In a follow-up preview, Jörn Schattenberg, Louise Campbell, Mazen Noureddin, Ian Rowe and patient advocate Jeff McIntyre join Roger Green to discuss key presentations and posters of interest at the 73rd Annual Meeting of the American Association for the Study of Liver Diseases (AASLD). On November 4th-8th in Washington DC, as many as 10,000 attendees will convene in an effort to advance and disseminate the science and practice of hepatology, and to promote liver health and quality patient care.
Continuing from the previous conversation, Roger starts by questioning what the right long-term commercial model for remote patient wellness management companies might be. He recalls the early successes of Jenny Craig or Nutrisystem, achieved from selling food and Weight Watchers by branding other companies’ white-or black-labled food products. Is there the potential for these companies to marry strong coaching to services offering high-quality, pre-cooked foods for an integrated offering? Jeff agrees and refers to his own experience working with groups on medically-tailored meals. He briefly notes the push he sees to get these meals paid for by Medicare or private insurers. Louise mentions that the kinds of programs described exist at Safari parks as part of a full-wellness program for the animals. From a different perspective, she notes the importance of patient volition in program success and that volitional assessments are not included in trial design. The app Tawazun Health recently launched adds a volitional element.
Roger returns to a comment made in the first preview episode. He had talked about finding improved primary care screening tools that might serve a dual purpose by also educating front-line providers how NAFLD often sits at the core of the galaxy of metabolic diseases. Two abstracts for posters are described that address this issue. The first of which is a meta-analysis of 121,975 patients. This study looked at significance of the odds and hazard ratios of the TyG index, computed based on triglyceride and glucose levels. It also investigated the ability over time for that ratio to predict disease. All results were highly significant and clinically meaningful. The second poster is titled Performance of Artificial Intelligence Enabled Electrocardiogram and the Prediction of Fatty Liver Disease. This paper showed that an AI analysis of ECG results based on a convolutional neural network produced an area under the curve far superior to FIB-4. It also showed to be superior or equal to BMI and simple metabolic parameters. Roger’s general point: using these metrics can improve on FIB-4 while, at the same time, focusing on the links between NAFLD and other metabolic diseases. As the conversation winds down, Mazen and Jörn agree with a few additional comments.