Predicting acute kidney injury onset using a random forest algorithm using electronic medical records of COVID-19 patients: the CRACoV-AKI model

Introduction: Acute kidney injury (AKI) is a serious and common complication of SARS-CoV-2 infection. Most risk assessment tools for AKI have been developed in the intensive care unit or in elderly populations. As the COVID-19 pandemic is transitioning into an endemic phase, there is an unmet need for prognostic scores tailored to the population of patients hospitalized for this disease. Objectives: We aimed to develop a robust predictive model for the occurrence of AKI in hospitalized patients with COVID-19. Patients and methods: Electronic medical records of all adult inpatients admitted between March 2020 and January 2022 were extracted from the database of a large, tertiary care center with a reference status in Lesser Poland. We screened 5806 patients with SARS-CoV-2 infection confirmed with a polymerase chain reaction test. After excluding individuals with lacking data on serum creatinine levels and those with a mild disease course (<⁠7 days of inpatient care), a total of 4630 records were considered. Data were randomly split into training (n = 3462) and test (n = 1168) sets. A random forest model was tuned with feature engineering based on expert advice and metrics evaluated in nested cross-validation to reduce bias. Results: Nested cross-validation yielded an area under the curve ranging between 0.793 and 0.807, and an average performance of 0.798. Model explanation techniques from a global perspective suggested that a need for respiratory support, chronic kidney disease, and procalcitonin concentration were among the most important variables in permutation tests. Conclusions: The CRACoV-AKI model enables AKI risk stratification among hospitalized patients with COVID-19. Machine learning–based tools may thus offer additional decision-making support for specialist providers.