Development and validation of a simple web-based tool for early prediction of COVID-19-associated death in kidney transplant recipients

Autor: Modelli de Andrade, Luis Gustavo [UNESP], de Sandes-Freitas, Tainá Veras, Requião-Moura, Lúcio R., Viana, Laila Almeida, Cristelli, Marina Pontello, Garcia, Valter Duro, Alcântara, Aline Lima Cunha, Esmeraldo, Ronaldo de Matos, Abbud Filho, Mario, Pacheco-Silva, Alvaro, de Lima Carneiro, Erika Cristina Ribeiro, Manfro, Roberto Ceratti, Costa, Kellen Micheline Alves Henrique, Simão, Denise Rodrigues, de Sousa, Marcos Vinicius, Santana, Viviane Brandão Bandeira de Mello, Noronha, Irene L., Romão, Elen Almeida, Zanocco, Juliana Aparecida, Arimatea, Gustavo Guilherme Queiroz, De Boni Monteiro de Carvalho, Deise, Tedesco-Silva, Helio, Medina-Pestana, José, Keitel, Elizete, Costa de Oliveira, Claudia Maria, Neri, Beatriz de Oliveira, Fernandes Charpiot, Ida Maria Maximina, Ferreira, Teresa Cristina Alves, Vicari, Alessandra Rosa, Pereira, Tomás, Coelho, Maria Eduarda Heinzen de Almeida, Mazzali, Marilda, Ferreira, Gustavo Fernandes, Campos, Juliana Bastos, Rocha, Nicole Gomes Campos, Saldanha, Anita Leme da Rocha, Martinez, Tania Leme da Rocha, Romão, João Egídio, Teixeira Araújo, Maria Regina, Braga, Sibele Lessa, Deboni, Luciane Mônica, Krüger, Franco Silveira da Mota, Neto, Miguel Moysés, Claudino, Auro Buffani, Cláudio de Oliveira, Lívia, Matuck, Tereza Azevedo, Bignelli, Alexandre Tortoza, Hokazono, Silvia Regina, Suassuna, José Hermógenes Rocco, Rioja, Suzimar da Silveira, Madeira, Rafael Lage, Vilaça, Sandra Simone, Calazans, Carlos Alberto Chalabi, Calazans, Daniel Costa Chalabi, Malafronte, Patrícia, Miorin, Antonio, de Aguiar, Filipe Carrilho, Andrade, Larissa Guedes da Fonte, de Carvalho, Fabiana Loss, Martins, Karoline Sesiuk, Pinheiro, Hélady Sanders, Sertório, Emiliana Spadarotto, Pereira, André Barreto, Machado, David José Barros, Pozzi, Carolina Maria, Kroth, Leonardo Viliano, Filho, Lauro Monteiro Vasconcellos, Maciel, Rafael Fabio, Silva, Amanda Maíra Damasceno, Baptista, Ana Paula Maia, de Souza, Pedro Augusto Macedo, Lasmar, Marcus Faria, Saber, Luciana Tanajura Santamaria, Palma, Lilian Monteiro Pereira, de Barros Almeida, Ricardo Augusto Monteiro
Přispěvatelé: Universidade Estadual Paulista (UNESP), Federal University of Ceará, Hospital Universitário Walter Cantídio, Hospital Geral de Fortaleza, Universidade de São Paulo (USP), Fundação Oswaldo Ramos, Hospital Israelita Albert Einstein, Santa Casa de Misericórdia de Porto Alegre, Medical School FAMERP, Federal University of Maranhão, Federal Univertisy of Rio Grande do Sul, Onofre Lopes University Hospital, Hospital Santa Isabel, Universidade Estadual de Campinas (UNICAMP), Hospital de Base de Brasília, Hospital Beneficência Portuguesa de São Paulo (BP), Hospital Santa Marcelina, University of Brasília - UnB, Hospital São Francisco na Providência de Deus
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Scopus
Repositório Institucional da UNESP
Universidade Estadual Paulista (UNESP)
instacron:UNESP
American Journal of Transplantation
ISSN: 0449-4776
Popis: Made available in DSpace on 2022-04-28T19:44:11Z (GMT). No. of bitstreams: 0 Previous issue date: 2022-02-01 This analysis, using data from the Brazilian kidney transplant (KT) COVID-19 study, seeks to develop a prediction score to assist in COVID-19 risk stratification in KT recipients. In this study, 1379 patients (35 sites) were enrolled, and a machine learning approach was used to fit models in a derivation cohort. A reduced Elastic Net model was selected, and the accuracy to predict the 28-day fatality after the COVID-19 diagnosis, assessed by the area under the ROC curve (AUC-ROC), was confirmed in a validation cohort. The better calibration values were used to build the applicable ImAgeS score. The 28-day fatality rate was 17% (n = 235), which was associated with increasing age, hypertension and cardiovascular disease, higher body mass index, dyspnea, and use of mycophenolate acid or azathioprine. Higher kidney graft function, longer time of symptoms until COVID-19 diagnosis, presence of anosmia or coryza, and use of mTOR inhibitor were associated with reduced risk of death. The coefficients of the best model were used to build the predictive score, which achieved an AUC-ROC of 0.767 (95% CI 0.698–0.834) in the validation cohort. In conclusion, the easily applicable predictive model could assist health care practitioners in identifying non-hospitalized kidney transplant patients that may require more intensive monitoring. Trial registration: ClinicalTrials.gov NCT04494776. Department of Internal Medicine Universidade Estadual Paulista-UNESP Department of Clinical Medicine Federal University of Ceará Hospital Universitário Walter Cantídio Hospital Geral de Fortaleza Department of Medicine Nephrology Division Federal University of São Paulo Department of Transplantation Hospital do Rim Fundação Oswaldo Ramos Renal Transplant Unit Hospital Israelita Albert Einstein Santa Casa de Misericórdia de Porto Alegre Hospital de Base Medical School FAMERP Federal University of Maranhão Hospital de Clínicas de Porto Alegre Federal Univertisy of Rio Grande do Sul Division of Nephrology and Kidney Transplantation Onofre Lopes University Hospital Hospital Santa Isabel Division of Nephrology School of Medical Sciences Renal Transplant Unit Renal Transplant Research Laboratoy University of Campinas – UNICAMP Hospital de Base de Brasília Hospital Beneficência Portuguesa de São Paulo (BP) Division of Nephrology School of Medicine of Ribeirão Preto University of Sao Paulo Hospital Santa Marcelina Hospital Universitário de Brasília University of Brasília - UnB, DF Hospital São Francisco na Providência de Deus Department of Internal Medicine Universidade Estadual Paulista-UNESP
Databáze: OpenAIRE
Externí odkaz: