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 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.

Differentiation of Spontaneous Bacterial Peritonitis from Secondary Peritonitis in Patients with Liver Cirrhosis: Retrospective Multicentre Study.

Ascitic fluid infection is a serious complication of liver cirrhosis. The distinction between the more common spontaneous bacterial peritonitis (SBP) and the less common secondary peritonitis in patients with liver cirrhosis is crucial due to the varying treatment approaches. This retrospective multicentre study was conducted in three German hospitals and analysed 532 SBP episodes and 37 secondary peritonitis episodes. Overall, >30 clinical, microbiological, and laboratory parameters were evaluated to identify key differentiation criteria. Microbiological characteristics in ascites followed by severity of illness and clinicopathological parameters in ascites were the most important predictors identified by a random forest model to distinguish between SBP and secondary peritonitis. To establish a point-score model, a least absolute shrinkage and selection operator (LASSO) regression model selected the ten most promising discriminatory features. By aiming at a sensitivity of 95% either to rule out or rule in SBP episodes, two cut-off scores were defined, dividing patients with infected ascites into a low-risk (score ≥ 45) and high-risk group (score < 25) for secondary peritonitis. Overall, the discrimination of secondary peritonitis from SBP remains challenging. Our univariable analyses, random forest model, and LASSO point score may help clinicians with the crucial differentiation between SBP and secondary peritonitis.

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.

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 decompensated liver cirrhosis undergoing abdominocentesis between 2006 and 2020 were included. Overall, 34 clinical, drug, and laboratory features were evaluated using machine learning to identify key differentiation criteria and integrate them into a point-score model. In total, 11 discriminatory features were selected using a Lasso regression model to establish a point-score model. Considering pre-test probabilities for infected ascites of 10%, 15%, and 25%, the negative and positive predictive values of the point-score model for infected ascites were 98.1%, 97.0%, 94.6% and 14.9%, 21.8%, and 34.5%, respectively. Besides the main model, a simplified model was generated, containing only features that are fast to collect, which revealed similar predictive values. Our point-score model appears to be a promising non-invasive approach to rule out infected ascites in clinical routine with high negative predictive values in patients with hydropic decompensated liver cirrhosis, but further external validation in a prospective study is needed.