Liver transplantation (LT) is recognized as an effective approach that offers survival benefits for patients with acute-on-chronic liver failure (ACLF). However, controversies remain regarding the LT selection criteria, and meta-analyses reporting overall survival outcomes across different ACLF severity grades are lacking.

To depict a comprehensive postoperative picture of patients with ACLF of varying severity and contribute to updating LT selection.

Systematic searches in Web of Science, EMBASE, PubMed, and Cochrane databases were performed, from inception to December 26, 2023, for studies exploring post-transplant outcomes among ACLF patients, stratified by severity grades as identified by the European Association for the Study of the Liver-Chronic Liver Failure criteria. The primary outcome of interest was the survival rate within one year, with post-transplant complications as secondary outcomes. Additionally, the subgroup analysis examined region-specific one-year survival rates.

A total of 17 studies involving 28025 participants were included. Patients with ACLF-1 and ACLF-2 have favorable survival within one year, with survival rates reaching 87% [95% confidence interval (CI): 84%-91%] and 86% (95%CI: 81%-91%), respectively. Despite the relatively lower survival (73%, 95%CI: 66%-80%) and higher incidence of infection (48%, 95%CI: 29%-67%) observed in ACLF-3 patients, their survival exceeds that of those who do not undergo LT. Moreover, post-transplant survival was highest in North America across all ACLF grades.

LT can provide survival advantages for ACLF patients. To optimize the utilization of scarce donor organs and improve prognosis, comprehensive preoperative health evaluations are essential, especially for ACLF-3 patients.

To develop a nomogram model based on the albumin-bilirubin (ALBI) score for predicting the 90-day prognosis of patients with acute-on-chronic liver failure (ACLF) and to evaluate its predictive efficacy.

Clinical data of 290 ACLF patients at the Third People's Hospital of Nantong City, collected from December 2020 to December 2023, were analyzed. The data were divided into a training set (n = 200) and a validation set (n = 90), with August 2022 as the cut-off date. Patients in the training set were categorized into an improvement group (n = 133) and a mortality group (n = 67) based on their 90-day outcomes. The predictive power of baseline parameters was assessed using univariate and multivariate logistic regression to construct model. Model performance was assessed using receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis (DCA) and the Hosmer-Lemeshow test.

Creatinine (CR) [odds ratio (OR) = 1.013, 95% confidence interval (CI): 1.004-1.022], ALBI (OR = 10.831, 95% CI: 4.009-33.247), Gender (OR = 1.931, 95% CI: 0.973-3.870) and ascites (OR = 3.032, 95% CI: 1.249-8.178) were identified as independent prognostic factors. The prognostic model formula was derived as prognostic index (PI) = -0.591 + 0.658 × Gender + 1.109 × ascites + 0.012 × CR + 2.382 × ALBI. The area under the curve (AUC) was 0.804 (95% CI: 0.741-0.866), with a specificity of 85.0% and a sensitivity of 65.7% at a cut-off of 0.425. The AUC of the validation set was 0.811 (95% CI: 0.697-0.926). The Hosmer-Lemeshow test indicated a good model fit with a p-value of 0.287 for the training set and 0.423 for the validation set. Calibration curves demonstrated the accuracy of the model, and DCA results suggested that the model was clinically useful when the threshold was below 0.6.

The nomogram model incorporating ALBI with CR, Gender and ascites can predict the 90-day prognosis of ACLF patients, potentially helping to optimize treatment strategies and improve patient outcomes.

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.

Due to development of an immune-dysregulated phenotype, advanced liver disease in all forms predisposes patients to sepsis acquisition, including by opportunistic pathogens such as fungi. Little data exists on fungal infection within a medical intensive liver unit (MILU), particularly in relation to acute on chronic liver failure.

To investigate the impact of fungal infections among critically ill patients with advanced liver disease, and compare outcomes to those of patients with bacterial infections.

From our prospective registry of MILU patients from 2018-2022, we included 27 patients with culture-positive fungal infections and 183 with bacterial infections. We compared outcomes between patients admitted to the MILU with fungal infections to bacterial counterparts. Data was extracted through chart review.

All fungal infections were due to Candida species, and were most frequently blood isolates. Mortality among patients with fungal infections was significantly worse relative to the bacterial cohort (93% vs 52%, P < 0.001). The majority of the fungal cohort developed grade 2 or 3 acute on chronic liver failure (ACLF) (90% vs 64%, P = 0.02). Patients in the fungal cohort had increased use of vasopressors (96% vs 70%, P = 0.04), mechanical ventilation (96% vs 65%, P < 0.001), and dialysis due to acute kidney injury (78% vs 52%, P = 0.014). On MILU admission, the fungal cohort had significantly higher Acute Physiology and Chronic Health Evaluation (108 vs 91, P = 0.003), Acute Physiology Score (86 vs 65, P = 0.003), and Model for End-Stage Liver Disease-Sodium scores (86 vs 65, P = 0.041). There was no significant difference in the rate of central line use preceding culture (52% vs 40%, P = 0.2). Patients with fungal infection had higher rate of transplant hold placement, and lower rates of transplant; however, differences did not achieve statistical significance.

Mortality was worse among patients with fungal infections, likely attributable to severe ACLF development. Prospective studies examining empiric antifungals in severe ACLF and associations between fungal infections and transplant outcomes are critical.

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.

Solid organ transplantation confronts numerous challenges ranging from donor organ shortage to post-transplant complications. Here, we provide an overview of the latest attempts to address some of these challenges using artificial intelligence (AI). We delve into the application of machine learning in pretransplant evaluation, predicting transplant rejection, and post-operative patient outcomes. By providing a comprehensive overview of AI's current impact, this review aims to inform clinicians, researchers, and policy-makers about the transformative power of AI in enhancing solid organ transplantation and facilitating personalized medicine in transplant care.

Acute-on-chronic liver failure (ACLF) is a clinical syndrome characterized by severe hepatic dysfunction leading to multiorgan failure in patients with end-stage liver disease. ACLF is a challenging clinical syndrome with a rapid clinical course and high short-term mortality. There is no single uniform definition of ACLF or consensus in predicting ACLF-related outcomes, which makes comparing studies difficult and standardizing management protocols challenging. This review aims to provide insights into the common prognostic models that define and grade ACLF.

Left ventricular hypertrophy (LVH) is the most frequent cardiac complication among end-stage kidney disease (ESKD) patients, which has been identified as predictive of adverse outcomes. Emerging evidence has suggested that immune system is implicated in the development of cardiac hypertrophy in multiple diseases. We applied machine learning models to exploring the relation between immune status and LVH in ESKD patients.

A cohort of 506 eligible patients undergoing immune status assessment and standard echocardiography simultaneously in our center were retrospectively analyzed. The association between immune parameters and the occurrence of LVH were evaluated through univariate and multivariate logistic analysis. To develop a predictive model, we utilized four distinct modeling approaches: support vector machine (SVM), logistic regression (LR), multi-layer perceptron (MLP), and random forest (RF).

In comparison to the non-LVH group, ESKD patients with LVH exhibited significantly impaired immune function, as indicated by lower cell counts of CD3⁺ T cells, CD4⁺ T cells, CD8⁺ T cells, and B cells. Additionally, multivariable Cox regression analysis revealed that a decrease in CD3⁺ T cell count was an independent risk factor for LVH, while a decrease in NK cell count was associated with the severity of LVH. The RF model demonstrated superior performance, with an average area under the curve (AUC) of 0.942.

Our findings indicate a strong association between immune parameters and LVH in ESKD patients. Moreover, the RF model exhibits excellent predictive ability in identifying ESKD patients at risk of developing LVH. Based on these results, immunomodulation may represent a promising approach for preventing and treating this disease.

Critically ill patients presenting with acute on chronic liver failure (ACLF) represent a particularly vulnerable population due to various considerations surrounding the syndrome definition, lack of robust prospective evaluation of outcomes, and allocation of resources such as organs for transplantation. Ninety-day mortality related to ACLF is high and patients who do leave the hospital are frequently readmitted. Artificial intelligence (AI), which encompasses various classical and modern machine learning techniques, natural language processing, and other methods of predictive, prognostic, probabilistic, and simulation modeling, has emerged as an effective tool in various areas of healthcare. These methods are now being leveraged to potentially minimize physician and provider cognitive load and impact both short-term and long-term patient outcomes. However, the enthusiasm is tempered by ethical considerations and a current lack of proven benefits. In addition to prognostic applications, AI models can likely help improve the understanding of various mechanisms of morbidity and mortality in ACLF. Their overall impact on patient-centered outcomes and countless other aspects of patient care remains unclear. In this review, we discuss various AI approaches being utilized in healthcare and discuss the recent and expected future impact of AI on patients with ACLF through prognostic modeling and AI-based approaches.

Acute-on-chronic liver failure (ACLF) is a syndrome characterized by decompensation in individuals with chronic liver disease, generally secondary to one or more extra-hepatic organ failures, implying an elevated mortality rate. Acute decompensation (AD) is the term used for one or more significant consequences of liver disease in a short time and is the most common reason for hospital admission in cirrhotic patients. The European Association for the Study of Liver-Chronic-Liver Failure (EASL-CLIF) Group modified the intensive care Sequential Organ Failure Assessment score into CLIF-SOFA, which detects the presence of ACLF in patients with or without AD, classifying it into three grades.

To investigate the role of the EASL-CLIF definition for ACLF and the ability of CLIF-SOFA, CLIF-C ACLF, and CLIF-C AD scores for prognosticating ACLF or AD.

This study is a literature review using a standardized search method, conducted using the steps following the guidelines for reporting systematic reviews set out by the PRISMA statement. For specific keywords, relevant articles were found by searching PubMed, ScienceDirect, and BioMed Central-BMC. The databases were searched using the search terms by one reviewer, and a list of potentially eligible studies was generated based on the titles and abstracts screened. The data were then extracted and assessed on the basis of the Reference Citation Analysis (https://www.referencecitationanalysis.com/).

Most of the included studies used the EASL-CLIF definition for ACLF to identify cirrhotic patients with a significant risk of short-term mortality. The primary outcome in all reviewed studies was mortality. Most of the study findings were based on an area under the receiver operating characteristic curve (AUROC) analysis, which revealed that CLIF-SOFA, CLIF-C ACLF, and CLIF-C AD scores were preferable to other models predicting 28-d mortality. Their AUROC scores were higher and able to predict all-cause mortality at 90, 180, and 365 d. A total of 50 articles were included in this study, which found that the CLIF-SOFA, CLIF-C ACLF and CLIF-C AD scores in more than half of the articles were able to predict short-term and long-term mortality in patients with either ACLF or AD.

CLIF-SOFA score surpasses other models in predicting mortality in ACLF patients, especially in the short-term. CLIF-SOFA, CLIF-C ACLF, and CLIF-C AD are accurate short-term and long-term mortality prognosticating scores.