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Microbial genome count in cerebrospinal fluid compared with clinical characteristics in pneumococcal and Haemophilus influenzae type b meningitis in children
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Diagnostic Microbiology and Infectious Disease 63 (2009) 16 – 23 www.elsevier.com/locate/diagmicrobio
Microbial genome count in cerebrospinal fluid compared with clinical characteristics in pneumococcal and Haemophilus influenzae type b meningitis in children☆,☆☆ Irmeli Roinea,⁎, Annika Saukkoriipib , Maija Leinonenb , Heikki Peltolac LatAm Meningitis Study Group1 b
a Faculty of Health Sciences, University Diego Portales, Santiago, Chile Department of Child and Adolescent Health, National Public Health Institute, Oulu, Finland c HUCH Hospital for Children and Adolescent, 00029 Helsinki, Finland Received 23 May 2008; accepted 11 September 2009
Abstract Cerebrospinal fluid genome counts were determined by quantitative real-time polymerase chain reaction from 121 children: 36 with Streptococcus pneumoniae and 85 with Haemophilus influenzae meningitis. To examine the interactions of genome count and to determine its prognostic importance, we projected the results against findings on admission and different outcomes. The genome count varied vastly in both meningitides ranging from 0 to 9 250 000/μL. The genome quantity was weakly associated with only some of the patient findings on admission. High counts predicted neurologic (odds ratio [OR] = 1.36; 95% confidence interval [CI], 1.09–1.69; P = 0.006 for 1 log increase) but not audiologic sequelae. They also predicted death in S .pneumoniae (OR = 2.05; 95% CI, 1.08–3.87; P = 0.03) but not in H. influenzae meningitis. © 2008 Elsevier Inc. All rights reserved. Keywords: Genome count; Bacterial meningitis; Outcome; Sequelae; Real-time PCR
1. Introduction High bacterial loads in the cerebrospinal fluid (CSF) of children with bacterial meningitis have been associated ☆ Some of the present results have been presented as an abstract at the 24th annual meeting of the European Society for Paediatric Infectious Diseases—ESPID, in Basel, Switzerland, May 3 to 5, 2006. ☆☆ The GlaxoSmithKline supported the initial phase of the ISRCTN35932399 study Brentford, Middlesex, TW89GS, United Kingdom. For the genome count determinations, partial funding was obtained from the Alfred Kordelin, Päivikki and Sakari Sohlberg, and Sigfrid Jusélius Funds. ⁎ Corresponding author. Tel.: +56-2-6762916; fax: +56-2-2351032. E-mail address: [email protected] (I. Roine). 1 Antonio Gonzalez Mata, Hospital Pediatrico Dr. Augustin Zubillaga, Barquisimeto, Venezuela; Ines Zavala, Hospital de Niños Dr. Roberto Gilbert, Guayaquil, Ecuador; Antonio Arbo, Instituto de Medicina Tropical, Universidad Nacional de Asunción, Asunción, Paraguay; Greta Miño Hospital del Niño Dr. Francisco de Icaza Bustamante, Guayaquil, Ecuador; Jose Goyo Rivas, Hospital Universitario de los Andes, Mérida, Venezuela.
0732-8893/$ – see front matter © 2008 Elsevier Inc. All rights reserved. doi:10.1016/j.diagmicrobio.2008.09.005
with a higher frequency of positive Gram stains (Bingen et al., 1990; La Scolea and Dryja, 1984), delay in culture sterilization (Bingen et al., 1990; Feldman, 1976), increased antimicrobial resistance (Feldman, 1976), and severe disease (Carrol et al., 2007; Feldman, 1977). The load may vary million-fold without obvious reasons, young age being perhaps an exception (Bingen et al., 1990; Carrol et al., 2007). Earlier studies have quantified the microbial load by counting bacterial colony forming units on growth media (Bingen et al., 1990; Feldman, 1976, 1978; La Scolea and Dryja, 1984). This approach probably underestimates the load, especially in patients who have received preadmission antimicrobials (Feldman, 1978), because only viable organisms are detected (Hackett et al., 2002). We used quantitative real-time polymerase chain reaction (PCR), which determines the microbial load more accurately, counting both viable and nonviable microorganisms (Saukkoriipi et al., 2003), to examine if genome quantity is related to clinical
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findings on admission and to determine its prognostic importance for different outcomes in childhood Streptococcus pneumoniae (pneumococcal) and Haemophilus influenzae type b (Hib) meningitis. 2. Materials and methods 2.1. Setup and data collection Cerebrospinal fluid samples, taken from the diagnostic tap on admission, were collected during 1999 to 2003 as part of the childhood bacterial meningitis study (ISRCTN35932399; Peltola et al., 2007) carried out in Latin America (participating centers are listed on front page). That study enrolled previously healthy children aged 2 months to 14 years presenting with probable bacterial meningitis. The protocol, approved by local ethical committees, stated that a part of the CSF sample was to be frozen for further analysis. The patients were enrolled only if consented by the legal guardian. Bacterial culture, Gram stain, and the detection of capsular antigens of S. pneumoniae and H. influenzae by latex agglutination were performed as described earlier (Peltola et al., 2007). During the meningitis study (Peltola et al., 2007), the study pediatrician had registered predefined patient characteristics on admission and continued recording throughout the course of disease. Nutritional status was determined as the z-score for weight/age by the World Health Organization nutritional survey calculator. At discharge, a thorough neurologic evaluation was carried out by a pediatric neurologist or the study pediatrician. Three predefined outcome measures were used: death, neurologic sequelae, and hearing impairment. “Severe neurologic sequelae” were defined as blindness, quadriplegia or paresis, hydrocephalus needing a shunt, or severe psychomotor retardation (does not sit or walk, speak, or establish contact, or requires institutionalization). In addition to these, “any neurologic sequelae” included hemiparesis, monoparesis, ataxia, and moderate psychomotor retardation (Glascoe et al., 1992). Hearing was measured after exclusion of middle ear disease separately for each ear using brain stem auditory evoked potentials in patients below 4 years of age (112 of 121 patients, 93%) or normal audiometry. The test results were interpreted by local specialists. The better ear's inability to recognize sounds at or above 80 dB was considered “severe hearing impairment” and when above 40 dB as “any hearing impairment”. 2.2. Real-time PCR analysis DNA extractions and PCR analyses were carried out in Finland in 2004 and early 2005. Cerebrospinal fluid samples were kept at −20 °C until analysis. DNA was extracted from 200 μL of CSF sample using QIAamp® DNA Mini Kit (QIAGEN, Hilden, Germany) according to the tissue protocol described in the instructions of the manufacturer
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or later from 100 μL of CSF sample using the MagNA Pure LC instrument and MagNA Pure LC DNA extraction Kit III (both from Roche Diagnostics, Mannheim, Germany). In both of the methods, lysis of sample was done overnight, and the extracted DNA was eluted into 100 μL of elusion buffer. The concentration of sample during DNA extraction using QIAamp® DNA Mini Kit was taken into account when calculating genome counts. General precautions were taken to avoid contamination during DNA extraction, including use of a separate room designated for DNA extraction only. At least every 16th sample was a negative control containing sterile distilled water instead of sample. The extracted DNA was stored at −20 °C. Real-time PCRs were performed using the LightCycler® 1.2 instrument (Roche Diagnostics). For detection and quantification of S. pneumoniae, our previously published method (Saukkoriipi et al., 2002) was used. In this method, a 206-bp fragment of the pneumolysin encoding gene is amplified, and fluorescent-labeled hybridization probes are used for detection of amplification products. In real-time pneumolysin PCR, an 8-μL DNA sample volume was used. For Hib, a conventional PCR method (Falla et al., 1994) was modified. The same forward primer was used, but a new reverse primer was designed because the length of the amplicon in the conventional PCR was rather long for real-time PCR. The sequence of the new reverse primer HibA was 5′-GCTAAGATGAAGTTATGGCGAA3′, and the size of the amplified fragment was 348 bp. In addition, a pair of fluorescent-labeled sequence-specific hybridization probes for detection of amplification products were designed by O. Landt (Tib-Molbiol, Berlin, Germany) based on the published sequence of the capB locus (GenBank accession no. X78559). The sequences of the hybridization probes were 5′-CATTTAGCAGACGACCAAAG GTATCTTG-fluorescein-3′ and 5′-LCRed640GTATAG CCTCGCCCCCAGAATTC-phosphate-3′ (TibMolbiol). The 20-μL reaction volume of real-time Hib-PCR contained 1× LightCycler® FastStart DNA Master Hybridization Probes solution (Roche Diagnostics), 4 mmol/L MgCl2, 0.5 μmol/L of each primer, 0.2 μmol/L of each hybridization probe, and 8 μL of sample DNA. The real-time Hib-PCR program consisted of an initial denaturation step at 95 °C for 10 min, 50 cycles of amplification each consisting of denaturation at 95 °C for 10 s, annealing at 59 °C for 10 s, and elongation at 72 °C for 14 s, and finally, a cooling step at 40 °C for 30 s. Fluorescence was measured in each cycle after the annealing step. In each PCR, every 7th specimen was a sterile distilled water control. Standard precautions were taken to prevent carryover contamination including the separation preparation of master mix, addition of template, and PCR into 3 different rooms. The fluorescence data were analyzed using the data analysis program included in the LightCycler® 4.0 Software (Roche Diagnostics). In both of the real-time PCR assays, an external standard curve was used for
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quantification. The external standards were prepared by extracting DNA from cultured pneumococcus (ATCC® 6305, American Type Culture Collection) and Hib and measuring the DNA concentrations using the Quant-iT™ PicoGreen® dsDNA Assay Kit (Invitrogen Molecular Probes™, Eugene, OR) or spectrophotometrically. For pneumococcus, the standard curve was prepared by amplifying 4 replicates of standards containing 5 × 105, 5 × 104, 5 × 103, 5 × 102, 50, and 5 pneumococcal genome equivalents (calculated based on the genome size of S. pneumoniae, GenBank accession number AE005672). A standard containing 5 × 102 copies was used as a calibrator and positive control in each run. For Hib, 4 replicates of dilutions containing 105, 104, 103, 102, 10, and 1 genome equivalents/μL (calculated based on a genome size of 1950 kb) (Kauc, 1992) were amplified. A standard containing 8 × 102 copies was used as a calibrator and positive control in each run. All dilutions of standards were made in H2O containing 10 ng/mL MS2 RNA (Roche Diagnostics). The average amplification efficiencies of the pneumococcus and Hib standards were calculated from the standard curves of the PCRs. The amplification efficiency of samples was studied by analyzing the dilution series of 1 sample positive by pneumolysin PCR and 1 positive by Hib PCR. The threshold detecting cycles were plotted versus the logarithm of the concentrations of the diluted samples, and the amplification efficiencies were calculated from the slope of the lines. In quantitative PCR, 5 or more of bacterial genomes/μL of CSF was considered as a cutoff value for positivity. 2.3. Statistical analysis Results describing continuous variables are shown as mean values with SD. If the distribution was not normal, they were log transformed before analysis, and their values are shown as median values with the interquartile value (IQR, the spread of values containing the central 50% of the data). The genome counts were projected against patient characteristics, history of disease, and laboratory findings on admission using Student's t test, Mann–Whitney U test, simple regression, or Spearman's correlation, whichever is appropriate. The relationship between the genome count and the outcomes are expressed as odds ratio (OR) with 95% confidence interval (CI) for each 1 log unit increase in genome count, starting from 1. These results were obtained by binary logistic regression using the outcomes, 1 at a time, as the dependent variable and log transformed genome count as the independent variable alone (Table 4, univariate analysis), or together with the adjuvant treatments (Table 5, multivariate analysis). First, all patients were examined as a whole, then separately for pneumococcal and Hib meningitis. A P value below 0.05 is taken as significant.
3. Results 3.1. Patient population Cerebrospinal fluid samples with either pneumococcal or Hib etiology were available from 121 (48%) of the 252 patients with these etiologies in the ISRCTN35932399 trial. The samples came from Venezuela (49/121, 40%), Ecuador (47/121, 39%), and Paraguay (25/121, 21%). Fifty-one/121 (42%) patients had received preadmission antimicrobials, 88% orally, with a median duration of 3 days (range, 0–15; mode, 1 day). As treatment, in addition to ceftriaxone (Peltola et al., 2007), 27 patients had received adjuvant dexamethasone (9 pneumococcus, 18 Hib), 25 dexamethasone and glycerol (7 pneumococcus, 18 Hib), 28 glycerol (7 pneumococcus, 21 Hib), and 41 placebo (13 pneumococcus, 28 Hib). Etiologic diagnosis was based in 76 (63%) CSF samples on culture (24 pneumococcal, 52 Hib), in 15 (12%) on a positive latex agglutination test with a concordant Gram stain result (3 pneumococcal, 12 Hib), in 7 (6%) on a positive latex result (all Hib), in 6 (5%) on a positive Gram stain (4 pneumococcal, 2 Hib), and in 17 (14%) on a real-time PCR test result (N5 genomes/μL) (5 pneumococcal, 12 Hib). Thirty-six patients had pneumococcal and 85 patients had Hib meningitis. The characteristics of the patients are presented in Table 1.
Table 1 Characteristics of the series Characteristic
Age, median (months) Females Z-score weight/age, mean ± SD History N48 h Convulsions Focal paresis Otitis media Prior antimicrobials Glasgow Coma Score, median CSF, median values Leucocytes, 103/μL Polymorphonuclears, 103/μL Glucose (mg/dL) Protein (g/dL) Blood, median values White cell count, 106/μL Platelet count, 109/μL Outcome Death Neurologic sequelae Severe Any Hearing impairment Severe Any a b
Pneumococcus
Hib
n = 36
n = 85
6 (40)a 9 [25]b -0.95 ± 1.56 1/33 [3] 20/35 [57] 0/35 [–] 2/35 [6] 14/35 [40] 10 (6)
8 (7) 31 [36] -0.50 ± 1.41 12/76 [16] 33/82 [40] 9/82 [11] 10/80 [13] 37/85 [44] 13 (4)
0.55 0.22 0.12 0.06 0.09 0.06 0.28 0.72 0.002
1288 (3198) 911 (2395) 12 (25) 191 (148)
2253 (5364) 1925 (4899) 13 (24) 139 (129)
0.04 0.08 0.76 0.04
16.2 (14.4) 275 (227)
12.2 (10.4) 232 (190)
0.34 0.12
9 [25]
10 [12]
0.07
4/27 [15] 11/27 [41]
11/73 [5] 33/73 [45]
0.97 0.69
1/24 [4] 11/24 [46]
9/71 [13] 31/71 [44]
0.24 0.85
In parenthesis: interquartile value. In brackets: percentages.
P
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Table 2 Comparison of CSF bacterial genome count with continuous patient characteristics All patients, n = 121
Pneumococcus, n = 36
Hib, n = 85
P
rs
P
rs
P
b
−0.20 −0.10 −0.18 −0.003
0.03 0.32 0.05 0.97
−0.27 −0.03 −0.22 −0.14
0.12 0.88 0.20 0.41
−0.18 −0.10 0.10 −0.06
0.09 0.36 0.37 0.58
b0.001 −0.003 −0.07 0.12
0.99 0.98 0.48 0.24
−0.39 −0.44 −0.21 0.36
0.02 0.01 0.23 0.046
0.18 0.20 −0.002 −0.002
0.10 0.09 0.99 0.99
−0.31 −0.004
0.0009 0.97
−0.36 −0.06
0.04 0.74
−0.27 b0.001
0.02 0.99
rs Age (months) Weight/age z-score Glasgow Coma Score Prior antimicrobials (days) CSF White cell count, 103/μL Polymorphonuclears, 103/μL Glucose (mg/dL) Protein (g/dL) Blood White cell count, 106/μL Platelet count, 109/μL
a
a
Spearman's correlation coefficient. The value of −0.20 indicates that when age increases, the genome count tends to decrease. Because the result is far away from −1, which indicates perfect inverse correlation, the effect of 1 variable on the other variable is weak, yet greater than what would be expected in a random sample, because P value is below 0.05. b
Pneumococcal patients were more severely ill than Hib patients (Table 1), as shown by the Glasgow Coma Score, the median being 10 versus 13, respectively (P = 0.002). The CSF white cell response was lower in pneumococcal meningitis (P = 0.04), which also presented with a higher CSF protein concentration (P = 0.04). 3.2. Amplification efficiency The amplification efficiencies of the standards in both pneumolysin and Hib PCR were 1.97 (the optimal efficiency being 2). The amplification efficiencies of the CSF samples analyzed were 1.97 in pneumolysin PCR and 1.92 in Hib PCR. 3.3. Cerebrospinal fluid genome counts Cerebrospinal fluid genome counts varied enormously, ranging for the entire group from 0 to 9.3 × 106/μL (median, 4.5 × 103/μL; IQR, 3.2 × 105), overlapping widely in
pneumococcal and Hib patients. The median in pneumococcal cases (4.6 × 104/μL; IQR, 4.0 × 105) was higher than for Hib meningitis (3.5 × 103/μL; IQR, 2.5 × 105; P = 0.16). There were 11 patients (3 pneumococcal, 8 Hib) whose CSF genome count was less than 5/μL (0 in 5 cases, 1 in 4 cases, 2 in 1 case, and 3 in 1 case; 2 of these samples had been collected in 1999, 6 in 2000, 2 in 2001, and 1 in 2003). Their etiologic diagnosis had been based on a positive CSF culture in 6 cases (4 pneumococcal, 2 Hib) and on a positive CSF latex result in 5 cases (1 pneumococcal, 4 Hib), with a concordant Gram stain result in 4 cases (1 pneumococcal, 3 Hib). Seven of these patients (7/11, 64%, 2 pneumococcal, 5 Hib) had received preadmission antimicrobials, but the difference was not significant compared with the rest of the patients (44/109 [data not available for 1 patient], 40%, P = 0.20). Genome counts obtained from samples that had used the 1st DNA extraction method (n = 49; range, 0–9 250 000/μL; median, 2954; IQR, 336 313) did not differ (P = 0.68) from
Table 3 Median CSF bacterial genome count × 103/μL according to nominal patient characteristics and outcome of disease Variables
All patients, n = 121
Pneumococcus, n = 36
Variable
Male sex Prior antimicrobials Prior convulsions History N48 h Otitis media Focal paresis Positive CSF culture Death Any neurologic sequelae Any audiologic sequelae a
Hib, n = 85
Variable
Variable
Present
Absent
P
Present
Absent
P
Present
Absent
P
3.5 4.5 16.4 1.2 109.5 21.2 45.1 201.9 20.3 3.2
33.2 16.4 3.0 4.8 3.5 3.7 1.1 3.4 0.8 3.5
0.33 0.04 0.08 0.84 0.71 0.19 0.005 0.07 0.006 0.72
21.1 2.1 173.4 3a 1.1 – 145.0 277.1 71.3 1.8
71.3 201.9 1.8 46.2 71.3 21.1 2.1 3.1 1.0 21.1
0.69 0.06 0.01 0.14 0.20 – 0.28 0.02 0.04 0.66
3.1 1.3 3.3 1.6 160.9 21.2 15.6 13.2 15.0 4.5
21.2 5.6 4.5 3.9 1.6 3.0 0.4 3.5 0.8 3.7
0.14 0.25 0.94 0.61 0.24 0.13 0.007 0.99 0.04 0.87
There was only 1 patient in this category.
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the ones using the 2nd method (n = 72; range, 0–8 025 000/ μL; median, 10 785; IQR, 288 094). The median genome count in samples collected from 1999 to 2000 (5.6 × 103/μL, n = 74) did not differ (P = 0.93) from that of samples collected from 2001 to 2003 (2.9 × 103/μL, n = 47). 3.4. Pneumococcal and Hib meningitis combined Higher genome counts were associated with younger age (Spearman's correlation = −0.20, P = 0.03, Table 2) and more positive CSF cultures (P = 0.005, Table 3), but sex, nutritional status, or a preadmission history of more than 48 h of symptoms did not have a significant effect on the count (Tables 2 and 3). Increasing genome counts correlated with decreasing blood white cell count (Spearman's correlation = −0.31, P = 0.0009 for all meningitides together) and showed a borderline association with decreasing Glasgow Coma Score (Spearman's correlation = −0.18, P = 0.05). No correlation was observed between the genome count and CSF glucose and protein concentrations, or with the number of days of having received preadmission antimicrobials (Table 2). Patients with focal paresis or otitis media did not differ from those without these disorders (Table 3). In the univariate analysis regarding the outcomes, only neurologic sequelae were significantly associated with the genome count (P = 0.006, Table 3). Each log unit elevated their risk by 36% (OR = 1.36; 95% CI, 1.09–1.69; P = 0.007, Table 4). Death and severe neurologic sequelae showed a similar tendency without reaching significance (OR = 1.29; 95% CI, 0.98–1.69; P = 0.07, and OR = 1.26; 95% CI, 0.93– 1.70; P = 0.14, respectively). Hearing impairment, regardless of its extent, was not associated with the count (OR = 0.97; 95% CI, 0.70–1.35; P = 0.87, for severe, and OR = 0.97; 95% CI, 0.79–1.19; P = 0.77, for any hearing impairment). In the multivariate analysis regarding the outcomes, examining the genome count together with adjuvant treatments (Table 5), the genome count is an independent predictor for both neurologic sequelae (OR = 1.31; 95% CI, 1.04–1.66; P = 0.02) and death (OR = 1.37; 95% CI, 1.001– 1.89; P = 0.049), whereas adjuvant treatments play a secondary role (P N 0.5).
None of the values of the tested variables, the frequencies, or the outcomes (Tables 1–3) differed between the patients who had and had not received preadmission antimicrobials (P N 0.05), with the exception of the CSF genome count, which was lower (P = 0.04) in patients with preadmission antimicrobials (Table 3). 3.5. Pneumococcal versus Hib meningitis Contrary to Hib meningitis, in pneumococcal meningitis, an increase in the genome count clearly increased the risk of death (OR = 2.05; 95% CI, 1.08–3.87; P = 0.03, Table 4), also when analyzed together with adjuvant treatments (OR = 2.56; 95% CI, 1.11–5.87; P = 0.03, Table 5). Other differences were a positive association between an increasing genome count and lower CSF white cell and polymorphonuclear cell counts (Spearman's correlation = −0.39, P = 0.02, and Spearman's correlation = −0.44, P = 0.01, respectively) in pneumococcal but not Hib meningitis (Table 2). Only in pneumococcal meningitis was a higher count associated with a history of convulsions (P = 0.01, Table 3). In turn, only in Hib meningitis was the genome count associated with positive CSF cultures (P = 0.007, Table 3). In addition, a higher count showed a stronger association with neurologic sequelae in pneumococcal (univariate P = 0.046, multivariate P = 0.04) compared with Hib meningitis (univariate P = 0.05, multivariate P = 0.14, Tables 4 and 5). In both meningitides (Table 2), CSF genome count was inversely associated with the blood leukocyte count (Spearman's correlation = −0.37, P = 0.04 in pneumococcal, and Spearman's correlation = −0.27, P = 0.02 in Hib meningitis, Table 2). To test if a combination of patient characteristics explained better the great variation in the genome count than each alone, we formed a score in which 3 characteristics (age below 7 months, blood white cell count below 15 000/ μL, and CSF leukocyte count below 1000/μL) were given 1 point each. This score was projected against log CSF genome count by simple regression. The squared correlation coefficients were 0.12 for pneumococcal (log genome count = 3.4 + 0.63 × score, P = 0.05) and 0.07 for Hib meningitis (log genome count = 3.0 + 0.54 × score,
Table 4 Univariate analysis of the effect of 1 log unit increase of CSF bacterial genome count on the risk of adverse outcome Outcome
All cases, n = 116a OR
Death 1.29 Neurologic sequelae Severe 1.26 Any 1.36 Hearing impairment Severe 0.97 Any 0.97 a
Pneumococcus, n = 34
Hib, n = 82
95% CI
P
OR
95% CI
P
OR
95% CI
P
0.98–1.69
0.07
2.05
1.08–3.87
0.03
0.99
0.71–1.39
0.98
0.93–1.70 1.09–1.69
0.14 0.007
2.04 1.61
0.89–4.69 1.01–2.57
0.09 0.046
1.11 1.29
0.79–1.55 0.99–1.65
0.55 0.05
0.70–1.35 0.79–1.19
0.87 0.77
0.91 0.97
0.33–2.48 0.65–1.46
0.85 0.88
0.98 0.97
0.69–1.39 0.77–1.23
0.91 0.80
Five patients (2 pneumococcal, 3 Hib) with a CSF genome count of zero have no log result and therefore cannot be included in this analysis.
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Table 5 Multivariate analysis of the effects of 1 log unit increase of CSF bacterial genome count and adjuvant treatments on the risk of adverse outcome Outcome
All cases OR
Death CSF genome count Dexamethasoneb Dexamethasone + glycerolb Glycerolb Neurologic sequelae Severe CSF genome count Dexamethasone Dexamethasone + glycerol Glycerol Any CSF genome count Dexamethasone Dexamethasone + glycerol Glycerol Hearing impairment Severe CSF genome count Dexamethasone Dexamethasone + glycerol Glycerol Any CSF genome count Dexamethasone Dexamethasone + glycerol Glycerol
P 95% CI
Pneumococcus OR
95% CI n = 34 1.11–5.87 0.17–15.25 0.01–2.08 0.04–9.36 n = 25
a
P
Hib
P
OR
95% CI
0.03 0.68 0.15 0.73
1.08 1.07 1.12 0.87
n = 82 0.74–1.59 0.16–7.18 0.16–7.60 0.13–5.82 n = 71
0.68 0.94 0.91 0.89
1.37 1.20 0.67 0.67
n = 116 1.001–1.89 0.33–4.44 0.15–2.99 0.15–2.94 n = 96
0.049 0.78 0.60 0.59
2.56 1.60 0.13 0.63
1.21 1.16 0.49 0.63
0.88–1.66 0.28–4.85 0.09–2.72 0.14–2.87
0.23 0.84 0.41 0.55
4.86 –c – –
0.81–29.13
0.08
1.04 0.82 0.24 0.17
0.73–1.47 0.17–3.94 0.03–2.22 0.02–1.54
0.83 0.80 0.21 0.11
1.31 1.59 0.48 1.15
1.04–1.66 0.49–5.17 0.14–1.64 0.39–3.39 n = 91
0.02 0.44 0.24 0.80
2.45 10.88 0.56 21.23
1.04–5.78 0.46–260.33 0.03–9.70 0.90–499.24 n = 22
0.04 0.14 0.69 0.06
1.22 1.10 0.37 0.63
0.94–1.58 0.29–4.26 0.09–1.55 0.18–2.17 n = 69
0.14 0.89 0.17 0.47
0.92 0.78 0.76 0.61
0.65–1.30 0.13–4.73 0.12–4.61 0.10–3.68
0.64 0.78 0.76 0.59
0.77 –d – –
0.30–2.02
0.60
0.94 0.71 0.34 0.56
0.65–1.35 0.11–4.48 0.03–3.42 0.09–3.47
0.73 0.71 0.36 0.53
0.92 1.60 0.40 1.03
0.73–1.15 0.50–5.12 0.12–1.41 0.35–3.07
0.46 0.43 0.15 0.96
0.77 3.04 0.25 1.40
0.44–3.87 0.20–46.08 0.02–3.62 0.14–13.95
0.35 0.42 0.31 0.78
0.96 1.39 0.47 0.95
0.74–1.23 0.37–5.20 0.11–1.99 0.27–3.34
0.74 0.63 0.31 0.94
Binominal logistic regression analysis. a Five patients (2 pneumococcal, 3 Hib) with a CSF genome count of zero have no log result and therefore cannot be included in this analysis. b Compared with patients who received placebo adjuvant treatment. c Not evaluable because none of the 9 patients who received placebo adjuvant treatment had severe neurologic sequelae. d Not evaluable because none of the patients who received dexamethasone, glycerol, or placebo had severe hearing impairment.
P = 0.02). Thus, the combination explained of the count variation only 12% in pneumococcal and 7% in Hib meningitis (Rummel, 1976).
4. Discussion Bacterial load quantification may open new insights into understanding pathogenetic mechanisms of disease and new possibilities to patient management (van Haeften et al., 2003). Our results show that the vast variation in bacterial quantity in childhood bacterial meningitis was surprisingly independent of patient findings on admission but was related to the prognosis. Its predictive value was clearest for neurologic sequelae and survival, but only in pneumococcal meningitis. Hearing impairment showed no association in either meningitis. Although young age (Bingen et al., 1990; Carrol et al., 2007) was associated with a higher genome count, it explained only a fraction of its variation (Spearman's correlation = −0.20). Likewise, the association between the blood white cell count and CSF genome count was weak (Spearman's correlation = −0.31), suggesting that leukocy-
tosis has some effect, but relatively small, in curbing it. Even the combination of the clinical characteristics (age, blood, and CSF white cell response) explained only around 10% of the variation. Clearly, the major determinants of the millionfold load variation were not identified in our study. Delay in instituting meningitis treatment showed no identifiable effect on the genome count (Table 3) in either meningitides. Although surprising, this observation fits previous reports (Carrol et al., 2007; Feldman, 1977; Hackett et al., 2002) and the clinical experience (Kallio et al., 1994; Kilpi et al., 1993) that some children survive meningitis well, despite probably having had meningitis for a few days before admission. On the other hand, several days of delay is common in developing countries, and this almost certainly contributes to the high mortality still being 30% to 50% (Peltola, 2001). Preadmission antibiotics had a lowering impact on Hib and meningococcal, but not on pneumococcal load in an earlier Feldman study that used the colony counting technique (Feldman, 1978). In our data, using the more accurate method of counting both viable and nonviable organisms, a trend of this effect is seen also in the pneumococcal load (P = 0.06).
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One would expect that the number of invading bacteria influences the CSF white cell count and glucose and protein concentrations. Surprisingly, this was true only for the white cell count and only in pneumococcal meningitis. The variation in glucose and protein concentrations was independent of the bacterial load in our, as well as in a previous study (Feldman, 1977). Cerebrospinal fluid bacterial load obtained by culture is known to associate with severe meningitis and complications during recovery (Carrol et al., 2007; Feldman, 1977), and our results agree. The novel findings of our study are that the bacterial load 1) predicted death only in pneumococcal meningitis, 2) predicted neurologic sequelae with more strength in pneumococcal compared with Hib meningitis, and 3) did not seem to affect audiologic prognosis. This suggests potentially important differences in the mechanisms leading to neurologic and audiologic sequelae. The strength of our study is to have tested the modern modality for determining CSF bacterial load and to separate different outcomes. Freezing and thawing, and long storage of the CSF samples may have caused some error, particularly in samples with very low counts. In the 11 patient samples with an etiologic diagnosis based on a positive CSF culture or a positive CSF latex result, but a bacterial genome count less than 5/μL, either DNA degradation or a misdiagnosis by traditional bacteriology may have taken place. According to the data shown, it is unlikely that storage time played a role, but it is possible that a single sample at random had been handled suboptimally. However, overall, our results show a similar range and median value compared with those from a recent similar study (Carrol et al., 2007). In patients with pneumococcal meningitis, our genome count range was 0 to 9.2 × 106/μL (median, 4.6 × 104/μL) compared with Malawi children with a range of 0.4 to 6.2 × 105/μL (median, 5.8 × 104/μL) (Carrol et al., 2007). Another potential source of error in samples, leading to falsely low or even negative results, is incomplete removal of PCR inhibitors during DNA extraction. In our case, this hardly was a major problem considering that the median values of our samples were in the range of 104 copies/μL, but it may have had an affect on the result of an individual sample. The specificity of pneumolysin PCR for pneumococcus has been questioned in several recent studies, as also other αhemolytic streptococci have been found to harbor the ply gene (Carvalho et al., 2007; Whatmore et al., 2000). However, these viridans streptococci are not expected in the CSF. We have previously compared the sensitivities of real-time pneumolysin PCR and bacterial culture by analyzing several dilutions of a pneumococcal broth culture by both methods (Saukkoriipi et al., 2003). In that study, there was a strong correlation (Spearman's correlation = 0.983, P b 0.001) between the results obtained by quantitative culture and quantitative PCR, but the numbers of genomes detected by real-time pneumolysin PCR were 10 to 30 times higher than the numbers of colonies detected by culture, indicating a higher sensitivity of real-time PCR
compared with culture. On the other hand, PCR may also have detected bacteria with impaired viability. Most invasive Hib strains possess a duplication of the capsulation locus, and even multiple copies of the capsular locus in Hib strains have been demonstrated (Cerquetti et al., 2005; Corn et al., 1993). Thus, there is a possibility that a high Hib genome count in our study would be due, in some cases, to the presence of strains possessing multiple copies of the Hib capsular locus. We underline the theoretic nature of this explanation because multiple-copy strains were found in disease other than meningitis (Cerquetti et al., 2005). The CSF genome count had more importance for outcome than adjuvant treatments (Table 5), but these results should be considered preliminary, because the small sample size in a multivariate analysis may lead to missing significance, although it exists. Before definite conclusions, this matter should be analyzed in a bigger data set. In conclusion, the major determinants of the vast variation of CSF pneumococcal and Hib genome counts were not among the variables examined in the present study. Differences according to outcomes and etiology in the prognostic importance of the load suggest different mechanisms of the processes leading to them. Acknowledgments The authors thank Leena Kuisma and Elsi Äijälä for their excellent work in real-time PCR analysis. References Bingen E, Lambert-Zechovsky N, Mariani-Kurkdijan P, Doit C, Aujard Y, Fournerie F, Mathieu H (1990) Bacterial counts in cerebrospinal fluid of children with meningitis. Eur J Clin Microbiol Infect Dis 9:278–281. Carrol ED, Guiver M, Nkhoma S, Mankhambo LA, Marsh J, Balmer P, Banda DL, Jeffers G, White SA, Molyneux EM, Molyneux ME, Smyth RL, Hart CA (2007) High pneumococcal DNA loads are associated with mortality in Malawian children with invasive pneumococcal disease. Pediatr Infect Dis J 26:416–422. Carvalho MdaG, Tondella ML, McGaustland K, Weidlich L, McGee L, Mayer LW, Steigerwalt A, Whaley M, Facklam RR, Fields B, Carlone G, Ades EW, Dagan R, Sampson JS (2007) Evaluation and improvement of real-time PCR assays targeting lytAplypsaA genes for detection of pneumococcal DNA. J Clin Microbiol 45:2460–2466. Cerquetti M, Cardines R, Ciofi Degli Atti ML, Giufre M, Bella A, Sofia T, Mastrantonio P, Slack M (2005) Presence of multiple copies of capsulation b locus in invasive Haemophilus influenzae type vaccine failure. J Infect Dis 192:819–823. Corn PG, Anders J, Takala AK, Käyhty H, Hoiseth SK (1993) Genes involved in Haemophilus influenzae type b capsule expression are frequently amplified. J Infect Dis 167:356–364. Falla TJ, Crook DWM, Brophy LN, Maskell D, Kroll JS, Moxon ER (1994) PCR for capsular typing of Haemophilus influenzae. J Clin Microbiol 32:2382–2386. Feldman WE (1976) Concentrations of bacteria in cerebrospinal fluid of patients with bacterial meningitis. J Pediatr 88:549–552. Feldman WE (1977) Relation of concentrations of bacteria and bacterial antigen in cerebrospinal fluid to prognosis in patients with bacterial meningitis. N Engl J Med 296:433–435. Feldman WE (1978) Effect of prior antibiotic therapy on concentrations of bacteria in CSF. Am J Dis Child 132:672–674.
I. Roine et al. / Diagnostic Microbiology and Infectious Disease 63 (2009) 16–23 Glascoe FP, Byrne KE, Ashford LG, Johnson KL, Chang B, Strickland B (1992) Accuracy of the Denver-II in developmental screening. Pediatrics 89:1221–1225. Hackett SJ, Guiver M, Marsh J, Sills JA, Thomson AP, Kaczmarski EB, Hart CA (2002) Meningococcal bacterial DNA at presentation correlates with disease severity. Arch Dis Child 86:44–46. Kallio MJ, Kilpi T, Anttila M, Peltola H (1994) The effect of a recent previous visit to a physician on outcome after childhood bacterial meningitis. JAMA) 272(10):787–791. Kauc L (1992) Determination of genome size of Haemophilus influenzae Sb: analysis of DNA restriction fragments. Acta Microbiol Pol 41:13–24. Kilpi T, Anttila M, Kallio MJ, Peltola H (1993) Length of prediagnostic history related to the course and sequelae of childhood bacterial meningitis. Pediatr Infect Dis J 12:184–188. La Scolea Jr LJ, Dryja D (1984) Quantification of bacteria in cerebrospinal fluid and blood of children with meningitis and its diagnostic significance. J Clin Microbiol 19:187–190. Peltola H (2001) Burden of meningitis and other severe bacterial infections of children in Africa: implications for prevention. Clin Infect Dis 32: 64–75. Peltola H, Roine I, Fernández J, Zavala I, González Ayala S, González Mata A, Arbo A, Bologna R, Miño G, Goyo J, López E, Dourado de
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Andrade S, Sarna S (2007) Adjuvant glycerol and/or da prospective, randomized, double-blind, placebo-controlled trial. Clin Infect Dis 45: 1277–1286. Rummel RJ (1976) Understanding correlation. E-pub Honolulu: Department of political science, University of Hawai. Available at: http:// www.hawaii.edu/powerkills/UC.HTM. Cited 1 May 2007. Saukkoriipi A, Palmu A, Kilpi T, Leinonen M (2002) Real-time quantitative PCR for the detection of Streptococcus pneumoniae in the middle ear fluid of children with acute otitis media. Mol Cell Probes 16:385–390. Saukkoriipi A, Kaijalainen T, Kuisma L, Ojala A, Leinonen M (2003) Isolation of pneumococcal DNA from nasopharyngeal samples for realtime, quantitative PCR. Comparison of three methods. Mol Diagn 7:9–15. van Haeften R, Palladino S, Kay I, Keil T, Heath C, Waterer GW (2003) A quantitative LightCycler PCR to detect Streptococcus pneumoniae in blood and CSF. Diagn Microbiol Infect Dis 47:407–414. Whatmore AM, Efstratiou A, Pickerill AP, Broughton K, Woodard G, Sturgeon D, George R, Dowson CG (2000) Genetic relationship between clinical isolates of Streptococcus pneumoniae, Streptococcus oralis, and Streptococcus mitis: characterization of "Atypical" pneumococci and organisms allied to S. mitis harboring S. pneumoniae virulence factorencoding genes. Infect Immun 68:1374–1382.
Diagnostic Microbiology and Infectious Disease 63 (2009) 16 – 23 www.elsevier.com/locate/diagmicrobio
Microbial genome count in cerebrospinal fluid compared with clinical characteristics in pneumococcal and Haemophilus influenzae type b meningitis in children☆,☆☆ Irmeli Roinea,⁎, Annika Saukkoriipib , Maija Leinonenb , Heikki Peltolac LatAm Meningitis Study Group1 b
a Faculty of Health Sciences, University Diego Portales, Santiago, Chile Department of Child and Adolescent Health, National Public Health Institute, Oulu, Finland c HUCH Hospital for Children and Adolescent, 00029 Helsinki, Finland Received 23 May 2008; accepted 11 September 2009
Abstract Cerebrospinal fluid genome counts were determined by quantitative real-time polymerase chain reaction from 121 children: 36 with Streptococcus pneumoniae and 85 with Haemophilus influenzae meningitis. To examine the interactions of genome count and to determine its prognostic importance, we projected the results against findings on admission and different outcomes. The genome count varied vastly in both meningitides ranging from 0 to 9 250 000/μL. The genome quantity was weakly associated with only some of the patient findings on admission. High counts predicted neurologic (odds ratio [OR] = 1.36; 95% confidence interval [CI], 1.09–1.69; P = 0.006 for 1 log increase) but not audiologic sequelae. They also predicted death in S .pneumoniae (OR = 2.05; 95% CI, 1.08–3.87; P = 0.03) but not in H. influenzae meningitis. © 2008 Elsevier Inc. All rights reserved. Keywords: Genome count; Bacterial meningitis; Outcome; Sequelae; Real-time PCR
1. Introduction High bacterial loads in the cerebrospinal fluid (CSF) of children with bacterial meningitis have been associated ☆ Some of the present results have been presented as an abstract at the 24th annual meeting of the European Society for Paediatric Infectious Diseases—ESPID, in Basel, Switzerland, May 3 to 5, 2006. ☆☆ The GlaxoSmithKline supported the initial phase of the ISRCTN35932399 study Brentford, Middlesex, TW89GS, United Kingdom. For the genome count determinations, partial funding was obtained from the Alfred Kordelin, Päivikki and Sakari Sohlberg, and Sigfrid Jusélius Funds. ⁎ Corresponding author. Tel.: +56-2-6762916; fax: +56-2-2351032. E-mail address: [email protected] (I. Roine). 1 Antonio Gonzalez Mata, Hospital Pediatrico Dr. Augustin Zubillaga, Barquisimeto, Venezuela; Ines Zavala, Hospital de Niños Dr. Roberto Gilbert, Guayaquil, Ecuador; Antonio Arbo, Instituto de Medicina Tropical, Universidad Nacional de Asunción, Asunción, Paraguay; Greta Miño Hospital del Niño Dr. Francisco de Icaza Bustamante, Guayaquil, Ecuador; Jose Goyo Rivas, Hospital Universitario de los Andes, Mérida, Venezuela.
0732-8893/$ – see front matter © 2008 Elsevier Inc. All rights reserved. doi:10.1016/j.diagmicrobio.2008.09.005
with a higher frequency of positive Gram stains (Bingen et al., 1990; La Scolea and Dryja, 1984), delay in culture sterilization (Bingen et al., 1990; Feldman, 1976), increased antimicrobial resistance (Feldman, 1976), and severe disease (Carrol et al., 2007; Feldman, 1977). The load may vary million-fold without obvious reasons, young age being perhaps an exception (Bingen et al., 1990; Carrol et al., 2007). Earlier studies have quantified the microbial load by counting bacterial colony forming units on growth media (Bingen et al., 1990; Feldman, 1976, 1978; La Scolea and Dryja, 1984). This approach probably underestimates the load, especially in patients who have received preadmission antimicrobials (Feldman, 1978), because only viable organisms are detected (Hackett et al., 2002). We used quantitative real-time polymerase chain reaction (PCR), which determines the microbial load more accurately, counting both viable and nonviable microorganisms (Saukkoriipi et al., 2003), to examine if genome quantity is related to clinical
I. Roine et al. / Diagnostic Microbiology and Infectious Disease 63 (2009) 16–23
findings on admission and to determine its prognostic importance for different outcomes in childhood Streptococcus pneumoniae (pneumococcal) and Haemophilus influenzae type b (Hib) meningitis. 2. Materials and methods 2.1. Setup and data collection Cerebrospinal fluid samples, taken from the diagnostic tap on admission, were collected during 1999 to 2003 as part of the childhood bacterial meningitis study (ISRCTN35932399; Peltola et al., 2007) carried out in Latin America (participating centers are listed on front page). That study enrolled previously healthy children aged 2 months to 14 years presenting with probable bacterial meningitis. The protocol, approved by local ethical committees, stated that a part of the CSF sample was to be frozen for further analysis. The patients were enrolled only if consented by the legal guardian. Bacterial culture, Gram stain, and the detection of capsular antigens of S. pneumoniae and H. influenzae by latex agglutination were performed as described earlier (Peltola et al., 2007). During the meningitis study (Peltola et al., 2007), the study pediatrician had registered predefined patient characteristics on admission and continued recording throughout the course of disease. Nutritional status was determined as the z-score for weight/age by the World Health Organization nutritional survey calculator. At discharge, a thorough neurologic evaluation was carried out by a pediatric neurologist or the study pediatrician. Three predefined outcome measures were used: death, neurologic sequelae, and hearing impairment. “Severe neurologic sequelae” were defined as blindness, quadriplegia or paresis, hydrocephalus needing a shunt, or severe psychomotor retardation (does not sit or walk, speak, or establish contact, or requires institutionalization). In addition to these, “any neurologic sequelae” included hemiparesis, monoparesis, ataxia, and moderate psychomotor retardation (Glascoe et al., 1992). Hearing was measured after exclusion of middle ear disease separately for each ear using brain stem auditory evoked potentials in patients below 4 years of age (112 of 121 patients, 93%) or normal audiometry. The test results were interpreted by local specialists. The better ear's inability to recognize sounds at or above 80 dB was considered “severe hearing impairment” and when above 40 dB as “any hearing impairment”. 2.2. Real-time PCR analysis DNA extractions and PCR analyses were carried out in Finland in 2004 and early 2005. Cerebrospinal fluid samples were kept at −20 °C until analysis. DNA was extracted from 200 μL of CSF sample using QIAamp® DNA Mini Kit (QIAGEN, Hilden, Germany) according to the tissue protocol described in the instructions of the manufacturer
17
or later from 100 μL of CSF sample using the MagNA Pure LC instrument and MagNA Pure LC DNA extraction Kit III (both from Roche Diagnostics, Mannheim, Germany). In both of the methods, lysis of sample was done overnight, and the extracted DNA was eluted into 100 μL of elusion buffer. The concentration of sample during DNA extraction using QIAamp® DNA Mini Kit was taken into account when calculating genome counts. General precautions were taken to avoid contamination during DNA extraction, including use of a separate room designated for DNA extraction only. At least every 16th sample was a negative control containing sterile distilled water instead of sample. The extracted DNA was stored at −20 °C. Real-time PCRs were performed using the LightCycler® 1.2 instrument (Roche Diagnostics). For detection and quantification of S. pneumoniae, our previously published method (Saukkoriipi et al., 2002) was used. In this method, a 206-bp fragment of the pneumolysin encoding gene is amplified, and fluorescent-labeled hybridization probes are used for detection of amplification products. In real-time pneumolysin PCR, an 8-μL DNA sample volume was used. For Hib, a conventional PCR method (Falla et al., 1994) was modified. The same forward primer was used, but a new reverse primer was designed because the length of the amplicon in the conventional PCR was rather long for real-time PCR. The sequence of the new reverse primer HibA was 5′-GCTAAGATGAAGTTATGGCGAA3′, and the size of the amplified fragment was 348 bp. In addition, a pair of fluorescent-labeled sequence-specific hybridization probes for detection of amplification products were designed by O. Landt (Tib-Molbiol, Berlin, Germany) based on the published sequence of the capB locus (GenBank accession no. X78559). The sequences of the hybridization probes were 5′-CATTTAGCAGACGACCAAAG GTATCTTG-fluorescein-3′ and 5′-LCRed640GTATAG CCTCGCCCCCAGAATTC-phosphate-3′ (TibMolbiol). The 20-μL reaction volume of real-time Hib-PCR contained 1× LightCycler® FastStart DNA Master Hybridization Probes solution (Roche Diagnostics), 4 mmol/L MgCl2, 0.5 μmol/L of each primer, 0.2 μmol/L of each hybridization probe, and 8 μL of sample DNA. The real-time Hib-PCR program consisted of an initial denaturation step at 95 °C for 10 min, 50 cycles of amplification each consisting of denaturation at 95 °C for 10 s, annealing at 59 °C for 10 s, and elongation at 72 °C for 14 s, and finally, a cooling step at 40 °C for 30 s. Fluorescence was measured in each cycle after the annealing step. In each PCR, every 7th specimen was a sterile distilled water control. Standard precautions were taken to prevent carryover contamination including the separation preparation of master mix, addition of template, and PCR into 3 different rooms. The fluorescence data were analyzed using the data analysis program included in the LightCycler® 4.0 Software (Roche Diagnostics). In both of the real-time PCR assays, an external standard curve was used for
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quantification. The external standards were prepared by extracting DNA from cultured pneumococcus (ATCC® 6305, American Type Culture Collection) and Hib and measuring the DNA concentrations using the Quant-iT™ PicoGreen® dsDNA Assay Kit (Invitrogen Molecular Probes™, Eugene, OR) or spectrophotometrically. For pneumococcus, the standard curve was prepared by amplifying 4 replicates of standards containing 5 × 105, 5 × 104, 5 × 103, 5 × 102, 50, and 5 pneumococcal genome equivalents (calculated based on the genome size of S. pneumoniae, GenBank accession number AE005672). A standard containing 5 × 102 copies was used as a calibrator and positive control in each run. For Hib, 4 replicates of dilutions containing 105, 104, 103, 102, 10, and 1 genome equivalents/μL (calculated based on a genome size of 1950 kb) (Kauc, 1992) were amplified. A standard containing 8 × 102 copies was used as a calibrator and positive control in each run. All dilutions of standards were made in H2O containing 10 ng/mL MS2 RNA (Roche Diagnostics). The average amplification efficiencies of the pneumococcus and Hib standards were calculated from the standard curves of the PCRs. The amplification efficiency of samples was studied by analyzing the dilution series of 1 sample positive by pneumolysin PCR and 1 positive by Hib PCR. The threshold detecting cycles were plotted versus the logarithm of the concentrations of the diluted samples, and the amplification efficiencies were calculated from the slope of the lines. In quantitative PCR, 5 or more of bacterial genomes/μL of CSF was considered as a cutoff value for positivity. 2.3. Statistical analysis Results describing continuous variables are shown as mean values with SD. If the distribution was not normal, they were log transformed before analysis, and their values are shown as median values with the interquartile value (IQR, the spread of values containing the central 50% of the data). The genome counts were projected against patient characteristics, history of disease, and laboratory findings on admission using Student's t test, Mann–Whitney U test, simple regression, or Spearman's correlation, whichever is appropriate. The relationship between the genome count and the outcomes are expressed as odds ratio (OR) with 95% confidence interval (CI) for each 1 log unit increase in genome count, starting from 1. These results were obtained by binary logistic regression using the outcomes, 1 at a time, as the dependent variable and log transformed genome count as the independent variable alone (Table 4, univariate analysis), or together with the adjuvant treatments (Table 5, multivariate analysis). First, all patients were examined as a whole, then separately for pneumococcal and Hib meningitis. A P value below 0.05 is taken as significant.
3. Results 3.1. Patient population Cerebrospinal fluid samples with either pneumococcal or Hib etiology were available from 121 (48%) of the 252 patients with these etiologies in the ISRCTN35932399 trial. The samples came from Venezuela (49/121, 40%), Ecuador (47/121, 39%), and Paraguay (25/121, 21%). Fifty-one/121 (42%) patients had received preadmission antimicrobials, 88% orally, with a median duration of 3 days (range, 0–15; mode, 1 day). As treatment, in addition to ceftriaxone (Peltola et al., 2007), 27 patients had received adjuvant dexamethasone (9 pneumococcus, 18 Hib), 25 dexamethasone and glycerol (7 pneumococcus, 18 Hib), 28 glycerol (7 pneumococcus, 21 Hib), and 41 placebo (13 pneumococcus, 28 Hib). Etiologic diagnosis was based in 76 (63%) CSF samples on culture (24 pneumococcal, 52 Hib), in 15 (12%) on a positive latex agglutination test with a concordant Gram stain result (3 pneumococcal, 12 Hib), in 7 (6%) on a positive latex result (all Hib), in 6 (5%) on a positive Gram stain (4 pneumococcal, 2 Hib), and in 17 (14%) on a real-time PCR test result (N5 genomes/μL) (5 pneumococcal, 12 Hib). Thirty-six patients had pneumococcal and 85 patients had Hib meningitis. The characteristics of the patients are presented in Table 1.
Table 1 Characteristics of the series Characteristic
Age, median (months) Females Z-score weight/age, mean ± SD History N48 h Convulsions Focal paresis Otitis media Prior antimicrobials Glasgow Coma Score, median CSF, median values Leucocytes, 103/μL Polymorphonuclears, 103/μL Glucose (mg/dL) Protein (g/dL) Blood, median values White cell count, 106/μL Platelet count, 109/μL Outcome Death Neurologic sequelae Severe Any Hearing impairment Severe Any a b
Pneumococcus
Hib
n = 36
n = 85
6 (40)a 9 [25]b -0.95 ± 1.56 1/33 [3] 20/35 [57] 0/35 [–] 2/35 [6] 14/35 [40] 10 (6)
8 (7) 31 [36] -0.50 ± 1.41 12/76 [16] 33/82 [40] 9/82 [11] 10/80 [13] 37/85 [44] 13 (4)
0.55 0.22 0.12 0.06 0.09 0.06 0.28 0.72 0.002
1288 (3198) 911 (2395) 12 (25) 191 (148)
2253 (5364) 1925 (4899) 13 (24) 139 (129)
0.04 0.08 0.76 0.04
16.2 (14.4) 275 (227)
12.2 (10.4) 232 (190)
0.34 0.12
9 [25]
10 [12]
0.07
4/27 [15] 11/27 [41]
11/73 [5] 33/73 [45]
0.97 0.69
1/24 [4] 11/24 [46]
9/71 [13] 31/71 [44]
0.24 0.85
In parenthesis: interquartile value. In brackets: percentages.
P
I. Roine et al. / Diagnostic Microbiology and Infectious Disease 63 (2009) 16–23
19
Table 2 Comparison of CSF bacterial genome count with continuous patient characteristics All patients, n = 121
Pneumococcus, n = 36
Hib, n = 85
P
rs
P
rs
P
b
−0.20 −0.10 −0.18 −0.003
0.03 0.32 0.05 0.97
−0.27 −0.03 −0.22 −0.14
0.12 0.88 0.20 0.41
−0.18 −0.10 0.10 −0.06
0.09 0.36 0.37 0.58
b0.001 −0.003 −0.07 0.12
0.99 0.98 0.48 0.24
−0.39 −0.44 −0.21 0.36
0.02 0.01 0.23 0.046
0.18 0.20 −0.002 −0.002
0.10 0.09 0.99 0.99
−0.31 −0.004
0.0009 0.97
−0.36 −0.06
0.04 0.74
−0.27 b0.001
0.02 0.99
rs Age (months) Weight/age z-score Glasgow Coma Score Prior antimicrobials (days) CSF White cell count, 103/μL Polymorphonuclears, 103/μL Glucose (mg/dL) Protein (g/dL) Blood White cell count, 106/μL Platelet count, 109/μL
a
a
Spearman's correlation coefficient. The value of −0.20 indicates that when age increases, the genome count tends to decrease. Because the result is far away from −1, which indicates perfect inverse correlation, the effect of 1 variable on the other variable is weak, yet greater than what would be expected in a random sample, because P value is below 0.05. b
Pneumococcal patients were more severely ill than Hib patients (Table 1), as shown by the Glasgow Coma Score, the median being 10 versus 13, respectively (P = 0.002). The CSF white cell response was lower in pneumococcal meningitis (P = 0.04), which also presented with a higher CSF protein concentration (P = 0.04). 3.2. Amplification efficiency The amplification efficiencies of the standards in both pneumolysin and Hib PCR were 1.97 (the optimal efficiency being 2). The amplification efficiencies of the CSF samples analyzed were 1.97 in pneumolysin PCR and 1.92 in Hib PCR. 3.3. Cerebrospinal fluid genome counts Cerebrospinal fluid genome counts varied enormously, ranging for the entire group from 0 to 9.3 × 106/μL (median, 4.5 × 103/μL; IQR, 3.2 × 105), overlapping widely in
pneumococcal and Hib patients. The median in pneumococcal cases (4.6 × 104/μL; IQR, 4.0 × 105) was higher than for Hib meningitis (3.5 × 103/μL; IQR, 2.5 × 105; P = 0.16). There were 11 patients (3 pneumococcal, 8 Hib) whose CSF genome count was less than 5/μL (0 in 5 cases, 1 in 4 cases, 2 in 1 case, and 3 in 1 case; 2 of these samples had been collected in 1999, 6 in 2000, 2 in 2001, and 1 in 2003). Their etiologic diagnosis had been based on a positive CSF culture in 6 cases (4 pneumococcal, 2 Hib) and on a positive CSF latex result in 5 cases (1 pneumococcal, 4 Hib), with a concordant Gram stain result in 4 cases (1 pneumococcal, 3 Hib). Seven of these patients (7/11, 64%, 2 pneumococcal, 5 Hib) had received preadmission antimicrobials, but the difference was not significant compared with the rest of the patients (44/109 [data not available for 1 patient], 40%, P = 0.20). Genome counts obtained from samples that had used the 1st DNA extraction method (n = 49; range, 0–9 250 000/μL; median, 2954; IQR, 336 313) did not differ (P = 0.68) from
Table 3 Median CSF bacterial genome count × 103/μL according to nominal patient characteristics and outcome of disease Variables
All patients, n = 121
Pneumococcus, n = 36
Variable
Male sex Prior antimicrobials Prior convulsions History N48 h Otitis media Focal paresis Positive CSF culture Death Any neurologic sequelae Any audiologic sequelae a
Hib, n = 85
Variable
Variable
Present
Absent
P
Present
Absent
P
Present
Absent
P
3.5 4.5 16.4 1.2 109.5 21.2 45.1 201.9 20.3 3.2
33.2 16.4 3.0 4.8 3.5 3.7 1.1 3.4 0.8 3.5
0.33 0.04 0.08 0.84 0.71 0.19 0.005 0.07 0.006 0.72
21.1 2.1 173.4 3a 1.1 – 145.0 277.1 71.3 1.8
71.3 201.9 1.8 46.2 71.3 21.1 2.1 3.1 1.0 21.1
0.69 0.06 0.01 0.14 0.20 – 0.28 0.02 0.04 0.66
3.1 1.3 3.3 1.6 160.9 21.2 15.6 13.2 15.0 4.5
21.2 5.6 4.5 3.9 1.6 3.0 0.4 3.5 0.8 3.7
0.14 0.25 0.94 0.61 0.24 0.13 0.007 0.99 0.04 0.87
There was only 1 patient in this category.
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the ones using the 2nd method (n = 72; range, 0–8 025 000/ μL; median, 10 785; IQR, 288 094). The median genome count in samples collected from 1999 to 2000 (5.6 × 103/μL, n = 74) did not differ (P = 0.93) from that of samples collected from 2001 to 2003 (2.9 × 103/μL, n = 47). 3.4. Pneumococcal and Hib meningitis combined Higher genome counts were associated with younger age (Spearman's correlation = −0.20, P = 0.03, Table 2) and more positive CSF cultures (P = 0.005, Table 3), but sex, nutritional status, or a preadmission history of more than 48 h of symptoms did not have a significant effect on the count (Tables 2 and 3). Increasing genome counts correlated with decreasing blood white cell count (Spearman's correlation = −0.31, P = 0.0009 for all meningitides together) and showed a borderline association with decreasing Glasgow Coma Score (Spearman's correlation = −0.18, P = 0.05). No correlation was observed between the genome count and CSF glucose and protein concentrations, or with the number of days of having received preadmission antimicrobials (Table 2). Patients with focal paresis or otitis media did not differ from those without these disorders (Table 3). In the univariate analysis regarding the outcomes, only neurologic sequelae were significantly associated with the genome count (P = 0.006, Table 3). Each log unit elevated their risk by 36% (OR = 1.36; 95% CI, 1.09–1.69; P = 0.007, Table 4). Death and severe neurologic sequelae showed a similar tendency without reaching significance (OR = 1.29; 95% CI, 0.98–1.69; P = 0.07, and OR = 1.26; 95% CI, 0.93– 1.70; P = 0.14, respectively). Hearing impairment, regardless of its extent, was not associated with the count (OR = 0.97; 95% CI, 0.70–1.35; P = 0.87, for severe, and OR = 0.97; 95% CI, 0.79–1.19; P = 0.77, for any hearing impairment). In the multivariate analysis regarding the outcomes, examining the genome count together with adjuvant treatments (Table 5), the genome count is an independent predictor for both neurologic sequelae (OR = 1.31; 95% CI, 1.04–1.66; P = 0.02) and death (OR = 1.37; 95% CI, 1.001– 1.89; P = 0.049), whereas adjuvant treatments play a secondary role (P N 0.5).
None of the values of the tested variables, the frequencies, or the outcomes (Tables 1–3) differed between the patients who had and had not received preadmission antimicrobials (P N 0.05), with the exception of the CSF genome count, which was lower (P = 0.04) in patients with preadmission antimicrobials (Table 3). 3.5. Pneumococcal versus Hib meningitis Contrary to Hib meningitis, in pneumococcal meningitis, an increase in the genome count clearly increased the risk of death (OR = 2.05; 95% CI, 1.08–3.87; P = 0.03, Table 4), also when analyzed together with adjuvant treatments (OR = 2.56; 95% CI, 1.11–5.87; P = 0.03, Table 5). Other differences were a positive association between an increasing genome count and lower CSF white cell and polymorphonuclear cell counts (Spearman's correlation = −0.39, P = 0.02, and Spearman's correlation = −0.44, P = 0.01, respectively) in pneumococcal but not Hib meningitis (Table 2). Only in pneumococcal meningitis was a higher count associated with a history of convulsions (P = 0.01, Table 3). In turn, only in Hib meningitis was the genome count associated with positive CSF cultures (P = 0.007, Table 3). In addition, a higher count showed a stronger association with neurologic sequelae in pneumococcal (univariate P = 0.046, multivariate P = 0.04) compared with Hib meningitis (univariate P = 0.05, multivariate P = 0.14, Tables 4 and 5). In both meningitides (Table 2), CSF genome count was inversely associated with the blood leukocyte count (Spearman's correlation = −0.37, P = 0.04 in pneumococcal, and Spearman's correlation = −0.27, P = 0.02 in Hib meningitis, Table 2). To test if a combination of patient characteristics explained better the great variation in the genome count than each alone, we formed a score in which 3 characteristics (age below 7 months, blood white cell count below 15 000/ μL, and CSF leukocyte count below 1000/μL) were given 1 point each. This score was projected against log CSF genome count by simple regression. The squared correlation coefficients were 0.12 for pneumococcal (log genome count = 3.4 + 0.63 × score, P = 0.05) and 0.07 for Hib meningitis (log genome count = 3.0 + 0.54 × score,
Table 4 Univariate analysis of the effect of 1 log unit increase of CSF bacterial genome count on the risk of adverse outcome Outcome
All cases, n = 116a OR
Death 1.29 Neurologic sequelae Severe 1.26 Any 1.36 Hearing impairment Severe 0.97 Any 0.97 a
Pneumococcus, n = 34
Hib, n = 82
95% CI
P
OR
95% CI
P
OR
95% CI
P
0.98–1.69
0.07
2.05
1.08–3.87
0.03
0.99
0.71–1.39
0.98
0.93–1.70 1.09–1.69
0.14 0.007
2.04 1.61
0.89–4.69 1.01–2.57
0.09 0.046
1.11 1.29
0.79–1.55 0.99–1.65
0.55 0.05
0.70–1.35 0.79–1.19
0.87 0.77
0.91 0.97
0.33–2.48 0.65–1.46
0.85 0.88
0.98 0.97
0.69–1.39 0.77–1.23
0.91 0.80
Five patients (2 pneumococcal, 3 Hib) with a CSF genome count of zero have no log result and therefore cannot be included in this analysis.
I. Roine et al. / Diagnostic Microbiology and Infectious Disease 63 (2009) 16–23
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Table 5 Multivariate analysis of the effects of 1 log unit increase of CSF bacterial genome count and adjuvant treatments on the risk of adverse outcome Outcome
All cases OR
Death CSF genome count Dexamethasoneb Dexamethasone + glycerolb Glycerolb Neurologic sequelae Severe CSF genome count Dexamethasone Dexamethasone + glycerol Glycerol Any CSF genome count Dexamethasone Dexamethasone + glycerol Glycerol Hearing impairment Severe CSF genome count Dexamethasone Dexamethasone + glycerol Glycerol Any CSF genome count Dexamethasone Dexamethasone + glycerol Glycerol
P 95% CI
Pneumococcus OR
95% CI n = 34 1.11–5.87 0.17–15.25 0.01–2.08 0.04–9.36 n = 25
a
P
Hib
P
OR
95% CI
0.03 0.68 0.15 0.73
1.08 1.07 1.12 0.87
n = 82 0.74–1.59 0.16–7.18 0.16–7.60 0.13–5.82 n = 71
0.68 0.94 0.91 0.89
1.37 1.20 0.67 0.67
n = 116 1.001–1.89 0.33–4.44 0.15–2.99 0.15–2.94 n = 96
0.049 0.78 0.60 0.59
2.56 1.60 0.13 0.63
1.21 1.16 0.49 0.63
0.88–1.66 0.28–4.85 0.09–2.72 0.14–2.87
0.23 0.84 0.41 0.55
4.86 –c – –
0.81–29.13
0.08
1.04 0.82 0.24 0.17
0.73–1.47 0.17–3.94 0.03–2.22 0.02–1.54
0.83 0.80 0.21 0.11
1.31 1.59 0.48 1.15
1.04–1.66 0.49–5.17 0.14–1.64 0.39–3.39 n = 91
0.02 0.44 0.24 0.80
2.45 10.88 0.56 21.23
1.04–5.78 0.46–260.33 0.03–9.70 0.90–499.24 n = 22
0.04 0.14 0.69 0.06
1.22 1.10 0.37 0.63
0.94–1.58 0.29–4.26 0.09–1.55 0.18–2.17 n = 69
0.14 0.89 0.17 0.47
0.92 0.78 0.76 0.61
0.65–1.30 0.13–4.73 0.12–4.61 0.10–3.68
0.64 0.78 0.76 0.59
0.77 –d – –
0.30–2.02
0.60
0.94 0.71 0.34 0.56
0.65–1.35 0.11–4.48 0.03–3.42 0.09–3.47
0.73 0.71 0.36 0.53
0.92 1.60 0.40 1.03
0.73–1.15 0.50–5.12 0.12–1.41 0.35–3.07
0.46 0.43 0.15 0.96
0.77 3.04 0.25 1.40
0.44–3.87 0.20–46.08 0.02–3.62 0.14–13.95
0.35 0.42 0.31 0.78
0.96 1.39 0.47 0.95
0.74–1.23 0.37–5.20 0.11–1.99 0.27–3.34
0.74 0.63 0.31 0.94
Binominal logistic regression analysis. a Five patients (2 pneumococcal, 3 Hib) with a CSF genome count of zero have no log result and therefore cannot be included in this analysis. b Compared with patients who received placebo adjuvant treatment. c Not evaluable because none of the 9 patients who received placebo adjuvant treatment had severe neurologic sequelae. d Not evaluable because none of the patients who received dexamethasone, glycerol, or placebo had severe hearing impairment.
P = 0.02). Thus, the combination explained of the count variation only 12% in pneumococcal and 7% in Hib meningitis (Rummel, 1976).
4. Discussion Bacterial load quantification may open new insights into understanding pathogenetic mechanisms of disease and new possibilities to patient management (van Haeften et al., 2003). Our results show that the vast variation in bacterial quantity in childhood bacterial meningitis was surprisingly independent of patient findings on admission but was related to the prognosis. Its predictive value was clearest for neurologic sequelae and survival, but only in pneumococcal meningitis. Hearing impairment showed no association in either meningitis. Although young age (Bingen et al., 1990; Carrol et al., 2007) was associated with a higher genome count, it explained only a fraction of its variation (Spearman's correlation = −0.20). Likewise, the association between the blood white cell count and CSF genome count was weak (Spearman's correlation = −0.31), suggesting that leukocy-
tosis has some effect, but relatively small, in curbing it. Even the combination of the clinical characteristics (age, blood, and CSF white cell response) explained only around 10% of the variation. Clearly, the major determinants of the millionfold load variation were not identified in our study. Delay in instituting meningitis treatment showed no identifiable effect on the genome count (Table 3) in either meningitides. Although surprising, this observation fits previous reports (Carrol et al., 2007; Feldman, 1977; Hackett et al., 2002) and the clinical experience (Kallio et al., 1994; Kilpi et al., 1993) that some children survive meningitis well, despite probably having had meningitis for a few days before admission. On the other hand, several days of delay is common in developing countries, and this almost certainly contributes to the high mortality still being 30% to 50% (Peltola, 2001). Preadmission antibiotics had a lowering impact on Hib and meningococcal, but not on pneumococcal load in an earlier Feldman study that used the colony counting technique (Feldman, 1978). In our data, using the more accurate method of counting both viable and nonviable organisms, a trend of this effect is seen also in the pneumococcal load (P = 0.06).
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One would expect that the number of invading bacteria influences the CSF white cell count and glucose and protein concentrations. Surprisingly, this was true only for the white cell count and only in pneumococcal meningitis. The variation in glucose and protein concentrations was independent of the bacterial load in our, as well as in a previous study (Feldman, 1977). Cerebrospinal fluid bacterial load obtained by culture is known to associate with severe meningitis and complications during recovery (Carrol et al., 2007; Feldman, 1977), and our results agree. The novel findings of our study are that the bacterial load 1) predicted death only in pneumococcal meningitis, 2) predicted neurologic sequelae with more strength in pneumococcal compared with Hib meningitis, and 3) did not seem to affect audiologic prognosis. This suggests potentially important differences in the mechanisms leading to neurologic and audiologic sequelae. The strength of our study is to have tested the modern modality for determining CSF bacterial load and to separate different outcomes. Freezing and thawing, and long storage of the CSF samples may have caused some error, particularly in samples with very low counts. In the 11 patient samples with an etiologic diagnosis based on a positive CSF culture or a positive CSF latex result, but a bacterial genome count less than 5/μL, either DNA degradation or a misdiagnosis by traditional bacteriology may have taken place. According to the data shown, it is unlikely that storage time played a role, but it is possible that a single sample at random had been handled suboptimally. However, overall, our results show a similar range and median value compared with those from a recent similar study (Carrol et al., 2007). In patients with pneumococcal meningitis, our genome count range was 0 to 9.2 × 106/μL (median, 4.6 × 104/μL) compared with Malawi children with a range of 0.4 to 6.2 × 105/μL (median, 5.8 × 104/μL) (Carrol et al., 2007). Another potential source of error in samples, leading to falsely low or even negative results, is incomplete removal of PCR inhibitors during DNA extraction. In our case, this hardly was a major problem considering that the median values of our samples were in the range of 104 copies/μL, but it may have had an affect on the result of an individual sample. The specificity of pneumolysin PCR for pneumococcus has been questioned in several recent studies, as also other αhemolytic streptococci have been found to harbor the ply gene (Carvalho et al., 2007; Whatmore et al., 2000). However, these viridans streptococci are not expected in the CSF. We have previously compared the sensitivities of real-time pneumolysin PCR and bacterial culture by analyzing several dilutions of a pneumococcal broth culture by both methods (Saukkoriipi et al., 2003). In that study, there was a strong correlation (Spearman's correlation = 0.983, P b 0.001) between the results obtained by quantitative culture and quantitative PCR, but the numbers of genomes detected by real-time pneumolysin PCR were 10 to 30 times higher than the numbers of colonies detected by culture, indicating a higher sensitivity of real-time PCR
compared with culture. On the other hand, PCR may also have detected bacteria with impaired viability. Most invasive Hib strains possess a duplication of the capsulation locus, and even multiple copies of the capsular locus in Hib strains have been demonstrated (Cerquetti et al., 2005; Corn et al., 1993). Thus, there is a possibility that a high Hib genome count in our study would be due, in some cases, to the presence of strains possessing multiple copies of the Hib capsular locus. We underline the theoretic nature of this explanation because multiple-copy strains were found in disease other than meningitis (Cerquetti et al., 2005). The CSF genome count had more importance for outcome than adjuvant treatments (Table 5), but these results should be considered preliminary, because the small sample size in a multivariate analysis may lead to missing significance, although it exists. Before definite conclusions, this matter should be analyzed in a bigger data set. In conclusion, the major determinants of the vast variation of CSF pneumococcal and Hib genome counts were not among the variables examined in the present study. Differences according to outcomes and etiology in the prognostic importance of the load suggest different mechanisms of the processes leading to them. Acknowledgments The authors thank Leena Kuisma and Elsi Äijälä for their excellent work in real-time PCR analysis. References Bingen E, Lambert-Zechovsky N, Mariani-Kurkdijan P, Doit C, Aujard Y, Fournerie F, Mathieu H (1990) Bacterial counts in cerebrospinal fluid of children with meningitis. Eur J Clin Microbiol Infect Dis 9:278–281. Carrol ED, Guiver M, Nkhoma S, Mankhambo LA, Marsh J, Balmer P, Banda DL, Jeffers G, White SA, Molyneux EM, Molyneux ME, Smyth RL, Hart CA (2007) High pneumococcal DNA loads are associated with mortality in Malawian children with invasive pneumococcal disease. Pediatr Infect Dis J 26:416–422. Carvalho MdaG, Tondella ML, McGaustland K, Weidlich L, McGee L, Mayer LW, Steigerwalt A, Whaley M, Facklam RR, Fields B, Carlone G, Ades EW, Dagan R, Sampson JS (2007) Evaluation and improvement of real-time PCR assays targeting lytAplypsaA genes for detection of pneumococcal DNA. J Clin Microbiol 45:2460–2466. Cerquetti M, Cardines R, Ciofi Degli Atti ML, Giufre M, Bella A, Sofia T, Mastrantonio P, Slack M (2005) Presence of multiple copies of capsulation b locus in invasive Haemophilus influenzae type vaccine failure. J Infect Dis 192:819–823. Corn PG, Anders J, Takala AK, Käyhty H, Hoiseth SK (1993) Genes involved in Haemophilus influenzae type b capsule expression are frequently amplified. J Infect Dis 167:356–364. Falla TJ, Crook DWM, Brophy LN, Maskell D, Kroll JS, Moxon ER (1994) PCR for capsular typing of Haemophilus influenzae. J Clin Microbiol 32:2382–2386. Feldman WE (1976) Concentrations of bacteria in cerebrospinal fluid of patients with bacterial meningitis. J Pediatr 88:549–552. Feldman WE (1977) Relation of concentrations of bacteria and bacterial antigen in cerebrospinal fluid to prognosis in patients with bacterial meningitis. N Engl J Med 296:433–435. Feldman WE (1978) Effect of prior antibiotic therapy on concentrations of bacteria in CSF. Am J Dis Child 132:672–674.
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