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Prospective validation of the EASL management algorithm for acute kidney injury in cirrhosis.

Ann Thu MaCristina SoléAdrià JuanolaLaia EscudéLaura NapoleoneEmma AvitabileMartina Pérez-GuaschMarta CarolEnrico PompiliJordi Gratacós-GinésAnna SoriaAna Belén RubioMarta CerveraMaria José MoretaManuel Morales-RuizElsa SolàEsteban PochNúria FabrellasIsabel GrauperaElisa PosePere Ginès
Published in: Journal of hepatology (2024)
The occurrence of acute kidney injury (AKI) in patients with cirrhosis is associated with poor short-term mortality. Improving its rapid identification and prompt management was the focus of the recently proposed EASL AKI algorithm. This is the first prospective study demonstrating that high AKI response rates are achieved with the use of this algorithm, which includes identification of AKI, treatment of precipitating factors, a 2-day albumin challenge in patients with AKI ≥1B, and supportive therapy in patients with persistent AKI not meeting HRS-AKI criteria or terlipressin with albumin in those with HRS-AKI. These findings support the use of this algorithm in clinical practice.
Keyphrases
  • acute kidney injury
  • cardiac surgery
  • machine learning
  • deep learning
  • risk assessment
  • stem cells
  • coronary artery disease
  • neural network
  • mesenchymal stem cells
  • replacement therapy