Faculty Bibliography

BACKGROUND & AIMS/OBJECTIVE:Nonalcoholic steatohepatitis (NASH) is the leading indication for liver transplant (LT) in women and the elderly. Granular details into factors impacting survival in this population are needed to optimize management and improve outcomes. METHODS:Patients receiving LT for NASH cirrhosis from 1997 to 2017 across 7 transplant centers (NailNASH consortium) were analyzed. The primary outcome was all-cause mortality, and causes of death were enumerated. All outcomes were cross referenced with United Network for Organ Sharing and adjudicated at each individual center. Cox regression models were constructed to elucidate clinical factors impacting mortality. RESULTS:Nine hundred thirty-eight patients with a median follow-up of 3.8 years (interquartile range, 1.60-7.05 years) were included. The 1-, 3-, 5-, 10-, and 15-year survival of the cohort was 93%, 88%, 83%, 69%, and 46%, respectively. Of 195 deaths in the cohort, the most common causes were infection (19%), cardiovascular disease (18%), cancer (17%), and liver-related (11%). Inferior survival was noted in patients >65 years. On multivariable analysis, age >65 (hazard ratio [HR], 1.70; 95% confidence interval [CI], 1.04-2.77; P = .04), end-stage renal disease (HR, 1.55; 95% CI, 1.04-2.31; P = .03), black race (HR, 5.25; 95% CI, 2.12-12.96; P = .0003), and non-calcineurin inhibitors-based regimens (HR, 2.05; 95% CI, 1.19-3.51; P = .009) were associated with increased mortality. Statin use after LT favorably impacted survival (HR, 0.38; 95% CI, 0.19-0.75; P = .005). CONCLUSIONS:Despite excellent long-term survival, patients transplanted for NASH at >65 years or with type 2 diabetes mellitus at transplant had higher mortality. Statin use after transplant attenuated risk and was associated with improved survival across all subgroups, suggesting that careful patient selection and implementation of protocol-based management of metabolic comorbidities may further improve clinical outcomes.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Nonalcoholic fatty liver disease (NAFLD) is rapidly becoming the most common cause of chronic liver disease due to an increase in the prevalence of obesity. The development of NASH leads to an increase in morbidity and mortality. While the first line of treatment is lifestyle modifications, including dietary changes and increased physical activity, there are no approved pharmacological treatment agents for NAFLD and NASH currently. Due to its complex pathophysiology, different pathways are under investigation for drug development with the focus on metabolic pathways, inflammation, and slowing or reversing fibrosis. There are several agents advancing in clinical trials, and promising results have been seen with drugs that affect hepatic steatosis, inflammation, and fibrosis. This review will provide an overview on NAFLD and some of the mechanisms of disease that are being targeted with pharmacologic agents.

Sepsis is a leading cause of death and is the most expensive condition to treat in U.S. hospitals. Despite targeted efforts to automate earlier detection of sepsis, current techniques rely exclusively on using either standard clinical data or novel biomarker measurements. In this study, we apply machine learning techniques to assess the predictive power of combining multiple biomarker measurements from a single blood sample with electronic medical record data (EMR) for the identification of patients in the early to peak phase of sepsis in a large community hospital setting. Combining biomarkers and EMR data achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 0.81, while EMR data alone achieved an AUC of 0.75. Furthermore, a single measurement of six biomarkers (IL-6, nCD64, IL-1ra, PCT, MCP1, and G-CSF) yielded the same predictive power as collecting an additional 16 hours of EMR data(AUC of 0.80), suggesting that the biomarkers may be useful for identifying these patients earlier. Ultimately, supervised learning using a subset of biomarker and EMR data as features may be capable of identifying patients in the early to peak phase of sepsis in a diverse population and may provide a tool for more timely identification and intervention.