Abstract

Introduction: Minilaparoscopy (ML) increasingly establishes in the diagnosis of liver disease. We hereby present our results of a prospective study comparing ML and conventional laparoscopy (CL) in the diagnostic workup of patients with liver disease. Patients and methods: 96 patients were randomized either to undergo CL (n = 47) or ML (n = 49) for the diagnosis of suspected liver disease. Conventional laparoscopy was performed with a 11mm-standard Storz laparoscope (Storz, Tuttlingen, Germany) according to previously published guidelines. For minilaparoscopy we used a 1,9 mm-minioptic (Richard Wolf GmbH, Knittlingen, Germany). In all cases, we attempted to obtain a liver biopsy. Results: Laparoscopy could successfully be performed in 92/96 (96%) patients with simultaneous biopsies of the liver. Compared to CL, ML could be performed in a significantly shorter period of time (27,6 min vs.25,1 min, p≤.0,05). In four cases (1 CL and 3 ML), postoperative adhesions prevented sufficient inspection of the liver and in one further patient the technique was switched from CL to ML for the same reason. Minor, self-limiting bleeding after biopsy was observed during 7 examinations with either technique, 2 patients in the ML-group (liver cirrhosis stage Child-Pugh C with ascites) required surgery for uncontrollable bleeding. The patients` subjective perception of the examination was comparable in both groups. Macroscopic and microscopic findings equaled in both groups: During CL and ML, macroscopic signs of cirrhosis were found in 33/47 (70%) and 26/49 (53%) patients, respectively. Histological confirmation of these findings could be obtained in 76% and 77%, respectively. On the other hand, liver cirrhosis was diagnosed by histology in 1/14 (7%) and 1/23 (4%) patients without macroscopic signs of cirrhosis. Discussion: The diagnostic gain of laparoscopy with minioptics in the workup of liver disease seems to be comparable with the results obtained by CL. An advantage is a lower degree of invasiveness and a shorter examination time in ML.

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