A Novel Machine Learning-Based Point-Score Model as a Non-Invasive Decision-Making Tool for Identifying Infected Ascites in Patients with Hydropic Decompensated Liver Cirrhosis: A Retrospective Multicentre Study

• Main Authors: Silvia Würstle (Author), Alexander Hapfelmeier (Author), Siranush Karapetyan (Author), Fabian Studen (Author), Andriana Isaakidou (Author), Tillman Schneider (Author), Roland M. Schmid (Author), Stefan von Delius (Author), Felix Gundling (Author), Julian Triebelhorn (Author), Rainer Burgkart (Author), Andreas Obermeier (Author), Ulrich Mayr (Author), Stephan Heller (Author), Sebastian Rasch (Author), Tobias Lahmer (Author), Fabian Geisler (Author), Benjamin Chan (Author), Paul E. Turner (Author), Kathrin Rothe (Author), Christoph D. Spinner (Author), Jochen Schneider (Author)
• Format: Book
• Published: MDPI AG, 2022-11-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.

This study is aimed at assessing the distinctive features of patients with infected ascites and liver cirrhosis and developing a scoring system to allow for the accurate identification of patients not requiring abdominocentesis to rule out infected ascites. A total of 700 episodes of patients with d...