Geographical, temporal, and demographical distribution of cases:
Burden of invasive infections vary between different geographic regions as well as patients’ demographics (1,12).
Hence, we wanted to profile the distribution of bacterial pathogens isolated from blood and CSF cultures over patients’ demographics, geographic location, and morphology of the causative agent. To determine the demographic distribution of invasive infections and the phenotypic characteristics of pathogens, we performed univariate descriptive analyses on 3068 cases as tabulated in Table 1.
Table 1. Attributes of pathogens isolated from blood and cerebrospinal fluid.
| Number of bacterial pathogens (n = 3068) |
Age (years) |
≤ 5 | 1061 (34.6%) |
6–18 | 262 (8.5%) |
19–45 | 711 (23.2%) |
46–65 | 550 (17.9%) |
> 65 | 484 (15.8%) |
Sex |
Female | 1245 (40.6%) |
Male | 1823 (59.4%) |
Site of Infection |
Blood | 2917 (95.1%) |
CSF | 151 (4.9%) |
Year of Isolation |
2011 | 262 (8.5%) |
2012 | 296 (9.6%) |
2013 | 493 (16.1%) |
2014 | 750 (24.4%) |
2015 | 1267 (41.3%) |
Location of sample collection |
Punjab | 2701 (87.9%) |
KPK | 338 (11%) |
Sindh | 25 (1%) |
Baluchistan | 1 (0%) |
No information | 3 (.1%) |
Gram stain | |
Gram-positive bacteria | 1433 (46.7%) |
Gram-negative bacteria | 1635 (53.3%) |
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Attributes of pathogens isolated from blood and cerebrospinal fluid (CSF) cultures between 2011 and 2015 in Pakistan. Data are n (%). |
A total of 3068 microorganisms were isolated from blood and CSF cultures between 2011 and 2015. These cases were reported from 44 cities in 4 provinces. Highest number of cases were reported from Punjab (87.9%), followed by Khyber Pakhtunkhwa (11%), Sindh (1%) and Baluchistan (0.033%). The median number of cases from a city was 4 with an interquartile range of 2–15. Out of 44 cities, less than 11 cases were reported from 31 cities during the five-year study period. Highest number of cases were reported from Lahore (60.5%), Faisalabad (18.3%), and Abbottabad (8.3%). Detailed distribution of cases over geographic location is given in the Supplementary Material (S1 Table).
Temporal and demographical distribution of pathogens isolated from Blood and CSF cultures:
The incidence rates of pathogen-specific invasive infections fluctuate on the basis of their temporal, spatial, and demographical characteristics (13).
Hence, the next logical step was to determine the distribution patterns of pathogens isolated from blood and CSF specimens. To investigate the temporal and demographical distribution of the bacterial species, univariate analyses were performed. Over 75% of these pathogens were from one of the seven most common bacterial species. These included CoNS (41.7%), E. coli (10.6%), Stenotrophomonas. maltophilia (S. maltophilia) (6.2%), Acinetobacter species (6.2%), S. Typhi (6.1%), S. aureus (5.9%), and Klebseilla pneumoniae (K. pneumoniae) (5%). Coagulase-negative Staphylococci was isolated from the highest number of patients throughout the study period. Detailed distribution of common pathogens isolated from blood and CSF cultures is given in Table 2.
Table 2: Profile of common bacterial species isolated from blood and cerebrospinal fluid (CSF) cultures with corresponding demographical and temporal distribution
Organism | Age (years) | Sex | Year | Total |
< 5 | 6–18 | 19–45 | 46–65 | > 65 | Female | Male | 2011 | 2012 | 2013 | 2014 | 2015 |
Coagulase-negative staphylococci | 444 (41.8%) | 74 (28.2%) | 297 (41.8%) | 255 (19.9%) | 209 (43.2%) | 557 (44.7%) | 722 (39.6%) | 101 (38.5%) | 111 (37.5%) | 198 (40.2%) | 337 (44.9%) | 532 (42%) | 1279 (41.7%) |
Escherichia coli | 57 (5.4%) | 11 (4.2%) | 58 (8.2%) | 90 (27.7%) | 109 (22.5%) | 144 (11.6%) | 181 (9.9%) | 44 (16.8%) | 53 (17.9%) | 73 (14.8%) | 66 (8.8%) | 89 (7%) | 325 (10.6%) |
Stenotrophomonas maltophilia | 215 (20.3%) | .. | 8 (1.1%) | .. | 7 (1.4%) | 76 (6.1%) | 160 (8.8%) | .. | .. | .. | 46 (6.1%) | 185 (14.6%) | 236 (7.7%) |
Acinetobacter species | 85 (8%) | 9 (3.4%) | 41 (5.8%) | 35 (18.3%) | 21 (4.3%) | 81 (6.5%) | 110 (6%) | 15 (5.7%) | 16 (5.4%) | 26 (5.3%) | 44 (5.9%) | 90 (7.1%) | 191 (6.2%) |
Salmonella enterica serovar Typhi | 16 (1.5%) | 82 (31.3%) | 86 (12.1%) | .. | .. | 81 (6.5%) | 107 (5.9%) | 13 (5%) | 20 (6.8%) | 33 (6.7%) | 58 (7.7%) | 64 (5.1%) | 188 (6.1%) |
Staphylococcus aureus | 41 (3.9%) | 13 (5%) | 52 (7.3%) | 45 (25%) | 29 (6%) | 71 (5.7%) | 109 (6%) | 19 (7.3%) | 20 (6.8%) | 33 (6.7%) | 47 (6.3%) | 61 (4.8%) | 180 (5.9%) |
Klebsiella pneumoniae | 66 (6.2%) | 8 (3.1%) | 26 (3.7%) | 29 (19.1%) | 23 (4.8%) | 53 (4.3%) | 99 (5.4%) | .. | .. | 29 (5.9%) | 51 (6.8%) | 71 (5.6%) | 152 (5%) |
Data are n (% isolates in a column). Empty cells indicate number of cases < 6. These cases are included in the total. |
Temporal and demographical AMR trends in pathogens isolated from blood and CSF cultures
Due to the variations in the treatment approaches as well as the differential ability of pathogens to acquire and disseminate resistance, resistance trends differ in different pathogens. The ability of resistant pathogens to cause infections is also dependent on host-related factors including age, gender, and co-morbidities (14). Hence, after identification of common pathogens, we wanted to determine temporal and demographical AMR trends in them. Susceptibility data was not available for all years throughout the study period on S. maltophilia and K. pneumoniae. Hence, these two pathogens could not be analyzed. Each of the remaining five pathogens was analyzed separately. For each isolate, susceptibility data for all antimicrobials was not available. To account for missing values, available case approach was employed to analyze resistance trends for each antimicrobial. As a result, the number of data points (n) varied between analyses involving different antimicrobials in a pathogen.
In the case of gram-negative organisms, we found out that resistance against fluoroquinolones has increased in E. coli from 50–74.2% between 2011 and 2015 (Table 3 and Fig. 2). Further, an increasing resistance trend against cefipime, a fourth generation cephalosporin, was also observed in E. coli (Table 3 and Fig. 2). While increasing resistance rates were observed for most of the tested antimicrobials, we found decreasing resistance trends against amikacin and gentamicin in E. coli (Table 3 and Fig. 2). Next, increasing resistance trends were observed against most tested antimicrobials in Acinetobacter species (Table 4 and Fig. 3). Of these, the most alarming finding was the steep increase in carbapenem resistance in Acinetobacter species from 50% in 2011 to 95.5% in 2015 (Table 4 and Fig. 3). In the case of S. Typhi, our results have indicated an increasing resistance trend against fluoroquinolones with resistance rates reaching up to 60% in 2015. We have also reported emerging resistance against 3rd and emergence of 4th generation cephalosporins resistance in S. Typhi (Table 5 and Fig. 4). Sex-wise comparisons showed that the rate of isolation of resistant gram-negative pathogens were independent of patients’ sex. Evaluation of age-wise resistance trends highlighted that rate of isolation of resistant pathogens is age dependent. Detailed AMR trends in E. coli, Acinetobacter species, and S. Typhi have been tabulated in Table 3–5, and shown in Figs. 2–4.
Amongst gram-positive pathogens, we found significantly decreasing resistance trends against amikacin, doxycycline, and trimethoprim-sulfamethoxazole in CoNS as well as in S. aureus (Tables 6 and 7; Figs. 5 and 6). While we unexpected did not observe any increasing resistance trend in S. aureus, resistance had increased in CoNS for a range of antimicrobials including 3rd and 4th generation cephalosporins as shown in Table 6 and Fig. 5. Our results also indicated that rate of isolation of resistant gram-positive species varied with patients’ demographic for a wide range of antimicrobials (Tables 6 and 7). Detailed AMR trends in CoNS and S. aureus have been tabulated in Tables 6 and 7, and shown in Figs. 5 and 6, respectively.
Co-resistance trends in pathogens isolated from blood and CSF cultures
Multidrug resistance (MDR) has emerged as a major public health problem globally as well as in Pakistan. Co-resistance to multiple drugs emerges with the selection of strains which are resistant to multiple antimicrobials. Clonal expansion of MDR clones is faster as compared to strains resistant to a single antimicrobial (15). Hence, it is important to identify those antimicrobials which do not exhibit resistance with any other type of antimicrobials.
To investigate this, antimicrobials belonging to the same class and exhibiting identical resistance profiles in isolates of a given species were merged and entries with missing data were excluded. Chi-square test was used to identify patterns of co-resistance in each pathogen and these patterns are tabulated in Table 8 and Supplementary materials (S2-S6 Tables). Evaluation of E. coli showed that resistance against all antimicrobials was significantly associated except β-lactams, aminoglycosides, fluoroquinolones, and tetracycline. Resistance against these four antimicrobials in E. coli was found to be independent of resistance against other antimicrobials (Table 8 and Fig. 2). We then evaluated Acinetobacter species and our results showed that cefoperazone-sulbactam resistance is not significantly associated with resistance against aminoglycosides, trimethoprim-sulfamethoxazole and tetracycline (Table 8 and Fig. 3). For S. Typhi, we only detected a significant co-resistance between nalidixic acid and fluoroquinolones (Table 8 and Fig. 4). In case of CoNS, we observed a significant co-resistance between all antimicrobials except doxycycline. Doxycycline resistance in CoNS was significantly associated only with pencillin resistance and macrolide resistance (Table 8 and Fig. 5). Analysis of S. aureus showed that penicillin resistance was not associated with resistance against any other antimicrobials. Furthermore, doxycycline resistance in S. aureus was found to be independent of resistance to all antimicrobials except macrolides and tobramycin (Table 8and Fig. 6). The co-resistance proportions, p-values, and associated odds ratios for all tested antimicrobials are provided in Table 8 and Supplementary materials (S2-S6 Tables). The co-resistance trends are E. coli, Acinetobacter species, S. Typhi, CoNS, and S. aureus have been visualized in Figs. 2–6.
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