Objective(s): To measure serum C-reactive protein (CRP) titer as a predictive diagnosis of acute hepatitis C virus (HCV)
infection.
Methodology: Two hundred and ten patients with acute HCV infection and 234 apparently healthy individuals as
control group were enrolled in this study in Baghdad medical city (Teaching Laboratories). The patents include
74(35.2%) females and 136 (64.8%) males with mean age (27±16.5) years. The control group includes 114 (48.7%)
females and 120 (51.3%) males with mean age (26±5.8) years. Blood samples were collected from out patients from
Alfadul in Baghdad city. Sera were separated and stored at 20 0
C. The diagnosis of acute HCV infection was based on
detection of HC Ag and anti- HCV IgM and standard liver function tests. Determination of CRP titer was assessed by
semi-quantitative tube agglutination test. All data were statistically analyzed.
Results: Based on 95% percentile, the baseline CRP titer in healthy individuals was 1:8 (16mg/l) and for patients 1:512
(1024mg/l). There was a statistically significant increase in the mean CRP titer in patients with acute HCV infection
compared to healthy individuals (P< 0.001) .The validly of CRP titer 1: 64 as a cut –off value to predict HCV infection
provide a sensitivity and specificity of 100 % and 96% respectively. Furthermore, there was a significant correlation
between CRP titer and liver function test values.
Recommendation:
Therefore, in further studies, we recommends the evaluation of C- reactive protein titer in patients with acute
hepatitis B Virus infection and patients with non–infectious diseases such as cardiovascular disease, diabetes mellitus
and hyperlipidemia infection, and compare between them.
Abstract:
Interest in the topic of prediction has increased in recent years and appeared modern methods such as Artificial Neural Networks models, if these methods are able to learn and adapt self with any model, and does not require assumptions on the nature of the time series. On the other hand, the methods currently used to predict the classic method such as Box-Jenkins may be difficult to diagnose chain and modeling because they assume strict conditions.
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