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Erschienen in:
01.09.2011 | Original Article—Liver, Pancreas, and Biliary Tract
Novel classification of acute liver failure through clustering using a self-organizing map: usefulness for prediction of the outcome
Erschienen in: Journal of Gastroenterology | Ausgabe 9/2011
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Background
Patients with acute liver failure are classified according to the interval between the onset of hepatitis symptoms and the development of hepatic encephalopathy. We examined the validity of such classifications.
Methods
The subjects were 1,022 patients enrolled in a nationwide survey in Japan. The intervals between the onset of the hepatitis symptoms and the development of encephalopathy were 10 days or less in 472 patients (group-A), between 11 and 56 days in 468 patients (group-B), and longer than 56 days in 82 patients (group-C). Data on a total of 104 items collected from the patients were subjected to clustering using a self-organizing map.
Results
The patients were classified into three clusters. The first cluster consisted of 411 patients (group-A: 57%, group-B: 39%, group-C: 4%). Their incidence of complications was low; 34% underwent liver transplantation (LT), and their survival rate was 90%, while 94% of those treated without transplant were rescued. The second cluster consisted of 320 patients (21, 65, and 14% groups A, B, and C, respectively), who showed a high incidence of complications; the survival rate was 7% in the patients treated conservatively without LT. Sixteen percent underwent LT and survival rate of these patients was 52%. There was a third cluster, of 291 patients (59, 34, and 7% groups A, B, and C, respectively). Without LT, 81% of the patients died. Seven percent were treated by LT and their survival rate was 60%.
Conclusions
Clustering revealed that patients with acute liver failure could be classified into three clusters independent of the interval between the onset of disease symptoms and the development of encephalopathy. This technique may be useful, since the outcomes of the patients differed markedly among the clusters.