Liver fibrosis is usually accompanied by abnormal activation of hepatic stellate cells. Exosomal microRNAs play important roles in the development of liver fibrosis. Our previous study confirmed that Ellagic acid (EA) is one of the main bloodstream components of Scutellaria barbata, a traditional Chinese medicinal herb. This study aimed to explore the effects of EA on hepatic stellate cells in liver fibrosis. First, a CCl4-induced mouse model of liver fibrosis was used to evaluate the effects of EA administration on the histopathology and histochemical staining of liver tissue in mice with liver fibrosis, while simultaneously assessing serum markers of liver function; Second, RT-PCR was used to detect fibrosis-related genes, inflammation-related genes and matrix metalloproteinase-related genes. Third, exosome-enriched fraction miRNAs from liver tissues were sequenced and PCR-verified for differential expression. Finally, in vitro experiments were performed on murine hepatic stellate cells (JS-1) and human hepatic stellate cells (LX-2) using flow cytometry and Western blotting (WB) to apoptosis-related proteins. Animal studies demonstrated that Scutellaria barbata and its natural active component EA can significantly ameliorate hepatic histopathology in the mouse model of liver fibrosis. Furthermore, EA effectively improves serum liver function parameters, reduces the expression of fibrosis-related, inflammation-related and matrix metalloproteinase-related genes, and markedly reduces elevated miR-182-5p expression in the fibrotic model. Cellular experiments demonstrated that inhibition of miR-182-5p promotes the expression of proteins associated with apoptosis in hepatic stellate cells, thereby accelerating apoptosis in these cells. In summary, EA from S. barbata may accelerates hepatic stellate cell apoptosis during liver fibrosis by inhibiting miR-182-5p.

Background:Ciprofol is increasingly used in surgical procedures, and anesthesiologists have observed that it provides deeper sedation compared to propofol. However, it remains unclear whether the use of ciprofol alone, without combining opioids, is sufficient for upper gastrointestinal endoscopy. This study aims to address this question. Objective:To determine whether ciprofol alone is non-inferior to ciprofol combined with fentanyl regarding sedation success and safety. Methods:In this randomized, double-blind trial, 344 adult patients (ASA I-II, aged 18-70 years) undergoing elective upper gastrointestinal endoscopy were randomized to receive either ciprofol with saline (CS group) or ciprofol with fentanyl (CF group). Participants in both groups received an initial ciprofol dose of (0.4 mg/kg). The CF group received (1 µg/kg) intravenously before ciprofol administration, while the CS group received an equivalent volume of saline. Additional ciprofol doses (0.15-0.30 mg/kg) were administered as needed. The primary outcome was sedation success, defined as procedure completion with no more than two additional ciprofol doses within any 5-minute interval. Secondary outcomes included the incidence of hypotension and hypoxemia, as well as adverse events. Results:Sedation success rates were 99.4% for CS and 100% for CF, demonstrating non-inferiority (difference: -0.6%, 95% CI: -0.02, 0.01). The CS group had lower respiratory depression rates and better hemodynamic stability but higher intraoperative coughing (18.1% vs 2.9%, P=0.01). Induction and recovery times were slightly longer in the CS group, and postoperative dizziness was more common (15.2% vs 7%, P=0.03). Conclusion:Ciprofol alone is non-inferior to ciprofol with fentanyl for sedation in upper gastrointestinal endoscopy and offers advantages in respiratory and hemodynamic stability. However, it is associated with increased coughing, minor delays in induction and recovery, and more postoperative dizziness.

Background. Alcohol liver disease (ALD) may coexist with hepatitis C (HCV) in many transplant recipients (alcoholic cirrhosis with hepatitis C [AHC]). Our objective was to determine whether there were differences in postliver transplantation outcomes of patients with AHC when compared with those with alcoholic cirrhosis (AC) and/or alcoholic hepatitis (AH). Methods. Using UNOS explant data sets (2016–2020), the survival probabilities of AC, AH, and AHC were compared by Kaplan-Meier survival analysis. Cox proportional-hazard regression analysis was used to determine outcomes after adjusting for disease confounders. The outcomes were also compared with predirect antiviral agent (DAA) period. Results. During study period, 8369 biopsy-proven ALD liver transplant recipients were identified. Of those, 647 had AHC (HCV + alcohol), 353 had AH, and 7369 had AC. MELD-Na score (28.7 ± 9.5 versus 23.8 ± 10.7; P < 0.001) and presence of ACLF-3 (19% versus 11%; P < 0.001) were higher in AC + AH as compared with AHC. AHC and AC+AH has similar adjusted mortality at 1-y, but 3-y (hazard ratios, 1.76; 95% confidence intervals, 1.32-2.35; P < 0.0001) and 5-y (hazard ratios, 1.64; 95% confidence intervals, 1.24-2.15; P = 0.0004) mortality rates were higher in AHC. Survival improved in the DAA era (2016–2020) compared with 2009 to 2013 in AHC, but remained worse in AHC group versus AC and/or AH. Malignancy-related mortality was higher in AHC (15% versus 9.3% in AC) in the DAA era. Conclusions. AHC was associated with lower 3- and 5-y post-LT survival as compared with ALD without HCV and the worse outcomes in AHC group continued in the DAA era.

BACKGROUND & AIMS:There is a need to reduce the screen failure rate (SFR) in metabolic dysfunction-associated steatohepatitis (MASH) clinical trials (MASH+F2-3; MASH+F4) and identify people with high-risk MASH (MASH+F2-4) in clinical practice. We aimed to evaluate non-invasive tests (NITs) screening approaches for these target conditions. METHODS:This was an individual participant data meta-analysis for the performance of NITs against liver biopsy for MASH+F2-4, MASH+F2-3 and MASH+F4. Index tests were the FibroScan-AST (FAST) score, liver stiffness measured using vibration-controlled transient elastography (LSM-VCTE), the fibrosis-4 score (FIB-4) and the NAFLD fibrosis score (NFS). Area under the receiver operating characteristics curve (AUROC) and thresholds including those that achieved 34% SFR were reported. RESULTS:We included 2281 unique cases. The prevalence of MASH+F2-4, MASH+F2-3 and MASH+F4 was 31%, 24% and 7%, respectively. Area under the receiver operating characteristics curves for MASH+F2-4 were .78, .75, .68 and .57 for FAST, LSM-VCTE, FIB-4 and NFS. Area under the receiver operating characteristics curves for MASH+F2-3 were .73, .67, .60, .58 for FAST, LSM-VCTE, FIB-4 and NFS. Area under the receiver operating characteristics curves for MASH+F4 were .79, .84, .81, .76 for FAST, LSM-VCTE, FIB-4 and NFS. The sequential combination of FIB-4 and LSM-VCTE for the detection of MASH+F2-3 with threshold of .7 and 3.48, and 5.9 and 20 kPa achieved SFR of 67% and sensitivity of 60%, detecting 15 true positive cases from a theoretical group of 100 participants at the prevalence of 24%. CONCLUSIONS:Sequential combinations of NITs do not compromise diagnostic performance and may reduce resource utilisation through the need of fewer LSM-VCTE examinations.

BACKGROUND & AIMS:There is a paucity of studies on older patients (≥65 years) who develop acute on chronic liver failure (ACLF). The objectives of our study were to determine clinical characteristics and outcomes of older patients listed for liver transplantation (LT). METHODS:Adults listed for LT with estimated ACLF (Est-ACLF) between 2005 and 2021 were identified using the United Network for Organ Sharing database and subdivided into older and younger age (18-64 years) groups. Kaplan-Meier survival analyses were used to evaluate survival, and a competing-risk model (Fine-Gray) was used to evaluate risk factors for survival on the waitlist. Logistic regression was done to evaluate risk factors. RESULTS:A total of 4313 older (14%) and 26,628 younger (86%) patients were listed for LT, and 2142 (49.6%) and 16,931 (63.5%) were transplanted, respectively. Older patients had a higher 30-day waitlist mortality than younger patients (20.4% vs 16.7%; P < .0001); this was more pronounced in Est-ACLF-2 (23.7% vs 14.8%; P < .0001) and Est-ACLF-3 (43.3% vs 29.9%; P < .0001). One-year post-LT, patient survival in older patients with Est-ACLF grades 1, 2, and 3 were 86.4%, 85.5%, and 77% respectively; younger patients had better survival across all Est-ACLF grades. When adjusted for transplant eras, respiratory failure was the only independent risk factor for increased 1-year post-LT mortality in older patients. CONCLUSION:Older patients with Est-CLF had significantly higher waitlist mortality than younger patients, but had acceptable 1-year post-LT survival including those with Est-ACLF-3; therefore, age alone should not be considered as a contraindication for LT. Older patients with respiratory failure should be carefully selected for LT.

Patients with acute on chronic liver failure (ACLF-3) have a very high short-term mortality without liver transplantation (LT). Our objective was to determine whether early LT (ELT; ≤ 7 days from listing) had an impact on 1 year patient (PS) in patients with ACLF-3 compared to late LT (LLT; days 8–28 from listing). All adults with ACLF-3 listed for LT with the United Network for Organ Sharing (UNOS) between 2005 and 2021 were included. We excluded status one patients and those with liver cancer or listed for multi-organ or living donor transplants. ACLF patients were identified using the European Association for the Study of the Liver-Chronic Liver Failure criteria. Patients were categorized as ACLF-3a and ACLF-3b. During the study period, 7607 patients were listed with ACLF-3 (3a-4520, 3b-3087); 3498 patients with ACLF-3 underwent ELT and 1308 had LLT. The overall 1 year PS after listing was 64.4

BACKGROUND:We have recently shown that the European Association for the Study of the Liver-Chronic Liver Failure Consortium (EASL-CLIF) criteria showed a better sensitivity to detect acute-on-chronic liver failure (ACLF) with a better prognostic capability than the North American Consortium for the Study of End-Stage Liver Disease criteria.AIM:To simplify EASL-CLIF criteria for ease of use without sacrificing its sensitivity and prognostic capability.METHODS:Using the United Network for Organ Sharing data (January 11, 2016, to August 31, 2020), we modified EASL-CLIF (mEACLF) criteria; the modified mEACLF criteria included six organ failures (OF) as in the original EASL-CLIF, but renal failure was defined as creatinine ≥ 2.35 mg/dL and coagulation failure was defined as international normalized ratio (INR) ≥ 2.0. The mEACLF grades (0, 1, 2, and ≥ 3) directly reflected the number of OF.RESULTS:Of the 40357 patients, 14044 had one or more OF, and 9644 had ACLF grades 1-3 by EASL-CLIF criteria. By the mEACLF criteria, 15574 patients had one or more OF. The area under the receiver operating characteristic (AUROC) for 30-d all-cause mortality by OF was 0.842 (95%CI: 0.831-0.853) for mEACLF and 0.835 (95%CI: 0.824-0.846) for EASL-CLIF (P = 0.006), and AUROC for 30-d transplant-free mortality by OF was 0.859 (95%CI: 0.849-0.869) for mEACLF and 0.851 (95%CI: 0.840-0.861) for EASL-CLIF (P = 0.001). The AUROC of 30-d all-cause mortality by ACLF grades was 0.842 (95%CI: 0.831-0.853) for mEACLF and 0.793 (95%CI: 0.781-0.806) for EASL-CLIF (P < 0.0001). The AUROC of 30-d transplant-free mortality by ACLF was 0.859 (95%CI: 0.848-0.869) for mEACLF and 0.805 (95%CI: 0.793-0.817) for EASL-CLIF (P < 0.0001).CONCLUSION:Our study showed that EASL-CLIF criteria for ACLF grades could be simplified for ease of use without losing its prognostication capability and sensitivity.

It is essential to accurately distinguish small benign hyperplastic colon polyps (HP) from sessile serrated lesions (SSL) or adenomatous polyps (TA) based on endoscopic appearances. Our objective was to determine the accuracy and inter-observer agreements for the endoscopic diagnosis of small polyps. High-quality endoscopic images of 30 small HPs, SSLs, and TAs were used randomly to create two-timed PowerPoint slide sets—one with and another one without information on polyp size and location. Seven endoscopists viewed the slides on two separate occasions 90 days apart, identified the polyp type, and graded their confidence level. Overall and polyp-specific accuracies were assessed for the group and individual endoscopists. Chi-square tests and Kappa (κ) statistics were used to compare differences as appropriate. When polyp size and location were provided, overall accuracy was 67.1

INTRODUCTION AND OBJECTIVES:Lower antibody (Ab) responses after SARS-CoV-2 vaccination have been reported in liver transplant (LT) recipients and those with chronic liver diseases (CLD). The role of a booster dose in those with poor responses to initial vaccination is not well defined.METHODS:In this prospective study, we determined antibody (Ab) response to spike protein after a booster dose in LT recipients and those with chronic liver diseases (CLD) with and without cirrhosis after they had a poor response to an initial standard regimen.RESULTS:Of the 80 patients enrolled, 45 had LT, and 35 had CLD (18 with cirrhosis). A booster dose was given at a median of 138.5 days after the completion of the standard regimen. After the booster dose, 58 (73%, 31 LT, 27 CLD) had good response (≥250 U/mL), and 22 (28%, 14 LT, and 8 CLD) had poor response (7 undetectable and 15 with low Ab levels). No patient had any serious adverse events. The antibody responses were lower in those who had undetectable Ab (80 U/mL) than those who had low levels of Ab (0.80-249 U/mL) after the standard vaccination regimen (42% vs. 87%, p=0.0001). The antibody responses after homologous and heterologous booster doses were similar.CONCLUSIONS:We have shown that a booster dose will enhance Ab responses in LT recipients and those with CLD who had poor responses after an initial vaccine regimen.

The guidelines recommend surveillance with liver ultrasound (US) for early detection of hepatocellular carcinoma (HCC). The accuracy of US is operator-dependent and is limited in those with severe obesity or ascites. Magnetic resonance imaging (MRI) may have higher accuracy for detecting HCC, especially early HCC. To assess the accuracy of US for HCC detection using MRI as the clinical gold standard. We performed a retrospective study of 223 patients with cirrhosis who had both abdominal US and MRI within 6 months of each other, between October 2013 and November 2018. Using MRI as the gold standard for detecting HCC, the test characteristics of US and concordance with MRI were evaluated, and potential patient characteristics associated with the false-negative US were examined. Of 223 patients, MRI detected HCC in 70 patients while US detected HCC only in 38; thus, the overall sensitivity of US was 54% in detecting HCC, using MRI as the reference standard. The prevalence-adjusted and bias-adjusted kappa (PABAK) was 0.65 for the entire group. However, PABAK was only 0.04 when the subgroup with HCC (n = 70) was analysed. Higher Model for End-Stage Liver Disease (MELD) score was the only factor that was associated with decreased US sensitivity, with the odds of false-negative US increasing by 9.8% for each 1-point increase in MELD. Our study showed that MRI performed better than the US in detecting HCC in patients with cirrhosis. Patients with higher MELD may require an MRI for HCC surveillance for early diagnosis of HCC.

Introduction: Long-term symptoms following COVID-19 have been reported, and are often referred to as post-acute COVID-19 syndrome (PACS) or ‘Long COVID’. We assessed the prevalence of long-term symptoms, especially gastrointestinal symptoms, in patients who tested positive for COVID-19 in comparison to a contemporary control group who had flu-like symptoms but tested negative for the COVID-19 infection. Methods: In this single-center, longitudinal prospective study, we randomly selected 320 adult patients who were screened for SARS-CoV-2 infection using polymerase chain reaction (PCR) testing between March 13, 2020, and May 1, 2020. A follow-up telephone survey was conducted between December 1, 2020, and January 31, 2021, a median of 270 days after the index testing. The survey assessed long-term symptoms, especially gastrointestinal symptoms, and impact on physical, mental and socioeconomic outcomes. Results: 221 (69%) of 320 patients responded to our telephone survey; of those 112/160 were COVID-19 positive (cases) and 109/160 were COVID-19 negative (controls). The median follow-up time after initial testing was 270 days (240-300 days). At the time of index testing, 117 patients (53%; 70% cases and 36% controls) had at least three symptoms. At follow-up (10 months), 47 of 221 patients (21%; 36% cases and 6% controls) had at least one persistent symptom. Most common were shortness of breath (20, [18%]) and cough (13, [12%]) among the COVID 19 cohort and headache (3, [3%]) among the controls. Amongst the assessed gastrointestinal symptoms, the percent changes were not significantly different between cases and controls. Six of 60 (10%) cases with loss of taste or smell at baseline had persistent symptoms at follow-up (Figure). A significant limitation in the activity of daily living (ADL) was reported among cases (20% vs 2%, p , 0.001) compared to the control group. 33 patients (21, [19%] in cases 12 [11%] controls, P5ns) reported either reduced or worse QoL in comparison to baseline. Time to return to work was longer among cases, and the COVID infection had a higher financial impact (38% vs 28%, P5ns). Conclusion: At a 10-month follow-up, our study showed that while shortness of breath and cough persisted, most gastrointestinal symptoms, with the exception of loss of taste or smell, had resolved in comparison to the control group.

Universal definition and prognostication in acute-on-chronic liver failure – an unmet need!Journal of HepatologyVol. 76Issue 1PreviewWe read the study by Li and Thuluvath1 with great interest that highlighted the differences in prevalence and mortality between acute-on-chronic liver failure (ACLF) defined by the North American Consortium for the study of End-stage Liver Disease (NACSELD)2 and the European Association for the Study of the Liver – Chronic Liver Failure Consortium (EASL-CLIF)3 criteria. The authors conclude that the EASL-CLIF criteria are better than the NACSELD criteria for the detection and prognostication of ACLF. Full-Text PDF EASL-CLIF criteria outperform NACSELD criteria for diagnosis and prognostication in ACLFJournal of HepatologyVol. 75Issue 5PreviewThere is no consensus on the best definition for acute-on-chronic liver failure (ACLF). In this study, we compared the prevalence and 30-day all-cause and transplant-free mortality of patients with ACLF identified by European Association for the Study of the Liver-Chronic Liver Failure Consortium (EASL-CLIF) and North American Consortium for the Study of End-stage Liver Disease (NACSELD) criteria. Full-Text PDF An unbiased analysis of ACLF diagnosis and not an incorporation biasTo the Editor:We want to thank Dr. Verma and colleagues for their interest in our recent manuscript. The authors raise several concerns that merit our responses.[1]Verma N. Mehtani R. Duseja A. Universal definition and prognostication in acute-on-chronic liver failure – an unmet need!.J Hepatol. 2022; 76: 241-242Abstract Full Text Full Text PDF Scopus (2) Google Scholar The objective of our study was to determine the comparative performance of EASL-CLIF and NACSELD criteria to identify patients with ACLF and not to determine the mortality risks in patients since many previous studies have reported that in detail using the UNOS datasets.[2]Thuluvath P.J. Thuluvath A.J. Hanish S. Savva Y. Liver transplantation in patients with multiple organ failures: feasibility and outcomes.J Hepatol. 2018; 69: 1047-1056Abstract Full Text Full Text PDF PubMed Scopus (88) Google ScholarWe did not exclude 10.6% of patients with ACLF as implicated (10.6% of all listed patients, see Fig. 1). Our study was on adults, and status 1B is reserved for children less than 18 years old. Only 261 (2 living donors and 259 multiple organs) patients with ACLF-3 were excluded from our analysis. The impact of exclusion, if any, is applicable to both ACLF groups. Our study was exclusively in those listed for liver transplantation; hence, 30-day mortality rates could not be compared to the CANONIC study.Another concern was that we analyzed the data based on organ failures (OFs) at the time of listing. The dynamic nature of ACLF grades is unlikely to have a significant differential effect on one set of criteria. Moreover, we have shown that the median time from listing to liver transplant (5 days) or death (10 days) was 5-10 days in patients with ACLF and 3 or more OFs.[2]Thuluvath P.J. Thuluvath A.J. Hanish S. Savva Y. Liver transplantation in patients with multiple organ failures: feasibility and outcomes.J Hepatol. 2018; 69: 1047-1056Abstract Full Text Full Text PDF PubMed Scopus (88) Google Scholar Additionally, the purpose of our study was not to compare mortality rates based on the dynamic changes in the number of OFs between listing and transplant.Dr. Verma et al. also suggest that our study involves incorporation bias. For a better understanding for our readers, we are going to elaborate on this broad terminology. The incorporation bias is often associated with the interpretation of laboratory diagnostic tests, especially when there are no ‘gold standards’, but it could also apply to other clinical tests.[3]Karch A. Koch A. Zapf A. Zerr I. Karch A. Partial verification bias and incorporation bias affected accuracy estimates of diagnostic studies for biomarkers that were part of an existing composite gold standard.J Clin Epidemiol. 2016 Oct; 78: 73-82Abstract Full Text Full Text PDF PubMed Scopus (16) Google Scholar,[4]Worster A. Carpenter C. Incorporation bias in studies of diagnostic tests: how to avoid being biased about bias.CJEM. 2008; 10: 174-175Crossref PubMed Scopus (72) Google Scholar It also happens when the diagnostic test or the components of the test are already incorporated in the gold standard resulting in an overestimation of the sensitivity and specificity of the diagnostic test. In this situation, we would have overestimated NACSELD’s ability if bias were involved. Another type of incorporation bias is when reporting a test based on the clinical history and findings. A contemporary example will be to report a chest X-ray after knowing that the patient had respiratory symptoms and confirmed COVID-19. We are not sure how this sort of incorporation bias could have applied to our study. The criteria for ACLF diagnosis include a combination of laboratory tests, physical findings ('judgment'), and treatments. We applied the documented observations from the datasets to examine the prevalence of ACLF and their 30-day mortality rates. When ‘death’ is used as an end-point, incorporation bias may not be applicable. Our critics are perhaps referring to a broad terminology when the gold standard and diagnostic test are not entirely independent, but it is unavoidable for our study.Another suggestion was to compare a continuous numeric scoring system (CLIF-C ACLF scores) with a binary NACSELD model. We do not believe it is logical and identifying the “best” prediction model for 30-day mortality was not our primary objective. To assert that 51.2% of patients who had grade-3 ACLF by EASL-CLIF criteria did not have ACLF by NACSELD because of ‘inappropriate exclusions or presence of liver or coagulation failure among study population' is mere speculation and not based on objective evidence. Verma et al. also suggest that “it was strange to note that 18.6% patients with grade-III EASL-ACLF did not have any OF by NACSELD criteria”. Precisely, that was our point. We do not believe that a competing risk analysis would have altered our conclusions regarding the poor sensitivity in detecting ACLF by NACSELD. The all-cause mortality rates for EASL-CLIF ACLF grade 3 and NACSELD ACLF (>2 OF) were similar (25.8% vs. 28.2%) in our study, but when patients with no OF by NACSELD were stratified by EASL-CLIF grades 0-3, the transplant-free mortality rates ranged from 1.5% to 86.0%.By citing one of their recent publications, Verma et al. suggest that "the sensitivity of the NACSELD-definition is better than EASL-ACLF for mortality prediction”.[5]Verma N. Dhiman R.K. Singh V. Duseja A. Taneja S. Choudhury A. et al.Comparative accuracy of prognostic models for short-term mortality in acute-on-chronic liver failure patients: CAP-ACLF.Hepatol Int. 2021; 15: 753-765Crossref PubMed Scopus (4) Google Scholar In that study, the sensitivity, specificity, positive predictive value, and negative predictive value for NACSELD-ACLF-binary were reported as 100%, 0.0%, 65.1%, and 0.0% respectively. The highest sensitivity of the NACSELD-ACLF binary was reached by keeping specificity at 0%. This observation was perhaps overlooked by Verma and colleagues.Indeed, we did not compare APASL-ACLF with either EASL-CLIF or NACSELD. We had stated the reasons for that in our manuscript. More importantly, we had cited a comparative study of EASL-CLIF and APASL using a Veteran Affairs administrative dataset where they found that 76.1% of patients with EASL-ACLF did not fulfil APASL criteria.[6]Mahmud N. Kaplan D.E. Taddei T.H. Goldberg D.S. Incidence and mortality of acute on chronic liver failure using two definitions in patients with compensated cirrhosis.Hepatology. 2019; 90: 2150-2163Crossref Scopus (93) Google Scholar Moreover, the 30-day mortality was 37.6% for those who met the EASL-CLIF criteria suggesting that APASL criteria probably missed 75% of patients with ACLF with a very high short-term mortality.Finally, we believe that a serious debate on ACLF is fundamental to identifying the best model and understanding its role in managing our patients.Financial supportThe authors received no financial support to produce this manuscript.Authors’ contributionsPaul Thuluvath and Feng LI drafted the response. An unbiased analysis of ACLF diagnosis and not an incorporation biasTo the Editor:We want to thank Dr. Verma and colleagues for their interest in our recent manuscript. The authors raise several concerns that merit our responses.[1]Verma N. Mehtani R. Duseja A. Universal definition and prognostication in acute-on-chronic liver failure – an unmet need!.J Hepatol. 2022; 76: 241-242Abstract Full Text Full Text PDF Scopus (2) Google Scholar The objective of our study was to determine the comparative performance of EASL-CLIF and NACSELD criteria to identify patients with ACLF and not to determine the mortality risks in patients since many previous studies have reported that in detail using the UNOS datasets.[2]Thuluvath P.J. Thuluvath A.J. Hanish S. Savva Y. Liver transplantation in patients with multiple organ failures: feasibility and outcomes.J Hepatol. 2018; 69: 1047-1056Abstract Full Text Full Text PDF PubMed Scopus (88) Google ScholarWe did not exclude 10.6% of patients with ACLF as implicated (10.6% of all listed patients, see Fig. 1). Our study was on adults, and status 1B is reserved for children less than 18 years old. Only 261 (2 living donors and 259 multiple organs) patients with ACLF-3 were excluded from our analysis. The impact of exclusion, if any, is applicable to both ACLF groups. Our study was exclusively in those listed for liver transplantation; hence, 30-day mortality rates could not be compared to the CANONIC study.Another concern was that we analyzed the data based on organ failures (OFs) at the time of listing. The dynamic nature of ACLF grades is unlikely to have a significant differential effect on one set of criteria. Moreover, we have shown that the median time from listing to liver transplant (5 days) or death (10 days) was 5-10 days in patients with ACLF and 3 or more OFs.[2]Thuluvath P.J. Thuluvath A.J. Hanish S. Savva Y. Liver transplantation in patients with multiple organ failures: feasibility and outcomes.J Hepatol. 2018; 69: 1047-1056Abstract Full Text Full Text PDF PubMed Scopus (88) Google Scholar Additionally, the purpose of our study was not to compare mortality rates based on the dynamic changes in the number of OFs between listing and transplant.Dr. Verma et al. also suggest that our study involves incorporation bias. For a better understanding for our readers, we are going to elaborate on this broad terminology. The incorporation bias is often associated with the interpretation of laboratory diagnostic tests, especially when there are no ‘gold standards’, but it could also apply to other clinical tests.[3]Karch A. Koch A. Zapf A. Zerr I. Karch A. Partial verification bias and incorporation bias affected accuracy estimates of diagnostic studies for biomarkers that were part of an existing composite gold standard.J Clin Epidemiol. 2016 Oct; 78: 73-82Abstract Full Text Full Text PDF PubMed Scopus (16) Google Scholar,[4]Worster A. Carpenter C. Incorporation bias in studies of diagnostic tests: how to avoid being biased about bias.CJEM. 2008; 10: 174-175Crossref PubMed Scopus (72) Google Scholar It also happens when the diagnostic test or the components of the test are already incorporated in the gold standard resulting in an overestimation of the sensitivity and specificity of the diagnostic test. In this situation, we would have overestimated NACSELD’s ability if bias were involved. Another type of incorporation bias is when reporting a test based on the clinical history and findings. A contemporary example will be to report a chest X-ray after knowing that the patient had respiratory symptoms and confirmed COVID-19. We are not sure how this sort of incorporation bias could have applied to our study. The criteria for ACLF diagnosis include a combination of laboratory tests, physical findings ('judgment'), and treatments. We applied the documented observations from the datasets to examine the prevalence of ACLF and their 30-day mortality rates. When ‘death’ is used as an end-point, incorporation bias may not be applicable. Our critics are perhaps referring to a broad terminology when the gold standard and diagnostic test are not entirely independent, but it is unavoidable for our study.Another suggestion was to compare a continuous numeric scoring system (CLIF-C ACLF scores) with a binary NACSELD model. We do not believe it is logical and identifying the “best” prediction model for 30-day mortality was not our primary objective. To assert that 51.2% of patients who had grade-3 ACLF by EASL-CLIF criteria did not have ACLF by NACSELD because of ‘inappropriate exclusions or presence of liver or coagulation failure among study population' is mere speculation and not based on objective evidence. Verma et al. also suggest that “it was strange to note that 18.6% patients with grade-III EASL-ACLF did not have any OF by NACSELD criteria”. Precisely, that was our point. We do not believe that a competing risk analysis would have altered our conclusions regarding the poor sensitivity in detecting ACLF by NACSELD. The all-cause mortality rates for EASL-CLIF ACLF grade 3 and NACSELD ACLF (>2 OF) were similar (25.8% vs. 28.2%) in our study, but when patients with no OF by NACSELD were stratified by EASL-CLIF grades 0-3, the transplant-free mortality rates ranged from 1.5% to 86.0%.By citing one of their recent publications, Verma et al. suggest that "the sensitivity of the NACSELD-definition is better than EASL-ACLF for mortality prediction”.[5]Verma N. Dhiman R.K. Singh V. Duseja A. Taneja S. Choudhury A. et al.Comparative accuracy of prognostic models for short-term mortality in acute-on-chronic liver failure patients: CAP-ACLF.Hepatol Int. 2021; 15: 753-765Crossref PubMed Scopus (4) Google Scholar In that study, the sensitivity, specificity, positive predictive value, and negative predictive value for NACSELD-ACLF-binary were reported as 100%, 0.0%, 65.1%, and 0.0% respectively. The highest sensitivity of the NACSELD-ACLF binary was reached by keeping specificity at 0%. This observation was perhaps overlooked by Verma and colleagues.Indeed, we did not compare APASL-ACLF with either EASL-CLIF or NACSELD. We had stated the reasons for that in our manuscript. More importantly, we had cited a comparative study of EASL-CLIF and APASL using a Veteran Affairs administrative dataset where they found that 76.1% of patients with EASL-ACLF did not fulfil APASL criteria.[6]Mahmud N. Kaplan D.E. Taddei T.H. Goldberg D.S. Incidence and mortality of acute on chronic liver failure using two definitions in patients with compensated cirrhosis.Hepatology. 2019; 90: 2150-2163Crossref Scopus (93) Google Scholar Moreover, the 30-day mortality was 37.6% for those who met the EASL-CLIF criteria suggesting that APASL criteria probably missed 75% of patients with ACLF with a very high short-term mortality.Finally, we believe that a serious debate on ACLF is fundamental to identifying the best model and understanding its role in managing our patients. An unbiased analysis of ACLF diagnosis and not an incorporation biasTo the Editor:We want to thank Dr. Verma and colleagues for their interest in our recent manuscript. The authors raise several concerns that merit our responses.[1]Verma N. Mehtani R. Duseja A. Universal definition and prognostication in acute-on-chronic liver failure – an unmet need!.J Hepatol. 2022; 76: 241-242Abstract Full Text Full Text PDF Scopus (2) Google Scholar The objective of our study was to determine the comparative performance of EASL-CLIF and NACSELD criteria to identify patients with ACLF and not to determine the mortality risks in patients since many previous studies have reported that in detail using the UNOS datasets.[2]Thuluvath P.J. Thuluvath A.J. Hanish S. Savva Y. Liver transplantation in patients with multiple organ failures: feasibility and outcomes.J Hepatol. 2018; 69: 1047-1056Abstract Full Text Full Text PDF PubMed Scopus (88) Google ScholarWe did not exclude 10.6% of patients with ACLF as implicated (10.6% of all listed patients, see Fig. 1). Our study was on adults, and status 1B is reserved for children less than 18 years old. Only 261 (2 living donors and 259 multiple organs) patients with ACLF-3 were excluded from our analysis. The impact of exclusion, if any, is applicable to both ACLF groups. Our study was exclusively in those listed for liver transplantation; hence, 30-day mortality rates could not be compared to the CANONIC study.Another concern was that we analyzed the data based on organ failures (OFs) at the time of listing. The dynamic nature of ACLF grades is unlikely to have a significant differential effect on one set of criteria. Moreover, we have shown that the median time from listing to liver transplant (5 days) or death (10 days) was 5-10 days in patients with ACLF and 3 or more OFs.[2]Thuluvath P.J. Thuluvath A.J. Hanish S. Savva Y. Liver transplantation in patients with multiple organ failures: feasibility and outcomes.J Hepatol. 2018; 69: 1047-1056Abstract Full Text Full Text PDF PubMed Scopus (88) Google Scholar Additionally, the purpose of our study was not to compare mortality rates based on the dynamic changes in the number of OFs between listing and transplant.Dr. Verma et al. also suggest that our study involves incorporation bias. For a better understanding for our readers, we are going to elaborate on this broad terminology. The incorporation bias is often associated with the interpretation of laboratory diagnostic tests, especially when there are no ‘gold standards’, but it could also apply to other clinical tests.[3]Karch A. Koch A. Zapf A. Zerr I. Karch A. Partial verification bias and incorporation bias affected accuracy estimates of diagnostic studies for biomarkers that were part of an existing composite gold standard.J Clin Epidemiol. 2016 Oct; 78: 73-82Abstract Full Text Full Text PDF PubMed Scopus (16) Google Scholar,[4]Worster A. Carpenter C. Incorporation bias in studies of diagnostic tests: how to avoid being biased about bias.CJEM. 2008; 10: 174-175Crossref PubMed Scopus (72) Google Scholar It also happens when the diagnostic test or the components of the test are already incorporated in the gold standard resulting in an overestimation of the sensitivity and specificity of the diagnostic test. In this situation, we would have overestimated NACSELD’s ability if bias were involved. Another type of incorporation bias is when reporting a test based on the clinical history and findings. A contemporary example will be to report a chest X-ray after knowing that the patient had respiratory symptoms and confirmed COVID-19. We are not sure how this sort of incorporation bias could have applied to our study. The criteria for ACLF diagnosis include a combination of laboratory tests, physical findings ('judgment'), and treatments. We applied the documented observations from the datasets to examine the prevalence of ACLF and their 30-day mortality rates. When ‘death’ is used as an end-point, incorporation bias may not be applicable. Our critics are perhaps referring to a broad terminology when the gold standard and diagnostic test are not entirely independent, but it is unavoidable for our study.Another suggestion was to compare a continuous numeric scoring system (CLIF-C ACLF scores) with a binary NACSELD model. We do not believe it is logical and identifying the “best” prediction model for 30-day mortality was not our primary objective. To assert that 51.2% of patients who had grade-3 ACLF by EASL-CLIF criteria did not have ACLF by NACSELD because of ‘inappropriate exclusions or presence of liver or coagulation failure among study population' is mere speculation and not based on objective evidence. Verma et al. also suggest that “it was strange to note that 18.6% patients with grade-III EASL-ACLF did not have any OF by NACSELD criteria”. Precisely, that was our point. We do not believe that a competing risk analysis would have altered our conclusions regarding the poor sensitivity in detecting ACLF by NACSELD. The all-cause mortality rates for EASL-CLIF ACLF grade 3 and NACSELD ACLF (>2 OF) were similar (25.8% vs. 28.2%) in our study, but when patients with no OF by NACSELD were stratified by EASL-CLIF grades 0-3, the transplant-free mortality rates ranged from 1.5% to 86.0%.By citing one of their recent publications, Verma et al. suggest that "the sensitivity of the NACSELD-definition is better than EASL-ACLF for mortality prediction”.[5]Verma N. Dhiman R.K. Singh V. Duseja A. Taneja S. Choudhury A. et al.Comparative accuracy of prognostic models for short-term mortality in acute-on-chronic liver failure patients: CAP-ACLF.Hepatol Int. 2021; 15: 753-765Crossref PubMed Scopus (4) Google Scholar In that study, the sensitivity, specificity, positive predictive value, and negative predictive value for NACSELD-ACLF-binary were reported as 100%, 0.0%, 65.1%, and 0.0% respectively. The highest sensitivity of the NACSELD-ACLF binary was reached by keeping specificity at 0%. This observation was perhaps overlooked by Verma and colleagues.Indeed, we did not compare APASL-ACLF with either EASL-CLIF or NACSELD. We had stated the reasons for that in our manuscript. More importantly, we had cited a comparative study of EASL-CLIF and APASL using a Veteran Affairs administrative dataset where they found that 76.1% of patients with EASL-ACLF did not fulfil APASL criteria.[6]Mahmud N. Kaplan D.E. Taddei T.H. Goldberg D.S. Incidence and mortality of acute on chronic liver failure using two definitions in patients with compensated cirrhosis.Hepatology. 2019; 90: 2150-2163Crossref Scopus (93) Google Scholar Moreover, the 30-day mortality was 37.6% for those who met the EASL-CLIF criteria suggesting that APASL criteria probably missed 75% of patients with ACLF with a very high short-term mortality.Finally, we believe that a serious debate on ACLF is fundamental to identifying the best model and understanding its role in managing our patients. To the Editor: We want to thank Dr. Verma and colleagues for their interest in our recent manuscript. The authors raise several concerns that merit our responses.[1]Verma N. Mehtani R. Duseja A. Universal definition and prognostication in acute-on-chronic liver failure – an unmet need!.J Hepatol. 2022; 76: 241-242Abstract Full Text Full Text PDF Scopus (2) Google Scholar The objective of our study was to determine the comparative performance of EASL-CLIF and NACSELD criteria to identify patients with ACLF and not to determine the mortality risks in patients since many previous studies have reported that in detail using the UNOS datasets.[2]Thuluvath P.J. Thuluvath A.J. Hanish S. Savva Y. Liver transplantation in patients with multiple organ failures: feasibility and outcomes.J Hepatol. 2018; 69: 1047-1056Abstract Full Text Full Text PDF PubMed Scopus (88) Google Scholar We did not exclude 10.6% of patients with ACLF as implicated (10.6% of all listed patients, see Fig. 1). Our study was on adults, and status 1B is reserved for children less than 18 years old. Only 261 (2 living donors and 259 multiple organs) patients with ACLF-3 were excluded from our analysis. The impact of exclusion, if any, is applicable to both ACLF groups. Our study was exclusively in those listed for liver transplantation; hence, 30-day mortality rates could not be compared to the CANONIC study. Another concern was that we analyzed the data based on organ failures (OFs) at the time of listing. The dynamic nature of ACLF grades is unlikely to have a significant differential effect on one set of criteria. Moreover, we have shown that the median time from listing to liver transplant (5 days) or death (10 days) was 5-10 days in patients with ACLF and 3 or more OFs.[2]Thuluvath P.J. Thuluvath A.J. Hanish S. Savva Y. Liver transplantation in patients with multiple organ failures: feasibility and outcomes.J Hepatol. 2018; 69: 1047-1056Abstract Full Text Full Text PDF PubMed Scopus (88) Google Scholar Additionally, the purpose of our study was not to compare mortality rates based on the dynamic changes in the number of OFs between listing and transplant. Dr. Verma et al. also suggest that our study involves incorporation bias. For a better understanding for our readers, we are going to elaborate on this broad terminology. The incorporation bias is often associated with the interpretation of laboratory diagnostic tests, especially when there are no ‘gold standards’, but it could also apply to other clinical tests.[3]Karch A. Koch A. Zapf A. Zerr I. Karch A. Partial verification bias and incorporation bias affected accuracy estimates of diagnostic studies for biomarkers that were part of an existing composite gold standard.J Clin Epidemiol. 2016 Oct; 78: 73-82Abstract Full Text Full Text PDF PubMed Scopus (16) Google Scholar,[4]Worster A. Carpenter C. Incorporation bias in studies of diagnostic tests: how to avoid being biased about bias.CJEM. 2008; 10: 174-175Crossref PubMed Scopus (72) Google Scholar It also happens when the diagnostic test or the components of the test are already incorporated in the gold standard resulting in an overestimation of the sensitivity and specificity of the diagnostic test. In this situation, we would have overestimated NACSELD’s ability if bias were involved. Another type of incorporation bias is when reporting a test based on the clinical history and findings. A contemporary example will be to report a chest X-ray after knowing that the patient had respiratory symptoms and confirmed COVID-19. We are not sure how this sort of incorporation bias could have applied to our study. The criteria for ACLF diagnosis include a combination of laboratory tests, physical findings ('judgment'), and treatments. We applied the documented observations from the datasets to examine the prevalence of ACLF and their 30-day mortality rates. When ‘death’ is used as an end-point, incorporation bias may not be applicable. Our critics are perhaps referring to a broad terminology when the gold standard and diagnostic test are not entirely independent, but it is unavoidable for our study. Another suggestion was to compare a continuous numeric scoring system (CLIF-C ACLF scores) with a binary NACSELD model. We do not believe it is logical and identifying the “best” prediction model for 30-day mortality was not our primary objective. To assert that 51.2% of patients who had grade-3 ACLF by EASL-CLIF criteria did not have ACLF by NACSELD because of ‘inappropriate exclusions or presence of liver or coagulation failure among study population' is mere speculation and not based on objective evidence. Verma et al. also suggest that “it was strange to note that 18.6% patients with grade-III EASL-ACLF did not have any OF by NACSELD criteria”. Precisely, that was our point. We do not believe that a competing risk analysis would have altered our conclusions regarding the poor sensitivity in detecting ACLF by NACSELD. The all-cause mortality rates for EASL-CLIF ACLF grade 3 and NACSELD ACLF (>2 OF) were similar (25.8% vs. 28.2%) in our study, but when patients with no OF by NACSELD were stratified by EASL-CLIF grades 0-3, the transplant-free mortality rates ranged from 1.5% to 86.0%. By citing one of their recent publications, Verma et al. suggest that "the sensitivity of the NACSELD-definition is better than EASL-ACLF for mortality prediction”.[5]Verma N. Dhiman R.K. Singh V. Duseja A. Taneja S. Choudhury A. et al.Comparative accuracy of prognostic models for short-term mortality in acute-on-chronic liver failure patients: CAP-ACLF.Hepatol Int. 2021; 15: 753-765Crossref PubMed Scopus (4) Google Scholar In that study, the sensitivity, specificity, positive predictive value, and negative predictive value for NACSELD-ACLF-binary were reported as 100%, 0.0%, 65.1%, and 0.0% respectively. The highest sensitivity of the NACSELD-ACLF binary was reached by keeping specificity at 0%. This observation was perhaps overlooked by Verma and colleagues. Indeed, we did not compare APASL-ACLF with either EASL-CLIF or NACSELD. We had stated the reasons for that in our manuscript. More importantly, we had cited a comparative study of EASL-CLIF and APASL using a Veteran Affairs administrative dataset where they found that 76.1% of patients with EASL-ACLF did not fulfil APASL criteria.[6]Mahmud N. Kaplan D.E. Taddei T.H. Goldberg D.S. Incidence and mortality of acute on chronic liver failure using two definitions in patients with compensated cirrhosis.Hepatology. 2019; 90: 2150-2163Crossref Scopus (93) Google Scholar Moreover, the 30-day mortality was 37.6% for those who met the EASL-CLIF criteria suggesting that APASL criteria probably missed 75% of patients with ACLF with a very high short-term mortality. Finally, we believe that a serious debate on ACLF is fundamental to identifying the best model and understanding its role in managing our patients. Financial supportThe authors received no financial support to produce this manuscript. The authors received no financial support to produce this manuscript. Authors’ contributionsPaul Thuluvath and Feng LI drafted the response. Paul Thuluvath and Feng LI drafted the response. The authors declare no conflicts of interest that pertain to this work. Please refer to the accompanying ICMJE disclosure forms for further details. Supplementary dataThe following is the supplementary data to this article: Download .pdf (.47 MB) Help with pdf files Multimedia component 1 The following is the supplementary data to this article: Download .pdf (.47 MB) Help with pdf files Multimedia component 1

Background & Aims: There is no consensus on the best defini-tion for acute-on-chronic liver failure (ACLF). In this study, we compared the prevalence and 30-day all-cause and transplant free mortality of patients with ACLF identified by European Association for the Study of the Liver-Chronic Liver Failure Consortium (EASL-CLIF) and North American Consortium for the Study of End-stage Liver Disease (NACSELD) criteria. Methods: We performed this comparative analysis using the United Network for Organ Sharing (UNOS) data from January 11, 2016 to August 31, 2020. Results: A total of 10,198 (21%) adult patients had EASL-CLIF ACLF grade 1-3, but of these only 15.3% had ACLF by NACSELD. Of the 2,562 with EASL-CLIF ACLF grade 3, only 48.8% had NACSELDACLF, 16.8% had no organ failure (OF) and 34.4% had 1 OF. The 30-day all-cause mortality was 1.5%, 7.7%, 13.3% and 25.8% for EASL-CLIF grade 0-3, respectively, and it was 15.4% and 28.1% in those without and with NACSELD-ACLF. When EASL-CLIF grade 3 patients were stratified by NACSELD OF, the mortality ranged from 18.6% with no OF to 41.0% with 4 OFs. The 30-day transplant-free mortality in those with no OF by NACSELD was 2.7%, but when the same group is stratified by EASL-CLIF grades 0-3, the mortality rates were 1.5%, 10.5%, 43.5% and 86%, respectively; the mortality rates ranged from 3.0% to 75.7% in those with 1 OF by NACSELD. Conclusions: There is a clear discordance in the prevalence and 30-day mortality rates of patients with ACLF identified by the EASL-CLIF and NACSELD criteria. EASL-CLIF criteria have a better sensitivity to detect ACLF and have a better prognostic capability. Lay summary: There is no consensus on the definition of acute on-chronic liver failure. European (EASL-CLIF) and North American (NACSELD) consortia have each proposed a commonly used definition. In this study, we compared the prevalence and shortterm (30-day) mortality based on these definitions. Using a very large data set, we observed that there was a significant discordance in the prevalence and mortality based on these criteria. EASL-CLIF criteria appeared to be more sensitive to identify acute-on-chronic liver failure, and were better at predicting all cause and short-term mortality. (C) 2021 European Association for the Study of the Liver. Published by Elsevier B.V. All rights reserved.