Acute Kidney Injury (AKI) is a major complication of cardiac surgery and its most severe forms are associated with significant morbidity and mortality .
Identification of patients at risk may facilitate early use of management protocoolos recommended by KDIGO (Kidney Disease: Improving Global Outcomes), such as haemodynamic and volume optimisation, monitoring of renal function and discontinuation or reduction of nephrotoxic drugs . The rate associated with cardiac surgery varies according to its different historical definitions, from 0.3% to 29.7% . Cases requiring renal replacement therapy (RRT) occur between 1 .2%-3.0% in the studied cardiac surgery cohorts and their presence is a n independent predictor of mortality.
Because AKI still lacks effective treatments in addition to support and elimination of the cause, early and specific diagnosis is an unmet but critical need f o r successful and personalised patient management.
Creatinine, urea and urine output have been and are the main ways to diagnose and treat renal failure. Other types of biomarkers that seem to be related to risk of postoperative AKI are currently under study. Although more research is needed, they may eventually become predictive diagnostic tools. Several studies have indicated that the urinary level of NGAL excreted intraoperatively and after surgery is effective in predicting AKI in both adult and paediatric populations. Similar results have been obtained with other urinary biomarkers, such as the cell cycle arrest biomarkers TIMP-2 and IGFBP7) KIM-1, NAG, IL-18 and L-FAP . The major limitation of these biomarkers is that they are not easily accessible in all hospitals and clinical settings. Recently Demirjian developed a predictive model in which they observed that perioperative change in serum creatinine and postoperative blood urea nitrogen, serum sodium, potassium, bicarbonate and albumin from the first metabolic panel after cardiac surgery show good predictive value for moderate to severe AKI within 72 hours and 14 days after the surgical procedure.
3\. Hypothesis
The score developed by Demirjian et al has a good predictive ability for moderate to severe AKI after cardiac surgery in the European population.
4\. Objectives
4.1 Main
* Confirm the usefulness and externally validate the scale.
* Compare this scale with other models of AKI prediction after cardiac surgery: Thakar, Mehta, ISR, Callejas, Leicester, CRATE.
4.2 Secondary
* Identify new risk factors for Acute Kidney Injury (AKI)
* Understanding the incidence of AKI after cardiac surgery in Europe
* Validating a model for predicting discharge dialysis after cardiac surgery
* Investigate the performance of these models in specific surgeries: coronary surgery without cardiopulmonary bypass and heart transplantation.
* Assess the performance of other scales and compare them.
* Determine the prevalence and risk factors for Acute Kidney Disease (AKD) (renal injury beyond 7 days, up to 90 days).
* Determine the prevalence and risk factors for new Chronic Kidney Disease (CKD) (follow-up for 120 days or 4 months).
* Determine the prevalence and risk factors for CKD progression, defined by a one-stage progression in patients with pre-existing CKD. Follow-up for 120 days (4 months).
