Acute kidney injury (AKI) is a common problem in critically ill patients and is associated with increased morbidity and mortality. Since 2012, AKI has been defined according to the KDIGO (Kidney Disease Improving Global Outcome) guidelines. As some biomarkers are now available that can provide useful clinical information, a new definition including a new stage 1S has been proposed by an expert group of the Acute Disease Quality Initiative (ADQI). At this stage, classic AKI criteria are not yet met, but biomarkers are already positive defining subclinical AKI. This stage 1S is associated with a worse patient outcome, regardless of the biomarker chosen. The PrevAKI and PrevAKI-Multicenter trial also showed that risk stratification with a biomarker and implementation of the KDIGO bundle (in the high-risk group) can reduce the rate of moderate and severe AKI. In the absence of a successful clinical trial, conservative management remains the primary focus of treatment. This mainly involves optimization of hemodynamics and an individualized (restrictive) fluid management. The STARRT-AKI trial has shown that there is no benefit from accelerated initiation of renal replacement therapy. However, delaying too long might be associated with potential harm, as shown in the AKIKI2 study. Prospective studies are needed to determine whether artificial intelligence will play a role in AKI in the future, helping to guide treatment decisions and improve outcomes.
Keyphrases
- acute kidney injury
- cardiac surgery
- artificial intelligence
- clinical trial
- study protocol
- phase iii
- phase ii
- machine learning
- quality improvement
- big data
- healthcare
- deep learning
- primary care
- clinical practice
- double blind
- early onset
- type diabetes
- case report
- randomized controlled trial
- adipose tissue
- open label
- metabolic syndrome
- skeletal muscle
- risk assessment
- cross sectional
- social media
- combination therapy
- health information