KDIGO Classification in Predicting the Outcomes of Acute Kidney Injury in Patients in Intensive Care Units of a Tertiary Care Centre

Background: Acute kidney injury (AKI) is a common occurrence in intensive care units. Mortality is more for patients with AKI than those without AKI. KDIGO classification system is a recent tool to stage AKI. Outcomes of AKI depend on stage of the disease, underlying aetiologies and interventions. Objectives were to study the ability of KDIGO classification in predicting the outcomes of acute kidney injury in patients admitted to the intensive care units of a tertiary care centre, to predict the mortality among acute kidney injury patients admitted for intensive care and to study the clinical and etiological profile of acute kidney injury in such patients. Materials and methods: 153 subjects from Medical ICUs admitted with AKI were included. The period of study was from 1st January 2016 to 31st December 2016. After getting proper consent, details were taken in proforma. Patients coming under exclusion criteria were excluded. Data of 153 patients entered in excel sheet and with various statistical tools the data were analysed. Results: Globally subjects with AKI in ICU, the mortality is about 40 to 60%. Duration of ICU stay among Stage III patients were comparatively longer than those in stage II and stage I (p<0.05). Patients undergoing RRT hold higher mortality. AKI patients who need HD or PD as Renal replacement had comparatively higher rates of mortality than those doesn't require RRT (p<0.05). There was no significant association between age of the patients and mortality (P>0.05). There was no significant association between mortality and gender. Binary logistic regression model for mortality was performed to predict the independent risk factors of mortality. The regression analysis revealed that staging according to KDIGO, sepsis, hypertension and diabetes has independent predictability in mortality. Conclusion: We concluded that higher the stage of AKI, the higher will be the mortality and also staging can predict mortality. So staging AKI patients with KDIGO classification holds statistical significance.