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

BMC Nephrology

Research

Daniela Ponce¹  Mônica Aparecida da Paula Sordi¹  Claudio Moisés Valiense de Andrade²  Marcos André Gonçalves²  Polianna Delfino Pereira³  Magda Carvalho Pires⁴  Vivian Costa Morais de Assis⁵  Ana Carolina Pitanga dos Santos⁵  Manuela Furtado Sacioto⁵  João Victor Baroni Neves⁵  André Soares de Moura Costa⁶  Angélica Gomides dos Reis Gomes⁶  Luís César de Castro⁷  Natália da Cunha Severino Sampaio⁸  Bruno Porto Pessoa⁹  Flavia Maria Borges Vigil^1,10  Felício Roberto Costa^1,11  Euler Roberto Fernandes Manenti^1,12  Luciane Kopittke^1,13  Fernando Anschau^1,13  Elayne Crestani Pereira^1,14  Fernando Graça Aranha^1,14  Rochele Mosmann Menezes^1,15  Marcelo Carneiro^1,15  Frederico Bartolazzi^1,16  Silvia Ferreira Araújo^1,17  Danyelle Romana Alves Rios^1,18  Evelin Paola de Almeida Cenci^1,19  Karen Brasil Ruschel^1,19  Lucas Moyses Carvalho de Oliveira^2,20  Letícia do Nascimento^2,21  Joanna d’Arc Lyra Batista^2,22  Vanessa das Graças José Ventura^2,23  Katia de Paula Farah^2,23  Milena Soriano Marcolino^2,24  Maria Aparecida Camargos Bicalho^2,25  Beatriz Figueiredo Lima^2,26  Alzira de Oliveira Jorge^2,27  Pedro Gibson Paraíso^2,28  Genna Maira Santos Grizende^2,29  Alisson Alves Asevedo^3,30  Christiane Corrêa Rodrigues Cimini^3,31  Gabriella Genta Aguiar^3,32 

[1] Botucatu Medical School, Universidade Estadual Paulista “Júlio de Mesquita Filho”, Av. Prof. Mário Rubens Guimarães Montenegro, Botucatu, Brazil;Computer Science Department, Universidade Federal de Minas Gerais, Av. Presidente Antônio Carlos, 6627, Belo Horizonte, Brazil;Department of Internal Medicine, Medical School, Universidade Federal de Minas Gerais, Av. Professor Alfredo Balena, 190, Belo Horizonte, Brazil;Institute for Health Technology Assessment (IATS/ CNPq), R. Ramiro Barcelos, 2359, Porto Alegre, Brazil;Department of Statistics, Universidade Federal de Minas Gerais, Av. Presidente Antônio Carlos, 6627, Belo Horizonte, Brazil;Faculdade de Ciências Médicas de Minas Gerais, Al. Ezequiel Dias, 275, Belo Horizonte, Minas Gerais, Brazil;Hospitais da Rede Mater Dei, Av. Do Contorno, 9000, Belo Horizonte, Brazil;Hospital Bruno Born, Av. Benjamin Constant, 881, Lajeado, Brazil;Hospital Eduardo de Menezes, R. Dr. Cristiano Rezende, 2213, Belo Horizonte, Brazil;Hospital Júlia Kubitschek, Av. Professor Alfredo Balena, 190, Belo Horizonte, Brazil;Hospital Metropolitano Doutor Célio de Castro, R. Dona Luiza, 311, Belo Horizonte, Brazil;Hospital Metropolitano Odilon Behrens, R. Formiga, 50, Belo Horizonte, Brazil;Hospital Mãe de Deus, R. José de Alencar, 286, Porto Alegre, Brazil;Hospital Nossa Senhora da Conceição, Av. Francisco Trein, 326, Porto Alegre, Brazil;Hospital SOS Cárdio, Rod. SC-401, 121, Florianópolis, Brazil;Hospital Santa Cruz, R. Fernando Abott, 174, Santa Cruz Do Sul, Brazil;Hospital Santo Antônio, Pç. Dr. Márcio Carvalho Lopes Filho, 501, Curvelo, Brazil;Hospital Semper, Al. Ezequiel Dias, 389, Belo Horizonte, Brazil;Hospital São João de Deus (Fundação Geraldo Correa), R. Do Cobre, 800, Divinópolis, Brazil;Hospital Universitário Canoas, Av. Farroupilha, 8001, Canoas, Brazil;Hospital Universitário Ciências Médicas de Minas Gerais, R. Dos Aimorés, 2896, Belo Horizonte, Brazil;Hospital Universitário de Santa Maria, Av. Roraima, 1000, Prédio 22, Santa Maria, Brazil;Institute for Health Technology Assessment (IATS/ CNPq), R. Ramiro Barcelos, 2359, Porto Alegre, Brazil;Medical School, Universidade Federal da Fronteira Sul, SC-484 Km 02, Chapecó, Brazil;Medical School and University Hospital, Universidade Federal de Minas Gerais, Av. Professor Alfredo Balena, 190, Belo Horizonte, Brazil;Medical School and University Hospital, Universidade Federal de Minas Gerais, Av. Professor Alfredo Balena, 190, Belo Horizonte, Brazil;Department of Internal Medicine, Medical School, Universidade Federal de Minas Gerais, Av. Professor Alfredo Balena, 190, Belo Horizonte, Brazil;Institute for Health Technology Assessment (IATS/ CNPq), R. Ramiro Barcelos, 2359, Porto Alegre, Brazil;Telehealth Center, University Hospital, Universidade Federal de Minas Gerais, Av. Professor Alfredo Balena, 110, Belo Horizonte, Brazil;Medical School and University Hospital, Universidade Federal de Minas Gerais, Av. Professor Alfredo Balena, 190, Belo Horizonte, Brazil;Hospital João XXIII, Av. Professor Alfredo Balena, 400, Belo Horizonte, Brazil;Medical School and University Hospital, Universidade Federal de Minas Gerais, Av. Professor Alfredo Balena, 190, Belo Horizonte, Brazil;Hospital Metropolitano Odilon Behrens, R. Formiga, 50, Belo Horizonte, Brazil;Medical School and University Hospital, Universidade Federal de Minas Gerais, Av. Professor Alfredo Balena, 190, Belo Horizonte, Brazil;Hospital Risoleta Tolentino Neves, R. das Gabirobas, 01, Belo Horizonte, Brazil;Orizonti Instituto de Saúde E Longevidade, Av. José Do Patrocínio Pontes, 1355, Belo Horizonte, Brazil;Santa Casa de Misericórdia de Belo Horizonte, Av. Francisco Sales, 1111, Belo Horizonte, Brazil;Universidade Federal Dos Vales Do Jequitinhonha E Mucuri (UFVJM), R. Cruzeiro, 01., Teófilo Otoni, Minas Gerais, Brazil;Universidade Federal Dos Vales Do Jequitinhonha E Mucuri (UFVJM), R. Cruzeiro, 01., Teófilo Otoni, Minas Gerais, Brazil;Hospital Santa Rosália, R. Do Cruzeiro, 01, Teófilo Otoni, Brazil;Universidade José Do Rosário Vellano (UNIFENAS), R. Boaventura, 50, Belo Horizonte, Brazil;

关键词: COVID-19;    Acute kidney injury;    Kidney replacement therapy;    Score predictive;    Risk prediction;    Mortality;   

DOI  :  10.1186/s12882-023-03341-9

 received in 2023-04-28, accepted in 2023-09-20,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundAcute 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.MethodsThis 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.ResultsA 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]).ConclusionThe 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.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

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