Developing a ‘traffic light’ test with potential for rational early diagnosis of liver fibrosis and cirrhosis in the community
Open Access
- 1 September 2012
- journal article
- Published by Royal College of General Practitioners in British Journal of General Practice
- Vol. 62 (602) , e616-e624
- https://doi.org/10.3399/bjgp12x654588
Abstract
Background Liver disease develops silently and presents late, with often fatal complications. Aim To develop a ‘traffic light’ test for liver disease suitable for community use that could enhance assessment of liver risk and allow rational referral of more severe disease to specialist care. Design and setting Two cohorts from Southampton University Hospital Trust Liver Unit: model development and a validation cohort to evaluate prognosis. Method A total of 1038 consecutive liver patients (inpatient and outpatient) (development n = 397, validation n = 641) for whom the relevant blood tests had been performed, were followed for a mean of 46 months (range 13–89 months). Blood tests for: hyaluronic acid (HA), procollagen-3 N-terminal peptide (P3NP), and platelet count were combined in a diagnostic algorithm to stage liver disease. Results A simple clinical rule combined: HA, P3NP, and platelet count into a ‘traffic light’ algorithm, grading the results red — high risk, amber — intermediate risk, and green — low risk. In the validation cohort, no green subjects died or developed varices or ascites (n = 202); in the amber group, 9/267 (3.3%) died, 0/267 developed varices, and 2/267 (0.7%) developed ascites; in the red group, 24/172 died (14%), 24/172 (14%) developed varices, and 20/172 developed (11.6%) ascites. Survival was reduced in red (PP<0.012) groups compared with green. Conclusion A simple blood test triages liver disease into three prognostic groups; used in the community, it could enhance the management of risk factors in primary care and rationalise secondary care referrals, including the many patients with fatty liver and relatively minor elevations in alanine transaminase.Keywords
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