In the emergency department (ED) a prompt diagnosis and appropriate treatment for all diseases improve a patient's outcome.
Acute kidney injury (AKI) is defined as an abrupt deficiency of renal function over a period of hours to days resulting in a failure of the kidney to excrete nitrogenous
waste products and to maintain fluid and
electrolyte homeostasis. AKI diagnosis could be very challenging for ED physicians because it is often very difficult to obtain some anamnestic data such as daily urine output or a preexisting value of BUN and serum
creatinine. The incidence of AKI is progressively increasing in EDs and the mortality rates of these patients range from 50 to 80% in multiorgan failure. For ED physicians it is also crucial to distinguish AKI from prerenal
azotemia (volume depletion promptly resolved through administration of fluids) at the time of patient presentation. Moreover, a rapid diagnosis of AKI leads to stop the progressive kidney damage on the basis of an appropriate therapeutic approach. Recent studies have demonstrated that by using a new
biomarker,
neutrophil gelatinase-associated lipocalin (NGAL), it is possible to obtain an accurate and fast diagnosis of AKI. It is well known that in patients with
cardiovascular diseases such as
stroke,
coronary artery diseases and
congestive heart failure, high levels of
creatinine are strictly related to a higher mortality. In the ED the occurrence of AKI in patients with acute worsening of cardiac function like acute decompensated
heart failure is very common. Moreover, managing acute
heart failure strictly depends on renal function. Therefore, a multimarker approach including NGAL+BNP (today easily obtained by a POCT system) could have a tremendous impact on an appropriate diagnosis, treatment and a supposed better patient outcome. Furthermore, an evaluation of total body fluid content is of great utility. We propose a new model of management for ED patients with
cardiorenal syndromes using a multimarker approach and non-invasive evaluation of body fluid content by bioelectrical impedance vector analysis.