S3-E30.1 - Highlighting Intriguing #ILC2022 Abstracts: Machine Learning Identified NASH Biomarkers and a Cost-Effectiveness Study episode artwork

EPISODE · Jun 18, 2022 · 16 MIN

S3-E30.1 - Highlighting Intriguing #ILC2022 Abstracts: Machine Learning Identified NASH Biomarkers and a Cost-Effectiveness Study

from Surfing the MASH Tsunami · host HEP Dynamics LLC

Send us Fan MailNext week, >10,000 Fatty Liver stakeholders are expected to journey to London (rail strike and all) for the International Liver Congress (#ILC2022), the first meeting at this level since the pandemic started to include an in-person attendance option. This week, NASH Tsunami identifies some of the most important and intriguing non-embargoed presentations at #ILC2022. This conversation explores two of these: OS097, Machine learning algorithms identify novel biomarker combinations for NAFLD, from Jenny Lee (Netherlands); and FRI094, Clinical and economic evaluation of community-based preventative screening strategies for NAFLD in people with Type-2 diabetes melllitus, from Roberta Forlano (UK).Jörn Schattenberg selected OS097 as the first paper for the Surfers to discuss due to its robust dataset (720 liver-biopsied patients recruited prospectively at centers across Europe; 53% with NASH, 26% with advanced fibrosis and 7% with cirrhosis.) and intriguing task (identify the most robust biomarkers for predicting levels of steatosis, ballooning, inflammation and fibrosis independently). The AUCs for fibrosis were robust (0.90 in the test group and 0.84 in the validation set), while the AUCs for the steatosis measures were weaker (0.79 and 0.74, respectively.) Jörn notes that the histological markers vary between the four targets, with steatosis and ballooning predicted most strongly by BMI, inflammation by hemoglobin and fibrosis by VCTE. After questions, Jörn goes on to note that the CK-18 M 30 emerges as a predictor for both steatosis and fibrosis.The key question comes from Stephen, who asks how diabetes came into this equation in terms of metric, severity and length of disease. Jörn states that the only diabetes metric in the set was A1c, which was the 5th strongest for fibrosis. He also notes that length of disease is exceptionally challenging to determine in a study that relies on patient self-reports.Louise Campbell responds next, selecting FRI094, a clinical and economic assessment of community-based screening of Type 2 diabetes mellitus patients for Fatty Liver disease. This study began at Imperial while Louise still worked there. The study reported 17% of NAFLD patients with "significant" fibrosis (kPa>8.1 by VCTE), 11% had advanced fibrosis and 3% had cirrhosis (defined as kPa >12,1 by VCTE.) Any of these patients might have been defined by clinical, radiological or histological means.After questions, Louise would go on to note that all approaches were determine to be cost effective against a metric of 20,000 English pounds.Again, the pivotal question came from Stephen, who suggested that a kPa of 12.1 (the definition of cirrhosis here) would fit half of his practice. He used this point to remind listeners of the poor positive predictive value of VCTE as a single measure.When Stephen and Louise finish this point, Louise notes the positive recommendation on cost-effectivesness of community screening while acknowledging the issue around scoring of cirrhosis. Jörn then closes the discussion of this paper (and the conversation) by agreeing with Stephen's comment on the VCTE kPa and cirrhosis, but noting that the high levels indicate a serious challenge (even if not a level of cirrhosis).

Episode metadata supplied by the publisher feed · Published Jun 18, 2022

Embed this episode

Send us Fan Mail Next week, >10,000 Fatty Liver stakeholders are expected to journey to London (rail strike and all) for the International Liver Congress (#ILC2022), the first meeting at this level since the pandemic started to include an in-person attendance option. This week, NASH Tsunami identifies some of the most important and intriguing non-embargoed presentations at #ILC2022. This conversation explores two of these: OS097, Machine learning algorithms identify novel biomarker combina...

Distinct summary based on available episode metadata or transcript content.

NOW PLAYING

S3-E30.1 - Highlighting Intriguing #ILC2022 Abstracts: Machine Learning Identified NASH Biomarkers and a Cost-Effectiveness Study

0:00 16:17

No transcript for this episode yet

We transcribe on demand. Request one and we'll notify you when it's ready — usually under 10 minutes.

No similar episodes found.

No similar podcasts found.

Frequently Asked Questions

How long is this episode of Surfing the MASH Tsunami?

This episode is 16 minutes long.

When was this Surfing the MASH Tsunami episode published?

This episode was published on June 18, 2022.

Can I download this Surfing the MASH Tsunami episode?

Yes. Use the download control on the episode player to save the publisher-provided media file.
URL copied to clipboard!