LINAS‐Score: prognostic model for mortality assessment in patients with cirrhotic liver and infected ascites

Background and Aim Patients with liver cirrhosis often face a grave threat from infected ascites (IA). However, a well‐established prognostic model for this complication has not been established in routine clinical practice. Therefore, we aimed to assess mortality risk in patients with liver cirrhos...

Full description

Saved in:
Bibliographic Details
Published in:Journal of gastroenterology and hepatology Vol. 39; no. 9; pp. 1876 - 1884
Main Authors: Würstle, Silvia, Schneider, Tillman, Karapetyan, Siranush, Hapfelmeier, Alexander, Isaakidou, Andriana, Studen, Fabian, Schmid, Roland M., Delius, Stephan, Rothe, Kathrin, Burgkart, Rainer, Obermeier, Andreas, Triebelhorn, Julian, Erber, Johanna, Voit, Florian, Geisler, Fabian, Spinner, Christoph D., Schneider, Jochen, Wagner, Laura
Format: Journal Article
Language:English
Published: Australia Wiley Subscription Services, Inc 01-09-2024
Subjects:
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Background and Aim Patients with liver cirrhosis often face a grave threat from infected ascites (IA). However, a well‐established prognostic model for this complication has not been established in routine clinical practice. Therefore, we aimed to assess mortality risk in patients with liver cirrhosis and IA. Methods We conducted a retrospective study across three tertiary hospitals, enrolling 534 adult patients with cirrhotic liver and IA, comprising 465 with spontaneous bacterial peritonitis (SBP), 34 with bacterascites (BA), and 35 with secondary peritonitis (SP). To determine the attributable mortality risk linked to IA, these patients were matched with 122 patients with hydropic decompensated liver cirrhosis but without IA. Clinical, laboratory, and microbiological parameters were assessed for their relation to mortality using univariable analyses and a multivariable random forest model (RFM). Least absolute shrinkage and selection operator (Lasso) regression model was used to establish an easy‐to‐use mortality prediction score. Results The in‐hospital mortality risk was highest for SP (39.0%), followed by SBP (26.0%) and BA (25.0%). Besides illness severity markers, microbiological parameters, such as Candida spp., were identified as the most significant indicators for mortality. The Lasso model determined 15 parameters with corresponding scores, yielding good discriminatory power (area under the receiver operating characteristics curve = 0.89). Counting from 0 to 83, scores of 20, 40, 60, and 80 corresponded to in‐hospital mortalities of 3.3%, 30.8%, 85.2%, and 98.7%, respectively. Conclusion We developed a promising mortality prediction score for IA, highlighting the importance of microbiological parameters in conjunction with illness severity for assessing patient outcomes.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0815-9319
1440-1746
1440-1746
DOI:10.1111/jgh.16637