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Viral load as predictor of Crimean-Congo hemorrhagic fever outcome.

Duh D, Saksida A, Petrovec M, Ahmeti S, Dedushaj I, Panning M, Drosten C, Avsic-Zupanc T - Emerging Infect. Dis. (2007)

Bottom Line: We used quantitative real-time reverse transcription-PCR to measure viral load in serum from 24 patients in Kosovo who had acute Crimean-Congo hemorrhagic fever.Viral load correlated with clinical disease and antibodies and could be used as a predictor of disease outcome.

View Article: PubMed Central - PubMed

Affiliation: Institute of Microbiology and Immunology, Ljubljana, Slovenia.

ABSTRACT
We used quantitative real-time reverse transcription-PCR to measure viral load in serum from 24 patients in Kosovo who had acute Crimean-Congo hemorrhagic fever. Viral load correlated with clinical disease and antibodies and could be used as a predictor of disease outcome.

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Related in: MedlinePlus

Correlation between clinical outcome, serologic data, and Crimean-Congo hemorrhagic fever (CCHF) viral load measurements. A) Viral load versus immunoglobulin (Ig) M result taken during the first week of illness. B) Viral load versus outcome. Average viral loads were 1.6 × 109 copies/mL in persons who died and 5 × 106 copies/mL in persons who survived (difference highly significant, p<0.0001). The dot is a datum point that has been identified as an outlier. C) Statistically significant difference (p<0.001) in CCHF viral load and day of illness between group who died and group who survived. D) No correlation in viral load and day of illness between severe and moderate CCHF cases. E) Inverse correlation of quantitative IgG levels with viral loads (p<0.0001) in samples taken after first week of illness. Black dot, >1 sample; *, first week samples.
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Figure 1: Correlation between clinical outcome, serologic data, and Crimean-Congo hemorrhagic fever (CCHF) viral load measurements. A) Viral load versus immunoglobulin (Ig) M result taken during the first week of illness. B) Viral load versus outcome. Average viral loads were 1.6 × 109 copies/mL in persons who died and 5 × 106 copies/mL in persons who survived (difference highly significant, p<0.0001). The dot is a datum point that has been identified as an outlier. C) Statistically significant difference (p<0.001) in CCHF viral load and day of illness between group who died and group who survived. D) No correlation in viral load and day of illness between severe and moderate CCHF cases. E) Inverse correlation of quantitative IgG levels with viral loads (p<0.0001) in samples taken after first week of illness. Black dot, >1 sample; *, first week samples.

Mentions: From the 24 patients, 43 serum samples were tested by real-time RT-PCR and ELISA (Table). Viral loads ranged from 102 to 1010 copies per milliliter of serum, depending on the day of illness, the severity of disease, and the results of serologic analyses (Table). Whether early laboratory findings could serve as prognostic markers for outcome was explored. Because prognostic information is most relevant in the first week of disease, only samples taken up to day 7 of symptoms from any patient were included in the analysis. Average sampling days did not differ between patients who died and those who survived (5.0 and 3.9 days, respectively, p = 0.28, analysis of variance [ANOVA]). No patient from either group had detectable IgG in the first week. Although we suspected that early development of IgM might correlate with good outcome, such correlation was not found. Of those patients in whom IgM was detected up to day 7, 5 (62%) died. Of those without IgM in the first week, 3 (42%) died (insignificant difference in 1-way ANOVA, p = 0.6). Furthermore, the presence of IgM in the first week did not correlate with viral load, which suggests that virus levels in the first week can be regarded as an independent prognostic parameter. Namely, viral load seemed to be strongly related to the clinical classification (p<0.001), with the average log value 9.25 (1.78 × 109) in the group of patients who died and 6.91 (8.06 × 106) in the group who survived (Figure).


Viral load as predictor of Crimean-Congo hemorrhagic fever outcome.

Duh D, Saksida A, Petrovec M, Ahmeti S, Dedushaj I, Panning M, Drosten C, Avsic-Zupanc T - Emerging Infect. Dis. (2007)

Correlation between clinical outcome, serologic data, and Crimean-Congo hemorrhagic fever (CCHF) viral load measurements. A) Viral load versus immunoglobulin (Ig) M result taken during the first week of illness. B) Viral load versus outcome. Average viral loads were 1.6 × 109 copies/mL in persons who died and 5 × 106 copies/mL in persons who survived (difference highly significant, p<0.0001). The dot is a datum point that has been identified as an outlier. C) Statistically significant difference (p<0.001) in CCHF viral load and day of illness between group who died and group who survived. D) No correlation in viral load and day of illness between severe and moderate CCHF cases. E) Inverse correlation of quantitative IgG levels with viral loads (p<0.0001) in samples taken after first week of illness. Black dot, >1 sample; *, first week samples.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3375790&req=5

Figure 1: Correlation between clinical outcome, serologic data, and Crimean-Congo hemorrhagic fever (CCHF) viral load measurements. A) Viral load versus immunoglobulin (Ig) M result taken during the first week of illness. B) Viral load versus outcome. Average viral loads were 1.6 × 109 copies/mL in persons who died and 5 × 106 copies/mL in persons who survived (difference highly significant, p<0.0001). The dot is a datum point that has been identified as an outlier. C) Statistically significant difference (p<0.001) in CCHF viral load and day of illness between group who died and group who survived. D) No correlation in viral load and day of illness between severe and moderate CCHF cases. E) Inverse correlation of quantitative IgG levels with viral loads (p<0.0001) in samples taken after first week of illness. Black dot, >1 sample; *, first week samples.
Mentions: From the 24 patients, 43 serum samples were tested by real-time RT-PCR and ELISA (Table). Viral loads ranged from 102 to 1010 copies per milliliter of serum, depending on the day of illness, the severity of disease, and the results of serologic analyses (Table). Whether early laboratory findings could serve as prognostic markers for outcome was explored. Because prognostic information is most relevant in the first week of disease, only samples taken up to day 7 of symptoms from any patient were included in the analysis. Average sampling days did not differ between patients who died and those who survived (5.0 and 3.9 days, respectively, p = 0.28, analysis of variance [ANOVA]). No patient from either group had detectable IgG in the first week. Although we suspected that early development of IgM might correlate with good outcome, such correlation was not found. Of those patients in whom IgM was detected up to day 7, 5 (62%) died. Of those without IgM in the first week, 3 (42%) died (insignificant difference in 1-way ANOVA, p = 0.6). Furthermore, the presence of IgM in the first week did not correlate with viral load, which suggests that virus levels in the first week can be regarded as an independent prognostic parameter. Namely, viral load seemed to be strongly related to the clinical classification (p<0.001), with the average log value 9.25 (1.78 × 109) in the group of patients who died and 6.91 (8.06 × 106) in the group who survived (Figure).

Bottom Line: We used quantitative real-time reverse transcription-PCR to measure viral load in serum from 24 patients in Kosovo who had acute Crimean-Congo hemorrhagic fever.Viral load correlated with clinical disease and antibodies and could be used as a predictor of disease outcome.

View Article: PubMed Central - PubMed

Affiliation: Institute of Microbiology and Immunology, Ljubljana, Slovenia.

ABSTRACT
We used quantitative real-time reverse transcription-PCR to measure viral load in serum from 24 patients in Kosovo who had acute Crimean-Congo hemorrhagic fever. Viral load correlated with clinical disease and antibodies and could be used as a predictor of disease outcome.

Show MeSH
Related in: MedlinePlus