Some adipose-related parameters exhibit distinct prognostic value in patients with cirrhosis. However, the magnitude and direction of the association between individual adipose parameter and mortality in patients with cirrhosis are unclear.

This study aimed to evaluate the association between individual adipose parameter and mortality in patients with cirrhosis using the meta-analysis method.

The PubMed, Embase, Web of Science, China Biological Medicine, WanFang, and China National Knowledge Infrastructure databases were searched from inception through December 15, 2023, to identify eligible studies. The impact of each adipose parameter on mortality was assessed by the pooled unadjusted or adjusted hazard ratio (HR) with 95% confidence intervals (CIs) using the random effects model.

A total of 33 studies involving 9626 patients were included in our analysis, with 11 adipose parameters evaluated. The pooled prevalence of sarcopenic obesity (SO) and myosteatosis in patients with cirrhosis was 15.5% and 34.4%, respectively. In adjusted analysis, each unit increase in subcutaneous adipose tissue index (SATI) (HR: 0.99, 95% CI: 0.98-1.00) or muscle attenuation (MA) (HR: 0.94, 95% CI: 0.90-0.98) and each unit decrease in visceral-to-subcutaneous adipose tissue ratio (VSR) (HR: 1.92, 95% CI: 1.45-2.54) showed an independent association with a decreased risk of mortality. However, concurrent myosteatosis (HR: 1.88, 95% CI: 1.48-2.40) or SO (HR: 2.77, 95% CI: 1.95-3.93) significantly increased the risk of mortality in patients with cirrhosis.

Decreased SATI or MA, increased VSR, and concurrent myosteatosis or SO were independently associated with a higher risk of mortality in patients with cirrhosis.

Transjugular intrahepatic portosystemic shunt (TIPS) is an established procedure for managing portal hypertension in cirrhotic patients, but the impact of post-TIPS overt hepatic encephalopathy (OHE) on survival remains controversial. While its effect on short-term survival is well-documented, its long-term implications remain unclear.

This study aims to investigate the long-term impact of post-TIPS OHE on mortality in cirrhotic patients for variceal bleeding, focusing on the timing and predictive value of OHE beyond the first year post-TIPS.

A multicenter, retrospective cohort study was conducted involving 3262 cirrhotic patients who underwent TIPS for variceal bleeding at seven Chinese tertiary centers between January 2010 and June 2020. Clinical data, including demographics, procedure details, post-TIPS complications and survival outcomes, were collected. The primary endpoints were all-cause mortality and OHE, with follow-up until death, liver transplantation or 60 months. Propensity score matching minimised confounding effects, and multivariate Fine-Grey competing risk models identified independent mortality predictors.

During a median follow-up of 1077 days, 33.2% developed post-TIPS OHE, associated with higher MELD and Child-Pugh scores. Among these, 19.3% died, with a median time from OHE onset to death of 947 days. Post-TIPS OHE was not linked to early survival (within 12 months) but emerged as an independent predictor of long-term mortality beyond 24 months, consistent across various clinical scenarios.

Post-TIPS OHE does not affect short-term survival but significantly increases long-term mortality risk. These findings highlight the need for continuous monitoring and tailored interventions to improve long-term outcomes in post-TIPS patients.

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.

Hepatocellular carcinoma (HCC) with coexisting portal vein tumor thrombus (PVTT) is associated with poor patient outcomes. The efficacy and safety of neoadjuvant therapy in patients with HCC with PVTT remain a subject of debate. In the present study, a comprehensive search of electronic databases, including PubMed, Web of Science, Embase and the Cochrane Library, was conducted to identify studies evaluating the outcomes of neoadjuvant therapy in patients with HCC and PVTT. The primary outcomes assessed were overall survival (OS) and relapse-free survival (RFS), with complication rates as a secondary outcome. A total of six studies comprising 750 patients were included in the present meta-analysis. The neoadjuvant therapy group exhibited significantly superior OS [hazard ratio (HR), 0.39; P<0.001] and RFS (HR, 0.31; P<0.001) compared with the primary hepatectomy control group. Compared with the control group, neoadjuvant radiotherapy improved OS (HR, 0.34; P<0.001) and RFS (HR, 0.24; P=0.004). While the neoadjuvant intervention subgroup exhibited an improved OS compared with controls (HR, 0.37; P=0.001), no significant difference in RFS was observed (HR, 0.11; P=0.095). Geographical analysis revealed that the Chinese subgroup demonstrated a significantly improved OS and RFS (HR, 0.41 for both; P<0.001), compared with the control group. However, the Japanese and Korean subgroups showed no improvement in OS (HR, 0.25; P=0.057) compared with the control group, and the results did not reach statistical significance. There were no significant differences between the groups in terms of blood transfusion, blood loss, operation time, bile leakage, ascites, peritoneal infection, postoperative bleeding, complications or mortality (all P>0.05). Overall, neoadjuvant therapy significantly improved survival outcomes in patients with HCC and PVTT without increasing complication rates, supporting its efficacy and manageable safety profile.

Topiramate (TPM) is a broad-spectrum antiepileptic drug (AED) commonly prescribed for approved and off-label uses. Routine monitoring is suggested for clinical usage of TPM in special population due to its broad side effect profile. Therefore, it is crucial to further explore its pharmacokinetic characteristics. Physio-chemical properties of TPM were initially determined from online database and further optimized while establishing the PBPK model for healthy adults using the PK-Sim software. The model was then extrapolated to patients with renal impairment and patients who were hepatically impaired. A drug-drug interaction (DDI) model was also built to simulate plasma TPM concentrations while concomitantly used with carbamazepine (CBZ). The goodness-of-fit method and average fold error (AFE) method were used to compare the differences between predicted and observed values to assess the accuracy of the PBPK model. Almost all of the predicted concentration fell within twofold error range of corresponding observed concentrations. The AFE ratio of predicted to observed values of Cmax and AUC0-inf was all within 0.5 and 2. It is recommended that the doses be reduced to 70%, 50%, and 40% of the healthy adult dose for the chronic kidney disease (CKD) stage 3, stage 4, and stage 5 patients, respectively, and reduced to ~70%, and 35% for the Child-Pugh-B, and Child-Pugh C scored patient with hepatic impairment, respectively. If TPM is co-administered with CBZ, increasing TPM doses to 150%-175% of the monotherapy dose is recommended according to model simulation.

Cirrhosis, a leading cause of global mortality, necessitates an accurate assessment of disease severity and prognosis. While traditional scoring systems like Child-Turcotte-Pugh (CTP) and model for end-stage liver disease (MELD) are used to assess the severity, specific biomarkers are lacking. This study explores serum prolactin levels as a potential biomarker for evaluating cirrhosis severity and predicting short-term mortality.

A prospective observational study was conducted from December 2021 to December 2023. After a thorough clinical examination, serum prolactin levels were measured. The correlation between prolactin levels and established severity scores [CTP, MELD, chronic liver failure consortium organ failure (CLIF-C OF), and MELD-sodium (MELD-Na)] was analyzed. The study also evaluated the prognostic value of prolactin levels in predicting 28-day and 90-day mortality.

A total of 90 patients with liver cirrhosis were included. There were 82% men, with a mean age of 47.6 years. Alcohol was the most common cause of cirrhosis (73%). The median (interquartile range (IQR)) serum prolactin level was 29 (10-54) ng/mL, with higher levels correlating with increased disease severity: CTP (r = 0.73), MELD (r = 0.64), MELD-Na (r = 0.67), and CLIF-C OF (r = 0.82) scores. Elevated prolactin levels were significantly associated with increased mortality, with an area under the receiver operating characteristic curve of 0.83 for predicting 28-day mortality and 0.79 for 90-day mortality. A prolactin cut-off of 35.12 ng/mL demonstrated high sensitivity (93% and 77%, respectively) and specificity (63% and 72%, respectively) for 28-day and 90-day mortality prediction.

Serum prolactin levels significantly correlated with the severity of cirrhosis and also effectively predicted the short-term mortality. Prolactin may offer a noninvasive and cost-effective adjunct for severity assessment and short-term prognosis in cirrhosis.

Singh R, Ramadoss R, Mohan P, Vairappan B. Utility of Serum Prolactin Levels as a Marker for Disease Severity and Short-term Prognosis in Patients with Cirrhosis: A Prospective Observational Study. Indian J Crit Care Med 2025;29(3):244-250.

Splenectomy is an effective yet invasive intervention for alleviating portal pressure in patients with hepatitis cirrhosis. However, the current prognostic indicators for predicting long-term overall survival of these patients have several limitations.

To assess the potential of preoperative total bilirubin-albumin (B/A) ratio as a prognostic indicator for patients with hepatitis cirrhosis undergoing splenectomy.

A total of 257 patients diagnosed with hepatitis cirrhosis were retrospectively enrolled in the study. Normality test, t-test, Wilcoxon test, χ 2 test, or Fisher's exact test was employed to analyze the intraoperative and postoperative conditions of the patients. Receiver operating characteristic (ROC) curve analysis was utilized to depict the 10-year overall survival rate.

During the follow-up period, 85.99% of the patients survived, with a median survival time of 64.6 months. Multivariate analysis revealed that total serum B/A ratio was an independent risk factor for overall survival (P = 0.037). ROC curve analysis demonstrated that a B/A ratio of 0.87 was the optimal cut-off value. Consequently, the patients were categorized into two groups: High B/A group (n = 64) and low B/A group (n = 193). The median follow-up time for the high B/A group and low B/A group was 56.8 months and 67.2 months, respectively (P = 0.045). Notably, the high B/A group exhibited a significantly lower 10-year overall survival compared to the low B/A group (P < 0.001). Patients with hepatocellular carcinoma (HCC) had lower overall survival rates. Patients with a high B/A ratio exhibited a lower overall survival than those with a low B/A rate in the overall cohort and the subgroups of patients with HCC or not, early Child-Pugh grade, low albumin-bilirubin grade, and model for end-stage liver disease score ≥ 10 (log-rank test, P < 0.001 for all).

The B/A ratio can serve as an effective prognostic indicator for overall survival in patients with hepatitis B virus-related cirrhosis following splenectomy, and a higher B/A ratio may suggest a poorer prognosis.

Cirrhotic patients face heightened energy demands, leading to rapid glycogen depletion, protein degradation, oxidative stress, and inflammation, which drive disease progression and complications. These disruptions cause cellular damage and parenchymal changes, resulting in vascular alterations, portal hypertension, and liver dysfunction, significantly affecting patient prognosis.

To analyze the association between Child-Turcotte-Pugh (CTP) scores and different nutritional indicators with survival in a 15-year follow-up cohort.

This was a retrospective cohort study with 129 cirrhotic patients of both sexes aged > 18 years. Diagnosis of cirrhosis was made by liver biopsy. The first year of data collection was 2007, and data regarding outcomes were collected in 2023. Data were gathered from medical records, and grouped by different methods, including CTP, handgrip strength, and triceps skinfold cutoffs. The prognostic values for mortality were assessed using Kaplan-Meier curves and multivariate binary logistic regression models.

The coefficient for CTP was the only statistically significant variable (Wald = 5.193, P = 0.023). This suggests that with a negative change in CTP classification score, the odds of survival decrease 52.6%. The other evaluated variables did not significantly predict survival outcomes in the model. Kaplan-Meier survival curves also indicated that CTP classification was the only significant predictor.

Although different classifications showed specific differences in stratification, only CTP showed significant predictive potential. CTP score remains a simple and effective predictive tool for cirrhotic patients even after longer follow-up.

Chronic liver disease is a growing global health problem, leading to hepatic decompensation characterized by an array of clinical and biochemical complications. Several scoring systems have been introduced in assessing the severity of hepatic decompensation with the most frequent ones are Child-Pugh score, model of end-stage liver disease (MELD) score, and MELD-Na score. Anemia is frequently observed in cirrhotic patients and is linked to worsened clinical outcomes. Although studies have explored anemia in liver disease, few have investigated the correlation of hemoglobin level with the severity of hepatic decompensation.

To determine the relationship between hemoglobin levels and the severity of decompensated liver disease and comparing the strength of this correlation using the Child-Pugh, MELD, and MELD-Na scores.

This cross-sectional study was conducted at a tertiary care hospital with 652 decompensated liver disease patients enrolled in the study. Data was collected on demographics, clinical history, and laboratory findings, including hemoglobin levels, bilirubin, albumin, prothrombin time (international normalized ratio), sodium, and creatinine. The Child-Pugh, MELD, and MELD-Na scores were calculated. Statistical analysis was performed using Statistical Package for the Social Sciences version 26, and correlations between hemoglobin levels and severity scores were assessed using Spearman's correlation coefficient.

The study included 405 males (62.1%) and 247 females (37.9%) with an average age of 58.8 years. Significant inverse correlations were found between hemoglobin levels and Child-Pugh, MELD, and MELD-Na scores (P < 0.01), with the MELD scoring system being the strongest correlator among all. One-way analysis of variance revealed significant differences in hemoglobin levels across the severity groups of each scoring system (P = 0.001). Tukey's post hoc analysis confirmed significant internal differences among each severity group.

Understanding the correlation between hemoglobin and liver disease severity can improve patient management by offering insights into prognosis and guiding treatment decisions.

Nonalcoholic fatty liver disease (NAFLD), recently renamed metabolic-associated fatty liver disease (MAFLD), is the most prevalent liver disease worldwide. It is associated with an increased risk of developing hepatocellular carcinoma (HCC) in the background of cirrhosis or without cirrhosis. The prevalence of NAFLD-related HCC is increasing all over the globe, and HCC surveillance in NAFLD cases is not that common. In the present review, we attempt to summarize promising treatments and clinical trials focused on NAFLD, nonalcoholic steatohepatitis (NASH), and HCC in the past five to seven years. We categorized the trials based on the type of intervention. Most of the trials are still running, with only a few completed and with conclusive results. In clinical trial NCT03942822, 25 mg/day of milled chia seeds improved NAFLD condition. Completed trial NCT03524365 concluded that Rouxen-Y gastric bypass (RYGB) or sleeve gastrectomy (SG) results in histological resolution of NASH without worsening of fibrosis, while NCT04677101 validated sensitivity/accuracy of blood biomarkers in predicting NASH and fibrosis stage. Moreover, trials with empagliflozin (NCT05694923), curcuvail (NCT06256926), and obeticholic acid (NCT03439254) were completed but did not provide conclusive results. However, trial NCT03900429 reported effective improvement in fibrosis by at least one stage, without worsening of NAFLD activity score (NAS), as well as improvement in lipid profile of the NASH patients by 80 or 100 mg MGL-3196 (resmetirom). Funded by Madrigal Pharmaceuticals, Rezdiffra (resmetirom), used in the clinical trial NCT03900429, is the first FDA-approved drug for the treatment of NAFLD/NASH.

The impairment of liver function strongly limits the therapeutic options for hepatocellular carcinoma (HCC), and the assessment of liver function is key to finding the appropriate therapy for patients suffering from this disease. Furthermore, preexisting liver dysfunction has a negative impact on the prognosis of patients in addition to the malignant potential of HCC. Hence, defining the optimal treatment of patients with HCC requires a comprehensive examination with liver function being a crucial part of it.

This review will provide an overview of the currently existing methods for evaluating the liver function in patients with HCC. Assessment of liver function includes scoring systems but also functional and technical methods. In addition, the role of these tests in different treatment facilities such as liver resection, transplantation, interventional and systemic therapy is summarized.

A comprehensive pretherapeutic assessment of the liver function includes laboratory-based scoring systems, as well as imaging- and non-imaging-based functional tests. Combining diverse parameters can help to improve the safety and efficacy of HCC therapy particularly in patients with compromised liver function. Future research should focus on optimizing pretherapeutic assessment recommendations for each therapy.

This study aimed to develop nomograms for predicting post-hepatectomy liver failure (PHLF) in patients with hepatocellular carcinoma (HCC), using deep learning analysis of Gadoxetic acid-enhanced hepatobiliary (HBP) MRI.

This retrospective study analyzed patients who underwent gadoxetic acid-enhanced MRI and hepatectomy for HCC between 2016 and 2020 at two referral centers. Using a deep learning algorithm, volumes and signal intensities of whole non-tumor liver, expected remnant liver, and spleen were measured on HBP images. Two multivariable logistic regression models were formulated to predict PHLF, defined and graded by the International Study Group of Liver Surgery: one based on whole non-tumor liver measurements (whole liver model) and the other on expected remnant liver measurements (remnant liver model). The models were presented as nomograms and a web-based calculator. Discrimination performance was evaluated using the area under the receiver operating curve (AUC), with internal validation through 1000-fold bootstrapping.

The study included 1760 patients (1395 male; mean age ± standard deviation, 60 ± 10 years), with 137 (7.8%) developing PHLF. Nomogram predictors included sex, gamma-glutamyl transpeptidase, prothrombin time international normalized ratio, platelets, extent of liver resection, and MRI variables derived from the liver volume, liver-to-spleen signal intensity ratio, and spleen volume. The whole liver and the remnant liver nomograms demonstrated strong predictive performance for PHLF (optimism-corrected AUC of 0.78 and 0.81, respectively) and symptomatic (grades B and C) PHLF (optimism-corrected AUC of 0.81 and 0.84, respectively).

Nomograms based on deep learning analysis of gadoxetic acid-enhanced HBP images accurately stratify the risk of PHLF.

Question Can PHLF be predicted by integrating clinical and MRI-derived volume and functional variables through deep learning analysis of gadoxetic acid-enhanced MRI? Findings Whole liver and remnant liver nomograms demonstrated strong predictive performance for PHLF with the optimism-corrected area under the curve of 0.78 and 0.81, respectively. Clinical relevance These nomograms can effectively stratify the risk of PHLF, providing a valuable tool for treatment decisions regarding hepatectomy for HCC.

Cirrhosis represents a significant global health challenge, characterized by high morbidity and mortality rates that severely impact human health. Timely and precise prognostic assessments of liver cirrhosis are crucial for improving patient outcomes and reducing mortality rates as they enable physicians to identify high-risk patients and implement early interventions. This paper features a thorough literature review on the prognostic assessment of liver cirrhosis, aiming to summarize and delineate the present status and constraints associated with the application of traditional prognostic tools in clinical settings. Among these tools, the Child-Pugh and Model for End-Stage Liver Disease (MELD) scoring systems are predominantly utilized. However, their accuracy varies significantly. These systems are generally suitable for broad assessments but lack condition-specific applicability and fail to capture the risks associated with dynamic changes in patient conditions. Future research in this field is poised for deep exploration into the integration of artificial intelligence (AI) with routine clinical and multi-omics data in patients with cirrhosis. The goal is to transition from static, unimodal assessment models to dynamic, multimodal frameworks. Such advancements will not only improve the precision of prognostic tools but also facilitate personalized medicine approaches, potentially revolutionizing clinical outcomes.

Diagnosis code classification is a common method for cohort identification in cirrhosis research, but it is often inaccurate and augmented by labor-intensive chart review. Natural language processing using large language models (LLMs) is a potentially more accurate method. To assess LLMs' potential for cirrhosis cohort identification, we compared code-based versus LLM-based classification with chart review as a "gold standard."

We extracted and conducted a limited chart review of 3788 discharge summaries of cirrhosis admissions. We engineered zero-shot prompts using a Generative Pre-trained Transformer 4 to determine whether cirrhosis and its complications were active hospitalization problems. We calculated positive predictive values (PPVs) of LLM-based classification versus limited chart review and PPVs of code-based versus LLM-based classification as a "silver standard" in all 3788 summaries. Compared to gold standard chart review, code-based classification achieved PPVs of 82.2% for identifying cirrhosis, 41.7% for HE, 72.8% for ascites, 59.8% for gastrointestinal bleeding, and 48.8% for spontaneous bacterial peritonitis. Compared to the chart review, Generative Pre-trained Transformer 4 achieved 87.8%-98.8% accuracies for identifying cirrhosis and its complications. Using LLM as a silver standard, code-based classification achieved PPVs of 79.8% for identifying cirrhosis, 53.9% for HE, 55.3% for ascites, 67.6% for gastrointestinal bleeding, and 65.5% for spontaneous bacterial peritonitis.

LLM-based classification was highly accurate versus manual chart review in identifying cirrhosis and its complications. This allowed us to assess the performance of code-based classification at scale using LLMs as a silver standard. These results suggest LLMs could augment or replace code-based cohort classification and raise questions regarding the necessity of chart review.

Because secondary progressive multiple sclerosis (SPMS) is associated with worse prognosis, early predictive tools are needed. We aimed to use systematic literature review and advanced methods to create and validate a clinical tool for estimating individual patient risk of transition to SPMS over five years.

Data from the Jacobs Multiple Sclerosis Center (JMSC) and the Multiple Sclerosis Center Amsterdam (MSCA) was collected between 1994 and 2022. Participants were relapsing-remitting adult patients at initial evaluation. We created the tool in four stages: (1) identification of candidate predictors from systematic literature review, (2) ordinal cutoff determination, (3) feature selection, (4) feature weighting.

Patients in the development/internal-validation/external-validation datasets respectively (n = 787/n = 522/n = 877) had a median age of 44.1/42.4/36.6 and disease duration of 7.7/6.2/4.4 years. From these, 12.6 %/10.2 %/15.4 % converted to SPMS (median=4.9/5.2/5.0 years). The DAAE Score was named from included predictors: Disease duration, Age at disease onset, Age, EDSS. It ranges from 0 to 12 points, with risk groups of very-low=0-2, low=3-7, medium=8-9, and high≥10. Risk of transition to SPMS increased proportionally across these groups in development (2.7 %/7.4 %/18.8 %/40.2 %), internal-validation (2.9 %/6.8 %/26.8 %/36.5 %), and external-validation (7.5 %/9.6 %/22.4 %/37.5 %).

The DAAE Score estimates individual patient risk of transition to SPMS consistently across datasets internationally using clinically-accessible data. With further validation, this tool could be used for clinical risk estimation.

To evaluate the correlation between non-tumoral liver volume (NTLV) by computed tomography (CT) volumetry and indocyanine green retention at 15 minutes (ICG-r15%), Child-Pugh score (CTP) and model for end-stage liver diseases (MELD) score in cirrhotic patients having hepatocellular carcinoma (HCC) (group A) and in cirrhotics without HCC (group B).

As many as 111 consecutive patients with liver cirrhosis, who underwent triple-phase CT abdomen, were retrospectively included in our study. They were classified into group A (cirrhosis with HCC, n = 69) and group B (cirrhosis only, n = 42). Segmental liver volume, tumor and NTLV were calculated using Myrian XP-Liver segmentation software. In group B, NTLV was the same as the total liver volume (TLV). The correlation of NTLV with ICG-r15%, CTP and MELD scores was analyzed using appropriate correlation tests for each group.

NTLV had a good and significant negative correlation with ICG-r15% (ρ =  - 512; p < 0.001) in group A, but not in group B. It also had a significant negative correlation with CTP (ρ =  - 251; p = 0.038) and MELD (ρ =  - 323; p = 0.007) scores only in group A. Furthermore, ICG-r15% had a good and significant positive correlation with CTP and MELD scores in both groups (p < 0.05).

NTLV showed a significant negative correlation with ICG-r15% in cirrhotic patients with HCC, but not in cirrhotic patients without HCC. Therefore, CT volumetry can be a valuable tool to predict the functional hepatic volume in patients of cirrhosis with HCC subjected for hepatectomy, where a facility of ICG-r15% is not available. However, further studies are needed to validate our findings in cirrhotic only patients.

Clinically significant portal hypertension (CSPH) seriously affects the feasibility and safety of surgical treatment for hepatocellular carcinoma (HCC) patients. The aim of this study was to establish a new surgical scheme defining risk classification of post-hepatectomy liver failure (PHLF) to facilitate the surgical decision-making and identify suitable candidates for individual hepatectomy among HCC patients with CSPH.

Hepatectomy is the preferred treatment for HCC. Surgeons must maintain a balance between the expected oncological outcomes of HCC removal and short-term risks of severe PHLF and morbidity. CSPH aggravates liver decompensation and increases the risk of severe PHLF thus complicating hepatectomy for HCC.

Multivariate logistic regression and stochastic forest algorithm were performed, then the independent risk factors of severe PHLF were included in a nomogram to determine the risk of severe PHLF. Further, a conditional inference tree (CTREE) through recursive partitioning analysis validated supplement the misdiagnostic threshold of the nomogram.

This study included 924 patients, of whom 137 patients (14.8%) suffered from mild-CSPH and 66 patients suffered from (7.1%) with severe-CSPH confirmed preoperatively. Our data showed that preoperative prolonged prothrombin time, total bilirubin, indocyanine green retention rate at 15 min, CSPH grade, and standard future liver remnant volume were independent predictors of severe PHLF. By incorporating these factors, the nomogram achieved good prediction performance in assessing severe PHLF risk, and its concordance statistic was 0.891, 0.850 and 0.872 in the training cohort, internal validation cohort and external validation cohort, respectively, and good calibration curves were obtained. Moreover, the calculations of total points of diagnostic errors with 95% CI were concentrated in 110.5 (range 76.9-178.5). It showed a low risk of severe PHLF (2.3%), indicating hepatectomy is feasible when the points fall below 76.9, while the risk of severe PHLF is extremely high (93.8%) and hepatectomy should be rigorously restricted at scores over 178.5. Patients with points within the misdiagnosis threshold were further examined using CTREE according to a hierarchic order of factors represented by the presence of CSPH grade, ICG-R15, and sFLR.

This new surgical scheme established in our study is practical to stratify risk classification in assessing severe PHLF, thereby facilitating surgical decision-making and identifying suitable candidates for individual hepatectomy.

The model for end-stage liver disease (MELD) was updated to MELDNa and recently to MELD3.0 to predict survival of cirrhotic patients. We validated the prognostic performance of MELD3.0 and compared with MELDNa and MELD amongst cirrhotic inpatients.

Demographical, clinical, biochemical, and survival data of cirrhotic inpatients in Singapore General Hospital (SGH) from 01 January 2018 to 31 December 2018, were studied retrospectively. Patients were followed up from first admission in 2018 until death or until 01 April 2023. Area under the receiver operating characteristic curves (AUROC) were computed for the discriminative effects of MELD3.0, MELDNa, and MELD to predict 30-, 90-, and 365-day mortalities. AUROC was compared with DeLong's test. The cutoff MELD3.0 score for patients at high risk of 30-day mortality was determined using Youden's Index. Survival curves of patients with MELD3.0 score above and below the cutoff were estimated with Kaplan-Meier method and compared with log-rank analysis.

Totally 862 patients were included (median age 71.0 years [interquartile range, IQR: 64.0-79.0], 65.4% males, 75.8% Chinese). Proportion of patients with Child-Turcotte-Pugh classes A/B/C were 55.5%/35.5%/9.0%. Median MELD3.0/MELDNa/MELD scores were 12.2 (IQR: 8.7-18.3)/11.0 (IQR: 8.0-17.5)/10.3 (IQR: 7.8-15.0). Median time of follow-up was 51.9 months (IQR: 8.5-59.6). The proportion of 30-/90-/365-day mortalities was 5.7%/13.2%/26.9%. AUROC of MELD3.0/MELDNa/MELD in predicting 30-, 90-, and 365-day mortalities, respectively, were 0.823/0.793/0.783, 0.754/0.724/0.707, 0.682/0.654/0.644 (P < 0.05). Optimal cutoff to predict 30-day mortality was MELD3.0 > 19 (sensitivity = 67.4%, specificity = 82.4%). Patients with MELD3.0 > 19, compared with patients with MELD3.0 ≤ 19, had shorter median time to death (98.0 days [IQR: 28.8-398.0] vs 390.0 days [IQR: 134.3-927.5]), and higher proportion of 30-day mortality (68.8% vs 43.0%) (P < 0.001).

MELD3.0 performs better than MELDNa and MELD in predicting mortality in cirrhotic inpatients. MELD3.0 > 19 predicts higher 30-day mortality.

Liver transplantation is the only treatment for patients with liver failure. As demand for liver transplantation grows, it remains a challenge to predict the short- and long-term survival of the liver graft. Recently, artificial intelligence models have been used to evaluate the short- and long-term survival of the liver transplant. To make the models more accurate, suitable liver transplantation characteristics must be used as input to train them. In this narrative review, we reviewed studies concerning liver transplantations published in the PubMed, Web of Science, and Cochrane databases between 2017 and 2022. We picked out 17 studies using our selection criteria and analyzed them, evaluating which medical characteristics were used as input for creation of artificial intelligence models. In eight studies, models estimating only short-term liver graft survival were created, while in five of the studies, models for the prediction of only long-term liver graft survival were built. In four of the studies, artificial intelligence algorithms evaluating both the short- and long-term liver graft survival were created. Medical characteristics that were used as input in reviewed studies and had the biggest impact on the accuracy of the model were the recipient's age, recipient's body mass index, creatinine levels in the recipient's serum, recipient's international normalized ratio, diabetes mellitus, and recipient's model of end-stage liver disease score. To conclude, in order to define important liver transplantation characteristics that could be used as an input for artificial intelligence algorithms when predicting liver graft survival, more models need to be created and analyzed, in order to fully support the results of this review.

The aim of this study was to identify predictive risk factors associated with 90-day mortality after hepatic resection (HR) in hepatocellular carcinoma (HCC).

All patients undergoing elective resection for HCC from a single- institutional and prospectively maintained database were included. Multivariate regression analysis was conducted to identify pre- and intraoperative as well as histopathological predictive factors of 90-day mortality after elective HR.

Between August 2004 and October 2021, 196 patients were enrolled (148 male /48 female). The median age of the study cohort was 68.5 years (range19-84 years). The rate of major hepatectomy (≥ 3 segments) was 43.88%. Multivariate analysis revealed patient age ≥ 70 years [HR 2.798; (95% CI 1.263-6.198); p = 0.011], preoperative chronic renal insufficiency [HR 3.673; (95% CI 1.598-8.443); p = 0.002], Child-Pugh Score [HR 2.240; (95% CI 1.188-4.224); p = 0.013], V-Stage [HR 2.420; (95% CI 1.187-4.936); p = 0.015], and resected segments ≥ 3 [HR 4.700; (95% 1.926-11.467); p = 0.001] as the major significant determinants of the 90-day mortality.

Advanced patient age, pre-existing chronic renal insufficiency, Child-Pugh Score, extended hepatic resection, and vascular tumor involvement were identified as significant predictive factors of 90-day mortality. Proper patient selection and adjustment of treatment strategies could potentially reduce short-term mortality.

Artificial intelligence (AI) is a speciality within computer science that deals with creating systems that can replicate the intelligence of a human mind and has problem-solving abilities. AI includes a diverse array of techniques and approaches such as machine learning, neural networks, natural language processing, robotics, and expert systems. An electronic literature search was conducted using the databases of "PubMed" and "Google Scholar". The period for the search was from 2000 to June 2023. The search terms included "artificial intelligence", "machine learning", "liver cancers", "liver tumors", "hepatectomy", "perioperative" and their synonyms in various combinations. The search also included all MeSH terms. The extracted articles were further reviewed in a step-wise manner for identification of relevant studies. A total of 148 articles were identified after the initial literature search. Initial review included screening of article titles for relevance and identifying duplicates. Finally, 65 articles were reviewed for this review article. The future of AI in liver cancer planning and management holds immense promise. AI-driven advancements will increasingly enable precise tumour detection, location, and characterisation through enhanced image analysis. ML algorithms will predict patient-specific treatment responses and complications, allowing for tailored therapies. Surgical robots and AI-guided procedures will enhance the precision of liver resections, reducing risks and improving outcomes. AI will also streamline patient monitoring, better hemodynamic management, enabling early detection of recurrence or complications. Moreover, AI will facilitate data-driven research, accelerating the development of novel treatments and therapies. Ultimately, AI's integration will revolutionise liver cancer care, offering personalised, efficient and effective solutions, improving patients' quality of life and survival rates.

Acute-on-chronic liver failure (ACLF) is associated with high short-term mortality, and early prediction is critical to reduce the deaths of ACLF patients. To date, however, the prognostic accuracy of current models for ACLF is unsatisfactory, particularly, in patients with hepatitis B virus (HBV) infection. This study aims to develop novel prognostic models based on the dynamic changes in variables to predict the short-term mortality of HBV-associated ACLF (HBV-ACLF).

A retrospective cohort study was conducted, with the population comprised in whom ACLF was confirmed.319 patients were enrolled and their clinical data were collected on Days 1 and 7 following hospital admission. Univariate and multivariate analyses were performed to identify risk factors for 28 and 90-day mortality. The dynamic alterations in the risk factors were further analyzed, and Days 1 and 7 prognostic models were constructed. Receiver operating characteristic (ROC) analysis were used to identify and compared the predictors of prognosis among our model.

Univariate and multivariate analyses revealed significant risk factors at Days 1 and 7, which when combined with the clinically important parameters, were used to establish the Days 1 and 7 prognostic models. For 28-day mortality, the predictive accuracy of the Day 1 prognostic model was significantly higher than that of the albumin-bilirubin (ALBI) model. For 90-day mortality, the predictive accuracy of the Days 1 and 7 prognostic models was significantly higher than that of the Model of End-Stage Liver Disease (MELD), MELD-sodium (MELD-Na), and ALBI prognostic models.

The prognostic models established in this study were superior to the existing prognostic scoring systems to accurately predict short-term mortality, and therefore, could be potential novel prognostic tools for HBV-ACLF.

Post-hepatectomy liver failure (PHLF) is a potentially life-threatening complication following liver resection. Hepatocellular carcinoma (HCC) often occurs in patients with chronic liver disease, which increases the risk of PHLF. This study aimed to investigate the ability of the combination of liver function and fibrosis markers (ALBI score and FIB-4 index) to predict PHLF in patients with HCC. Patients who underwent hepatectomy for HCC between August 2012 and September 2022 were considered for inclusion. Multivariable logistic regression analysis was used to identify factors associated with PHLF, and ALBI score and FIB-4 index were combined based on their regression coefficients. The performance of the combined ALBI-FIB4 score in predicting PHLF and postoperative mortality was compared with Child-Pugh score, MELD score, ALBI score, and FIB-4 index. A total of 215 patients were enrolled in this study. PHLF occurred in 35 patients (16.3%). The incidence of severe PHLF (grade B and grade C PHLF) was 9.3%. Postoperative 90-d mortality was 2.8%. ALBI score, FIB-4 index, prothrombin time, and extent of liver resection were identified as independent factors for predicting PHLF. The AUC of the ALBI-FIB4 score in predicting PHLF was 0.783(95%CI: 0.694-0.872), higher than other models. The ALBI-FIB4 score could divide patients into two risk groups based on a cut-off value of - 1.82. High-risk patients had a high incidence of PHLF of 39.1%, while PHLF just occurred in 6.6% of low-risk patients. Similarly, the AUCs of the ALBI-FIB4 score in predicting severe PHLF and postoperative 90-d mortality were also higher than other models. Preoperative ALBI-FIB4 score showed good performance in predicting PHLF and postoperative mortality in patients undergoing hepatectomy for HCC, superior to the currently commonly used liver function and fibrosis scoring systems.

Liver transplantation (LT) is a life-saving intervention for patients with end-stage liver disease. However, the equitable allocation of scarce donor organs remains a formidable challenge. Prognostic tools are pivotal in identifying the most suitable transplant candidates. Traditionally, scoring systems like the model for end-stage liver disease have been instrumental in this process. Nevertheless, the landscape of prognostication is undergoing a transformation with the integration of machine learning (ML) and artificial intelligence models.

To assess the utility of ML models in prognostication for LT, comparing their per formance and reliability to established traditional scoring systems.

Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, we conducted a thorough and standardized literature search using the PubMed/MEDLINE database. Our search imposed no restrictions on publication year, age, or gender. Exclusion criteria encompassed non-English stu dies, review articles, case reports, conference papers, studies with missing data, or those exhibiting evident methodological flaws.

Our search yielded a total of 64 articles, with 23 meeting the inclusion criteria. Among the selected studies, 60.8% originated from the United States and China combined. Only one pediatric study met the criteria. Notably, 91% of the studies were published within the past five years. ML models consistently demonstrated satisfactory to excellent area under the receiver operating characteristic curve values (ranging from 0.6 to 1) across all studies, surpassing the performance of traditional scoring systems. Random forest exhibited superior predictive capa bilities for 90-d mortality following LT, sepsis, and acute kidney injury (AKI). In contrast, gradient boosting excelled in predicting the risk of graft-versus-host disease, pneumonia, and AKI.

This study underscores the potential of ML models in guiding decisions related to allograft allocation and LT, marking a significant evolution in the field of prognostication.

The Albumin-Bilirubin (ALBI) score has been widely used to assess the prognosis in patients with cirrhosis and hepatocellular carcinoma. This study aimed to analyze the relationship between ALBI score and all-cause mortality in patients with hepatitis B virus (HBV) infection in general.

Patients aged ≥ 18 years with previous or current HBV infection from the National Health and Nutrition Examination Survey (NHANES) in the United States between 1999 and 2018 were enrolled in this retrospective cohort study. Weight univariate and multivariate Cox regression models were used to assess the relationship between ALBI score and all-cause mortality. The area under the receiver operating characteristic curve (AUC) was utilized to assess the predictive effect of ALBI score for all-cause mortality.

A total of 3,666 patients were included, of whom 925 (23.53 %) patients died. Compared with ALBI score ≤ -2.6, HBV-infected patients with ALBI score > -2.6 [hazard ratio (HR) = 1.75; 95 % confidence interval (CI): 1.43-2.14] were corrected with a higher all-cause mortality risk after adjusting for confounders. Stratified analyses showed that higher ALBI score was related to a higher risk of all-cause mortality in different patients with HBV infection (All P < 0.05). Furthermore, the ALBI score had good predictive ability for 1-year (AUC = 0.816, 95 %CI: 0.754-0.878), 3-year (AUC = 0.808, 95 %CI: 0.775-0.841), 5-year (AUC = 0.809, 95 %CI: 0.783-0.835), and 10-year (AUC = 0.806, 95 %CI: 0.784-0.827) all-cause mortality.

Higher ALBI score was related to a higher risk of all-cause mortality in patients with HBV infection, and the ALBI score showed a good predictive effect for short- and long-term all-cause mortality.

To determine the value of periportal hyperintensity sign from gadobenate dimeglumine (Gd-BOPTA)-enhanced hepatobiliary phase (HBP) magnetic resonance imaging (MRI) for predicting clinical outcomes in patients with decompensated cirrhosis.

A total of 199 cirrhotic patients who underwent Gd-BOPTA-enhanced MRI were divided into control group (n  =  56) and decompensated cirrhosis group (n  =  143). The presence of periportal hyperintensity sign on HBP MRI was recorded. The Cox regression model was used to investigate the association between periportal hyperintensity sign and clinical outcomes.

There was a significant difference in the frequency of periportal hyperintensity sign on HBP between compensated and decompensated cirrhotic patients (p  <  0.05). After a median follow-up of 29.0 months (range, 1.0-90.0 months), nine out of 143 patients (6.2%) with decompensated cirrhosis died. Periportal hyperintensity sign on HBP MRI was a significant risk factor for death (hazard ratio (HR)  =  23.677; 95% confidence interval (CI)  =  4.759-117.788; p  =  0.0001), with an area under the curve (AUC) of 0.844 (95% CI  =  0.774-0.899). Thirty patients (20.9%) developed further decompensation. Periportal hyperintensity sign on HBP MRI was also a significant risk factor for further decompensation (HR  =  2.594; 95% CI  =  1.140-5.903; p  =  0.023).

Periportal hyperintensity sign from Gd-BOPTA-enhanced HBP MRI is valuable for predicting clinical outcomes in patients with decompensated cirrhosis.

Periportal hyperintensity sign from gadobenate dimeglumine-enhanced hepatobiliary phase magnetic resonance imaging is a new noninvasive method to predict clinical outcomes in patients with decompensated cirrhosis.

• There was a significant difference in the frequency of periportal hyperintensity sign on HBP between compensated and decompensated cirrhotic patients. • Periportal hyperintensity sign on the hepatobiliary phase was a significant risk factor for death in patients with decompensated cirrhosis. • Periportal hyperintensity sign on the hepatobiliary phase was a significant risk factor for further decompensation in patients with decompensated cirrhosis.

We report on the feasibility and outcomes of liver stereotactic body radiation therapy (SBRT) for hepatocellular carcinoma (HCC) with single-photon emission computed tomography (SPECT) functional treatment planning in patients with Child-Pugh (CP) B/C cirrhosis.

Liver SPECT with 99mTc-sulfur colloid was coregistered to treatment planning computed tomography (CT) for the guided avoidance of functional hepatic parenchyma during SBRT. Functional liver volumes (FLVs) obtained from SPECT were compared with anatomic liver volumes defined on the planning CT. Radiation dose constraints were adapted exclusively to FLV. Local control, toxicity, and survival were reported with at least 6 months of radiographic follow-up. Pre- and posttransplant outcomes were analyzed in a subset of patients who completed SBRT as a bridge to liver transplant. Model of End-Stage Liver Disease was used to score hepatic function before and after SBRT completion.

With a median follow-up of 32 months, 45 patients (58 lesions) with HCC and CP-B/C cirrhosis received SBRT to a median dose of 45 Gy (3-5 fractions). FLV loss (34%, P < .001) was observed in all patients, and the functional and anatomic liver volumes matched well in a control group of noncirrhotic/non-HCC patients. Despite marked functional parenchyma retraction, the amount of FLV on SPECT exposed to the threshold irradiation was significantly less than the CT liver volumes (P < .001) because of the optimized beam placement during dosimetry planning. Twenty-three patients (51%) successfully completed orthotopic liver transplant, with a median time to transplant of 9.2 months. With 91% in-field local control, the overall 2-year survival was 65% (90% after the orthotopic liver transplant), with no incidence of radiation-induced liver disease observed within 3 to 4 months or accelerated CP class migration from B to C within the first 6 months post-SBRT. Mean Model of End-Stage Liver Disease-Na score was not significantly elevated at 3-month intervals after SBRT completion.

Functional treatment planning with 99mTc sulfur colloid SPECT/CT allows identification and avoidance of functional hepatic parenchyma in patients with CP-B/C cirrhosis, leading to low toxicity and satisfactory transplant outcomes.

Patients with cirrhosis who have gastrointestinal bleeding have high short-term mortality, but the best modality for risk calculation remains in debate. Liver severity indices, such as Child-Turcotte-Pugh (CTP) and Model-for-End-Stage-Liver Disease (MELD) score, are well-studied in portal hypertensive bleeding, but there is a paucity of data confirming their accuracy in non-portal hypertensive bleeding and overall acute upper gastrointestinal bleeding (UGIB), unrelated to portal hypertension.

This study aims to better understand the accuracy of current mortality risk calculators in predicting mortality for patients with any type of UGIB, which could allow for earlier risk stratification and targeted intervention prior to endoscopy to identify the bleeding source.

In a large US single-center cohort, we investigated and recalibrated the model performance of CTP and MELD scores to predict six-week mortality risk for both sources of UGIB (portal hypertensive and non-portal hypertensive).

Both CTP- and MELD-based models have excellent discrimination in predicting six-week mortality for all types of bleeding sources. However, only a CTP-based model demonstrates calibration for all bleeding, regardless of bleeding etiology. Median predicted 6-week mortality by CTP class A, B, and C estimates a risk of 1%, 7%, and 35% respectively.

Our study corroborates findings in the literature that CTP- and MELD-based models have similar discriminative abilities for predicting 6-week mortality in hospitalized cirrhosis patients presenting with either portal hypertensive or non-portal hypertensive UGIB. CTP class is an effective clinical decision tool that can be used, even prior to endoscopy, to accurately risk stratify a patient with known cirrhosis presenting with any UGIB into low, moderate, and severe risk groupings.

This study aimed to investigate the pharmacokinetic/pharmacodynamic (PK/PD) target attainment of various tigecycline dosing regimens in real-world patients with impaired liver function.

The clinical data and serum concentrations of tigecycline were extracted from the patients' electronic medical records. Patients were classified into Child-Pugh A, Child-Pugh B, and Child-Pugh C groups, according to the severity of liver impairment. Furthermore, the minimum inhibition concentration (MIC) distribution and PK/PD targets of tigecycline from the literature were used to obtain a proportion of PK/PD targets attainment of various tigecycline dosing regimens at different infected sites.

The pharmacokinetic parameters revealed significantly higher values in moderate and severe liver failure (groups Child-Pugh B and Child-Pugh C) than those in mild impairment (Child-Pugh A). Considering the target area under the time-concentration curve (AUC 0-24 )/MIC ≥4.5 for patients with pulmonary infection, most patients with high-dose (100 mg, every 12 hours) or standard-dose (50 mg, every 12 hours) for tigecycline achieved the target in groups Child-Pugh A, B, and C. Considering the target AUC 0-24 /MIC ≥6.96 for patients with intra-abdominal infection, when MIC ≤1 mg/L, more than 80% of the patients achieved the target. For an MIC of 2-4 mg/L, only patients with high-dose tigecycline in groups Child-Pugh B and C attained the treatment target. Patients experienced a reduction in fibrinogen values after treatment with tigecycline. In group Child-Pugh C, all 6 patients developed hypofibrinogenemia.

Severe hepatic impairment may attain higher PK/PD targets, but carries a high risk of adverse reactions.

We present a rapid and high-resolution photoacoustic imaging method for evaluating the liver function reserve (LFR). To validate its accuracy, we establish alcoholic liver disease (ALD) models and employ dual-wavelength spectral unmixing to assess oxygen metabolism. An empirical mathematical model fits the photoacoustic signals, obtaining liver metabolism curve and LFR parameters. Liver oxygen metabolism significantly drops in ALD with the emergence of abnormal hepatic lobular structure. ICG half-life remarkably extends from 241 to 568 s in ALD. A significant decline in LFR occurs in terminal region compared to central region, indicated by a 106.9 s delay in ICG half-life, likely due to hepatic artery and vein damage causing hypoxia and inadequate nutrition. Reduced glutathione repairs LFR with a 43% improvement by reducing alcohol-induced oxidative damage. Scalable photoacoustic imaging shows immense potential for assessing LFR in alcoholic-related diseases, providing assistance to early detection and management of liver disease.

Poststroke epilepsy poses a significant clinical challenge for individuals recovering from strokes, leading to a less favorable long-term outlook and increased mortality rates. Existing studies have primarily concentrated on administering antiseizure or anticonvulsant treatments only after the onset of late-onset seizures, without intervening during the epileptogenesis phase following a stroke.

This research protocol is designed to conduct a randomized controlled trial to assess whether the early, preventive introduction of low-dose antiepileptic drug therapy (levetiracetam [LEV] or perampanel [PER]) in patients who have experienced middle cerebral artery (MCA) infarction can reduce the risk of developing poststroke epilepsy (primary prevention).

Participants with MCA infarction, either with or without reperfusion treatments, will be recruited and promptly receive preventive intervention within 72 hours of the stroke occurrence. These participants will be randomly assigned to receive either PER (4 mg per day), LEV (1000 mg per day), or a placebo that matches the active drugs. This treatment will continue for 12 weeks after allocation. Brain magnetic resonance imaging will be used to confirm the presence of MCA territory infarction, and an electroencephalography will be used to ensure the absence of epileptiform discharges or electrographic seizures at the time of the stroke. All participants will undergo follow-up assessments for 72 weeks after allocation.

The primary outcome under evaluation will be the incidence of poststroke epilepsy in the 3 groups following the 18-month study period. Secondary outcomes will encompass the time to the occurrence of the first seizure, the severity of seizures, any treatment-related adverse events, and the modified Rankin scale score at 3 and 18 months. Exploratory outcomes will involve comparing the effectiveness and safety of PER and LEV.

We anticipate that the intervention groups will experience a lower incidence and reduced severity of poststroke epilepsy compared to the control group after 18 months. We aim to establish evidence supporting the potential preventive effects of LEV and PER on poststroke seizures and epilepsy in patients with MCA infarction, as well as to explore the antiepileptogenic potential of both LEV and PER in patients with major ischemic strokes.

ClinicalTrials.gov NCT04858841; https://clinicaltrials.gov/study/NCT04858841.

DERR1-10.2196/49412.

Cirrhosis entails high risk of serious infections and abated efficiency of vaccination, but the underlying mechanisms are only partially understood. This study aimed at characterizing innate and adaptive immune functions, including antigen-specific T cell responses to COVID-19 vaccination, in patients with compensated and decompensated cirrhosis.

Immune phenotype and function in peripheral blood from 42 cirrhotic patients and 44 age-matched healthy controls were analysed after two doses of the mRNA-based COVID-19 vaccines [BNT162b2 (Pfizer BioNTech) or mRNA-1273 (Moderna)].

Cirrhotic patients showed significantly reduced blood counts of antigen-presenting dendritic cells (DC) and high counts of monocytic myeloid-derived suppressor cells (M-MDSC) as compared to healthy controls. In addition, monocytic cells recovered from cirrhotic patients showed impaired expression of the antigen-presenting molecule HLA-DR and the co-stimulatory molecule CD86 upon Toll-like receptor (TLR) stimulation. These features were more prominent in patients with decompensated cirrhosis (Child-Pugh classes B & C). Interestingly, while patients with compensated cirrhosis (Child-Pugh class A) showed an inflammatory profile with myeloid cells producing the proinflammatory cytokines IL-6 and TNF, decompensated patients produced reduced levels of these cytokines. Cirrhotic patients, in particular those with more advanced end-stage liver disease, mounted reduced antigen-specific T cell reactivity to COVID-19 vaccination. Vaccine efficiency inversely correlated with levels of M-MDSC.

These results implicate MDSC as mediators of immunosuppression, with ensuing deficiency of vaccine-specific T cell responses, in cirrhosis.

In patients with compensated alcohol-related cirrhosis, reliable prognostic biomarkers are lacking. Keratin-18 and hepatocyte-derived large extracellular vesicle (lEV) concentrations reflect disease activity, but their ability to predict liver-related events is unknown.

We measured plasma keratin-18 and hepatocyte lEV concentrations in 500 patients with Child-Pugh class A alcohol-related cirrhosis. The ability of these hepatocyte-derived biomarkers, alone or combined with model for end-stage liver disease (MELD) and FibroTest scores, to predict liver-related events at 2 years was analyzed, taking into account the alcohol consumption at inclusion and during follow-up.

Keratin-18 and hepatocyte lEV concentrations increased with alcohol consumption. In patients without active alcohol consumption at enrollment (n = 419), keratin-18 concentration predicted liver-related events at 2 years, independently of FibroTest and MELD. Patients with both keratin-18 concentrations >285 U/L and FibroTest >0.74 had a 24% cumulative incidence of liver-related events at 2 years, vs. 5% to 14% in other groups of patients. Similar results were obtained when combining keratin-18 concentrations >285 U/L with MELD >10. In patients with active alcohol consumption at enrollment (n = 81), hepatocyte lEVs predicted liver-related events at 2 years, independently of FibroTest and MELD. Patients with both hepatocyte lEV concentrations >50 U/L and FibroTest >0.74 had a 62% cumulative incidence of liver-related events at 2 years, vs. 8% to 13% in other groups of patients. Combining hepatocyte lEV concentrations >50 U/L with MELD >10 had a lower discriminative ability. Similar results were obtained when using decompensation of cirrhosis, defined according to Baveno VII criteria, as an endpoint.

In patients with Child-Pugh class A alcohol-related cirrhosis, combining hepatocyte-derived biomarkers with FibroTest or MELD scores identifies patients at high risk of liver-related events, and could be used for risk stratification and patient selection in clinical trials.

In patients with compensated alcohol-related cirrhosis, reliable predictors of outcome are lacking. In patients with Child-Pugh class A alcohol-related cirrhosis, combining hepatocyte-derived biomarkers (keratin-18 and hepatocyte-large extracellular vesicles) with FibroTest or MELD scores identifies those at high risk of liver-related events at 2 years. The identified patients at high risk of liver-related events are the target-of-choice population for intensive surveillance (e.g., referral to tertiary care centers; intensive control of risk factors) and inclusion in clinical trials.

Acute variceal hemorrhage is a major decompensating event in patients with cirrhosis and is associated with high 6-week mortality risk. Many prognostic models based on clinical and laboratory parameters have been developed to risk stratify patients on index bleeding presentation, including those based on the Model for End-Stage Liver Disease (MELD) and Child-Turcotte-Pugh (CTP). However, consensus on model performance remains unclear.

Using a large US multicenter cohort of hospitalized patients with cirrhosis who presented with acute variceal hemorrhage, this study evaluates, recalibrates, and compares liver severity index-based models, including the more recent MELD 3.0 model, to investigate their predictive performance on 6-week mortality. Models were also recalibrated and externally validated using additional external centers.

All recalibrated MELD-based and CTP-based models had excellent discrimination to identify patients at higher risk for 6-week mortality on initial presentation. The recalibrated CTP score model maintained the best calibration and performance within the validation cohort. Patients with low CTP scores (Class A, score 5-6) were strongly associated with < 5% mortality, while high CTP score (Class C, score > 9) were associated with > 20% mortality.

Use of liver severity index-based models accurately predict 6-week mortality risk for patients admitted to the hospital with acute variceal hemorrhage and supports the utilization of these models in future clinical trials as well as their use in clinical practice.

Posthepatectomy liver failure is one of the main causes of death in patients after hepatectomy. This study intends to establish a prediction model to predict the risk of posthepatectomy liver failure and provide a scientific basis for further reducing the incidence of posthepatectomy liver failure.

This was a retrospective analysis of 1,172 patients with hepatocellular carcinoma undergoing partial hepatectomy. Using univariate and multivariate logistic regression analyses and stepwise regression, a prediction model for posthepatectomy liver failure was established based on the independent risk factors for posthepatectomy liver failure and validated by bootstrapping with 100 resamples, and the receiver operating characteristic curve was used to evaluate the predictive value of the prediction model.

The incidence rate of posthepatectomy liver failure was 22.7% (266/1172). The results showed that the indocyanine green retention rate at 15 minutes (odds ratio = 1.05, P = .002), alanine transaminase (odds ratio = 1.02, P < .001), albumin rate (odds ratio = 0.92, P < .001), total bilirubin (odds ratio = 1.04, P < .001), prothrombin time (odds ratio = 2.44, P < .001), aspartate aminotransferase-neutrophil ratio (odds ratio = 0.95, P < .001), and liver fibrosis index (odds ratio = 1.35, P < .001) were associated with posthepatectomy liver failure. These 7 independent risk factors for posthepatectomy liver failure were integrated into a nomogram prediction model, the predictive efficiency for posthepatectomy liver failure (area under the curve = 0.818, 95% confidence interval 0.789-0.848) was significantly higher than in other predictive models with a liver fibrosis index (area under the curve = 0.651), indocyanine green R15 (area under the curve = 0.669), albumin-bilirubin score (area under the curve = 0.709), albumin-indocyanine green evaluation score (area under the curve = 0.706), model for end-stage liver disease score (area under the curve = 0.636), and Child‒Pugh (area under the curve = 0.551) (all P < .001). The risk of posthepatectomy liver failure in the high-risk posthepatectomy liver failure group (score ≥152) was higher than that in the posthepatectomy liver failure low-risk group (score <152).

This study developed and validated a nomogram model to predict the risk of posthepatectomy liver failure before surgery that can effectively predict the risk of posthepatectomy liver failure in patients with hepatocellular carcinoma.

Post-hepatectomy liver failure (PHLF) remains a complication with the potential risk of mortality for hepatocellular carcinoma (HCC) patients. The systemic inflammatory response (SIR) has been demonstrated to be associated with a bad prognosis of liver cirrhosis and tumors. This study aims to evaluate the incremental prognostic value of inflammatory markers in predicting PHLF in patients with HCC.

Clinical characteristics and variables were retrospectively collected in 2824 patients diagnosed with HCC who underwent radical hepatectomy from the First Medical Center of the General Hospital of the People's Liberation Army. A recently published prognostic model for PHLF was used as the reference model. The increase in AUC (ΔAUC), integrated discrimination improvement (IDI), and the continuous version of the net reclassification improvement (NRI) were applied for quantifying the incremental value of adding the inflammatory markers to the reference model. A p value < 0.05 was considered statistically significant.

The reference PHLF model showed acceptable prediction performance in the current cohort, with an AUC of 0.7492 (95%CI, 0.7191-0.7794). The calculated ΔAUC associated with procalcitonin (PCT) was the only one that was statistically significant (p < 0.05), with a value of 0.0044, and demonstrated the largest magnitude of the increase in AUC. The continuous NRI value associated with the systemic immune-inflammation index (SII) was 35.79%, second only to GPS (46.07%). However, the inflammatory markers of the new models with statistically significant IDI only included WBC count, lymphocyte count, and SII. IDI associated with SII, meanwhile, was the maximum (0.0076), which was consistent with the performance of using the ΔAUC (0.0044) to assess the incremental value of each inflammatory variable.

Among a wide range of inflammatory markers, only PCT and SII have potential incremental prognostic value for predicting PHLF in patients with radical resectable HCC.

In alcohol-associated cirrhosis, an accurate estimate of the risk of death is essential for patient care. We developed individualized prediction charts for 5-year liver-related mortality among outpatients with alcohol-associated cirrhosis that take into account the impact of abstinence.

We collected data on outpatients with alcohol-associated cirrhosis in a prospective registry. The model was derived, internally and externally validated, and compared with the Child-Pugh and the Model For End-Stage Liver Disease (MELD) scores.

A total of 527 and 127 patients were included in the derivation and validation data sets, respectively. A model was developed based on the 3 variables independently associated with liver-related mortality in multivariate analyses (age, Child-Pugh score, and abstinence). In the derivation data set, the model combining age, Child-Pugh score, and abstinence outperformed the Child-Pugh and the MELD scores. In the validation data set, the Brier score was lower for the model (0.166) compared with the Child-Pugh score (0.196, p = 0.008) and numerically lower compared with the MELD score (0.190) (p = 0.06). The model had the greatest AUC (0.77; 95% CI 0.68-0.85) compared with the Child-Pugh score (AUC = 0.66; 95% CI 0.56-0.76, p = 0.01) and was numerically higher than that of the MELD score (AUC = 0.66; 95% CI 0.56-0.78, p = 0.06). Also, the Akaike and Bayesian information criterion scores were lower for the model (2163; 2172) compared with the Child-Pugh (2213; 2216) or the MELD score (2205; 2208).

A model combining age, Child-Pugh score, and abstinence accurately predicts liver-related death at 5 years among outpatients with alcohol-associated cirrhosis. In this study, the model outperformed the Child-Pugh and the MELD scores, although the AUC and the Brier score of the model were not statically different from the MELD score in the validation data set.