Umberto Vespasiani‐Gentilucci

University Name
Country flag
Italy

Research Interests

Explore related searches

Contact this professor

LinkedIn
ORCID
Google Scholar

Articles (7)

Poor accuracy and sustainability of the first‐step FIB4 EASL pathway for stratifying steatotic liver disease risk in the general population

Summary Background and Aims The European Association for the Study of the Liver introduced a clinical pathway (EASL CP) for screening significant/advanced fibrosis in people at risk of steatotic liver disease (SLD). We assessed the performance of the first‐step FIB4 EASL CP in the general population across different SLD risk groups (MASLD, Met‐ALD and ALD) and various age classes. Methods We analysed a total of 3372 individuals at risk of SLD from the 2017–2018 National Health and Nutrition Examination Survey (NHANES17‐18), projected to 152.3 million U.S. adults, 300,329 from the UK Biobank (UKBB) and 57,644 from the Biobank Japan (BBJ). We assessed liver stiffness measurement (LSM) ≥8 kPa and liver‐related events occurring within 3 and 10 years (3/10 year‐LREs) as outcomes. We defined MASLD, MetALD, and ALD according to recent international recommendations. Results FIB4 sensitivity for LSM ≥ 8 kPa was low (27.7%), but it ranged approximately 80%‐90% for 3‐year LREs. Using FIB4, 22%–57% of subjects across the three cohorts were identified as candidates for vibration‐controlled transient elastography (VCTE), which was mostly avoidable (positive predictive value of FIB4 ≥ 1.3 for LSM ≥ 8 kPa ranging 9.5%–13% across different SLD categories). Sensitivity for LSM ≥ 8 kPa and LREs increased with increasing alcohol intake (ALD>MetALD>MASLD) and age classes. For individuals aged ≥65 years, using the recommended age‐adjusted FIB4 cut‐off (≥2) substantially reduced sensitivity for LSM ≥ 8 kPa and LREs. Conclusions The first‐step FIB4 EASL CP is poorly accurate and feasible for individuals at risk of SLD in the general population. It is crucial to enhance the screening strategy with a first‐step approach able to reduce unnecessary VCTEs and optimise their yield.

Year:

2024

Collaborators (6)

Sami Qadri

University of Helsinki

FINLAND

Jussi Pihlajamäki

University of Eastern Finland

FINLAND

Luca V. C. Valenti

-

ITALY

Paolo Gallo

Università Campus Bio-Medico

ITALY

Stefano Romeo

-

SWEDEN

Antonio Picardi

Professore Associato di Medicina Interna

Università Campus Bio-Medico di Roma

ITALY
Social connections

How do I reach out?

Sign in for free to see their profile details and contact information.

Meet Kite AI