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

• Autor(a) principal: Modelli de Andrade, Luis Gustavo [UNESP]
• Data de Publicação: 2022
• Outros Autores: 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
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1111/ajt.16807; http://hdl.handle.net/11449/222352

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.