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Acute Kidney Injury (AKI) is a syndrome characterized by a sudden decline in kidney function, potentially leading to renal replacement therapy. Despite its reversible nature, AKI can cause increased mortality, morbidity, and later cardiovascular complications, imposing economic burdens. The current limited utility of biomarkers for early AKI detection upon ICU admission necessitates evaluating innovative predictive tools.
A retrospective observational study was conducted from January 31 to September 30, 2020, among patients aged >18 admitted to ICU at Fundación Cardioinfantil, Bogotá, Colombia. Exclusion criteria included renal replacement therapy dependency before admission, prior AKI diagnosis, kidney transplant history, referral from another ICU, or ICU discharge before 48 hours. Demographic and lab data collected for seven days post-admission were applied to Malhotra et al. renal angina tool (1). AKI was defined per KDIGO classification. The Youden method estimated an optimal cutoff point, and the GiViTi model assessed calibration.
Using Malhotra et al. original cutoff point, the tool had good sensitivity but poor specificity. Although an optimal cutoff improved specificity, it reduced sensitivity; the discrimination capacity of the tool is is adequate. We recommend the original cutoff point of 5 for this population, as the tool effectively identifies low-risk patients, potentially minimizing AKI preventive interventions. This suggests a more targeted, cost-effective AKI management in ICU settings, beneficial in emerging economies. The low calibration indicates a need for a modified tool to better predict AKI risk in this population.
References.
1. Malhotra, R., Kashani, K. B., Macedo, E., Kim, J., Bouchard, J., Wynn, S., ... & Mehta, R. (2017). A risk prediction score for acute kidney injury in the intensive care unit. Nephrology Dialysis Transplantation, 32(5), 814-822.