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Results Chapter on CMV Infection Research

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Published: Tue, 12 Sep 2017

Results

This study has been conducted on 366 patients with suspected CMV infection attending pediatric department at Zagazig University Hospital.

Table (1): Age distribution of the studied patients (except for neonates with congenital anomalies) (N=344)    

Studied patients (N=344)

No.

%

Age (years)

Mean ± SD

9.9 ± 3.4

Median (Range)

10.0 (3.5 – 18.0)

The mean age and standard deviation (SD) of ages of the studied patients (except for neonates with congenital anomalies) in years as shown in table (1) was 9.9 ± 3.4.

Table (2): Age distribution of neonates with congenital anomalies (N=22)

Studied patients (N=22)

No.

%

Age of neonates with congenital anomalies group (days)

Mean ± SD

Median (Range)

4.1 ± 1.6

4.0 (2.0 – 7.0)

Table (2) shows that The mean age and standard deviation (SD) of ages ofneonates with congenital anomalies were 4.1 ± 1.6 days

Table (3): Sex distribution of the studied patients (N=366)

Studied patients (N=366)

No.

%

Sex

Male

202

55.2%

Female

164

44.8%

Table(3) shows that 55.2% (202 out of 366) of the studied patients were males, while 44.8% were females.

Figure (1): Pie diagram showing sex distribution of the studied patients (N=366)

Table (4): Distribution of the risk factors among the studied patients (N=366)

Risk factors

Studied patients

(N=366)

No.

%

  • Malignant hematological disease with chemotherapy

43

11.7 %

  • Receiving repeated blood transfusion

164

44.8 %

  • Fever of unknown origin

16

4.4 %

  • Critically ill patients lying in the ICUs with prolonged hospitalization

28

7.7 %

  • Receiving corticosteroids or other immunosuppressives for long period

22

6 %

  • Chronic renal failure with haemodialysis

64

17.5 %

  • Fever with pancytopenia

7

1.9 %

  • Neonates with congenital anomalies

22

6 %

As shown in table (4) and figure (2),44.8% of the studied patients were receiving repeated blood transfusion, 17.5% were suffering from chronic renal failure and receiving haemodialysis, 11.7% were suffering from Malignant hematological disease and receiving chemotherapy, 7.7% were critically ill patients lying in the ICUs with prolonged hospitalization, 6% were receiving immunosuppressive agents for long period, 6% were  neonates with congenital anomalies, 4.4% had fever of unknown origin, and 1.9% suffered from fever with pancytopenia.

Figure (2): Pie diagram showing Distribution of the risk factors in the studied patients (N=366).

Table (5): Results of ELISA IgM and IgG for CMV in the enrolled patients (N=366)

ELISA results

Studied patients

(N=366)

IgM

  • Positive

60

16.4 %

  • Negative

306

83.6 %

IgG

  • Positive

93

25.4 %

  • Negative

273

74.6 %

Over all seropositivity

  • Positive both IgM and IgG

109

29.8 %

As shown in table (5), out of the 366 studied patients, 60 (16.4%) and 93 (25.4%) were positive for CMV IgM and IgG in an ELISA test respectively.

Table (6): Agreement between ELISA IgM and IgG in the studied patients (N=366)

ELISA IgM

ELISA IgG

Total

#Test

P-value

Negative

Positive

Negative

No.

257

49

306

0.469

0.000*

(HS)

%

94.1 %

52.7 %

83.6 %

Positive

No.

16

44

60

%

5.9 %

47.3 %

16.4 %

Total

No.

273

93

366

%

100.0 %

100.0 %

100.0 %

#   Kappa measure of agreement

P< 0.05 is significant.

Statistical Significance

Standards for strength of agreement for the kappa coefficient:

≤0=poor,

.01-.20=slight,

.21-.40=fair,

.41-.60=moderate,

.61-.80=substantial, and

.81-1=almost perfect.

Table 6 shows that there is a moderate agreement between ELISA IgM and IgG in the detection of CMV in children with high statistical significance.

Table (7): Prevalence of CMV IgM seropositivity among different risk groups

Risk Factors

No.

Studied patients

(N=366)

Positive IgM

No.

%

  • Malignant hematological disease with chemotherapy

(43)

8

18.6 %

  • Receiving repeated blood transfusion

(164)

36

21.9 %

  • Fever of unknown origin

(16)

8

50 %

  • Critically ill patients lying in the ICUs with prolonged hospitalization

(28)

0

0 %

  • Receiving corticosteroids or other immunosuppressives for long period

(22)

0

0 %

  • Chronic renal failure with haemodialysis

(64)

8

12.5 %

  • Fever with pancytopenia

(7)

0

0 %

  • Neonates with congenital anomalies

(22)

0

0 %

Table (7) and figure (3) show that the highest prevalence (50%) of CMV IgM seropositivity was reported from patients suffering from fever of unknown origin.

Figure (3): Bar chart showing prevalence of CMV IgM seropositivity among different risk groups

Table (8): Association between CMV IgM seropositivity and different risk factors

Risk factors

No.

Studied patients

(N=366)

Test

p-value

ELISA IgM

Positive

(N=60)

Negative

(N=306)

No.

%

No.

%

  • Malignant hematological disease with chemotherapy

(43)

8

18.6 %

35

81.4%

#11.17

0.010

(S)

  • Receiving repeated blood transfusion

(164)

36

21.9 %

128

78%

  • Fever of unknown origin

(16)

8

50 %

8

50%

  • Chronic renal failure with haemodialysis

(64)

8

12.5 %

56

87.5%

#   chi square test

P< 0.05 is significant.

*statistical Significance

Table (9): Prevalence of CMV IgG seropositivity among different risk groups

Risk factors

No.

Studied patients

(N=366)

Positive IgG

No.

%

  • Malignant hematological disease with chemotherapy

(43)

0

0 %

  • Receiving repeated blood transfusion

(164)

63

38.4 %

  • Fever of unknown origin

(16)

0

0 %

  • Critically ill patients lying in the ICUs with prolonged hospitalization

(28)

0

0 %

  • Receiving corticosteroids or other immunosuppressives for long period

(22)

0

0 %

  • Chronic renal failure with haemodialysis

(64)

8

12.5 %

  • Fever with pancytopenia

(7)

0

0 %

  • Neonates with congenital anomalies

(22)

22

100 %

Table (9) and figure (4) show that the highest prevalence (100%) of CMV IgG seropositivity was reported from neonates with congenital anomalies.

Figure (4): Bar chart showing prevalence of CMV IgG seropositivity among different risk groups.

Table (10): Association between CMV IgG seropositivity and different risk factors

Risk factors

No.

Studied patients

(N=366)

Test

p-value

ELISA IgG

Positive

(N=93)

Negative

(N=273)

No.

%

No.

%

  • Receiving repeated blood transfusion

(164)

63

38.4%

101

61.6%

53.96

0.000*

(HS)

  • Chronic renal failure with haemodialysis

(64)

8

12.5%

56

87.5%

  • Neonates with congenital anomalies

(22)

22

100%

0

0%

#   chi square test

P< 0.05 is significant.

*highly statistical Significance

Table (11): Results of real time PCR for CMV in the enrolled patients (N=366)

Real time PCR

Studied patients (N=366)

  • Positive

36

9.8%

  • Negative

330

90.2%

Table (11) shows that 9.8% (36 out of 366) of the studied patients were positive for CMV in real time PCR test.

Table (12): Results of nested PCR for CMV in the enrolled patients (N=366)

Nested PCR

Studied patients (N=366)

  • Positive

29

7.9%

  • Negative

337

92.1%

Table (12) shows that 7.9% (29 out of 366) of the studied patients were positive for CMV in nested PCR test.

Figure (4): Results of real time PCR and nested PCR for CMV in the enrolled patients.

 

Figure (5): 1st run nested PCR showing band at 435 bp.

Figure (6): 2ndrun nested PCR showing band at 159 bp.

Table (13): Prevalence of CMV infection in the studied patients (using real time PCR as a gold standard test)

Risk factors

No.

Studied patients

(N=366)

Positive

No.

%

  • Malignant hematological disease with chemotherapy

(43)

36

83.7%

  • Receiving repeated blood transfusion

(164)

0

0%

  • Fever of unknown origin

(16)

0

0%

  • Critically ill patients lying in the ICUs with prolonged hospitalization

(28)

0

0%

  • Receiving corticosteroids or other immunosuppressives for long period

(22)

0

0%

  • Chronic renal failure with haemodialysis

(64)

0

0%

  • Fever with pancytopenia

(7)

0

0%

  • Neonates with congenital anomalies

(22)

0

0%

As shown in table (13), CMV infection (using real time PCR as a gold standard test) was only reported from patients suffering from malignant hematological disease and receiving chemotherapy, where  83.7% of these patients were positive for CMV.

Figure (7):

Figure (8):

Table (14): Titer of CMV viremia in patients with malignant hematological disease receiving chemotherapy

Quantitative PCR

Studied patients (N=366)

Mean ± SD

6907.30 ± 15846.04

Median (Range)

623.50 (3.70 – 57500)

The mean titer and SD of titers of CMV viremia in patients with malignant hematological disease receiving chemotherapy as shown in table (14) was 6907.30 ± 15846.04.

Table (15): Results of Nested PCR for CMV among different risk groups

Risk factors

No.

Studied patients

(N=366)

Positive

No.

%

  • Malignant hematological disease with chemotherapy

(43)

29

67.4%

  • Receiving repeated blood transfusion

(164)

0

0%

  • Fever of unknown origin

(16)

0

0%

  • Critically ill patients lying in the ICUs with prolonged hospitalization

(28)

0

0%

  • Receiving corticosteroids or other immunosuppressives for long period

(22)

0

0%

  • Chronic renal failure with haemodialysis

(64)

0

0%

  • Fever with pancytopenia

(7)

0

0%

  • Neonates with congenital anomalies

(22)

0

0%

Twenty nine out of 43 patients suffering from malignant hematological disease with chemotherapy with a percentage of 67.4 were positive for CMV in a nested PCR test as shown in table (15).

Table (16): Relation between ELISA IgM and real time PCR and nested PCR in the studied patients (N=366)

Agreement between ELISA IgM and real time PCR and nested PCR in the studied patients (N=366)

Laboratory findings

ELISA

Test

P-value

Positive IgM

(N=60)

Negative IgM

(N=306)

No.

%

No.

%

Real time PCR

  • Positive (n=36)

8

22.2 %

28

77.8 %

# 0.05

0. 320

(NS)

  • Negative (n=330)

52

15.8 %

278

84.2 %

Nested PCR

  • Positive

8

27.6 %

21

72.4 %

#0.082

0.090

(NS)

  • Negative

52

15.4 %

285

84.6 %

#   Kappa measure of agreement

P< 0.05 is significant.

Statistical Significance

Standards for strength of agreement for the kappa coefficient:

≤0=poor,

.01-.20=slight,

.21-.40=fair,

.41-.60=moderate,

.61-.80=substantial, and

.81-1=almost perfect.

As shown in table 16, there is poor statistical agreement between ELISA IgM and PCR reactions in the detection of CMV in children with no significance.

Table (17): Relation between ELISA IgG and real time PCR and nested PCR in the studied patients (N=366)

Agreement between ELISA IgG and real time PCR and nested PCR in the studied patients (N=366)

Laboratory findings

ELISA

Test

P-value

Positive IgG

(N=93)

Negative IgG

(N=273)

No.

%

No.

%

Real time PCR

  • Positive (n=36)

0

0 %

36

100 %

# -0.137

0.001*

(HS)

  • Negative (n=330)

93

28.2 %

237

71.8 %

Nested PCR

  • Positive

0

0 %

29

100 %

#-0.165

0.000*

(HS)

  • Negative

93

27.6 %

244

72.4 %

#   Kappa measure of agreement

P< 0.05 is significant.

*highly statistical Significance

Standards for strength of agreement for the kappa coefficient:

≤0=poor,

.01-.20=slight,

.21-.40=fair,

.41-.60=moderate,

.61-.80=substantial, and .81-1=almost perfect.

A high statistically significant non-agreement is present between ELISA IgG and PCR reactions in the detection of CMV in childrenas shown in table 17.

Table (18): Relation between real time PCR and nested PCR in the studied patients (N=366)

Agreement between real time PCR and nested PCR in the studied patients (N=366)

Laboratory findings

Nested PCR

Test

P-value

Positive

(N=29)

Negative (N=337)

No.

%

No.

%

Real time PCR

  • Positive (n=36)

29

100 %

7

2.1 %

# 0.882

0.000*

(HS)

  • Negative (n=330)

0

0 %

330

97.9 %

#   Kappa measure of agreement

P< 0.05 is significant.

*highly statistical Significance

Standards for strength of agreement for the kappa coefficient:

≤0=poor,

.01-.20=slight,

.21-.40=fair,

.41-.60=moderate,

.61-.80=substantial, and .81-1=almost perfect.

Table 18 shows that there is an almost perfect statistical agreement between real time PCR and nested PCR in the detection of CMV in children with high significance.

Table (19): Relation between real time PCR and nested


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