dc.contributor | Universidade Estadual Paulista (UNESP) | |
dc.contributor | School of Medicine | |
dc.contributor | Universidad de La República | |
dc.date.accessioned | 2022-04-29T08:38:19Z | |
dc.date.accessioned | 2022-12-20T03:00:33Z | |
dc.date.available | 2022-04-29T08:38:19Z | |
dc.date.available | 2022-12-20T03:00:33Z | |
dc.date.created | 2022-04-29T08:38:19Z | |
dc.date.issued | 2021-12-01 | |
dc.identifier | Scientific Reports, v. 11, n. 1, 2021. | |
dc.identifier | 2045-2322 | |
dc.identifier | http://hdl.handle.net/11449/230191 | |
dc.identifier | 10.1038/s41598-021-03894-5 | |
dc.identifier | 2-s2.0-85122537958 | |
dc.identifier.uri | https://repositorioslatinoamericanos.uchile.cl/handle/2250/5410325 | |
dc.description.abstract | Acute 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. | |
dc.language | eng | |
dc.relation | Scientific Reports | |
dc.source | Scopus | |
dc.title | Development of a prediction score for in-hospital mortality in COVID-19 patients with acute kidney injury: a machine learning approach | |
dc.type | Artículos de revistas | |