Investigating the association of acute kidney injury (AKI) with COVID-19 mortality using data-mining scheme

Abstract

COVID-19 has caused significant challenges in kidney research and disease management. Data mining techniques such as logistic regression (LR) and decision tree (DT) were used to model data. All analyses were performed using SPSS 25 and Python 3. The incidence of acute kidney injury (AKI) was 14.1% and the overall mortality risk was 13% among COVID-19 patients. The mortality was associated with, AKI, age, marital status, smoking status, heart failure, chronic obstructive pulmonary disease, malignancy, and SPO2 level using LR. The accuracy, sensitivity, specificity, and area under the curve of the DT (and LR) classifier were 70% (85%), 73% (75%), 78% (79%), and 77% (81%), respectively. Based on the DT model, the variable most significantly associated with COVID-19 mortality was AKI followed by age, high WBC count, BMI, and lymphocyte count. It was concluded that the incidence of AKI was high, and AKI was identified as one of the important factors that played an effective role in mortality due to COVID-19.