“APRI and other simple tests for liver fibrosis ” APASL Consensus Meeting for Liver Fibrosis

1. “APRI and other simple tests for liver fibrosis” APASL Consensus Meeting for Liver Fibrosis June 11th 2014 Prof Seng Gee Lim Director of Hepatology, Dept of Gastroenterology and Hepatology National University Health System Singapore

2. Conceptualising the diagnostic process Test D Test A Test B Test C Diagnosis excluded Diagnosis Possible Treatment initiated 100% 0% Testing range Diagnostic threshold Treatment threshold The diagnostic threshold and treatment threshold are arbitrary levels set depending on the disease, the outcome, cost of therapy, adverse effects of therapy, and patient/physician preferences Users' Guides to the Medical Literature: A Manual of Evidence-Based Clinical Practice. Guyatt GH, Rennie D, eds. Chicago, IL: AMA Press; 2002.

3. Parameters for diagnostic studies based on EBM • Was there diagnostic uncertainty? • What is the baseline risk? • Was there a reference (gold) standard? • Did all patients receive the index test and reference standard? • Was the study population similar to that of the patients to be tested? • If cirrhosis is obvious, then test performs well • Prevalence affects results –spectrum bias • Liver biopsy is still the gold standard • Both tests must be done in all patients, not in some only • Again patients must have similar levels of diagnostic uncertainty Users' Guides to the Medical Literature: A Manual of Evidence-Based Clinical Practice. Guyatt GH, Rennie D, eds. Chicago, IL: AMA Press; 2002.

4. Scope of the questions (PICO) • In liver patients where cirrhosis is suspected, can non-invasive tests confirmcirrhosis compared to the gold standard of liver biopsy? • In liver patients where cirrhosis is suspected, can non-invasive tests excludecirrhosis compared to the gold standard of liver biopsy? • In patients with liver disease can non-invasive tests confirm≥F2 fibrosis compared to the gold standard of liver biopsy? • In patients with liver disease can non-invasive tests exclude≥F2 fibrosis compared to the gold standard of liver biopsy?

5. Outcome Parameters of diagnostic tests Sensitivity • Selection of test to rule out Specificity • Selection of test to rule in NPV • Confidence in ruling out disease with that test • Cannot be used to compare tests as results vary with disease prevalence PPV • Confidence in ruling in disease with that test • Cannot be used to compare tests as results vary with disease prevalence

6. Spectrum Bias or differing Baseline prevalence of disease • In diagnostic tests, the performance of the test may vary depending on the prevalence of the underlying disease • Eg if prevalence of cirrhosis is 8% compared to 16%, how will it affect test performance?

7. NPV and PPV changes with disease prevalenceBUT not sensitivity and specificity Hypothetical example Study A Study B • Prevalence = 8% • Sensitivity = 74% • Specificity = 84% • PPV = 27% • NPV = 97% • Prevalence = 16% • Sensitivity = 74% • Specificity = 84% • PPV = 42% • NPV = 95 %

8. Solution to spectrum bias • Do not use parameters affected by disease prevalence • Avoid NPV and PPV • Parameters not affected by disease prevalence include sensitivity, specificity, LR (+) and LR (-) and AUROC • DANA analysis as proposed by Poynard is not validated and unnecessary

9. Likelihood Ratios Positive Likelihood Ratio Negative Likelihood Ratio LR(-) = % who test (-) with disease % who test (-) without disease = False Negative rate True Negative rate = (1 - Sensitivity) Specificity LR(+) = % who test (+) with disease % who test (+) without disease = True Positive rate False Positive rate = Sensitivity (1 - Specificity) The unlikelihood ratio Users' Guides to the Medical Literature: A Manual of Evidence-Based Clinical Practice. Guyatt GH, Rennie D, eds. Chicago, IL: AMA Press; 2002.

10. Interpreting Likelihood Ratios Positive Likelihood Ratio Negative Likelihood Ratio LR(-) = 1 No effect = 0.9-0.2 Moderate effect <0.1 Large effects LR(+) = 1 No effect = 2-9 Moderate effect >10 Large effects Users' Guides to the Medical Literature: A Manual of Evidence-Based Clinical Practice. Guyatt GH, Rennie D, eds. Chicago, IL: AMA Press; 2002.

11. AUROC = accuracy • AUROC ≠ sensitivity 1- specificity • AUROC =0.5 means no effect • Can compare performance of different tests but differences may be hard to evaluate • Eg Test A (AUROC=0.72), Test B (AUROC =0.68) but may not be significant Test A Test B 1- specificity

12. Search Strategy • Pubmed clinical queries • Search terms “non-invasive, biomarkers, serum markers” AND “liver fibrosis” • Also search under related items • Manual sort for “systematic review” or “meta-analysis” • Only 2 suitable articles: • Udell et al, JAMA. 2012;307(8):832-842 • Poynard et al, AdvClinChem 2008;46:131-160.

13. Evaluation of the 2 Systematic Reviews

14. Evaluation of the 2 Systematic Reviews

15. Biomarkers identified • Poynard et al, AdvClinChem 2008;46:131-160.

16. Class I and II Biomarkers • Poynard et al, AdvClinChem 2008;46:131-160.

17. Summary findings of Poynard Meta-analysis to detect significant fibrosis (≥F2) • Poynard et al, AdvClinChem 2008;46:131-160.

18. Questions addressed by Poynard study • Are patented biomarkers better than non-patented? • Only APRI and FT were compared and there was a statistically better performance of FT • Are there differences between patented biomarkers? • The statistical data showed no differences • Are biomarkers effective in all types of chronic liver disease? • The authors only examined FT which showed similar performance in all types of liver disease. The other tests were not extensively tested in different populations in order to be conclusive • Is there a gray zone or indeterminate zone? • Like liver biopsy, the biomarkers were better able to distinguish between extremes of stage rather than adjacent stage.

19. Summary statistic for Udell study • Udell et al, JAMA. 2012;307(8):832-842

20. Performance of the ELF panel Xie, PLoSOne 2014;9:e92772

21. Conclusions • None of the meta-analyses were able to determine the best biomarker • Among all the potential indirect (simple) biomarkers, the Lok Index, APRI, and the BonaciniIndex were the most common • They were in general only able to generate moderate effects • While Bonacini >7 has LR(+) 9.4; the 95% CI, 2.6-37 is wide • Similarly for Lok Index <0.2 has LR (-) of 0.09 the 95% CI 0.03-0.31 was again rather wide

22. Recommendations Previous Consensus New recommendation Biomarkers of liver fibrosis generally can give moderate estimates in diagnosis or exclusion of significant fibrosis and liver cirrhosis. (A2) Patented tests such as Fibrotest (FT) and Enhanced Liver Fibrosis (ELF) may perform better than the non-patented tests such as APRI. (B2) They may be used either stepwise or in combination with other non-invasive tests using imaging or elastography to improve accuracy of liver fibrosis. (C1) • Noninvasive tests are useful for identifying only those patients with no fibrosis or with extreme levels of fibrosis (1a, A). • Staging of liver fibrosis in the intermediate range cannot be satisfactorily predicted by any of the available tests (1a, A). • A stepwise algorithm incorporating noninvasive markers of fibrosis may reduce the number of liver biopsies by about 30% (1a, A).