Development of a prediction score for in-hospital mortality in COVID-19 patients with acute kidney injury: a machine learning approach

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Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorSchool of Medicine-
Autor(es): dc.contributorUniversidad de La República-
Autor(es): dc.creatorPonce, Daniela-
Autor(es): dc.creatorde Andrade, Luís Gustavo Modelli-
Autor(es): dc.creatorGranado, Rolando Claure-Del-
Autor(es): dc.creatorFerreiro-Fuentes, Alejandro-
Autor(es): dc.creatorLombardi, Raul-
Data de aceite: dc.date.accessioned2025-08-21T17:27:08Z-
Data de disponibilização: dc.date.available2025-08-21T17:27:08Z-
Data de envio: dc.date.issued2022-04-29-
Data de envio: dc.date.issued2022-04-29-
Data de envio: dc.date.issued2021-11-30-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1038/s41598-021-03894-5-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/230191-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/230191-
Descrição: dc.descriptionAcute kidney injury (AKI) is frequently associated with COVID-19 and it is considered an indicator of disease severity. This study aimed to develop a prognostic score for predicting in-hospital mortality in COVID-19 patients with AKI (AKI-COV score). This was a cross-sectional multicentre prospective cohort study in the Latin America AKI COVID-19 Registry. A total of 870 COVID-19 patients with AKI defined according to the KDIGO were included between 1 May 2020 and 31 December 2020. We evaluated four categories of predictor variables that were available at the time of the diagnosis of AKI: (1) demographic data; (2) comorbidities and conditions at admission; (3) laboratory exams within 24 h; and (4) characteristics and causes of AKI. We used a machine learning approach to fit models in the training set using tenfold cross-validation and validated the accuracy using the area under the receiver operating characteristic curve (AUC-ROC). The coefficients of the best model (Elastic Net) were used to build the predictive AKI-COV score. The AKI-COV score had an AUC-ROC of 0.823 (95% CI 0.761–0.885) in the validation cohort. The use of the AKI-COV score may assist healthcare workers in identifying hospitalized COVID-19 patients with AKI that may require more intensive monitoring and can be used for resource allocation.-
Descrição: dc.descriptionDepartment of Internal Medicine Botucatu Medical School University of São Paulo State–UNESP, Avenida Professor Mario Rubens Montenegro-
Descrição: dc.descriptionDivision of Nephrology Hospital Obrero No. 2 − CNS Universidad Mayor de San Simon School of Medicine-
Descrição: dc.descriptionDivision of Nephrology School of Medicine Universidad de La República-
Descrição: dc.descriptionDepartment of Internal Medicine Botucatu Medical School University of São Paulo State–UNESP, Avenida Professor Mario Rubens Montenegro-
Idioma: dc.languageen-
Relação: dc.relationScientific Reports-
???dc.source???: dc.sourceScopus-
Título: dc.titleDevelopment of a prediction score for in-hospital mortality in COVID-19 patients with acute kidney injury: a machine learning approach-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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