Tag Archive for Hepatitis

Genotyping Hepatitis C Virus

The Hepatitis C virus (HCV) is a single-stranded RNA virus, with a genome of 9,500 nucleotides. The prevalence of HCV among the general population of Ethiopia is not known. Nonetheless, the impression at the lab is that prevalence is high. Egypt is know to have one of the highest HCV prevalence in the world. It is postulated that this is the result of mass treatment with praziquantel for schistosomiasis using contaminated syringes in the past. Mass treatment against schistosomiasis also occurred in Ethiopia, however, oral treatment were used. Intravenous drug users are also unknown of. Would it be facial tattoos, high number of cesarians, blood transfusions? The primarily mode of transmission is still a mystery.

About 75 – 85% of HCV-infected individuals develop chronic hepatitis, with up to 20% of chronically infected individuals developing cirrhosis. In patients with cirrhosis, the incidence of hepatocellular carcinoma is 1 – 4% per year. Genotype 1 and genotype 4 responds less well to interferon-based anti-HCV drug treatment than genotypes 2 and 3, especially in patients of African descent. Therefore, genotyping of HCV is recommended before the start of antiviral therapy.

Thanks to the lobbying of, among other PharmAccess, treatment for HCV is becoming more affordable in developing countries. Still, hepatitis C viral loads and genotyping is mandatory for managing the treatment.

The Abbott RealTime HCV genotype II assay is an RT-PCR assay for use with the Abbott mSample Preparation System reagents and with the Abbott m2000sp and m2000rt instruments for viral count and genotyping of the viral (HCV) RNA in human blood, serum plasma (EDTA) or tissue donors of HCV-infected individuals. The Abbott RealTime HCV assay is intended for use as an aid in the management of HCV-infected patients undergoing antiviral therapy. The assay measures HCV RNA levels at baseline and during treatment and can be utilized to predict sustained and non-sustained virological response to HCV therapy.

During the verification tests, some remarkable observations were made. There was failure of the internal controls and mismatch at low viral loads. But that’s not the interesting one.

The genotype 4e at the reference laboratory in Germany (most likely they use the Roche system) corresponds with a genotype 1 & 4 double reaction at our lab in Addis Ababa. Genotype 4e is a strain of which little is known. There are only few and partial RNA sequences available for this genotype. It is therefore hypothesized that the primers/probes for gt1 detection from the Abbott assay cross match with genotype 4e.

HCV genotyping

Conclusion

In case, with the Abbott system, a reaction occurs in which both genotype 1 & 4 are detected, the results should be interpreted as HCV genotype 4.

genosubtype-coverage

Validating molecular assay using R






Validation of Hepatitis B viral count using R-computing


Validation of Hepatitis B viral count using R-computing

This is an example on how R-computing can be used for validation of an quantitative assay. In this case two assays for Hepatitis B viral count are compared.

## Loading required package: seriation
## Loading required package: nlme

In a summary. 'Zero' values have been changed to '1' in order to be able to plot in logaritmic scale. The lower limit of detection (LLD) at home-lab is 10 IU/ml and the LLD at the reference-lab os 20 IU/ml. So, if the result is <20IU/ml, the detected value could be anywhere between 1 and 20. Therefore, the lower limit of detection has been set for home-lab at '5 IU/ml' and '10 IU/ml' for the reference lab.

summary(HepB_Web)
##       PIN              Ref_lab            Home_lab       
##  Min.   :14091022   Min.   :1.00e+00   Min.   :1.00e+00  
##  1st Qu.:14104055   1st Qu.:2.24e+02   1st Qu.:6.39e+02  
##  Median :14121724   Median :1.98e+03   Median :2.17e+03  
##  Mean   :14116291   Mean   :1.64e+07   Mean   :2.15e+07  
##  3rd Qu.:14132019   3rd Qu.:1.52e+05   3rd Qu.:8.42e+05  
##  Max.   :14132394   Max.   :1.70e+08   Max.   :2.88e+08
head(HepB_Web)
##        PIN Ref_lab Home_lab
## 1 14091022       1      184
## 2 14091023    3473     3473
## 3 14104024    2976     2558
## 4 14104025     988     1001
## 5 14104026   96670 20892951
## 6 14104141 1526000  1048129

To make it more easy, the set of values from Reference-lab = 'x'. The set of values from Home-lab = 'y'

Calculate the means and difference between the two sets (x and y)

# derive difference
mean(x)
## [1] 16447938
mean(y)
## [1] 21548265
# mean Ref_lab - mean Home_lab
mean(x)-mean(y)
## [1] -5100327

Because n=17 is small, the distribution of the differences should be approximately normal. Check using a boxplot and QQ plot. There is some skew.

HepB_Web$diff <- x-y
HepB_Web$diff
##  [1]       -183          0        418        -13  -20796281     477871
##  [7]         77   12039815       -176   34930655 -118402140       -282
## [13]         -9     -53972       -171      -1757          0        265
boxplot(HepB_Web$diff)

plot of chunk Boxplot

qqnorm(HepB_Web$diff)
qqline(HepB_Web$diff)

plot of chunk Boxplot
Shaphiro test of normality.

shapiro.test(HepB_Web$diff)
## 
##  Shapiro-Wilk normality test
## 
## data:  HepB_Web$diff
## W = 0.479, p-value = 5.294e-07

The normality test gives p < 0.003, which is small, so we
reject the null hypothesis that the values are distributed normally.

This means that we cannot use the student t-test. Instead, use the Mann-Whitney-Wilcoxon Test. We can decide whether the population distributions are identical without assuming them to follow the normal distribution.

wilcox.test(x, y, paired = TRUE)
## Warning: cannot compute exact p-value with zeroes
## 
##  Wilcoxon signed rank test with continuity correction
## 
## data:  x and y
## V = 59, p-value = 0.6603
## alternative hypothesis: true location shift is not equal to 0

p > 0.05 and therefore the H0 is NOT rejected.
The two populations are identical.

Just to see what happens in the Student T-test.
A paired t-test: one sample, two tests
H0 = no difference; H1 = mean of 2 tests are different
mu= a number indicating the true value of the mean
(or difference in means if you are performing a two sample test).

t.test(x, y, mu=0, paired=T, alternative="greater")
## 
##  Paired t-test
## 
## data:  x and y
## t = -0.7202, df = 17, p-value = 0.7594
## alternative hypothesis: true difference in means is greater than 0
## 95 percent confidence interval:
##  -17420746       Inf
## sample estimates:
## mean of the differences 
##                -5100327

p = 0.759. Because p is larger than alpha, we do NOT reject H0.
In other words, it is unlikely the observed agreements happened by chance.
However, because the populations do not have a normal distribution, we can not use the outcome if this test.

For correlation, three methods are used: pearson, kendall and spearman at a confidence level of 95%.

# correlation of the two methods
cor.test(x, y, 
         alternative = c("two.sided", "less", "greater"),
         method = c("pearson", "kendall", "spearman"),
         exact = NULL, conf.level = 0.95)
## 
##  Pearson's product-moment correlation
## 
## data:  x and y
## t = 11.19, df = 16, p-value = 5.646e-09
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.8472 0.9784
## sample estimates:
##    cor 
## 0.9416

The correlation with the spearman test is 0.9416175. Almost perfect correlation.

Plotting the two methods using logarithmic scales.

g <- ggplot(HepB_Web, aes(log(Home_lab), log(Ref_lab)))

# add layers
g + 
  geom_smooth(method="lm", se=TRUE, col="steelblue", size = 1) +
  geom_point(size = 3, aes(colour = x)) +
  scale_colour_gradient("IU/ml", high = "red", low = "blue", space = "Lab") +
  labs(y = "Reference lab (log IU/ml)") +
  labs(x = "Home lab (log IU/ml)") +
  theme_bw(base_family = "Helvetica", base_size = 14) +
  scale_x_continuous(breaks=c(0,4,8,12))

plot of chunk Plotting
Summary data on the correlation line.

regmod <- lm(y~x, data=HepB_Web)
summary(regmod)
## 
## Call:
## lm(formula = y ~ x, data = HepB_Web)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -76044958   1901358   1905277   1905580  47898082 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1.91e+06   5.98e+06   -0.32     0.75    
## x            1.43e+00   1.27e-01   11.19  5.6e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 23800000 on 16 degrees of freedom
## Multiple R-squared:  0.887,  Adjusted R-squared:  0.88 
## F-statistic:  125 on 1 and 16 DF,  p-value: 5.65e-09

The Bland-Altman Analysis. To check if there is a bias.

##      Ref_lab  Home_lab       diff
## 1          1       184       -183
## 2       3473      3473          0
## 3       2976      2558        418
## 4        988      1001        -13
## 5      96670  20892951  -20796281
## 6    1526000   1048129     477871
## 7        919       842         77
## 8   23250000  11210185   12039815
## 9        421       597       -176
## 10 101000000  66069345   34930655
## 11 170000000 288402140 -118402140
## 12       483       765       -282
## 13         1        10         -9
## 14    169800    223772     -53972
## 15       158       329       -171
## 16        22      1779      -1757
## 17         1         1          0
## 18     10970     10705        265
BlandAltman(x, y,
            x.name = "Reference lab IU/ml",
            y.name = "Home lab IU/ml",
            maintit = "Bland-Altman plot for HBV count",
            cex = 1,
            pch = 16,
            col.points = "black",
            col.lines = "blue",
            limx = NULL,
            limy = NULL,
            ymax = NULL,
            eqax = FALSE,
            xlab = NULL,
            ylab = NULL,
            print = TRUE,
            reg.line = FALSE,
            digits = 2,
            mult = FALSE)
## NOTE:
##  'AB.plot' and 'BlandAltman' are deprecated,
##  and likely to disappear in a not too distant future,
##  use 'BA.plot' instead.

plot of chunk unnamed-chunk-6

## 
## Limits of agreement:
## Reference lab IU/ml - Home lab IU/ml                           2.5% limit 
##                             -5100327                            -65195645 
##                          97.5% limit                             SD(diff) 
##                             54994992                             30047659

When the dots are around 0, the two test could be interchanged for a patient. There are, however, some outliners: large difference of viral count between the two labs. One difference can be accounted for; the upper limited value of the reference lab is '>170.000.000 IU/ml, whereas the home-lab produces an exact calculation of 288402140 IU/ml.

World Hepatitis Day 2014

From the website of International Clinical Laboratories.

In connection with World Hepatitis day, seminars were organised on friday 27th of June 2014 in Addis Abeba, Ethiopia. The organizers were International Clinical Laboratories and the Ethiopian Gastroenterology Organisation. Dr. Laurence Phillips from Abbott presented the latest updates on epidemiology and molecular detection of Hepatitis viruses.

[nggallery id=28]

In the morning a seminar was held at St. Paul’s hospital with around 40 clinicians. In the afternoon a seminar was held in Elilly hotel.

Abbott Hepatitis presentation notes

After the presentations, discussions were held with the audience. Hepatitis B is not an untreatable disease anymore. It’s preventable and curable, however, some of the medication in Ethiopia is available for treating HIV, but not Hepatitis. Diagnostics used to be to expensive for the majority of Ethiopians. Blood samples had to be send to Germany for viral count. A big step forwards gas been made now that the molecular assay can be conducted at ICL. The price is now reduced from 4000,-  birr to around 1200,- birr.

With these new developments, the national guidelines regarding Hepatitis B an C will have to be updated as soon as possible. Also, drugs for treating Hepatitis  need to be made available and affordable for all patients in Ethiopia.

The Banner and Posters from the Hepatitis Day can be downloaded here:

Hepatitis B poster small

Hepatitis C poster small

ICL Banner small

Helicobacter small