RESUMO
BACKGROUND: The renal angina index (RAI) is a tool that has been validated by several studies in the pediatric population to predict the development of severe acute kidney injury (AKI). The aims of this study were to evaluate the efficacy of the RAI in predicting severe AKI in critically ill patients with COVID-19 and to propose a modified RAI (mRAI) for this population. METHODS: This was a prospective cohort analysis of all COVID-19 patients receiving invasive mechanical ventilation (IMV) who were admitted to the intensive care unit (ICU) of a third-level hospital in Mexico City from 03/2020 to 01/2021. AKI was defined according to KDIGO guidelines. The RAI score was calculated for all enrolled patients using the method of Matsuura. Since all patients had the highest score for the condition (due to receiving IMV), the score corresponded to the delta creatinine (ΔSCr) value. The main outcome was severe AKI (stage 2 or 3) at 24 and 72 h after ICU admission. A logistic regression analysis was applied to search for factors associated with the development of severe AKI, and the data were applied to develop a mRAI and compare it vis-à-vis the efficacy of both scores (RAI and mRAI). RESULTS: Of the 452 patients studied, 30% developed severe AKI. The original RAI score was associated with AUCs of 0.67 and 0.73 at 24 h and 72 h, respectively, with a cutoff of 10 points to predict severe AKI. In the multivariate analysis adjusted for age and sex, a BMI ≥30 kg/m2, a SOFA score ≥6, and Charlson score were identified as risk factors for the development of severe AKI. In the new proposed score (mRAI), the conditions were summed and multiplied by the ΔSCr value. With these modifications, the AUC improved to 0.72 and 0.75 at 24 h and 72 h, respectively, with a cutoff of 8 points. CONCLUSIONS: The original RAI is a limited tool for patients with critical COVID-19 receiving IMV. The mRAI, with the parameters proposed in the present study, improves predictive performance and risk stratification in critically ill patients receiving IMV.
