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 elderly population.As the COVID-19 pandemic is transitioning into an endemic state, there is an unmet need for prognostic scores tailored to this population.Objectives: Development of 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 a large, tertiary care center with reference status in Lesser Poland.We screened 5806 patients with SARS-CoV-2 infection confirmed with polymerase chain reaction test.After excluding subjects with absent serum creatinine values or mild disease course (less than 7 days of inpatient care), 4630 patients were recruited.Data was randomly split into a training (N = 3462) and test (N = 1168) cohort.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 AUC (area under the curve) with a range of 0.793-0.807and an average performance of 0.798.Model explanation techniques from a global perspective suggest respiratory support, chronic kidney disease and procalcitonin are 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 decisionmaking support for specialist providers.