BMC Medicine (Sep 2022)

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

  • Flávio de Azevedo Figueiredo,
  • Lucas Emanuel Ferreira Ramos,
  • Rafael Tavares Silva,
  • Daniela Ponce,
  • Rafael Lima Rodrigues de Carvalho,
  • Alexandre Vargas Schwarzbold,
  • Amanda de Oliveira Maurílio,
  • Ana Luiza Bahia Alves Scotton,
  • Andresa Fontoura Garbini,
  • Bárbara Lopes Farace,
  • Bárbara Machado Garcia,
  • Carla Thais Cândida Alves da Silva,
  • Christiane Corrêa Rodrigues Cimini,
  • Cíntia Alcantara de Carvalho,
  • Cristiane dos Santos Dias,
  • Daniel Vitório Silveira,
  • Euler Roberto Fernandes Manenti,
  • Evelin Paola de Almeida Cenci,
  • Fernando Anschau,
  • Fernando Graça Aranha,
  • Filipe Carrilho de Aguiar,
  • Frederico Bartolazzi,
  • 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 Machado-Rugolo,
  • Karen Brasil Ruschel,
  • Karina Paula Medeiros Prado Martins,
  • Luanna Silva Monteiro Menezes,
  • Luciana Siuves Ferreira Couto,
  • Luís César de Castro,
  • Luiz Antônio Nasi,
  • Máderson Alvares de Souza Cabral,
  • Maiara Anschau Floriani,
  • Maíra Dias Souza,
  • Maira Viana Rego Souza-Silva,
  • Marcelo Carneiro,
  • Mariana Frizzo de Godoy,
  • Maria Aparecida Camargos Bicalho,
  • Maria Clara Pontello Barbosa Lima,
  • Márlon Juliano Romero Aliberti,
  • Matheus Carvalho Alves Nogueira,
  • Matheus Fernandes Lopes Martins,
  • Milton Henriques Guimarães-Júnior,
  • Natália da Cunha Severino Sampaio,
  • Neimy Ramos de Oliveira,
  • Patricia Klarmann Ziegelmann,
  • Pedro Guido Soares Andrade,
  • Pedro Ledic Assaf,
  • Petrônio José de Lima Martelli,
  • Polianna Delfino-Pereira,
  • Raphael Castro Martins,
  • Rochele Mosmann Menezes,
  • Saionara Cristina Francisco,
  • Silvia Ferreira Araújo,
  • Talita Fischer Oliveira,
  • Thainara Conceição de Oliveira,
  • Thaís Lorenna Souza Sales,
  • Thiago Junqueira Avelino-Silva,
  • Yuri Carlotto Ramires,
  • Magda Carvalho Pires,
  • Milena Soriano Marcolino

DOI
https://doi.org/10.1186/s12916-022-02503-0
Journal volume & issue
Vol. 20, no. 1
pp. 1 – 13

Abstract

Read online

Abstract Background Acute kidney injury (AKI) is frequently associated with COVID-19, and the need for kidney replacement therapy (KRT) is considered an indicator of disease severity. This study aimed to develop a prognostic score for predicting the need for KRT in hospitalised COVID-19 patients, and to assess the incidence of AKI and KRT requirement. Methods This study is part of a multicentre cohort, the Brazilian COVID-19 Registry. A total of 5212 adult COVID-19 patients were included between March/2020 and September/2020. Variable selection was performed using generalised additive models (GAM), and least absolute shrinkage and selection operator (LASSO) regression was used for score derivation. Accuracy was assessed using the area under the receiver operating characteristic curve (AUC-ROC). Results The median age of the model-derivation cohort was 59 (IQR 47–70) years, 54.5% were men, 34.3% required ICU admission, 20.9% evolved with AKI, 9.3% required KRT, and 15.1% died during hospitalisation. The temporal validation cohort had similar age, sex, ICU admission, AKI, required KRT distribution and in-hospital mortality. The geographic validation cohort had similar age and sex; however, this cohort had higher rates of ICU admission, AKI, need for KRT and in-hospital mortality. Four predictors of the need for KRT were identified using GAM: need for mechanical ventilation, male sex, higher creatinine at hospital presentation and diabetes. The MMCD score had excellent discrimination in derivation (AUROC 0.929, 95% CI 0.918–0.939) and validation (temporal AUROC 0.927, 95% CI 0.911–0.941; geographic AUROC 0.819, 95% CI 0.792–0.845) cohorts and good overall performance (Brier score: 0.057, 0.056 and 0.122, respectively). The score is implemented in a freely available online risk calculator ( https://www.mmcdscore.com/ ). Conclusions The use of the MMCD score to predict the need for KRT may assist healthcare workers in identifying hospitalised COVID-19 patients who may require more intensive monitoring, and can be useful for resource allocation.

Keywords