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.
【저자키워드】 Risk factors, Kidney diseases, 【초록키워드】 COVID-19, cross-sectional, disease severity, Diagnosis, Comorbidity, prospective cohort study, healthcare worker, Laboratory, Kidney injury, AKI, Characteristics, Accuracy, Latin America, multicentre, prognostic, registry, characteristic, resource, Intensive, in-hospital mortality, hospitalized COVID-19 patient, COVID-19 patient, Predictive, 95% CI, coefficient, validation cohort, KDIGO, approach, defined, develop, evaluated, can be used, condition, were used, cause, category, assist, build, Elastic, predictor variable, with COVID-19, 【제목키워드】 development, in-hospital mortality, COVID-19 patient, approach, acute kidney,