SummaryBackgroundData on liver transplantation (LT) in acute on chronic liver failure (ACLF) are scanty.AimTo perform meta‐analysis on outcomes after LT for ACLF compared with ACLF patients not receiving LT or with LT recipients for indications other than ACLF.MethodsWe pooled data from 12 studies on LT outcomes among ACLF patients.ResultsAmong nine studies, 22 238 LT recipients for ACLF vs 30 791 for non‐ACLF were younger by 1.1 years, less males (64% vs 66.4%), and higher model for end‐stage disease score by 14.5 (14.4‐14.6), P < 0.01 for all. Post‐transplant patient survival at 30 day, 90 day, 6 months, 1 year and 5 years was lower in ACLF: 96.2% vs 98.1%, 92.6% vs 96.2%, 89.9% vs 94.4%, 86.0% vs 91.9%, 66.9% vs 80.7% respectively, P < 0.01 for all. ACLF patients stayed longer in hospital and ICU by 5.7 and 10.5 days respectively, P < 0.001, with similar post‐transplant complications [74.4% vs 55.5%, P = 0.12]. Among three studies, 441 LT recipients for ACLF vs 301 ACLF patients not selected for LT had better 30 day and 1 year survival: 95.2% vs 60% and 85.3% vs 28.2% respectively, P < 0.001. Outcomes were worse in ACLF‐3 and better for ACLF‐1 and ACLF‐2 patients at the time of LT.ConclusionIn this pooled analysis with a large sample size across the globe, LT for select patients with ACLF provided survival benefit. However, larger prospective studies are needed to further refine selection criteria, especially for ACLF‐3 patients as basis for improving outcomes and optimal utilisation of scarce donor pool.

ObjectivesTo evaluate the role of apolipoprotein(Apo A-1) as a biomarker of coronary artery disease (CAD) and its comparison with the traditional marker high-density lipoprotein (HDL).MethodologyOne hundred patients proven to have coronary artery disease by angiography were recruited and their serum biomarkers were compared with 100 normal individuals adjusted for age and sex.ResultThe mean +/-standard deviation (SD) value of plasma Apo A-1 levels in the normal individuals were observed to be 207.42 +/- 41.35 (mg/dL) against 90.69 +/- 20.77 (mg/dL) in the cardiac patients. On the other hand the serum HDL levels were 52.93 +/-33.58 (mg/dL) in the normal individuals and 37.86 +/- 23.19 (mg/dL) in the cardiac patients. Both of these differences were statistically significant (p < 0.001). For Apo A-1, a large proportion of patients (85%) were found to be in the abnormal range when compared to the control group in which only 7% had an abnormal value. For HDL, a majority (70%) of the cardiac patients had abnormal values while 40% of the normal individuals also had abnormal values. The sensitivity of Apo A-1 for detecting CAD was 85%, while for HDL, it was only 69%. Similarly, the specificity of Apo A-1 for detecting CAD was 93%, while for HDL, it was 60%. When plotted on a receiver operating characteristic (ROC) curve, Apo A-1 had a much larger area under the curve when compared to HDL.ConclusionThis study suggests that Apo A-1 may, in fact, be more sensitive than HDL as a predictor of CAD. However, to completely elucidate its role as a biomarker, to set target serum levels and to increase its clinical use, further studies are required.

BackgroundAlcoholic hepatitis (AH) is a unique syndrome characterized by high short-term mortality. The impact of the academic status of a hospital (urban and teaching) on outcomes in AH is unknown.MethodsNational Inpatient Sample dataset (2006–2014) on AH admissions stratified to academic center (AC) or non-academic center (NAC) and analyzed for in-hospital mortality (IHM), hospital resource use, length of stay in days (d), and total charges (TC) in United States dollars (USD). Admission year was stratified to 2006–2008 (TMI), 2009–2011 (TM2), and 2012–2014 (TM3).ResultsOf 62,136 AH admissions, the proportion at AC increased from 46% in TM1 to 57% in TM3, Armitage trend, p &lt; 0.001. On logistic regression, TM3, younger age, black race, Medicaid and private insurance, and development of acute on chronic liver failure (ACLF) were associated with admission to an AC. Of 53,264 admissions propensity score matched for demographics, pay status, and disease severity, admissions to AC vs. NAC (26,622 each) were more likely to have liver disease complications (esophageal varices, ascites, and hepatic encephalopathy) and hospital-acquired infections (HAI), especially Clostridioides difficile and ventilator-associated pneumonia. Admissions to AC were more likely transfers from outside hospital (1.6% vs. 1.3%) and seen by palliative care (4.8% vs. 3.3%), p &lt; 0.001. Use of endoscopy, dialysis, and mechanical ventilation were similar. With similar IHM comparing AC vs. NAC (7.7% vs. 7.8%, p = 0.93), average LOS and number of procedures were higher at AC (7.7 vs. 7.1 d and 2.3 vs. 1.9, respectively, p &lt; 0.001) without difference on total charges ($52,821 vs. $52,067 USD, p = 0.28). On multivariable logistic regression model after controlling for demographics, ACLF grade, and calendar year, IHM was similar irrespective of academic status of the hospital, HR (95% CI): 1.01 (0.93–1.08, p = 0.70). IHM decreased over time, with ACLF as strongest predictor. A total of 63 and 22% were discharged to home and skilled nursing facility, respectively, without differences on academic status of the hospital.ConclusionAdmissions with AH to AC compared to NAC have higher frequency of liver disease complications and HAI, with longer duration of hospitalization. Prospective studies are needed to reduce HAI among hospitalized patients with AH.

The spread of infectious diseases such as COVID-19, flu influenza, malaria, dengue, mumps, and rubella in a population is a big threat to public health. The infectious diseases spread from one person to another person through close contact. Without proper planning, an infectious disease can become an epidemic and can result in large human and financial losses. To better respond to the spread of infectious disease and take measures for its control, the public health authorities need models and simulations to study the spread of such diseases. In this paper, an agent-based simulation engine is presented that models the spread of infectious diseases in the population. The simulation takes as an input the human-to-human interactions, population dynamics, disease transmissibility and disease states and shows the spread of disease over time. The simulation engine supports non-pharmaceutical interventions and shows its impact on the disease spread across locations. A unique feature of this tool is that it is generic; therefore, it can simulate a wide variety of infectious disease models (SIR), susceptible-infectious-susceptible (SIS) and susceptible-infectious (SI). The proposed simulation engine will help the policy-makers and public health authorities study the behavior of disease spreading; thus, allowing for better planning.

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