Our objective was to identify distinct clinical subtypes among critically ill patients with cirrhosis and analyze the clinical features and prognosis of each subtype.

We extracted routine clinical data within 24 h of ICU admission from the MIMIC-IV database. To determine the number of clinical subtypes, we employed the "elbow method," "cumulative distribution function (CDF) plot," and "consensus matrix." Consensus k-means, k-means, and SOM methods were used to identify different clinical subtypes of critically ill cirrhosis. We validated our findings using patients from the eICU database. The SHapley Additive exPlanations (SHAP) method was used to explore the features of each clinical subtype, and 28-day Kaplan-Meier curves were generated. Survival differences among the clinical subtypes were assessed using the log-rank test.

Our study included 2,586 patients from the MIMIC-IV database and 1,670 patients from the eICU database. Based on the clinical routine variables, we identified three clinical subtypes among patients in the MIMIC-IV database. Subtype A (N = 1424, 55.07 %) was labeled the "common subtype" and exhibited the lowest mortality. Subtype B (N = 703, 27.18 %) was classified as the "hyperinflammatory response subtype" and had a relatively high mortality. Subtype C (N = 459, 17.75 %) was identified as the "liver dysfunction subtype" and had the highest mortality. These findings were consistent with the results obtained from both the internal validation set (MIMIC-IV database) and the external validation set (eICU database).

Our study presents a novel and clinically applicable approach for subtyping critically ill cirrhosis.

Liver cirrhosis represents the final stage of liver diseases. The transition from the compensated to the decompensated form is a critical phase, as it is associated with a negative impact on patient prognosis. Therefore, having a tool to identify patients at higher risk of complications and mortality is an ideal goal. Currently, the validated scores for this purpose are the model for end-stage liver disease score and the Child-Pugh score. However, these scores have limitations, as they do not account for other factors associated with liver cirrhosis that are equally relevant from a prognostic perspective. Among these, alterations in body composition, particularly sarcopenia, increase the risk of mortality and should therefore be considered in the comprehensive assessment of patients with liver cirrhosis.

Upper gastrointestinal bleeding (UGIB) is a prevalent and severe complication of cirrhosis, often resulting from esophagogastric variceal bleeding (EVB). This condition poses significant life-threatening risks. Once bleeding occurs, the risk of recurrent episodes substantially increases, further compromising liver function and worsening patient outcomes. This study aims to identify risk factors for UGIB in cirrhotic patients using clinical examination data and to develop a non-invasive predictive model to improve diagnostic precision and efficiency.

Based on the inclusion and exclusion criteria, the study included 140 cirrhotic patients hospitalized at the First Affiliated Hospital of Nanjing Medical University between June 2022 and May 2023, who experienced UGIB within six months after discharge. These patients were compared with 151 cirrhotic patients hospitalized at the same hospital during the same period, who were discharged within six months without experiencing UGIB. General characteristics of the patients during hospitalisation, laboratory parameters on admission, and liver and spleen stiffness were retrospectively collected, and a retrospective case-control study was conducted. All patients were randomly assigned to the training and validation sets in a ratio of 7:3. Independent factors associated with UGIB were identified by univariate analysis, multivariate logistic regression analysis, and stepwise regression analysis, on the basis of which a predictive model was developed. The model's performance was assessed via receiver operating characteristic (ROC) curve and decision curve analysis (DCA) and was compared with established prognostic models, including the Child-Pugh and MELD scores.

The study analyzed 291 patients with cirrhosis, of whom 208 were allocated to the training set and 83 to the validation set. Independent predictors were identified, and predictive models were constructed using multivariate logistic regression analysis, and stepwise regression analysis in the training set, followed by validation in the validation set. The stepwise regression analysis identified ascites, spleen stiffness, albumin, fibrinogen, total cholesterol, and total bilirubin as independent predictors of UGIB (P < 0.05). These variables were incorporated into the predictive model. The area under the curve (AUC) for UGIB prediction was 0.956 in the training set and 0.909 in the validation set, demonstrating strong predictive performance. Furthermore, comparative analysis using ROC and DCA demonstrated that the developed model outperformed established scoring systems, such as the Child-Pugh score and the MELD score.

Ascites, spleen stiffness, albumin, fibrinogen, total cholesterol and total bilirubin as independent predictors of UGIB in cirrhotic patients.

We aimed to evaluate the prognostic impact of baseline clinical features and treatment procedure, including liver function measured with albumin-bilirubin (ALBI) formula and dosing methods in HCC patients treated with SIRT.

The study includes 82 consecutive patients with liver-dominant HCC treated with SIRT (90Y glass microspheres, TheraSphereTM) between October 2014 and September 2023. Twenty-five patients were treated with standard dosimetry, while for remaining patients, multi-compartment dosimetry was performed using Simplicit90YTM software. Impact of baseline patient's characteristics including presence of portal vein thrombosis (PVT), Child-Pugh score (CP), ALBI score, bilirubin levels, tumor size and prior locoregional liver-directed or systemic treatments was assessed through multivariable Cox proportional hazard model.

Median follow-up after treatment was 40.0 months (15.2-67.9). At univariable analysis, ALBI score and bilirubin levels were found to be independent prognostic factors for survival after SIRT (p = 0.001, respectively); furthermore, at Cox proportional hazards analysis, HR for death of ALBI 2 versus ALBI 1 was 10.54 (95% CI, 1.42-78.19, p = 0.021), while despite not significant, HR in patients with bilirubin levels over 1.1 mg/dl was 2.67 (0.75-9.44, p = 0.118). Conversely, no significant association was found between OS and cirrhosis, tumor size and PVT.

ALBI score demonstrated to impact OS in HCC patients treated with SIRT thus going beyond a simple prediction of treatment-related toxicity. The present results are relevant for the selection of HCC patients for SIRT in a real-world clinical setting.

Transjugular intrahepatic portosystemic shunt (TIPS) is an essential procedure for the treatment of portal hypertension but can result in hepatic encephalopathy (HE), a serious complication that worsens patient outcomes. Investigating predictors of HE after TIPS is essential to improve prognosis. This review analyzes risk factors and compares predictive models, weighing traditional scores such as Child-Pugh, Model for End-Stage Liver Disease (MELD), and albumin-bilirubin (ALBI) against emerging artificial intelligence (AI) techniques. While traditional scores provide initial insights into HE risk, they have limitations in dealing with clinical complexity. Advances in machine learning (ML), particularly when integrated with imaging and clinical data, offer refined assessments. These innovations suggest the potential for AI to significantly improve the prediction of post-TIPS HE. The study provides clinicians with a comprehensive overview of current prediction methods, while advocating for the integration of AI to increase the accuracy of post-TIPS HE assessments. By harnessing the power of AI, clinicians can better manage the risks associated with TIPS and tailor interventions to individual patient needs. Future research should therefore prioritize the development of advanced AI frameworks that can assimilate diverse data streams to support clinical decision-making. The goal is not only to more accurately predict HE, but also to improve overall patient care and quality of life.

Background: Microvascular flap surgery has become a routine option for defect correction. The role of von Willebrand factor antigen (VWF:Ag) in the pathophysiology of flap complications is not fully understood. We aim to investigate the predictive value of VWF:Ag for microvascular flap complications and explore the relationship between chronic inflammation and VWF:Ag. Methods: This prospective cohort study included 88 adult patients undergoing elective microvascular flap surgery. Preoperative blood draws were collected on the day of surgery before initiation of crystalloids. The plasma concentration of VWF:Ag as well as albumin, neutrophil-to-lymphocyte ratio (NLR), interleukin-6, and fibrinogen were determined. Results: The overall complication rate was 27.3%, and true flap loss occurred in 11.4%. VWF:Ag levels were higher in true flap loss when compared to patients without complications (217.94 IU/dL [137.27-298.45] vs. 114.14 [95.67-132.71], p = 0.001). Regression analysis revealed the association between VWF:Ag and true flap loss at the cutoff of 163.73 IU/dL (OR 70.22 [10.74-485.28], p = 0.043). Increased VWF:Ag concentrations were linked to increases in plasma fibrinogen (p < 0.001), C-reactive protein (p < 0.001), interleukin-6 (p = 0.032), and NLR (p = 0.019). Conclusions: Preoperative plasma VWF:Ag concentration is linked to biomarkers of inflammation and may be valuable in predicting complications in microvascular flap surgery.

The transition from compensated to decompensated cirrhosis is crucial, drastically reducing prognosis from a median survival of over 10 years to 2 years. There is currently an unmet need to accurately predict decompensation. We systematically reviewed and meta-analysed data regarding biomarker use to predict decompensation in individuals with compensated cirrhosis.

PubMed and EMBASE database searches were conducted for all studies from inception until February 2024. The study was carried out according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The Quality of Prognosis Studies framework was used to assess the risk of bias. The meta-analysis was conducted with a random effects model using STATA software.

Of the 652 studies initially identified, 63 studies (n=31 438 patients) were included in the final review, examining 49 biomarkers. 25 studies (40%) were prospective with the majority of studies looking at all-cause decompensation (90%). The most well-studied biomarkers were platelets (n=17), Model for End-Stage Liver Disease (n=17) and albumin (n=16). A meta-analysis revealed elevated international normalised ratio was the strongest predictor of decompensation, followed by decreased albumin. However, high statistical heterogeneity was noted (l2 result of 96.3%). Furthermore, 21 studies were assessed as having a low risk of bias (34%), 26 (41%) moderate risk and 16 (25%) high risk.

This review highlights key biomarkers that should potentially be incorporated into future scoring systems to predict decompensation. However, future biomarker studies should be conducted with rigorous and standardised methodology to ensure robust and comparable data.

The albumin-bilirubin (ALBI) score, which was proposed to assess the prognosis of patients with hepatocellular carcinoma, has gradually been extended to other liver diseases in recent years, including primary biliary cholangitis, liver cirrhosis, hepatitis, liver transplantation, and liver injury. The ALBI score is often compared with classical scores such as the Child-Pugh and model for end-stage liver disease scores or other noninvasive prediction models. It is widely employed because of its immunity to subjective evaluation indicators and ease of obtaining detection indicators. An increasing number of studies have confirmed that it is highly accurate for assessing the prognosis of patients with chronic liver disease; additionally, it has demonstrated good predictive performance for outcomes beyond survival in patients with liver diseases, such as decompensation events. This article presents a review of the application of ALBI scores in various non-malignant liver diseases.

The albumin-bilirubin (ALBI) score to assess the risk of decompensation in patients with initially compensated cirrhosis may improve their prognostic evaluation. This letter critically evaluates the research, which utilizes the ALBI score to forecast decompensation in cirrhosis patients over a three-year period. This score was initially developed to assess liver function in hepatocellular carcinoma, its prognostic utility for non-malignant liver diseases has now been explored, recognizing decompensation as a pivotal event that significantly affects patient's survival. Some concerns regarding the methodology of this research may be raised, particularly the exclusive use of radiological diagnosis, potentially including patients without definite cirrhosis and thus skewing the decompensation risk assessment. The reported predominance of variceal bleeding as a decompensating event conflicts with established literature, that often reports ascites as the initial decompensation manifestation. The letter highlights the absence of details on esophageal varices and their management, which could introduce bias in evaluating the ALBI score's predictive power. Furthermore, the letter points out the small sample size of patients with high-risk ALBI grades, potentially compromising the score's validity in this context. We suggest prospective future research to investigate the dynamic changes in the ALBI score over time to reinforce the validity of the ALBI score as a predictor of decompensation in non-malignant liver disease.