniel Taiar Marinho Oliveira Ferrara, Natalia da Cunha Severino Sampaio, Máderson Alvares de Souza Cabral, Angelinda Rezende Bhering, Emanuele Marianne Souza Kroger, Rafael Lima Rodrigues de Carvalho, Lucas de Deus Sousa, Ana Luiza Bahia Alves Scotton, Roger Mendes de Abreu, Fernando Graca Aranha, Meire Pereira de Figueiredo, Guilherme Fagundes Nascimento, Luanna da Silva Monteiro, Frederico Bartolazzi, Juliana Machado Rugolo, Maria Aparecida Camargos Bicalho, Luciana Siuves Ferreira Couto, Rochele Mosmann Menezes, Luciane Kopittke, Natalia Trifiletti Crespo, Daniela Ponce, Eric Boersma, Patricia Klarmann Ziegelmann, Amanda de Oliveira Maurilio, Carisi Anne Polanczyk, Raquel Lutkmeier, Christiane Correa Rodrigues cimini, Bruno Mateus de Castro, Giovanna Grunewald Vietta, José Miguel Chatkin, Neimy Ramos de Oliveira, Thaís Lorenna Souza Sales, Lilian Santos Pinheiro, Angelica Aparecida Coelho Madureira, Gisele Alsina Nader Bastos, Elayne Crestani Pereira, Fernanda Barbosa Lucas, Karen Cristina Jung Rech Pontes, Maria Angelica Pires Ferreira, Liliane Souto Pacheco, Raphael Castro Martins, Andre Soares de Moura Costa, Helena Duani, Roberta Xavier Campos, Andre Pinheiro Weber, Matheus Carvalho Alves Nogueira, Rodolfo Lucas Silva Mourato, Silvia Ferreira Araujo, Renan Goulart Finger, Adrian Sanchez Montalva, Reginaldo Aparecido Valacio, Daniel Vitorio Silveira, Magda Carvalho Pires, Maíra Viana Rego Souza e Silva, Marcela Goncalves Trindade Tofani, Milena Soriano Marcolino, Saionara Cristina Francisco, Karen Brasil Ruschel, Tatiani Oliveira Fereguetti, L. E. F. Ramos, Israel Júnior Borges do Nascimento, Thaiza Simonia Marinho Albino de Araujo, Silvana Mangeon Mereilles Guimaraes, Rafaela dos Santos Charao de Almeida, Marcus Vinicius de Melo Andrade, Joanna d'Arc Lyra Batista, Andressa Barreto Glaeser, Veridiana Baldon dos Santos Santos, Henrique Cerqueira Guimaraes, Vitor Augusto Lima do Vale, Joice Coutinho de Alvarenga, Heloisa Reniers Vianna, Ricardo Bertoglio Cardoso, Petrônio José de Lima Martelli, Glicia Cristina de Castro Madeira, Fernando Anschau, Tatiana Kurtz, Milton Henriques Guimaraes Junior, Maria Clara Pontello Barbosa Lima, Mariana Frizzo de Godoy, Luana Martins Oliveira, Kauane Aline Maciel dos Santos, Gabriela Petry Crestani, Luisa Elem Almeida Santos, Fernando Antônio Botoni, Carla Thais Candida Alves da Silva, Felipe Barbosa Vallt, Rufino de Freitas Silva, Cintia Alcantara de Carvalho, Barbara Lopes Farace, Diego Henrique de Vasconcelos, Luis Cesar Souto de Moura, Alexandre Vargas Schwarbold, Karina Paula Medeiros Prado Martins, Julia Drumond Parreiras de Morais, Luís César de Castro, Pedro Ledic Assaf, Maiara Anschau Floriani, Roberta Pozza, Maira Dias Souza, Isabela Moraes Gomes, Susany Anastacia Pereira, Thainara Conceicao de Oliveira, Yuri Carlotto Ramires, Carolina Marques Ramos, Israel Molina Romero, Caroline Danubia Gomes, Rafael Guimarães Tavares da Silva, Marilia Mastrocolla de Almeida Cardoso, Berta Raventós, Leonardo Seixas de Oliveira, Virginia Mara Reis Gomes, Talita Fischer Oliveira, Thulio Henrique Oliveira Diniz, Julia Di Sabatino Santos Guimaraes, Ana Paula Beck da Silva Etges, Helena Carolina Noal, Marcelo Carneiro, and Edilson Cezar

Subjects
medicine.medical_specialty, Percentile, Framingham Risk Score, biology, business.industry, C-reactive protein, Emergency department, Logistic regression, Internal medicine, Cohort, Risk of mortality, biology.protein, Medicine, business, Blood urea nitrogen
Abstract

ObjectiveTo develop and validate a rapid scoring system at hospital admission for predicting in-hospital mortality in patients hospitalized with coronavirus disease 19 (COVID-19), and to compare this score with other existing ones.DesignCohort studySettingThe Brazilian COVID-19 Registry has been conducted in 36 Brazilian hospitals in 17 cities. Logistic regression analysis was performed to develop a prediction model for in-hospital mortality, based on the 3978 patients that were admitted between March-July, 2020. The model was then validated in the 1054 patients admitted during August-September, as well as in an external cohort of 474 Spanish patients.ParticipantsConsecutive symptomatic patients (≥18 years old) with laboratory confirmed COVID-19 admitted to participating hospitals. Patients who were transferred between hospitals and in whom admission data from the first hospital or the last hospital were not available were excluded, as well those who were admitted for other reasons and developed COVID-19 symptoms during their stay.Main outcome measuresIn-hospital mortalityResultsMedian (25th-75th percentile) age of the model-derivation cohort was 60 (48-72) years, 53.8% were men, in-hospital mortality was 20.3%. The validation cohorts had similar age distribution and in-hospital mortality. From 20 potential predictors, seven significant variables were included in the in-hospital mortality risk score: age, blood urea nitrogen, number of comorbidities, C-reactive protein, SpO2/FiO2ratio, platelet count and heart rate. The model had high discriminatory value (AUROC 0.844, 95% CI 0.829 to 0.859), which was confirmed in the Brazilian (0.859) and Spanish (0.899) validation cohorts. Our ABC2-SPH score showed good calibration in both Brazilian cohorts, but, in the Spanish cohort, mortality was somewhat underestimated in patients with very high (>25%) risk. The ABC2-SPH score is implemented in a freely available online risk calculator (https://abc2sph.com/).ConclusionsWe designed and validated an easy-to-use rapid scoring system based on characteristics of COVID-19 patients commonly available at hospital presentation, for early stratification for in-hospital mortality risk of patients with COVID-19.Summary boxesWhat is already known on this topic?Rapid scoring systems may be very useful for fast and effective assessment of COVID-19 patients in the emergency department.The majority of available scores have high risk of bias and lack benefit to clinical decision making.Derivation and validation studies in low- and middle-income countries, including Latin America, are scarce.What this study addsABC2-SPH employs seven well defined variables, routinely assessed upon hospital presentation: age, number of comorbidities, blood urea nitrogen, C reactive protein, Spo2/FiO2 ratio, platelets and heart rate.This easy-to-use risk score identified four categories at increasing risk of death with a high level of accuracy, and displayed better discrimination ability than other existing scores.A free web-based calculator is available and may help healthcare practitioners to estimate the expected risk of mortality for patients at hospital presentation.

Published
2021
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9. 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
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10. Predictors of Venous Thromboembolism in COVID-19 Patients: Results of the COVID-19 Brazilian Registry

Author

Warley Cezar da Silveira, Lucas Emanuel Ferreira Ramos, Rafael Tavares Silva, Bruno Barbosa Miranda de Paiva, Polianna Delfino Pereira, Alexandre Vargas Schwarzbold, Andresa Fontoura Garbini, Bruna Schettino Morato Barreira, Bruno Mateus de Castro, Carolina Marques Ramos, Caroline Danubia Gomes, Christiane Corrêa Rodrigues Cimini, Elayne Crestani Pereira, Eliane Würdig Roesch, Emanuele Marianne Souza Kroger, Felipe Ferraz Martins Graça Aranha, Fernando Anschau, Fernando Antonio Botoni, Fernando Graça Aranha, Gabriela Petry Crestani, Giovanna Grunewald Vietta, Gisele Alsina Nader Bastos, Jamille Hemétrio Salles Martins Costa, Jéssica Rayane Corrêa Silva da Fonseca, Karen Brasil Ruschel, Leonardo Seixas de Oliveira, Lílian Santos Pinheiro, Liliane Souto Pacheco, Luciana Borges Segala, Luciana Siuves Ferreira Couto, Luciane Kopittke, Maiara Anschau Floriani, Majlla Magalhães Silva, Marcelo Carneiro, Maria Angélica Pires Ferreira, Maria Auxiliadora Parreiras Martins, Marina Neves Zerbini de Faria, Matheus Carvalho Alves Nogueira, Milton Henriques Guimarães Júnior, Natália da Cunha Severino Sampaio, Neimy Ramos de Oliveira, Nicole de Moraes Pertile, Pedro Guido Soares Andrade, Pedro Ledic Assaf, Reginaldo Aparecido Valacio, Rochele Mosmann Menezes, Saionara Cristina Francisco, Silvana Mangeon Meirelles Guimarães, Silvia Ferreira Araújo, Suely Meireles Rezende, Susany Anastácia Pereira, Tatiana Kurtz, Tatiani Oliveira Fereguetti, Carísi Anne Polanczyk, Magda Carvalho Pires, Marcos André Gonçalves, and Milena Soriano Marcolino

Subjects
Adult, Male, Anticoagulants, COVID-19, Venous Thromboembolism, Cohort Studies, Oxygen, C-Reactive Protein, Risk Factors, Emergency Medicine, Internal Medicine, Lactates, Humans, Female, Registries, Brazil
Abstract

Previous studies that assessed risk factors for venous thromboembolism (VTE) in COVID-19 patients have shown inconsistent results. Our aim was to investigate VTE predictors by both logistic regression (LR) and machine learning (ML) approaches, due to their potential complementarity. This cohort study of a large Brazilian COVID-19 Registry included 4120 COVID-19 adult patients from 16 hospitals. Symptomatic VTE was confirmed by objective imaging. LR analysis, tree-based boosting, and bagging were used to investigate the association of variables upon hospital presentation with VTE. Among 4,120 patients (55.5% men, 39.3% critical patients), VTE was confirmed in 6.7%. In multivariate LR analysis, obesity (OR 1.50, 95% CI 1.11-2.02); being an ex-smoker (OR 1.44, 95% CI 1.03-2.01); surgery ≤ 90 days (OR 2.20, 95% CI 1.14-4.23); axillary temperature (OR 1.41, 95% CI 1.22-1.63); D-dimer ≥ 4 times above the upper limit of reference value (OR 2.16, 95% CI 1.26-3.67), lactate (OR 1.10, 95% CI 1.02-1.19), C-reactive protein levels (CRP, OR 1.09, 95% CI 1.01-1.18); and neutrophil count (OR 1.04, 95% CI 1.005-1.075) were independent predictors of VTE. Atrial fibrillation, peripheral oxygen saturation/inspired oxygen fraction (SF) ratio and prophylactic use of anticoagulants were protective. Temperature at admission, SF ratio, neutrophil count, D-dimer, CRP and lactate levels were also identified as predictors by ML methods. By using ML and LR analyses, we showed that D-dimer, axillary temperature, neutrophil count, CRP and lactate levels are risk factors for VTE in COVID-19 patients.

Published
2021
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