Many different artificial liver support systems(biological and non-biological) have been developed,tested pre-clinically and some have been applied in clinical trials.Based on theoretical considerations a biological a...Many different artificial liver support systems(biological and non-biological) have been developed,tested pre-clinically and some have been applied in clinical trials.Based on theoretical considerations a biological artificial liver(BAL) should be preferred above the non-biological ones.However,clinical application of the BAL is still experimental.Here we try to analyze which hurdles have to be taken before the BAL will become standard equipment in the intensive care unit for patients with acute liver failure or acute deterioration of chronic liver disease.展开更多
Acute liver failure (ALF), also known as fulminant hepatic failure (FHF), is a devastating clinical syndrome with a high mortality of 60%-90%. An early and exact assessment of the severity of ALF together with predict...Acute liver failure (ALF), also known as fulminant hepatic failure (FHF), is a devastating clinical syndrome with a high mortality of 60%-90%. An early and exact assessment of the severity of ALF together with prediction of its further development is critical in order to determine the further management of the patient. A number of prognostic models have been used for outcome prediction in ALF patients but they are mostly based on the variables measured at one time point, mostly at admission. ALF patients rarely show a static state: rapid progress to a life threatening situation occurs in many patients. Since ALF is a dynamic process, admission values of prognostic variables change over time during the clinical course of the patient. Kumar et al developed a prognostic model [ALF early dynamic (ALFED)] based on early changes in values of variables which predicted outcome. ALFED is a model which seems to be worthwhile to test in ALF patients in other parts of the world with different aetiologies. Since the exact pathophysiology of ALF is not fully known and is certainly complex, we believe that adding promising variables involved in the pathophysiology of ALF to the dynamic approach might even further improve prognostic performance. We agree with Kumar et al that an improved dynamic prognostic model should be based on simplicity (easily to be performed at the bedside) and accuracy. Our comments presented in this paper may be considered as recommendations for future optimization of ALF prediction models.展开更多
文摘Many different artificial liver support systems(biological and non-biological) have been developed,tested pre-clinically and some have been applied in clinical trials.Based on theoretical considerations a biological artificial liver(BAL) should be preferred above the non-biological ones.However,clinical application of the BAL is still experimental.Here we try to analyze which hurdles have to be taken before the BAL will become standard equipment in the intensive care unit for patients with acute liver failure or acute deterioration of chronic liver disease.
文摘Acute liver failure (ALF), also known as fulminant hepatic failure (FHF), is a devastating clinical syndrome with a high mortality of 60%-90%. An early and exact assessment of the severity of ALF together with prediction of its further development is critical in order to determine the further management of the patient. A number of prognostic models have been used for outcome prediction in ALF patients but they are mostly based on the variables measured at one time point, mostly at admission. ALF patients rarely show a static state: rapid progress to a life threatening situation occurs in many patients. Since ALF is a dynamic process, admission values of prognostic variables change over time during the clinical course of the patient. Kumar et al developed a prognostic model [ALF early dynamic (ALFED)] based on early changes in values of variables which predicted outcome. ALFED is a model which seems to be worthwhile to test in ALF patients in other parts of the world with different aetiologies. Since the exact pathophysiology of ALF is not fully known and is certainly complex, we believe that adding promising variables involved in the pathophysiology of ALF to the dynamic approach might even further improve prognostic performance. We agree with Kumar et al that an improved dynamic prognostic model should be based on simplicity (easily to be performed at the bedside) and accuracy. Our comments presented in this paper may be considered as recommendations for future optimization of ALF prediction models.
