Development and validation of the MMCD score to predict kidney replacement therapy in COVID-19 patients
Authors: Figueiredo Flávio de Azevedo , Ramos Lucas Emanuel Ferreira , Silva Rafael Tavares , Pires Magda Carvalho , Ponce Daniela , de Carvalho Rafael Lima Rodrigues , Schwarzbold Alexandre Vargas , Maurílio Amanda de Oliveira , Alves Scotton Ana Luiza Bahia , Garbini Andresa Fontoura , Farace Bárbara Lopes , Garcia Bárbara Machado , da Silva Carla Thais Cândida Alves , Cimini Christiane Corrêa Rodrigues , de Carvalho Cíntia Alcantara , Dias Cristiane dos Santos , Silveira Daniel Vitorio , Manenti Euler Roberto Fernandes , de Almeida Cenci Evelin Paola , Anschau Fernando , Aranha Fernando Graça , de Aguiar Filipe Carrilho , Bartolazzi Frederico , Vietta Giovanna Grunewald , Nascimento Guilherme Fagundes , Noal Helena Carolina , Duani Helena , Vianna Heloisa Reniers , Guimarães Henrique Cerqueira , de Alvarenga Joice Coutinho , Chatkin José Miguel , de Moraes Júlia Parreiras Drumond , Rugolo Juliana Machado , Ruschel Karen Brasil , Prado Martins Karina Paula Medeiros , Menezes Luanna Silva Monteiro , Couto Luciana Siuves Ferreira , de Castro Luís César , Nasi Luiz Antônio , Cabral Máderson Alvares de Souza , Floriani Maiara Anschau , Souza Maíra Dias , Souza e Silva Maira Viana Rego , Carneiro Marcelo , de Godoy Mariana Frizzo , Bicalho Maria Aparecida Camargos , Barbosa Lima Maria Clara Pontello , Nogueira Matheus Carvalho Alves , Martins Matheus Fernandes Lopes , Guimarães-Júnior Milton Henriques , Sampaio Natália da Cunha Severino , de Oliveira Neimy Ramos , Ziegelmann Patricia Klarmann , Andrade Pedro Guido Soares , Assaf Pedro Ledic , de Lima Martelli Petrônio José , Pereira Polianna Delfino , Martins Raphael Castro , Menezes Rochele Mosmann , Francisco Saionara Cristina , Araújo Silvia Ferreira , Oliveira Talita Fischer , de Oliveira Thainara Conceição , Sales Thaís Lorenna Souza , Ramires Yuri Carlotto , Marcolino Milena Soriano 
Published: January 2022

ABSTRACTBackgroundAcute 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 hospitalized COVID-19 patients.MethodsThis study is part of the multicentre cohort, the Brazilian COVID-19 Registry. A total of 5,212 adult COVID-19 patients were included between March/2020 and September/2020. We evaluated four categories of predictor variables: (1) demographic data; (2) comorbidities and conditions at admission; (3) laboratory exams within 24 h; and (4) the need for mechanical ventilation at any time during hospitalization. Variable selection was performed using generalized additive models (GAM) and least absolute shrinkage and selection operator (LASSO) regression was used for score derivation. The accuracy was assessed using the area under the receiver operating characteristic curve (AUC-ROC). Risk groups were proposed based on predicted probabilities: non-high (up to 14.9%), high (15.0 – 49.9%), and very high risk (≥ 50.0%).ResultsThe 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 hospitalization. The validation cohort had similar age, sex, ICU admission, AKI, required KRT distribution and in-hospital mortality. Thirty-two variables were tested and four important predictors of the need for KRT during hospitalization were identified using GAM: need for mechanical ventilation, male gender, higher creatinine at admission, and diabetes. The MMCD score had excellent discrimination in derivation (AUROC = 0.929; 95% CI 0.918–0.939) and validation (AUROC = 0.927; 95% CI 0.911–0.941) cohorts an good overall performance in both cohorts (Brier score: 0.057 and 0.056, respectively). The score is implemented in a freely available online risk calculator (https://www.mmcdscore.com/).ConclusionThe use of the MMCD score to predict the need for KRT may assist healthcare workers in identifying hospitalized COVID-19 patients who may require more intensive monitoring, and can be useful for resource allocation.