Spanish investigators have developed a non-invasive diagnostic tool that
can accurately predict which HIV-positive patients who are co-infected with
hepatitis C virus will successfully respond to treatment for hepatitis C. Their
findings are reported in the November 15th edition of Clinical Infectious Diseases.
the diagnostic tool can be downloaded for free here.
Four variables were included in the investigators’ model.
Two of these – hepatitis C genotype and viral load – are well-known predictors of
treatment outcome. The researchers also included two novel diagnostic markers:
liver stiffness and genetic variations near the IL28B gene.
All these markers can be monitored without the need for an invasive liver biopsy, which the investigators believe makes their model especially
attractive for use in routine care.
It is estimated that approximately 20% of HIV-positive
individuals are co-infected with hepatitis C. In the era of modern HIV
treatment, liver disease caused by hepatitis C is a leading cause of illness
and death in co-infected patients.
Treatment for hepatitis C is available. Involving pegylated
interferon and ribavirin, its aim is an undetectable hepatitis C viral load six
months after the completion of therapy. This is often called a sustained
virological response (SVR), and only a third of co-infected patients achieve this
Poorer outcomes are seen in patients infected with hepatitis
C genotypes 1 and 4 (as opposed to 2 and 3), and in individuals with a higher
hepatitis C viral load at the start of therapy.
Recently, genetics have also been identified as predicting
treatment response. Improved outcomes were seen in patients with a variation in
the polymorphism rs12979860 close to the IL28B
gene. Patients with the CC genotype were twice as likely as individuals with
the CT or TT genotype to have a successful treatment response.
Assessment of liver stiffness using FibroScan has also been shown to be an accurate way of monitoring
liver damage in patients with hepatitis C. This method of monitoring involves
placing a small device against the patient’s skin and therefore, unlike liver
biopsy, does not require surgery.
Therefore, the investigators developed a predictive model
including these four variables.
Their main study population included a cohort of 159
co-infected patients who received hepatitis C therapy at the Hospital Carlos
III, Madrid, between November 2004 and December 2008. To validate their
results, the diagnostic tool was also tested on 86 co-infected patients who
received treatment at two other clinics.
The two cohorts were generally well matched. However,
individuals in the validation population had a higher baseline hepatitis C
viral load (p < 0.001) and lower liver stiffness scores (p = 0.03). Overall, approximately 55% of patients
in both cohorts had a sustained virological response.
However, the investigators stress that they excluded
“nonadherent patients and patients who discontinued therapy because of
side-effects and only evaluated the subjects who had completed the course of
therapy”. Moreover, 99% of patients were male and the average body mass index
(BMI) was well within the normal range (23 kg/m2).
In both groups of patients, hepatitis C genotype, liver
stiffness, the CT or TT polymorphism and hepatitis C viral load were predictive
of treatment outcome.
The strongest predictor of outcome was CT or TT polymorphism
versus CC (odds ratio [OR] = 0.170; 95% CI, 0.065 to 0.442; p < 0.001).
followed by baseline hepatitis C viral load (OR, 0.186; 95% CI,
0.091 to 0.381; p < 0.001), genotype 1 and 4 versus 2 and 3 (OR, 0.212; 95% CI,
0.078 to 0.577; p = 0.002), and increased liver stiffness (OR, 0.920; 95% CI,
0.865 to 0.977; p = 0.007).
Using these data, the investigators developed their tool to
predict treatment response.
They used the control cohorts to test the accuracy of their
model at three cut-off points. These assessed different degrees of sensitivity
Using the lowest point (highest sensitivity and lowest
negative predictive value), a lack of treatment response was correctly
predicted in 89% of patients.
A similar accuracy – 90% – was seen when the investigators used the highest cut off
(highest specificity and positive predictive value).
An intermediate cut off (highest sensitivity and specificity)
accurately predicted a successful treatment response in 83% of patients and a
lack of response in 79%.
Therefore, they assessed the accuracy of their model as
between “excellent” and “good”.
The investigators believe that their tool “may be of great
value for making adequate therapeutic decisions for HIV-HCV-coinfected
For example, use of current hepatitis C therapy could be
encouraged for patients with the greatest chance of responding to treatment.
Those with a low chance of sustained virological response
could be counselled to “wait for new direct-acting antiviral agents against
Although the investigators are encouraged by the accuracy of
their model, they nevertheless caution, “as with any diagnostic tool used in
clinical practice, misclassification may occur, and understanding the limits of
estimated outcomes with predictive indexes is important before its widespread