Objectives: The objectives of this study were to determine the bacterial profiles and prevalence of antibiotic resistance patterns of bacteria causing bacteremia in febrile children and to compare levels of inflammatory markers between children with and without bacteremia in Kuwait from 2015 to 2022. Materials and Methods: Isolates from all episodes of significant bacteremia (n = 96) during the study period were recorded and evaluated. Microorganisms were identified using standard microbiological methods. Antimicrobial susceptibility testing was carried out using the VITEK2 system and Etest method. Extended-spectrum β-lactamase (ESBL) production by Enterobacterales was detected by the double-disk diffusion method and VITEK2 system. Patient age, gender, and inflammatory markers were collected at admission and compared between patients with and without bacteremia. Results: A majority of the patients were infants (37, 40%) and newborns (13, 14%). The main ports of entry were the lower respiratory tract, the genitourinary tract, and the gastrointestinal tract. Streptococcus pneumoniae was the most common pathogen (16, 16.7%) followed by Escherichia coli (12, 12.5%), Staphylococcus aureus (10, 10.4%), and Streptococcus agalactiae (9, 9.4%). High rates of resistance to ampicillin, cefuroxime, ciprofloxacin, and trimethoprim-sulfamethoxazole were observed among the Enterobacterales. The prevalence of ESBL-producing E. coli and K. pneumoniae were 45% and 29%, respectively. The prevalence of methicillin-resistant S. aureus was 30%. Patients with bacteremia had significantly higher white blood cell (WBC) counts, absolute neutrophil count (ANC), C-reactive protein (CRP), and neutrophil-lymphocyte ratio (NLR). Conclusion: Continuous surveillance of the prevalence and antimicrobial susceptibility patterns of blood isolates is imperative for the formulation of antibiotic policy. WBC, ANC, CRP, and NLR could be valuable indicators of bacteremia in febrile children.

Highlights of the Study

  • Streptococcus pneumoniae, Escherichia coli, Staphylococcus aureus, and Group B streptococci were the leading causes of pediatric bacteremia.

  • The rate of drug resistance among Gram-negative organisms causing pediatric bacteremia is alarming.

  • Hematological inflammatory markers are valuable markers of pediatric bacteremia.

  • The selection of empiric antibiotics for pediatric bacteremia should be based on local patterns of antibiotic resistance.

Bacteremia in children is a potentially life-threatening condition that requires immediate and effective antimicrobial treatment [1]. The surveillance of pediatric bacteremia is central to monitoring the changing epidemiology, which in turn guides the choice of empirical therapy [2]. Such surveillance data can also be used to monitor the effectiveness of current vaccine programs and inform strategies aimed at preventing and reducing invasive infections in children by developing new vaccines and antimicrobial treatments [3]. There are no published data for the incidence or etiology of childhood bacteremia in Kuwait. We describe the epidemiological and microbiological features of children admitted to the hospital from whom blood cultures yielded bacterial pathogens. Microbiological cultures have several limitations, including difficulties in collecting an adequate blood volume for culture, potential delays due to incubation time, the possibility of false negatives due to prior antibiotic use, and a low yield of positive cultures in cases of rare or less severe bacterial bloodstream infections. An early diagnosis of bacteremia before obtaining the results of microbial culture would certainly facilitate the choice of antibiotic therapy and reduce patient mortality. Thus, it could be especially useful to find a suggestive biologic marker for sepsis in addition to clinical markers [4, 5].

Inflammatory markers capable of distinguishing febrile children with bacteremia from children without bacteremia would be clinically useful. This study evaluates the accuracy of ten proposed inflammatory markers of infection in febrile children with and without bacteremia; these include white blood cell (WBC) count, absolute neutrophil count (ANC), lymphocyte count (LYM), monocyte count (MONO), platelet count (PLT), hemoglobin (Hb) levels, neutrophil-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), platelet-to-WBC ratio (PWR), and C-reactive protein (CRP). The objectives of this study were to determine the bacterial profile and prevalence of antibiotic resistance patterns of bacteria causing bacteremia in febrile children and to compare inflammatory parameters between febrile children with and without bacteremia attending Al-Amiri Hospital (AAH) from 2015 to 2022.

Study Design and Setting

This retrospective observational cohort study was conducted in the Department of Microbiology of AAH, a 400-bed teaching hospital, including a 15-bed surgical intensive care unit; 25-bedded medical intensive care unit; and urology, renal dialysis, and kidney transplant units. This hospital serves about 400,000 people of different nationalities and provides clinical laboratory services to 22 polyclinics. In Kuwait, the term “pediatric patients” refers to patients aged 12 years or younger at the time of the diagnosis or treatment.

Subjects and Clinical Samples

We included all children with suspected bacteremia admitted to AAH between January 1, 2015, and December 31, 2022. Ninety-six febrile children with positive blood culture (case group) and 96 febrile children without positive blood culture (control group), hospitalized for clinical suspicion of bacteremia in pediatric wards at AAH, Kuwait, were studied. Data on patients’ age, gender, culture site, date of collection, and culture result were obtained from the Laboratory Information System (LIS).

The control group was chosen based on age and gender criteria to closely match the cases. Both cases and controls were pediatric patients under 12 years old, with an equal distribution of males and females in both groups. The control group was carefully selected to accurately reflect the population from which the cases originated. This population consisted of febrile, unwell children seeking medical care at the pediatric casualty unit of AAH. Children in the control group had undergone blood culture analysis and had negative results. All cases and controls met the implicit criteria for blood culture analysis, aiming for comparability in disease severity.

The sample size of the control group was matched to that of the case group to ensure a fair comparison. Random selection techniques were employed to minimize the risk of selection bias when selecting controls from the source population.

Blood Samples

Blood samples were collected at admission during the study period. For children aged up to 12 years, 1–5 mL of blood was drawn and inoculated into BD BACTEC PEDS PLUS/F culture vials or BACT/ALERT PF PLUS culture bottles. After inoculation of the bottle in the wards, they were immediately transported at ambient temperature to the microbiology laboratory of the hospital. The following automated blood culture systems were used: BD BACTEC FX (Becton Dickinson) and BACT/ALERT VIRTUO (bioMérieux, Marcy-l’Etoile, France). All specimens were processed using standard laboratory methods based on the UK Standards for Microbiology Investigations. Samples were recorded as positive, if they gave a positive signal within 5 days; otherwise, they were reported as negative [6].

Identification

All significant Gram-negative and Gram-positive bacteria were identified by the standard VITEK2 system (bioMerieux) or MicroScan (Beckman Coulter) system. Isolates with low scores on VITEK2 or MicroScan were subjected to further identification on matrix-assisted laser desorption/ionization time of flight (bioMerieux).

Antimicrobial Susceptibility Testing

Susceptibility testing of all isolates was carried out by determining MIC using the Etest method (bioMerieux) and an automated VITEK2 system. The results were interpreted according to the recommendations of the Clinical and Laboratory Standards Institute [7]. Extended-spectrum β-lactamase (ESBL) activity was detected by the double-disk synergy method using amoxicillin/clavulanic acid, cefotaxime, and ceftazidime disks. ESBL-producing strains were confirmed with the automated VITEK2 system according to the manufacturer’s instructions. The following strains were used for quality control of susceptibility tests: E. coli (ATCC 25922), Pseudomonas aeruginosa (ATCC 27853), Staphylococcus aureus (ATCC 25923), and Enterococcus faecalis (ATCC 929212).

Inflammatory Markers

Ten different inflammatory markers were evaluated, as part of routine testing; these included WBC, ANC, LYM, MONO, PLT, Hb, NLR, PLR, PWR, and CRP. The blood was collected at admission, and the blood morphology with full leukocyte differentiation was determined using the XN-9100 automated hematology system. CRP was estimated by laser immunonephelometry using the Beckman Coulter IMMAGE 800 analyzer (Beckman Coulter, Brea, CA, USA). We calculated the NLR by dividing the ANC by LYM, the PLR by dividing PLT by LYM, and the PWR by dividing PLT by WBC.

Statistical Analysis

We present descriptive statistics as median (IQR) for continuous variables and as frequencies (%) for categorical variables. Statistical analysis involved comparing the demographic characteristics and inflammatory markers at the time of admission in patients with bacteremia to those without bacteremia. The Mann-Whitney U test was used to compare continuous data that did not follow a normal distribution, while categorical data were compared using the χ2 test. Data analysis was conducted using the IBM Statistical Package for the Social Sciences software version 25.0. Receiver-operating characteristic (ROC) curves were developed for each biomarker.

Demographics and Epidemiology

The age and sex distribution of the patients with positive blood cultures is shown in Table 1. The overall rate of isolation reduced with increasing age. The highest incidence of bacteremia was found in patients aged less than 1 year (52.1%), and most of the patients (61.4%) were aged less than 2 years. Bacteremia was consistently more commonly reported in males (68.7%) than in females (31.3%) in all age-groups (Table 1). Only one case of mortality due to Neisseria meningitidis was reported during the study period. Based on the clinical information and further microbiology investigations, the sources of bacteremia were identified in 85 (88.5%) of the episodes; the lower respiratory tract (27, 28.1%) was the most common source of bacteremia, followed by the genitourinary tract (22, 23.0%), gastrointestinal tract (18, 18.7%), urinary tract (9, 10%), skin (10, 10.4%), and central line (8, 8.3%) (Table 2).

Table 1.

Gender and age distribution of pediatric patients with bacteremia in AAH in Kuwait (percentages in parentheses)

GenderAgeTotal
daysmonthsyears
0–28 days1–12 months1–22–33–44–55–66–77–88–99–1010–1111–12
Male 28 66 (69) 
Female 30 (31) 
Total 13 (14) 37 (40) 9 (9) 8 (8) 9 (9) 5 (5) 6 (6) 3 (3) 6 (6) 96 (100) 
GenderAgeTotal
daysmonthsyears
0–28 days1–12 months1–22–33–44–55–66–77–88–99–1010–1111–12
Male 28 66 (69) 
Female 30 (31) 
Total 13 (14) 37 (40) 9 (9) 8 (8) 9 (9) 5 (5) 6 (6) 3 (3) 6 (6) 96 (100) 
Table 2.

The source of bacteremia in febrile children in AAH in Kuwait

SourcePatients, n%Microorganism
Respiratory tract 27 28.1 Streptococcus pneumoniae (16), Streptococcus pyogenes(6), Moraxella catarrhalis (2), Hemophilus influenzae (2), Neisseria meningitidis (1) 
Genitourinary tract 22 23.0 Escherichia coli (12), Streptococcus agalactiae (9), Enterococcus faecalis (1) 
Gastrointestinal tract 18 18.7 Salmonella spp. (7), Brucella spp. (4), nutritionally variant streptococci (3), Klebsiella pneumoniae (2), Streptococcus anginosus (1), Enterococcus faecalis (1) 
Skin 10 10.4 Staphylococcus aureus (10) 
Central line 8.1 Staphylococcus epidermidis (4), Acinetobacter baumannii (2), Enterobacter cloacae (2) 
Unknown 11 11.4  
Total 96 100.0  
SourcePatients, n%Microorganism
Respiratory tract 27 28.1 Streptococcus pneumoniae (16), Streptococcus pyogenes(6), Moraxella catarrhalis (2), Hemophilus influenzae (2), Neisseria meningitidis (1) 
Genitourinary tract 22 23.0 Escherichia coli (12), Streptococcus agalactiae (9), Enterococcus faecalis (1) 
Gastrointestinal tract 18 18.7 Salmonella spp. (7), Brucella spp. (4), nutritionally variant streptococci (3), Klebsiella pneumoniae (2), Streptococcus anginosus (1), Enterococcus faecalis (1) 
Skin 10 10.4 Staphylococcus aureus (10) 
Central line 8.1 Staphylococcus epidermidis (4), Acinetobacter baumannii (2), Enterobacter cloacae (2) 
Unknown 11 11.4  
Total 96 100.0  

Incidence of Reported Bacteremia Cases

To find out about changes in the number of bloodstream infections over time, the duration of the study was broken into four periods: A (2015–2016), B (2017–2018), C (2019–2020), and D (2021–2022). The number of blood cultures processed was 5,503 in period A, 4,874 in period B, 2,701 in 2017 in period C, and 5,317 in period D (Table 3). During the study period, the total number of blood cultures processed was 18,395, and the number of bacteremia-positive samples was 211 (1.15%). Only 96 (0.53%) of the blood cultures were classified as true bacteremia, and 115 isolates (0.63%) were classified as contaminants, mostly by coagulase-negative staphylococci, Bacillus spp., and Corynebacterium spp. These contaminants were excluded from the analysis as they may have masked a true infection. A single organism was isolated from each of the 92 cultures obtained on admission, and two organisms were isolated from each 3 cultures. Thus, 98 organisms were isolated from 96 admission blood cultures.

Table 3.

Pathogens isolated from blood samples of children with bacteremia

MicroorganismNumber of microorganisms isolated from children with bacteremia
2015–20162017–20182019–20202021–2022Total
N = 5,503N = 4,874N = 2,701N = 5,317N = 18,395
Blood culture samples 
Gram-negative bacteria 
Escherichia coli 12 (12.5) 
Klebsiella pneumoniae 7 (7.2) 
Salmonella spp. 7 (7.2) 
Brucella spp. 4 (4.0) 
Moraxella catarrhalis 2 (2.0) 
Enterobacter spp. 2 (2.0) 
Haemophilus influenzae 2 (2.0) 
Acinetobacter spp. 2 (2.0) 
 Other Gram-negative bacillia 8 (8.1) 
     46 (47.0) 
Gram-positive bacteria 
Streptococcus pneumoniae 16 (16.3) 
Staphylococcus aureus 10 (10.2) 
Streptococcus agalactiae 9 (9.2) 
Streptococcus pyogenes 6 (6.2) 
 Nutritionally variant streptococci 3 (3.1) 
Enterococcus faecalis 2 (2.0) 
Staphylococcus epidermidis 4 (4.0) 
Streptococcus anginosus 1 (1.0) 
     51 (52.0) 
Yeast 
Candida spp. 1 (1.0) 
Total 27 29 16 26 98 (100.0) 
MicroorganismNumber of microorganisms isolated from children with bacteremia
2015–20162017–20182019–20202021–2022Total
N = 5,503N = 4,874N = 2,701N = 5,317N = 18,395
Blood culture samples 
Gram-negative bacteria 
Escherichia coli 12 (12.5) 
Klebsiella pneumoniae 7 (7.2) 
Salmonella spp. 7 (7.2) 
Brucella spp. 4 (4.0) 
Moraxella catarrhalis 2 (2.0) 
Enterobacter spp. 2 (2.0) 
Haemophilus influenzae 2 (2.0) 
Acinetobacter spp. 2 (2.0) 
 Other Gram-negative bacillia 8 (8.1) 
     46 (47.0) 
Gram-positive bacteria 
Streptococcus pneumoniae 16 (16.3) 
Staphylococcus aureus 10 (10.2) 
Streptococcus agalactiae 9 (9.2) 
Streptococcus pyogenes 6 (6.2) 
 Nutritionally variant streptococci 3 (3.1) 
Enterococcus faecalis 2 (2.0) 
Staphylococcus epidermidis 4 (4.0) 
Streptococcus anginosus 1 (1.0) 
     51 (52.0) 
Yeast 
Candida spp. 1 (1.0) 
Total 27 29 16 26 98 (100.0) 

aOthers: Proteus mirabilis (3); Citrobacter spp. (3); Serratia marcescens (2).

Etiological Isolates

Gram-positive cocci accounted for 52 (52.0%), and Gram-negative accounted for 46 (47.0%), respectively (Table 3). The average number of bloodstream infections during the study period was 24 cases per period. We observed a significant drop in the number of bloodstream infections during period C, from 26, 29, to 25 cases in periods A, B, and D to 16 cases in period C. Streptococcus pneumoniae was the most common pathogen (16, 16.7%) followed by Escherichia coli (12, 12.5%), S. aureus (10, 10.4%), Streptococcus agalactiae (9, 9.4%), Klebsiella pneumoniae (7, 7.3%), Salmonella spp. (7, 7.3%), and Streptococcus pyogenes (6, 6.2%), accounting for 70% of episodes (Table 3).

Antimicrobial Susceptibility Pattern

Table 4 presents susceptibility rates for Gram-negative bacterial isolates. Among E. coli, first-line antibiotic susceptibility rates were ampicillin (18%), amoxicillin-clavulanic acid (36%), trimethoprim-sulfamethoxazole (45%), ciprofloxacin (45%), and cefotaxime (55%). E. coli showed higher susceptibility to piperacillin-tazobactam (82%), meropenem (91%), amikacin (91%), and gentamicin (91%). K. pneumoniae displayed low susceptibility to ampicillin (0%), SXT (71%), cefuroxime (71%), amoxicillin-clavulanic acid (71%), and cefotaxime (71%). K. pneumoniae had high susceptibility to piperacillin-tazobactam (100%), meropenem (100%), amikacin (100%), ciprofloxacin (100%), and gentamicin (86%). Approximately 43% of Salmonella spp. isolates were sensitive to ampicillin, 29% to SXT, 14% to ciprofloxacin, and 71% to cefotaxime.

Table 4.

Frequency and percentage of antimicrobial susceptibility of Gram-negative bacilli isolated from children with bacteremia (data presented as N [%])

Antimicrobial agentsE. coli (n = 12)K. pneumonia (n = 7)Salmonella spp. (n = 7)M. catarrhalis (n = 2)H. influenza (n = 2)Pantoea spp. (n = 2)
Amikacin 11 (91) 7 (100) 2 (100) 2 (100) 
Amoxicillin clavulanic acid 5 (36) 5 (71) 2(100) 2 (100) 
Ampicillin 2 (18) 0 (0) 3 (43) 2 (100) 1 (50) 0 (0) 
Cefepime 6 (55) 5 (71) 
Cefotaxime 6 (55) 5 (71) 5 (71) 2 (100) 2 (100) 2 (100) 
Cefuroxime 5 (45) 5 (71) 2(100) 2 (100) 0 (0) 
Ciprofloxacin 5 (45) 7 (100) 1 (14) 2(100) 2 (100) 1 (50) 
Gentamicin 11 (91) 6 (86) 2 (100) 
Imipenem 11 (91) 7 (100) 7 (100) 2 (100) 
Piperacillin-tazobactam 11 (82) 7 (100) 2 (100) 2 (100) 
SMX 6 (45) 5 (71) 2 (29) 2 (100) 1 (50) 2 (100) 
Antimicrobial agentsE. coli (n = 12)K. pneumonia (n = 7)Salmonella spp. (n = 7)M. catarrhalis (n = 2)H. influenza (n = 2)Pantoea spp. (n = 2)
Amikacin 11 (91) 7 (100) 2 (100) 2 (100) 
Amoxicillin clavulanic acid 5 (36) 5 (71) 2(100) 2 (100) 
Ampicillin 2 (18) 0 (0) 3 (43) 2 (100) 1 (50) 0 (0) 
Cefepime 6 (55) 5 (71) 
Cefotaxime 6 (55) 5 (71) 5 (71) 2 (100) 2 (100) 2 (100) 
Cefuroxime 5 (45) 5 (71) 2(100) 2 (100) 0 (0) 
Ciprofloxacin 5 (45) 7 (100) 1 (14) 2(100) 2 (100) 1 (50) 
Gentamicin 11 (91) 6 (86) 2 (100) 
Imipenem 11 (91) 7 (100) 7 (100) 2 (100) 
Piperacillin-tazobactam 11 (82) 7 (100) 2 (100) 2 (100) 
SMX 6 (45) 5 (71) 2 (29) 2 (100) 1 (50) 2 (100) 

Prevalence of ESBL-Producing Isolates

The number of members of the family Enterobacterales that was positive for ESBL production by the disk diffusion susceptibility test essentially tallied with that detected by the VITEK2 system. Approximately 45% of the E. coli and 29% of K. pneumoniae were ESBL producers. Of note was the high prevalence of resistance of E. coli and K. pneumoniae to cefotaxime which is commonly used in our hospital.

Gram-Positive Bacterial Isolates

The susceptibility rates of Gram-positive bacterial isolates are shown in Table 5. Generally, Gram-positive bacteria had high susceptibility rates against most antibiotics; all the Gram-positive isolates were susceptible to vancomycin (VA); all S. pneumoniae, S. agalactiae, S. pyogenes, and Enterococcus spp. were susceptible to penicillin, linezolid (LZ), and VA; and all S. aureus isolates were susceptible to clindamycin, erythromycin, CIP, LZ, GEN, and VA; of the 10 S. aureus isolates, 9 (90%) were resistant to penicillin and 3 (30%) were resistant to methicillin (MRSA). Lastly, all Staphylococcus epidermidis isolates were sensitive to LZ, GEN, and VA but resistant to penicillin, clindamycin, erythromycin, and CIP.

Table 5.

Frequency and percentage of antimicrobial susceptibility of Gram-positive pathogens isolated from children with bacteremia (data presented as n [%])

MSSA (n = 7)MRSA (n = 3)S. pneumonia (n = 16)S. agalactiae (n = 9)S. epidermidis (n = 2)S. pyogenes (n = 6)Enterococcus spp. (n = 2)S. anginosus (n = 2)
Penicillin 1 (14) 0 (0) 16 (100) 9 (100) 0 (100) 6 (100) 2 (100) 2 (100) 
Ampicillin 1 (14) 0 (0) 16 (100) 9 (100) 0 (100) 6 (100) 2 (100) 2 (100) 
Cloxacillin 7 (100) 0 (0) 0 (100) 
Cephalexin 7 (100) 0 (0)  6 (100) 
CC 7 (100) (100) 9 (67) 0 (100) 6 (100) 2 (100) 
Co-amoxiclav 6 (86) (0) 16 (100) 0 (100) 6 (100) 
ERY 7 (100) (100) 8 (80) 9 (67) 0 (100) 6 (100) 2 (100) 
Ciprofloxacin 7 (100) 16 (100) 2 (100) 
Levofloxacin 16 (100)   
LZ 7 (100) 2 (100) 16 (100) 9 (100) 2 (100) 2 (100) 2 (100) 
Cefotaxime (0) 16 (100) 9 (100) 6 (100) 2 (100) 
Ceftriaxone (0) 16 (100) 9 (100) 6 (100) 2 (100) 
Fusidic acid 5 (71) (0) 0 (100) 
Gentamicin 7 (100) 2 (100) 2 (100) 2 (100) 
SXT 7 (100) 2 (100) 8 (80) 2 (100) 6 (100) 2 (100) 
VA 7 (100) 2 (100) 16 (100) 9 (100) 2 (100) 6 (100) 2 (100) 2 (100) 
MSSA (n = 7)MRSA (n = 3)S. pneumonia (n = 16)S. agalactiae (n = 9)S. epidermidis (n = 2)S. pyogenes (n = 6)Enterococcus spp. (n = 2)S. anginosus (n = 2)
Penicillin 1 (14) 0 (0) 16 (100) 9 (100) 0 (100) 6 (100) 2 (100) 2 (100) 
Ampicillin 1 (14) 0 (0) 16 (100) 9 (100) 0 (100) 6 (100) 2 (100) 2 (100) 
Cloxacillin 7 (100) 0 (0) 0 (100) 
Cephalexin 7 (100) 0 (0)  6 (100) 
CC 7 (100) (100) 9 (67) 0 (100) 6 (100) 2 (100) 
Co-amoxiclav 6 (86) (0) 16 (100) 0 (100) 6 (100) 
ERY 7 (100) (100) 8 (80) 9 (67) 0 (100) 6 (100) 2 (100) 
Ciprofloxacin 7 (100) 16 (100) 2 (100) 
Levofloxacin 16 (100)   
LZ 7 (100) 2 (100) 16 (100) 9 (100) 2 (100) 2 (100) 2 (100) 
Cefotaxime (0) 16 (100) 9 (100) 6 (100) 2 (100) 
Ceftriaxone (0) 16 (100) 9 (100) 6 (100) 2 (100) 
Fusidic acid 5 (71) (0) 0 (100) 
Gentamicin 7 (100) 2 (100) 2 (100) 2 (100) 
SXT 7 (100) 2 (100) 8 (80) 2 (100) 6 (100) 2 (100) 
VA 7 (100) 2 (100) 16 (100) 9 (100) 2 (100) 6 (100) 2 (100) 2 (100) 

CC, clindamycin; ERY, erythromycin.

Inflammatory Markers

WBC, ANC, CRP, and NLR ratios were significantly higher in patients with bacteremia compared to patients without bacteremia, whereas there were no significant differences between these groups in terms of LYM, MONO, PLT, Hb, PLR, and PWR (Table 6).

Table 6.

Comparison of demographic and inflammatory markers in febrile children with and without bacteremia

VariablesPatients with bacteremiaPatients without bacteremiap value
Number of patients 96 96  
Demographic data 
 Males, number, n (%) 66 (68.7) 61 (63.1)  
 Age, median (IQR), months 35.3 (26.57–44.06) 34.05 (25–43.08) 0.498 
Biomarker, median (IQR) 
 WBC count, ×109/L 14.00 (9.45–19.30) 11.00 (8.00–14.50) 0.004 
 ANC, ×109/L 8.400 (4.00–13.90) 5.30 (3.30–8.50) 0.001 
 LYM, ×109/L 1.85 (3.30–5.25) 3.40 (2.20–5.70) 0.202 
 MONO, ×109/L 1.100 (0.500–1.80) 1.10 (0.70–1.60) 0.809 
 PLT, ×109/L 337.00 (238.50–490.50) 326.00 (237.00–405.00) 0.398 
 Hb, g/L 111.00 (101.00–121.50) 114.00 (107.00–123.00) 0.028 
 CRP, mg/dL 67.00 (19.50–148.50) 13.30 (6.00–50.00) 0.000 
Derived parameters 
 NLR 2.33 (1.14–4.46) 1.55 (0.67–3.18) 0.001 
 PLR 105.09 (59.80–179.37) 90.00 (62.27–137.69) 0.092 
 Lymphocyte-monocyte ratio 2.80 (1.78–5.92) 3.17 (2.14–5.17) 0.589 
VariablesPatients with bacteremiaPatients without bacteremiap value
Number of patients 96 96  
Demographic data 
 Males, number, n (%) 66 (68.7) 61 (63.1)  
 Age, median (IQR), months 35.3 (26.57–44.06) 34.05 (25–43.08) 0.498 
Biomarker, median (IQR) 
 WBC count, ×109/L 14.00 (9.45–19.30) 11.00 (8.00–14.50) 0.004 
 ANC, ×109/L 8.400 (4.00–13.90) 5.30 (3.30–8.50) 0.001 
 LYM, ×109/L 1.85 (3.30–5.25) 3.40 (2.20–5.70) 0.202 
 MONO, ×109/L 1.100 (0.500–1.80) 1.10 (0.70–1.60) 0.809 
 PLT, ×109/L 337.00 (238.50–490.50) 326.00 (237.00–405.00) 0.398 
 Hb, g/L 111.00 (101.00–121.50) 114.00 (107.00–123.00) 0.028 
 CRP, mg/dL 67.00 (19.50–148.50) 13.30 (6.00–50.00) 0.000 
Derived parameters 
 NLR 2.33 (1.14–4.46) 1.55 (0.67–3.18) 0.001 
 PLR 105.09 (59.80–179.37) 90.00 (62.27–137.69) 0.092 
 Lymphocyte-monocyte ratio 2.80 (1.78–5.92) 3.17 (2.14–5.17) 0.589 

ROC Curve Analysis of Diagnostic Performance

We estimated the relevance of the different parameters by using area under the ROC curves (AUC). The ROC curve analysis was conducted to evaluate the diagnostic efficacy of different biomarkers. CRP demonstrated the greatest diagnostic potential for predicting bacteremia in febrile children among the biomarkers assessed, with an AUC of 0.71 (95% CI: 0.63–0.79) (Fig. 1; Table 7). This was followed by ANC with an AUC of 0.62 (95% CI: 0.53–0.71) and subsequently WBC with an AUC of 0.60 (95% CI: 0.49–0.70). Other biomarkers demonstrated lower AUC values than CRP, ANC, and WBC in the prediction of bacteremia in these children.

Fig. 1.

Inflammatory markers for differentiating bacteremia from non-bacteremia include the receiver-operating characteristic (ROC) curves of C-reactive protein (CRP), white blood cell (WBC) count, absolute neutrophil count (ANC), lymphocyte count (LYM), monocyte count (MONO), platelet count (PLT), hemoglobin (Hb) levels, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), platelet-to-WBC ratio (PWR), and neutrophil-lymphocyte ratio (NLR). The areas under the ROC curves for CRP, ANC, and WBC showed significant differences compared to those of the other markers.

Fig. 1.

Inflammatory markers for differentiating bacteremia from non-bacteremia include the receiver-operating characteristic (ROC) curves of C-reactive protein (CRP), white blood cell (WBC) count, absolute neutrophil count (ANC), lymphocyte count (LYM), monocyte count (MONO), platelet count (PLT), hemoglobin (Hb) levels, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), platelet-to-WBC ratio (PWR), and neutrophil-lymphocyte ratio (NLR). The areas under the ROC curves for CRP, ANC, and WBC showed significant differences compared to those of the other markers.

Close modal
Table 7.

AUC for test results of inflammatory markers

Variable(s)Area
WBC 0.606 
ANC 0.621 
LYM 0.471 
PLT 0.532 
MONO 0.503 
CRP 0.714 
NLR 0.615 
PLR 0.547 
LMR 0.481 
Hb 0.431 
Variable(s)Area
WBC 0.606 
ANC 0.621 
LYM 0.471 
PLT 0.532 
MONO 0.503 
CRP 0.714 
NLR 0.615 
PLR 0.547 
LMR 0.481 
Hb 0.431 

The test result variable(s) WBC, ANC, LYM, PLT, MONO, CRP, NLR, PLR, LMR, Hb have at least one tie between the positive actual state group and the negative actual state group. Statistics may be biased.

This is the first study to examine the bacterial profile and prevalence of antibiotic resistance patterns of bacteria causing bacteremia in children in Kuwait. In our study, the rate of blood culture contamination (0.6%) is lower than in other studies [8, 9], which suggests that poor technique is unlikely to have greatly influenced our results, reflecting the rigorous training undertaken by clinical staff. In this single-center retrospective study, we found a low incidence of bacteremia among study subjects. Analysis of our results showed that 18,299 patients tested negative (99.5%) and 96 patients tested positive (0.5%) during the study period. This observation is discordant with reports from Mozambique (8%) and Kenya (5.9%) where the positivity rates of blood cultures were higher [10, 11]; several factors may explain the lower positivity rate in our cultures. First, it is noteworthy that the hospital lacked a well-defined written policy for taking blood cultures from pediatric patients attending the pediatric casualty unit. The absence of clear guidelines might have influenced the decision-making process regarding blood culture orders. Second, the quantity of blood sampled for cultures directly affects the likelihood of detecting positive results. A reduction in the amount of blood collected can potentially impact the chance of identifying positive cultures [12]. However, we could not confirm the precise amount of blood withdrawn from each patient. Third, prehospital antibiotic usage is an important factor to consider because it can affect the rate of positive blood culture results. Interpreting negative blood cultures in febrile patients who received antibiotics before collection should be done cautiously [13]. However, in this study, there was inconsistent access to the patients’ history of previous antibiotic exposure.

We noted that there was a significant decrease in the number of blood cultures collected and in the number of bloodstream infections that occurred during period C (the COVID-19 period) compared to periods A, B, and D. This observation may be explained, in part, by the fact that daily emergency department visits during the COVID-19 pandemic decreased substantially compared to the pre-pandemic period [14]. One study showed that COVID-19 brought a greater reduction of emergency department utilization in pediatric patients than in adult patients [15]; the primary reason for the decrease in visits, as mentioned in other studies, is fear of COVID-19 [16]. In addition, there was a substantial decrease in seasonal influenza and other viral diseases, which easily spread pre-pandemic, but during COVID-19 have been limited by social distancing, reductions in the number of people allowed at gatherings, limits on the operation of schools and other spaces, and an emphasis on personal hygiene such as mask-wearing and handwashing [17].

In the current study, the highest incidence of bacteremia was found in patients aged less than 1 year (52.1%), and the majority of patients (61.4%) were aged less than 2 years. This observation may be explained by the fact that children who are younger than 48 months are at increased risk for bacteremia or sepsis due to the immaturity of their immune system [18]. In the current study, we found that most bacteremia patients (68.7%) were male. This might imply age-specific sex-related differences in the immune response to infection [19].

Our data indicate that S. pneumoniae was the most common cause of pediatric bacteremia, accounting for up to 17% of cases in the hospital. This may be related to serotypes of S. pneumoniae not included in the vaccine currently used (i.e., non-vaccine serotypes) or to the patients who may not have received the vaccines or to the immune status of children with invasive pneumococcal disease. As this finding was unexpected, more detailed information on serotyping of pneumococcal isolates, the timing of vaccination, and the underlying health of these children is necessary. Notably, the high prevalence rate of S. pneumoniae in children with bacteremia at our hospital is inconsistent with previous studies in Saudi Arabia, Iran, and India, which showed high prevalence rates of S. aureus and Enterobacterales [7, 20, 21]. E. coli was the second most frequent isolate in our study, a finding similar to previous reports from Saudi Arabia and India [20, 22]. E coli bacteremia was most common in children younger than 1 year and is usually associated with urinary tract infection (data not shown). This needs to be closely monitored as E. coli has been reported as a growing reservoir for ESBL, which mediate cephalosporin resistance that inhibits successful antibiotic treatment [23]. S. aureus accounts for 10% of bloodstream infections and may be associated with skin, soft tissues, or musculoskeletal infections. The high incidence of bacteremia due to S. aureus was striking, especially in the youngest age-group, as S. aureus can cause serious morbidity and mortality and can often be difficult to treat, due to resistance to multiple antibiotics [19]. In previous studies from Saudi Arabia and India, it was reported that S. aureus was the most prevalent [20, 22]. Our analysis revealed reductions in N. meningitidis and H. influenzae bacteremia over the study period. The relatively low occurrence of bacteremia due to N. meningitidis and H. influenzae probably reflects the efficacy of the meningitis C vaccine and the Hib vaccine introduced in 1990 and 2008, respectively.

Antibiotic resistance was found predominantly in Gram-negative organisms, a recognized global concern [24]. E. coli, Klebsiella spp., and Salmonella spp. account for 27% of bloodstream infections. High rates of these pathogens were resistant to ampicillin (81%), SXT (50%), AMC (50%), and CIP (50%). These antibiotics are used frequently in Kuwait for empirical treatment of respiratory and urinary tract infections in children. Based on our data, the use of these agents for empirical treatment of suspected bacteremia would not cover the majority of pathogens. The incidence of resistance to MEM, AN, and TZP was very low and consistent with previous studies [15, 24]. An alarmingly high rate of ESBL-producing E. coli and K. pneumoniae was responsible for pediatric bacteremia in our facility. Approximately 45% of the E. coli and 29% of K. pneumoniae were ESBL producers. The widespread resistance of E. coli and K. pneumoniae to cefotaxime, a drug used commonly in our hospital, was alarming. A compounding finding was that most of the ESBL-producing E. coli and K. pneumoniae strains were multidrug-resistant; specifically, most were resistant to AMC, CIP, and SXT. This study shows that the prevalence of E. coli and K. pneumoniae in AAH was much greater than that reported in other nations in the Middle East region [25].

The current data may reflect the increased use of orally administered agents to treat community-acquired infections, such as UTIs and respiratory tract infections. This overuse may select multidrug-resistant E. coli phenotypes, harboring the potential to disseminate within our country. Further studies on consumption of antimicrobials in the community level are needed to verify this assumption. In general, carbapenems, TZP, and AN agents showed excellent in vitro coverage of the pathogens isolated in this study. This information will be useful in choosing empiric therapy for seriously ill hospitalized patients with suspected bacteremia. Due to the small number of other pathogens found in our study, the antibiotic sensitivity pattern might not be conclusive and was thus not compared with other reports.

The second objective of this study was to evaluate the diagnostic performances of ten inflammatory markers in febrile children as a diagnostic marker of bacteremia. The use of biomarkers for screening and diagnosis in the adult population has been investigated previously.

Some studies suggest promising results for markers like CRP, procalcitonin, and IL-6 in identifying bacteremia in febrile children. These markers can enhance diagnostic accuracy but should be used in conjunction with clinical assessments. Combining multiple biomarkers may improve diagnosis [5, 26]. Compared to studies on the use of biomarkers in adults, studies in the pediatric age-group are still very limited. We assessed whether the inflammatory markers parameters at the time of admission are helpful in distinguishing febrile children with bacteremia from febrile children without bacteremia. Out of 10 inflammatory markers, only WBC, ANC, CRP, and NLR ratios were significantly higher in patients with bacteremia compared to patients without bacteremia. Thus, the analysis of inflammatory markers profiles may provide additional information in early diagnosis of bacteremia. More prospective and large-scale studies are warranted to confirm these findings. Limitations of this study include being conducted in a single center, lack of pneumococcal isolate serotyping, and not evaluating the combined use of various biomarkers for predicting bacteremia.

This study demonstrated that S. pneumoniae, E. coli, S. aureus, and Group B streptococci were the leading causes of pediatric bacteremia. Most patients (61.4%) were under 2 years. A high percentage of Gram-negative organisms causing bacteremia in children was highly resistant to the first- and second-line antibiotics for the therapy of bacteremia, but the carbapenems, TZP, and AN demonstrated excellent in vitro coverage for the pathogens. Our study highlights the need for further investigation to explain the increased rate of S. pneumoniae bacteremia. We observed that a large number of E. coli and K. pneumonia were ESBL producers and multidrug-resistant. Monitoring ESBL production and antimicrobial susceptibility testing is necessary to avoid treatment failure in patients with bacteremia. Our results showed that WBC, ANC, CRP, and NLR were valuable markers of bacteremia in febrile children and can guide the initiation or discontinuation of antibiotic therapy in patients with suspected bacteremia. Studies of this nature will contribute to developing antibiotic policies for pediatric bacteremia.

The strains used in this study were obtained as part of routine diagnostic services. Therefore, no ethical approval was required.

The authors have no conflicts of interest to declare.

None.

Khalifa Al Benwan: study design, data collection, data interpretation, and drafting the manuscript. Dalal Al Benwan: data entry, data analysis, and data interpretation. Both the authors edited and approved the final version of the manuscript.

All relevant data are presented in this article.

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