A stepwise algorithm using an at-a-glance first-line test for the non-invasive diagnosis of advanced liver fibrosis

Jérôme Boursier1, Victor de Ledinghen2, Vincent Leroy3

  • 1Hepatology Department, University Hospital, Angers, France; HIFIH Laboratory, UPRES 3859, SFR 4208, Bretagne Loire University, Angers, France.

Journal of Hepatology
|January 16, 2017
PubMed

Insights

A new easy liver fibrosis test (eLIFT) algorithm accurately detects advanced liver fibrosis in chronic liver disease patients. This reduces unnecessary referrals, improving patient management and specialist access.

Area of Science:

  • Hepatology and Gastroenterology
  • Diagnostic Medicine
  • Biomarker Development

Background:

  • Chronic liver diseases (CLD) are prevalent and often managed by non-hepatologists lacking access to advanced non-invasive fibrosis tests.
  • Inaccurate disease severity assessment leads to suboptimal patient management and unnecessary specialist referrals.

Purpose of the Study:

  • To implement a novel algorithm for advanced liver fibrosis detection in all CLD patients.
  • To introduce a new, widely accessible first-line non-invasive test for initial fibrosis screening.

Main Methods:

  • A diagnostic study randomized 3754 CLD patients with liver biopsy into derivation and validation sets.
  • A prognostic study involved longitudinal follow-up of 1275 CLD patients with baseline fibrosis tests.
  • Developed the easy liver fibrosis test (eLIFT) using age, gender, liver enzymes, and coagulation parameters.

Main Results:

  • The eLIFT test demonstrated comparable sensitivity to FIB4 but with fewer false positives, especially in older patients.
  • The eLIFT-FibroMeter with vibration controlled transient elastography (eLIFT-FMVCTE) algorithm achieved 76.1% sensitivity for advanced fibrosis and 92.1% for cirrhosis.
  • Patients identified as having no/mild fibrosis by the algorithm showed excellent liver-related prognosis, negating the need for specialist referral.

Conclusions:

  • The eLIFT-FMVCTE algorithm effectively identifies patients with advanced liver fibrosis, enabling timely specialist referral.
  • This algorithm improves CLD management by extending accurate fibrosis detection to all patients and reducing unnecessary referrals.
Abstract