Model for End-Stage Liver Disease-Lactate and Prediction of Inpatient Mortality in Patients With Chronic Liver Disease

Comparison of a Novel Score "NOD-ACLF" to Other Established Prognostic Scores for Prediction of Mortality in APASL-ACLF Patients: A Cohort Study from a Tertiary Care Center of North India

Background

Commonly used prognostic scores for acute on-chronic liver failure (ACLF) have complex calculations. We tried to compare the simple counting of numbers and types of organ dysfunction to these scores, to predict mortality in ACLF patients.

Methods

In this prospective cohort study, ACLF patients diagnosed on the basis of Asia Pacific Association for Study of the Liver (APASL) definition were included. Severity scores were calculated. Prognostic factors for outcome were analysed. A new score, the Number of Organ Dysfunctions in Acute-on-Chronic Liver Failure (NOD-ACLF) score was developed.

Results

Among 80 ACLF patients, 74 (92.5%) were male, and 6 were female (7.5%). The mean age was 41.0±10.7 (18-70) years. Profile of acute insult was; alcohol 48 (60%), sepsis 30 (37.5%), variceal bleeding 22 (27.5%), viral 8 (10%), and drug-induced 3 (3.8%). Profiles of chronic insults were alcohol 61 (76.3%), viral 20 (25%), autoimmune 3 (3.8%), and non-alcoholic steatohepatitis 2 (2.5%). Thirty-eight (47.5%) were discharged, and 42 (52.5%) expired. The mean number of organ dysfunction (NOD-ACLF score) was ->4.5, simple organ failure count (SOFC) score was >2.5, APASL ACLF Research Consortium score was >11.5, Model for End-Stage Liver Disease-Lactate (MELD-LA) score was >21.5, and presence of cardiovascular and respiratory dysfunctions were significantly associated with mortality. NOD-ACLF and SOFC scores had the highest area under the receiver operating characteristic to predict mortality among all these.

Conclusion

The NOD-ACLF score is easy to calculate bedside and is a good predictor of mortality in ACLF patients performing similar or better to other scores.

Major liver resections, perioperative issues and posthepatectomy liver failure: A comprehensive update for the anesthesiologist

Significant advances in surgical techniques and relevant medium- and long-term outcomes over the past two decades have led to a substantial expansion in the indications for major liver resections. To support these outstanding results and to reduce perioperative complications, anesthesiologists must address and master key perioperative issues (preoperative assessment, proactive intraoperative anesthesia strategies, and implementation of the Enhanced Recovery After Surgery approach). Intensive care unit monitoring immediately following liver surgery remains a subject of active and often unresolved debate. Among postoperative complications, posthepatectomy liver failure (PHLF) occurs in different grades of severity (A-C) and frequency (9%-30%), and it is the main cause of 90-d postoperative mortality. PHLF, recently redefined with pragmatic clinical criteria and perioperative scores, can be predicted, prevented, or anticipated. This review highlights: (1) The systemic consequences of surgical manipulations anesthesiologists must respond to or prevent, to positively impact PHLF (a proactive approach); and (2) the maximal intensive treatment of PHLF, including artificial options, mainly based, so far, on Acute Liver Failure treatment(s), to buy time waiting for the recovery of the native liver or, when appropriate and in very selected cases, toward liver transplant. Such a clinical context requires a strong commitment to surgeons, anesthesiologists, and intensivists to work together, for a fruitful collaboration in a mandatory clinical continuum.

A novel prognostic model to predict mortality in patients with acute-on-chronic liver failure in intensive care unit

Acute-on-chronic liver failure (ACLF) implies high short-term mortality rates and usually requires intensive care unit (ICU) admission. Proper prognosis for these patients is crucial for early referral for liver transplantation. The superiority of CLIF-C ACLF score in Asian patients with ACLF admitted to an ICU remains inconclusive when compared to other scoring systems. The purpose of the study is (i) to compare the predictive performance of original MELD, MELD-Lactate, CLIF-C ACLF, CLIF-C ACLF-Lactate, and APACHE-II scores for short-term mortality assessment. (ii) to build and validate a novel scoring system and to compare its predictive performance to that of the original five scores. Two hundred sixty-five consecutive cirrhotic patients with ACLF who were admitted to our ICU were enrolled. The prognostic values for mortality were assessed by ROC analysis. A novel model was developed and internally validated using fivefold cross-validation. Alcohol abuse was identified as the primary etiology of cirrhosis. The AUROC of the five prognostic scores were not significantly superior to each other in predicting 1-month and 3-month mortality. The newly developed prognostic model, incorporating age, alveolar-arterial gradient (A-a gradient), BUN, total bilirubin level, INR, and HE grades, exhibited significantly improved performance in predicting 1-month and 3-month mortality with AUROC of 0.863 and 0.829, respectively, as compared to the original five prognostic scores. The novel ACLF model seems to be superior to the original five scores in predicting short-term mortality in ACLF patients admitted to an ICU. Further rigorous validation is required.

MELD 3.0 in Advanced Chronic Liver Disease

The MELD (model for end-stage liver disease) 3.0 score was developed to replace the MELD-Na score that is currently used to prioritize liver allocation for cirrhotic patients awaiting liver transplantation in the United States. The MELD 3.0 calculator includes new inputs from patient sex and serum albumin levels and has new weights for serum sodium, bilirubin, international normalized ratio, and creatinine levels. It is expected that use of MELD 3.0 scores will reduce overall waitlist mortality modestly and improve access for female liver transplant candidates. The utility of MELD 3.0 and PELDcre (pediatric end-stage liver disease, creatinine) scores for risk stratification in cirrhotic patients undergoing major abdominal surgery, placement of a transjugular intrahepatic portosystemic shunt, and other interventions requires further study. This article reviews the background of the MELD score and the rationale to create MELD 3.0 as well as potential implications of using this newer risk stratification tool in clinical practice.

Predictors of 30-day mortality in patients diagnosed with hepatic encephalopathy on admission to the emergency department

BACKGROUND

The aim of this study is to compare the laboratory findings and disease severity scores of patients diagnosed with hepatic encephalopathy (HE) in the emergency department (ED) to predict 30-day mortality.

METHOD

The patients over 18 years old and diagnosed HE in the ED of a tertiary hospital were included in the study. Demographic and clinical characteristics, laboratory parameters, predisposing causes and outcomes of the patients included in the study were recorded in the data form. Severity of liver disease was assessed by Child Pugh Score (CPS), End-stage liver disease model (MELD), MELD-Na and MELD-Lactate scores.

RESULTS

Two hundred fifty-four patients diagnosed with HE were included in the study. 59.1% of the patients were male. The mean age of the patients was 65.2 ± 12.6 years. The mortality rate of the patients was 47.2%. When the receiver operating characteristic (ROC) analysis, which determines the predictive properties of laboratory parameters and disease severity scores, was examined, the area under curve value of the MELD-Lactate score (0.858 95% CI 0.812-0.904, P  < 0.001) was the highest. Binary logistic regression analysis for the estimation of patients' 30-day mortality showed that CPS and MELD-Lactate scores and blood ammonia and B-type natriuretic peptide levels were independent predictors of mortality.

CONCLUSION

According to the study data, MELD-Lactate and BNP levels in patients diagnosed with HE in the ED may help the clinician in the prediction of 30-day mortality in the early period.

Protein Lactylation Modification and Proteomics Features in Cirrhosis Patients after UC-MSC Treatment

Umbilical cord mesenchymal stem cell (UC-MSC) therapy improves liver function in liver cirrhosis patients. This study aimed to elucidate the therapeutic mechanism underlying cell therapy by analyzing changes in the modification and expression of proteins 1 month post-treatment with UC-MSCs. This prospective study included 11 cirrhosis patients who received MSC injection. The laboratory indexes before and after treatment were collected to evaluate the clinical treatment effect of UC-MSCs, and the protein expression and lactylation modification in the liver were comprehensively revealed. Meanwhile, weighted gene co-expression network analysis was used to analyze the co-expression protein modules and their relationship with clinical features. The patients with liver cirrhosis showed an improvement trend after receiving UC-MSC treatment; specifically, the liver protein synthesis function was significantly improved and the coagulation function was also significantly improved. Proteomics combined with lactic acid proteomics revealed 160 lysine lactylation (Kla) sites of 119 proteins. Functional analysis showed that the lactylation-modified proteins were enriched in the pathway of glucose and other substances' metabolism, and many key enzymes of glycolysis and gluconeogenesis were lactated. UC-MSC therapy has a certain clinical effect in the treatment of liver cirrhosis and may act by regulating material metabolism, because the lactylation protein points to energy metabolism.

Complications in Post-Liver Transplant Patients

Liver transplantation (LT) is the treatment of choice for liver failure and selected cases of malignancies. Transplantation activity has increased over the years, and indications for LT have been widened, leading to organ shortage. To face this condition, a high selection of recipients with prioritizing systems and an enlargement of the donor pool were necessary. Several authors published their case series reporting the results obtained with the use of marginal donors, which seem to have progressively improved over the years. The introduction of in situ and ex situ machine perfusion, although still strongly debated, and better knowledge and treatment of the complications may have a role in achieving better results. With longer survival rates, a significant number of patients will suffer from long-term complications. An extensive review of the literature concerning short- and long-term outcomes is reported trying to highlight the most recent findings. The heterogeneity of the behaviors within the different centers is evident, leading to a difficult comparison of the results and making explicit the need to obtain more consent from experts.

A Validated Composite Score Demonstrates Potential Superiority to MELD-Based Systems in Predicting Short-Term Survival in Patients with Liver Cirrhosis and Spontaneous Bacterial Peritonitis-A Preliminary Study

BACKGROUND

Spontaneous bacterial peritonitis (SBP) is a severe complication in cirrhosis patients with ascites, leading to high mortality rates if not promptly treated. However, specific prediction models for SBP are lacking.

AIMS

This study aimed to compare commonly used cirrhotic prediction models (CTP score, MELD, MELD-Na, iMELD, and MELD 3.0) for short-term mortality prediction and develop a novel model to improve mortality prediction.

METHODS

Patients with the first episode of SBP were included. Prognostic values for mortality were assessed using AUROC analysis. A novel prediction model was developed and validated.

RESULTS

In total, 327 SBP patients were analyzed, with HBV infection as the main etiologies. MELD 3.0 demonstrated the highest AUROC among the traditional models. The novel model, incorporating HRS, exhibited superior predictive accuracy for in-hospital in all patients and 3-month mortality in HBV-cirrhosis, with AUROC values of 0.827 and 0.813 respectively, surpassing 0.8.

CONCLUSIONS

MELD 3.0 score outperformed the CTP score and showed a non-significant improvement compared to other MELD-based scores, while the novel SBP model demonstrated impressive accuracy. Internal validation and an HBV-related cirrhosis subgroup sensitivity analysis supported these findings, highlighting the need for a specific prognostic model for SBP and the importance of preventing HRS development to improve SBP prognosis.

Intensive care management of acute-on-chronic liver failure

Acute-on-chronic liver failure (ACLF) is a clinical syndrome defined by an acute deterioration of the liver function associated with extrahepatic organ failures requiring intensive care support and associated with a high short-term mortality. ACLF has emerged as a major cause of mortality in patients with cirrhosis and chronic liver disease. ACLF has a unique pathophysiology in which systemic inflammation plays a key role; this provides the basis of novel therapies, several of which are now in clinical trials. Intensive care unit (ICU) therapy parallels that applied in the general ICU population in some organ failures but has peculiar differential characteristics in others. Critical care management strategies and the option of liver transplantation (LT) should be balanced with futility considerations in those with a poor prognosis. Nowadays, LT is the only life-saving treatment that can radically improve the long-term prognosis of patients with ACLF. This narrative review will provide insights on the current understanding of ACLF with emphasis on intensive care management.

Using machine learning methods to predict 28-day mortality in patients with hepatic encephalopathy

BACKGROUND

Hepatic encephalopathy (HE) is associated with marked increases in morbidity and mortality for cirrhosis patients. This study aimed to develop and validate machine learning (ML) models to predict 28-day mortality for patients with HE.

METHODS

A retrospective cohort study was conducted in the Medical Information Mart for Intensive Care (MIMIC)-IV database. Patients from MIMIC-IV were randomized into training and validation cohorts in a ratio of 7:3. Training cohort was used for establishing the model while validation cohort was used for validation. The outcome was defined as 28-day mortality. Predictors were identified by recursive feature elimination (RFE) within 24 h of intensive care unit (ICU) admission. The area under the curve (AUC) and calibration curve were used to determine the predictive performance of different ML models.

RESULTS

In the MIMIC-IV database, 601 patients were eventually diagnosed with HE. Of these, 112 (18.64%) experienced death within 28 days. Acute physiology score III (APSIII), sepsis related organ failure assessment (SOFA), international normalized ratio (INR), total bilirubin (TBIL), albumin, blood urea nitrogen (BUN), acute kidney injury (AKI) and mechanical ventilation were identified as independent risk factors. Validation set indicated that the artificial neural network (NNET) model had the highest AUC of 0.837 (95% CI:0.774-0.901). Furthermore, in the calibration curve, the NNET model was also well-calibrated (P = 0.323), which means that it can better predict the 28-day mortality in HE patients. Additionally, the performance of the NNET is superior to existing scores, including Model for End-Stage Liver Disease (MELD) and Model for End-Stage Liver Disease-Sodium (MELD-Na).

CONCLUSIONS

In this study, the NNET model demonstrated better discrimination in predicting 28-day mortality as compared to other models. This developed model could potentially improve the early detection of HE with high mortality, subsequently improving clinical outcomes in these patients with HE, but further external prospective validation is still required.

MELD-Lactate Predicts Poor Outcome in Variceal Bleeding in Cirrhosis

BACKGROUND

Predictors of poor outcome associated with variceal bleeding remain suboptimal. In patients with cirrhosis, serum lactate combined with Model for End-Stage Liver Disease (MELD-LA) improved prediction across heterogeneous populations. However, prognostic properties have not yet been assessed in the context of variceal bleeding.

AIMS

We aimed to evaluate the predictive performance of MELD-LA compared to MELD, lactate, and nadir hemoglobin in cirrhosis patients with variceal bleeding.

METHODS

In this multicenter study, we identified 472 patients with variceal bleeding from a German primary cohort (University Hospitals Hamburg/Frankfurt/Cologne), and two independent external validation cohorts [Veterans Affairs (VA), Baylor University]. Discrimination for 30-day mortality was analyzed and scores were compared. MELD-LA was evaluated separately in validation cohorts to ensure consistency of findings.

RESULTS

In contrast to nadir hemoglobin, MELD and peak-lactate at time of bleeding were significantly higher in 30-day non-survivors in the primary cohort (p = 0.708; p < 0.001). MELD-LA had excellent discrimination for 30-day mortality (AUROC 0.82, 95% CI 0.76-0.88), better than MELD and peak-lactate (AUROC 0.78, 95% CI 0.71-0.84; AUROC 0.73, 95% CI 0.66-0.81). MELD-LA predicted 30-day mortality independently of age, sex, severity of liver disease and vasopressor support (HR 1.29 per 1-point-increase of MELD-LA; 95% CI 1.19-1.41; p < 0.001). Similarly, MELD-LA demonstrated excellent discrimination for 30-day mortality in the VA (AUROC = 0.86, 95% CI 0.79-0.93) and Baylor cohort (AUROC = 0.85, 95% CI 0.74-0.95).

CONCLUSIONS

MELD-LA significantly improves discrimination of short-term mortality associated with variceal bleeding, compared to MELD, peak-lactate and nadir hemoglobin. Thus, MELD-LA might represent a useful and objective marker for risk assessment and therapeutic intervention in patients with variceal bleeding.

Updates on the Model for End-Stage Liver Disease Score and Impact on the Liver Transplant Waiting List: A Narrative Review

The model for end-stage liver disease (MELD) score is an established indicator of cirrhosis severity and a predictor of morbidity and mortality in patients undergoing transjugular intrahepatic portosystemic shunt (TIPS) creation and for allocation in liver transplantation. Since the adoption of the score, its use has been expanded to multiple new indications requiring model modifications, including relevant clinical and demographic variables, to increase predictive accuracy. The purpose of this report is to provide an update on the modifications made to the MELD score, comparing their performance with C statistics, advantages and disadvantages, and impact on mortality at 3 months after placing a TIPS or awaiting liver transplantation.

Factors predicting mortality in patients with alcoholic liver cirrhosis visiting the emergency department

Liver cirrhosis (LC) is a major cause of morbidity and mortality worldwide and is becoming a regional and healthcare burden. South Korea is one of the 10 countries with the highest age standardized prevalence of decompensated LC. Moreover, the proportion of patients with alcoholic LC is increasing and there has been no decrease in the incidence of decompensated alcoholic LC. Patients with decompensated LC frequently visit the emergency department (ED). Several studies focused on patients with LC who visited the ED, but the studies about alcoholic LC were limited. This study aimed to identify predicting factors for mortality in alcoholic LC patients visiting the ED. This was a retrospective study of alcoholic LC patients who visited an ED between November 2017 and June 2021. The baseline characteristics, complications of LC, model for end-stage liver disease (MELD) score, and laboratory values including lactate were assessed. The primary outcome was in-hospital mortality. In total, 433 patients with alcoholic LC were included for analysis and the in hospital mortality rate was 15.9% (n = 69). Univariate regression analyses identified that MELD score, lactate, platelet, international normalized ratio, bilirubin, creatinine, albumin, and C-reactive protein (CRP) predicted in-hospital mortality. Multivariate regression analysis showed that MELD score, lactate, albumin, and CRP were significantly associated with in-hospital mortality. MELD score, lactate, albumin, and CRP predicted the mortality in alcoholic LC patients visiting the ED.

High-risk liver transplant recipients with grade 3 acute on chronic liver failure should receive the good quality graft

BACKGROUND & AIM

We aimed to develop a risk score for LT recipients and donor selection among patients with ACLF-3.

METHODS AND RESULTS

A total of 7166 adult LT recipients (mean age 53 years, 63% males, 56% Caucasians, 42% obese, median MELD score 36.5) using deceased donor grafts in the UNOS database (01/2002-06/2018) who were in ACLF-3 at LT as per EASL-CLIF criteria were analysed. Cox regression model on the derivation dataset (N = 3583) showed recipient age, non-alcohol aetiology, pulmonary failure, brain failure and cardiovascular failure to be associated with 1-year patient survival. Observed and expected post-transplant 1-year survival showed excellent correlation (R = .920). Risk score from cox model on derivation dataset stratified 3583 recipients in validation cohort using cut-off scores 7.55 and 11.57 to low (N = 1211), medium (N = 1168) and high risk (N = 1199), with 1-year patient survival of 89%, 82% and 80% respectively. Based on poor versus good quality graft (donor risk index cut-off at 1.50), 1-year patient survival for low, medium and high-risk categories were 90 versus 89% (p = .490), 83 versus 82% (p = .390) and 83 versus 78% (p = .038) respectively. Among recipients with a high-risk score, donor factors of age ≥60 years, grafts obtained from national sharing and macro-steatosis >15% were associated with 1-year patient survival below 66%.

CONCLUSION

Among ACLF-3 liver transplant recipients, those with high risk at the time of transplant receiving better quality graft will improve post-transplant outcomes. Prospective studies using additional characteristics are needed to derive an accurate risk score model in predicting post-transplant outcomes among recipients with ACLF-3.

Establishment of MELD-lactate clearance scoring system in predicting death risk of critically ill cirrhotic patients

Background

To develop a scoring system related to the lactate clearance (ΔLA) to predict the mortality risk (MELD-ΔLA) for critically ill cirrhotic patients.

Methods

In this retrospective cohort study, 881 critically ill cirrhotic patients from the Medical Information Mart for Intensive Care (MIMIC-III) database were included eventually. The outcomes of our study were defined as ICU death, 28-day, 90-day and 1-year mortality. Predictors were identified by multivariate Cox analysis to develop the predictive scoring system. The C-index and area under the curve (AUC) of receiver operator characteristic curve (ROC) were used to identify the predicting performance of the MELD-ΔLA, sequential organ failure assessment (SOFA), chronic liver failure-sequential organ failure assessment (CLIF-SOFA), the model for end-stage liver disease (MELD), Child–Pugh, chronic liver failure consortium acute-on-chronic liver failure (CLIF-C ACLF), chronic liver failure consortium-acute decompensation (CLIF-C AD) and MELD-Na scoring systems. Additionally, subgroup analysis was also performed based on whether critically ill cirrhotic patients underwent liver transplantation.

Results

Creatinine, bilirubin, international normalized ratio (INR), lactate first, ΔLA and vasopressors were closely associated with ICU death of liver critically ill cirrhotic patients. The C-index of the MELD-ΔLA in ICU death was 0.768 (95% CI 0.736–0.799) and the AUC for the MELD-ΔLA scoring system in predicting 28-day, 90-day, and 1-year mortality were 0.774 (95% CI 0.743–0.804), 0.765 (95% CI 0.735–0.796), and 0.757 (95% CI 0.726–0.788), suggested that MELD-ΔLA scoring system has a good predictive value than SOFA, CLIF-SOFA, MELD, Child–Pugh, CLIF-C ACLF, CLIF-C AD) and MELD-Na scoring systems. Additionally, the study also confirmed the good predictive value of MELD-ΔLA scoring system for critically ill cirrhotic patients regardless of undergoing liver transplantation.

Conclusion

The developed MELD-ΔLA score is a simple scoring system in predicting the risk of ICU death, 28-day, 90-day and 1-year mortality for critically ill cirrhotic patients, which may have a good predictive performance.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12876-022-02351-5.

Are MELD and MELDNa Still Reliable Tools to Predict Mortality on the Liver Transplant Waiting List?

Liver transplantation is the only curative treatment for end-stage liver disease. Unfortunately, the scarcity of donor organs and the increasing pool of potential recipients limit access to this life-saving procedure. Allocation should account for medical and ethical factors, ensuring equal access to transplantation regardless of recipient's gender, race, religion, or income. Based on their short-term prognosis prediction, model for end-stage liver disease (MELD) and MELD sodium (MELDNa) have been widely used to prioritize patients on the waiting list for liver transplantation resulting in a significant decrease in waiting list mortality/removal. Recent concern has been raised regarding the prognostic accuracy of MELD and MELDNa due, in part, to changes in recipients' profile such as body mass index, comorbidities, and general condition, including nutritional status and cause of liver disease, among others. This review aims to provide a comprehensive view of the current state of MELD and MELDNa advantages and limitations and promising alternatives. Finally, it will explore future options to increase the donor pool and improve donor-recipient matching.

Prognostic Role of MELD-Lactate in Cirrhotic Patients' Short- and Long-Term Prognosis, Stratified by Causes of Cirrhosis

OBJECTIVES

Recently, model for end-stage liver disease-lactate (MELD-LA) proved to be a superior predicting factor of inpatient mortality in patients with chronic liver disease. The study's objective was to evaluate the ability of MELD-LA to predict both short- and long-term mortality in critically ill cirrhotic patients stratified by causes of cirrhosis.

MATERIALS AND METHODS

This was a retrospective observational research of 469 cirrhotic patients entering intensive care unit. Clinical parameters and prognostic scores were measured and collected in the first 24 hours after entering intensive care unit. Follow-up duration was at least 5 years. Independent relationship between MELD-LA and mortality was evaluated by multivariate logistic regression analyses. Discrimination of scoring system was evaluated by the area under the receiver operating characteristic curve. Calibration of the score was evaluated by Hosmer-Lemeshow goodness of fit test for significance.

RESULTS

The MELD-LA score (odds ratio: 1.179, 95% confidence interval: 1.112-1.250, P < 0.001) was an independent risk factor for 15-day mortality. The area under the curve of MELD-LA was the highest (0.808, 95% confidence interval: 0.765-0.852) in predicting 15-day mortality and it had superior calibration. We found MELD-LA showed the best discrimination ability in cirrhotic patients caused by both alcohol and hepatitis (0.783, 95% confidence interval: 0.651-0.915) or alcohol alone (0.805, 95% confidence interval: 0.743-0.867).

CONCLUSIONS

MELD-LA performs better for predicting short-term prognosis in critically ill cirrhotic patients, especially caused by both alcohol and hepatitis or alcohol alone.

ESVM-SWRF: Ensemble SVM-based sample weighted random forests for liver disease classification

Recently, a significant way to diagnose the disease is using the model of medical data mining. The most challenging task in the healthcare field is to face a large amount of data during disease analyzes and prediction. Once the data are transformed into valuable data by means of data mining models then the actual prediction and decision making is easier. The existing studies met few shortcomings because of higher execution time, more computational complexities, less scalability, slow convergence, and lack of providing the solution. In this article, we have proposed an ensemble SVM-based sample weighted random forests (eSVM-swRF) with novel improved colliding body optimization (NICBO) algorithm to predict liver diseases. The extraction, loading, transformation, and analysis (ELTA) are used to pre-process the patient data. The significant feature with a suitable model is generated depending upon the filter-based method. Based on eSVM-swRF, the parameter values such as penalty parameter (P), threshold (T), and mTry are optimized via a novel improved colliding boding optimization (NICBO) algorithm. The UCI dataset provides liver disease data for this study. The implementation platform of RapidMiner Studio version 7.6 with different evaluation measures is used to validate the performance of eSVM-swRF with the NICBO method. Anyway, the proposed method yields outstanding performance than other existing methods such as Particle Swarm Optimization-based Support Vector Machine (PSO-SVM), fuzzy adaptive, and neighbor weighted k-NN (FuzzyANWKNN), Naïve Bayes-based Support Vector Machine (NB-SVM), and Neural network.

A neutrophil-to-lymphocyte ratio-based prognostic model to predict mortality in patients with HBV-related acute-on-chronic liver failure

Background

Although the Asian Pacific Association for the Study of the Liver acute-on-chronic liver failure (ACLF) research consortium (AARC) ACLF score is easy to use in patients with hepatitis b virus-related ACLF (HBV-ACLF), serum lactate is not routinely tested in primary hospitals, and its value may be affected by some interference factors. Neutrophil-to-lymphocyte ratio (NLR) is used to assess the status of bacterial infection (BI) or outcomes in patients with various diseases. We developed an NLR-based AARC ACLF score and compared it with the existing model.

Methods

A total of 494 HBV-ACLF patients, enrolled in four tertiary academic hospitals in China with 90-day follow-up, were analysed. Prognostic performance of baseline NLR and lactate were compared between cirrhotic and non-cirrhotic subgroups via the receiver operating curve and Kaplan–Meier analyses. A modified AARC ACLF (mAARC ACLF) score using NLR as a replacement for lactate was developed (n = 290) and validated (n = 204).

Results

There were significantly higher baseline values of NLR in non-survivors, patients with admission BI, and those with higher grades of ACLF compared with the control groups. Compared with lactate, NLR better reflected BI status in the cirrhotic subgroup, and was more significantly correlated with CTP, MELD, MELD-Na, and the AARC score. NLR was an independent predictor of 90-day mortality, and was categorized into three risk grades (< 3.10, 3.10–4.78, and > 4.78) with 90-day cumulative mortalities of 8%, 21.2%, and 77.5% in the derivation cohort, respectively. The mAARC ACLF score, using the three grades of NLR instead of corresponding levels of lactate, was superior to the other four scores in predicting 90-day mortality in the derivation (AUROC 0.906, 95% CI 0.872–0.940, average P < 0.001) and validation cohorts (AUROC 0.913, 95% CI 0.876–0.950, average P < 0.01), with a considerable performance in predicting 28-day mortality in the two cohorts.

Conclusions

The prognostic value of NLR is superior to that of lactate in predicting short-term mortality risk in cirrhotic and non-cirrhotic patients with HBV-ACLF. NLR can be incorporated into the AARC ACLF scoring system for improving its prognostic accuracy and facilitating the management guidance in patients with HBV-ACLF in primary hospitals.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12876-021-02007-w.

Combination of Model for End-Stage Liver Disease and Lactate Predicts Death in Patients Treated With Salvage Transjugular Intrahepatic Portosystemic Shunt for Refractory Variceal Bleeding

BACKGROUND AND AIMS

Data about the prognosis of salvage transjugular intrahepatic portosystemic shunt (TIPS) using covered stents for refractory variceal bleeding caused by portal hypertension are scarce. We aimed to assess survival and to identify predictors of mortality in these patients.

APPROACH AND RESULTS

One hundred sixty-four patients with cirrhosis from five centers treated with salvage TIPS between 2007 and 2017 were retrospectively divided into a derivation cohort (83 patients) and a validation cohort (81 patients). Comparisons were performed using the Mann-Whitney and Fischer's exact test. Six-week overall survival (OS) was correlated with variables on the day of the TIPS using Kaplan-Meier curves with log-rank test and univariate/multivariate analyses using the Cox model. Eighty-three patients were included in the derivation cohort (male, 78%; age, 55 years, alcohol-associated cirrhosis, 88%; Model for End-Stage Liver Disease [MELD], 19 [15-27]; arterial lactate, 3.7 mmol/L [2.0-8.3]). Six-week OS rate was 58%. At multivariate analysis, the MELD score (OR, 1.064; 95% CI, 1.005-1.126; P = 0.028) and arterial lactate (OR, 1.063; 95% CI, 1.013-1.114; P = 0.032) were associated with 6-week OS. Six-week OS rates were 100% in patients with arterial lactate ≤2.5 mmol/L and MELD score ≤ 15 and 5% in patients with lactate ≥12 mmol/L and/or MELD score ≥ 30. The 81 patients of the validation cohort had similar MELD and arterial lactate level but lower creatinine level (94 vs 106 µmol/L, P = 0.008); 6-week OS was 67%. Six-week OS rates were 86% in patients with arterial lactate ≤2.5 mmol/L and MELD score ≤ 15 and 10% for patients with lactate ≥12 mmol/L and/or MELD score ≥ 30. In the overall cohort, rebleeding rate was 15.8% at 6 weeks, and the acute-on-chronic liver failure grade (OR, 1.699; 95% CI, 1.056-1.663; P = 0.040) was independently associated with rebleeding.

CONCLUSIONS

After salvage TIPS, 6-week mortality remains high and can be predicted by MELD score and lactate. Survival rate at 6 weeks was >85% in patients with arterial lactate ≤2.5 mmol/L and MELD score ≤ 15, while mortality was >90% for lactate ≥12 mmol/L and/or MELD score ≥ 30.

Evolution of liver transplant organ allocation policy: Current limitations and future directions

Since the adoption of the model for end-stage liver disease (MELD) score for organ allocation in 2002, numerous changes to the system of liver allocation and distribution have been made with the goal of decreasing waitlist mortality and minimizing geographic variability in median MELD score at time of transplant without worsening post-transplant outcomes. These changes include the creation and adoption of the MELD-Na score for allocation, Regional Share 15, Regional Share for Status 1, Regional Share 35/National Share 15, and, most recently, the Acuity Circles Distribution Model. However, geographic differences in median MELD at time of transplant remain as well as limits to the MELD score for allocation, as etiology of liver disease and need for transplant changes. Acute-on-chronic liver failure (ACLF) is a subset of liver failure where prevalence is rising and has been shown to have an increased mortality rate and need for transplantation that is under-demonstrated by the MELD score. This underscores the limitations of the MELD score and raises the question of whether MELD is the most accurate, objective allocation system. Alternatives to the MELD score have been proposed and studied, however MELD score remains as the current system used for allocation. This review highlights policy changes since the adoption of the MELD score, addresses limitations of the MELD score, reviews proposed alternatives to MELD, and examines the specific implications of these changes and alternatives for ACLF.

Association of Chronic Liver Disease and Mortality in Patients With Aneurysmal Subarachnoid Hemorrhage

BACKGROUND AND PURPOSE

Chronic liver disease (CLD) is a risk factor for increased morbidity and mortality in acutely ill patients. For patients with aneurysmal subarachnoid hemorrhage (aSAH), the association between CLD and mortality remains unknown.

METHODS

In this retrospective cohort study, we analyzed consecutive aSAH patients admitted to the West China Hospital between 2009 and 2019. The primary outcome was in-hospital all-cause mortality.

RESULTS

This study included 6228 cases of aSAH, 489 (7.9%) of whom also had CLD. In a propensity-matched analysis, CLD was associated with increased mortality in patients with aSAH compared with non-CLD (odds ratio, 2.04 [95% CI, 1.43-2.92]). In aSAH patients with CLD, a high Model for End-Stage Liver Disease score was still associated with an increased odds of mortality.

CONCLUSIONS

Among aSAH patients, CLD was associated with increased mortality compared with non-CLD. Among aSAH patients with CLD, a higher Model for End-Stage Liver Disease score was associated with an increased odds of mortality.

Comparative accuracy of prognostic models for short-term mortality in acute-on-chronic liver failure patients: CAP-ACLF

BACKGROUND

Multiple predictive models of mortality exist for acute-on-chronic liver failure (ACLF) patients that often create confusion during decision-making. We studied the natural history and evaluated the performance of prognostic models in ACLF patients.

METHODS

Prospectively collected data of ACLF patients from APASL-ACLF Research Consortium (AARC) was analyzed for 30-day outcomes. The models evaluated at days 0, 4, and 7 of presentation for 30-day mortality were: AARC (model and score), CLIF-C (ACLF score, and OF score), NACSELD-ACLF (model and binary), SOFA, APACHE-II, MELD, MELD-Lactate, and CTP. Evaluation parameters were discrimination (c-indices), calibration [accuracy, sensitivity, specificity, and positive/negative predictive values (PPV/NPV)], Akaike/Bayesian Information Criteria (AIC/BIC), Nagelkerke-R², relative prediction errors, and odds ratios.

RESULTS

Thirty-day survival of the cohort (n = 2864) was 64.9% and was lowest for final-AARC-grade-III (32.8%) ACLF. Performance parameters of all models were best at day 7 than at day 4 or day 0 (p < 0.05 for C-indices of all models except NACSELD-ACLF). On comparison, day-7 AARC model had the numerically highest c-index 0.872, best accuracy 84.0%, PPV 87.8%, R² 0.609 and lower prediction errors by 10-50%. Day-7 NACSELD-ACLF-binary was the simple model (minimum AIC/BIC 12/17) with the highest odds (8.859) and sensitivity (100%) but with a lower PPV (70%) for mortality. Patients with day-7 AARC score > 12 had the lowest 30-day survival (5.7%).

CONCLUSIONS

APASL-ACLF is often a progressive disease, and models assessed up to day 7 of presentation reliably predict 30-day mortality. Day-7 AARC model is a statistically robust tool for classifying risk of death and accurately predicting 30-day outcomes with relatively lower prediction errors. Day-7 AARC score > 12 may be used as a futility criterion in APASL-ACLF patients.

Validating a novel score based on interaction between ACLF grade and MELD score to predict waitlist mortality

BACKGROUND & AIMS

Among candidates listed for liver transplant (LT), the model for end-stage liver disease (MELD) score may not capture acute-on-chronic liver failure (ACLF) severity. Data on the interaction between ACLF and MELD score in predicting waitlist mortality are scarce.

METHODS

We analyzed the UNOS database (01/2002 to 06/2018) for LT listings in adults with cirrhosis and ACLF (without hepatocellular carcinoma). ACLF grades 1, 2, 3a, and 3b- were defined using the modified EASL-CLIF criteria.

RESULTS

Of 18,416 candidates with ACLF at listing (mean age 54 years, 69% males, 63% Caucasians), 90-day waitlist mortality (patient death or being too sick for LT) was 21.6% (18%, 20%, 25%, and 39% for ACLF grades 1, 2, 3a, and 3b, respectively). Using a Fine and Gray regression model, we identified an interaction between MELD and ACLF grade, with ACLF having a higher impact at lower MELD scores. Other variables included candidate's age, sex, liver disease etiology, listing MELD, ACLF grade, obesity, and performance status. A score developed using parameter estimates from the interaction model on the derivation cohort (n = 9,181) stratified the validation cohort (n = 9,235) into quartiles: Q1 (score <10.42), Q2 (10.42-12.81), Q3 (12.82-15.50), and Q4 (>15.50). Waitlist mortality increased with each quartile from 13%, 18%, 23%, and 36%, respectively. Observed vs. expected waitlist mortality deciles in the validation cohort showed good calibration (goodness of fit p = 0.98) and correlation (R = 0.99).

CONCLUSION

Among selected candidates who have ACLF at listing, MELD score and ACLF interact in predicting cumulative risk of 90-day waitlist mortality, with higher impact of ACLF grade at lower listing MELD score. Validating these findings in large prospective studies will support consideration of both MELD and ACLF when prioritizing transplant candidates and allocating liver grafts.

LAY SUMMARY

In patients with cirrhosis listed for liver transplantation, the presence of multiorgan failure, a condition referred to as acute-on-chronic liver failure, is associated with high waiting list mortality rates. Current organ allocation policy disadvantages patients with this condition. This study describes and validates a new scoring method that performs better than the currently available scoring systems. Further validation of this approach may reduce the deaths of patients with cirrhosis and acute-on-chronic liver failure on the transplant waiting list.

The Predictive Role of Model for End-Stage Liver Disease-Lactate and Lactate Clearance for In-Hospital Mortality Among a National Cirrhosis Cohort

The burden of cirrhosis hospitalizations is increasing. The admission Model for End-Stage Liver Disease-lactate (MELD-lactate) was recently demonstrated to be a superior predictor of in-hospital mortality compared with MELD in limited cohorts. We identified specific classes of hospitalizations where MELD-lactate may be especially useful and evaluated the predictive role of lactate clearance. This was a retrospective cohort study of 1036 cirrhosis hospitalizations for gastrointestinal bleeding, infection, or other portal hypertension-related indications in the Veterans Health Administration where MELD-lactate was measured on admission. Performance characteristics for in-hospital mortality were compared between MELD-lactate and MELD/MELD-sodium (MELD-Na), with stratified analyses of MELD categories (≤15, >15 to <25, ≥25) and reason for admission. We also incorporated day 3 lactate levels into modeling and tested for an interaction between day 1 MELD-lactate and day 3 lactate clearance. MELD-lactate had superior discrimination for in-hospital mortality compared with MELD or MELD-Na (area under the curve [AUC] 0.789 versus 0.776 versus 0.760, respectively; P < 0.001) and superior calibration. MELD-lactate had higher discrimination among hospitalizations with MELD ≤15 (AUC 0.763 versus 0.608 for MELD, global P = 0.01) and hospitalizations for infection (AUC 0.791 versus 0.674 for MELD, global P < 0.001). We found a significant interaction between day 1 MELD-lactate and day 3 lactate clearance; heat maps were created as clinical tools to risk-stratify patients based on these clinical data. MELD-lactate had significantly superior performance in predicting in-hospital mortality among patients hospitalized for infection and/or with MELD ≤15 when compared with MELD or MELD-Na. Incorporating day 3 lactate clearance may further improve prognostication.

Development and Validation of a Novel Risk Prediction Model Using Recursive Feature Elimination Algorithm for Acute-on-Chronic Liver Failure in Chronic Hepatitis B Patients With Severe Acute Exacerbation

Background: Patients with chronic hepatitis B (CHB) with severe acute exacerbation (SAE) are at a progression stage of acute-on-chronic liver failure (ACLF) but uniform models for predicting ACLF occurrence are lacking. We aimed to present a risk prediction model to early identify the patients at a high risk of ACLF and predict the survival of the patient. Methods: We selected the best variable combination using a novel recursive feature elimination algorithm to develop and validate a classification regression model and also an online application on a cloud server from the training cohort with a total of 342 patients with CHB with SAE and two external cohorts with a sample size of 96 and 65 patients, respectively. Findings: An excellent prediction model called the PATA model including four predictors, prothrombin time (PT), age, total bilirubin (Tbil), and alanine aminotransferase (ALT) could achieve an area under the receiver operating characteristic curve (AUC) of 0.959 (95% CI 0.941-0.977) in the development set, and AUC of 0.932 (95% CI 0.876-0.987) and 0.905 (95% CI 0.826-0.984) in the two external validation cohorts, respectively. The calibration curve for risk prediction probability of ACLF showed optimal agreement between prediction by PATA model and actual observation. After predictive stratification into different risk groups, the C-index of predictive 90-days mortality was 0.720 (0.675-0.765) for the PATA model, 0.549 (0.506-0.592) for the end-stage liver disease score model, and 0.648 (0.581-0.715) for Child-Turcotte-Pugh scoring system. Interpretation: The highlypredictive risk model and easy-to-use online application can accurately predict the risk of ACLF with a poor prognosis. They may facilitate risk communication and guidetherapeutic options.

Development, Validation, and Evaluation of a Simple Machine Learning Model to Predict Cirrhosis Mortality

Importance

Machine-learning algorithms offer better predictive accuracy than traditional prognostic models but are too complex and opaque for clinical use.

Objective

To compare different machine learning methods in predicting overall mortality in cirrhosis and to use machine learning to select easily scored clinical variables for a novel cirrhosis prognostic model.

Design, Setting, and Participants

This prognostic study used a retrospective cohort of adult patients with cirrhosis or its complications seen in 130 hospitals and affiliated ambulatory clinics in the integrated, national Veterans Affairs health care system from October 1, 2011, to September 30, 2015. Patients were followed up through December 31, 2018. Data were analyzed from October 1, 2017, to May 31, 2020.

Exposures

Potential predictors included demographic characteristics; liver disease etiology, severity, and complications; use of health care resources; comorbid conditions; and comprehensive laboratory and medication data. Patients were randomly selected for model development (66.7%) and validation (33.3%). Three different statistical and machine learning methods were evaluated: gradient descent boosting, logistic regression with least absolute shrinkage and selection operator (LASSO) regularization, and logistic regression with LASSO constrained to select no more than 10 predictors (partial pathway model). Predictor inclusion and model performance were evaluated in a 5-fold cross-validation. Last, the predictors identified in the most parsimonious (the partial path) model were refit using maximum-likelihood estimation (Cirrhosis Mortality Model [CiMM]), and its predictive performance was compared with that of the widely used Model for End Stage Liver Disease with sodium (MELD-Na) score.

Main Outcomes and Measures

All-cause mortality.

Results

Of the 107 939 patients with cirrhosis (mean [SD] age, 62.7 [9.6] years; 96.6% male; 66.3% white, 18.4% African American), the annual mortality rate ranged from 8.8% to 15.3%. In total, 32.7% of patients died within 3 years, and 46.2% died within 5 years after the index date. Models predicting 1-year mortality had good discrimination for the gradient descent boosting (area under the receiver operating characteristics curve [AUC], 0.81; 95% CI, 0.80-0.82), logistic regression with LASSO regularization (AUC, 0.78; 95% CI, 0.77-0.79), and the partial path logistic model (AUC, 0.78; 95% CI, 0.76-0.78). All models showed good calibration. The final CiMM model with machine learning-derived clinical variables offered significantly better discrimination than the MELD-Na score, with AUCs of 0.78 (95% CI, 0.77-0.79) vs 0.67 (95% CI, 0.66-0.68) for 1-year mortality, respectively (DeLong z = 17.00; P < .001).

Conclusions and Relevance

In this study, simple machine learning techniques performed as well as the more advanced ensemble gradient boosting. Using the clinical variables identified from simple machine learning in a cirrhosis mortality model produced a new score more transparent than machine learning and more predictive than the MELD-Na score.

Prognostic Value of Acute-On-Chronic Liver Failure (ACLF) Score in Critically Ill Patients with Cirrhosis and ACLF

Background

In the intensive care unit (ICU), critically ill patients with cirrhosis and acute-on-chronic liver failure (ACLF) continue to have high mortality rates. The AARC ACLF score is a simple, newly-developed score based on Asian ACLF patients, which performs well in prognosis. The present study attempted to verify the prognostic ability of AARC ACLF in non-Asian critically ill patients with cirrhosis and ACLF.

Material/Methods

We enrolled 786 patients. Relevant clinical data were collected within 24 h after admission to compare the differences between survivors and non-survivors, and all the patients were followed up for at least 180 days.

Results

The 28-day, 90-day, and 180-day mortality rates were 28.9% (227/786), 36.4% (286/786), and 40.3% (317/786), respectively. Multivariate Cox regression analysis showed that AARC ACLF score (HR: 1.375, 95% CI: 1.247–1.516, P<0.001) was an independent predictive factor of 28-day mortality, and the AUROC of the predictive ability in 28-day mortality of the AARC ACLF score was 0.754. In addition, the AARC ACLF score was regraded into 3 classes (low risk: AARC ACLF <9, intermediate risk: 9≤ AARC ACLF <12, and high risk: AARC ACLF ≥12). The AARC ACLF score can be used for dynamic assessment by retest at days 4–7.

Conclusions

The AARC ACLF score has a good predictive value for 28-day, 90-day, and 180-day mortality in non-Asian critically ill patients with cirrhosis and ACLF, which is not inferior to CLIF-C ACLFs_Lact and other models. It is easy to use at bedside, and it is dynamic and reliable.

Comparison of mortality risk in patients with cirrhosis and COVID-19 compared with patients with cirrhosis alone and COVID-19 alone: multicentre matched cohort

OBJECTIVE

Comorbid conditions are associated with poor prognosis in COVID-19. Registry data show that patients with cirrhosis may be at high risk. However, outcome comparisons among patients with cirrhosis+COVID-19 versus patients with COVID-19 alone and cirrhosis alone are lacking. The aim of this study was to perform these comparisons.

DESIGN

A multicentre study of inpatients with cirrhosis+COVID-19 compared with age/gender-matched patients with COVID-19 alone and cirrhosis alone was performed. COVID-19 and cirrhosis characteristics, development of organ failures and acute-on-chronic liver failure (ACLF) and mortality (inpatient death+hospice) were compared.

RESULTS

37 patients with cirrhosis+COVID-19 were matched with 108 patients with COVID-19 and 127 patients with cirrhosis from seven sites. Race/ethnicity were similar. Patients with cirrhosis+COVID-19 had higher mortality compared with patients with COVID-19 (30% vs 13%, p=0.03) but not between patients with cirrhosis+COVID-19 and patients with cirrhosis (30% vs 20%, p=0.16). Patients with cirrhosis+COVID-19 versus patients with COVID-19 alone had equivalent respiratory symptoms, chest findings and rates of intensive care unit transfer and ventilation. However, patients with cirrhosis+COVID-19 had worse Charlson Comorbidity Index (CCI 6.5±3.1 vs 3.3±2.5, p<0.001), lower presenting GI symptoms and higher lactate. Patients with cirrhosis alone had higher cirrhosis-related complications, maximum model for end-stage liver disease (MELD) score and lower BiPAP/ventilation requirement compared with patients with cirrhosis+COVID-19, but CCI and ACLF rates were similar. In the entire group, CCI (OR 1.23, 95% CI 1.11 to 1.37, p<0.0001) was the only variable predictive of mortality on multivariable regression.

CONCLUSIONS

In this multicentre North American contemporaneously enrolled study, age/gender-matched patients with cirrhosis+COVID-19 had similar mortality compared with patients with cirrhosis alone but higher than patients with COVID-19 alone. CCI was the only independent mortality predictor in the entire matched cohort.