Antimicrobial resistance among Enterobacteriaceae, Staphylococcus aureus, and Pseudomonas spp. isolates from clinical specimens from a hospital in Nairobi, Kenya

Design, setting and data source

This retrospective epidemiologic study used laboratory records of 1,217 clinical specimens submitted to the diagnostic laboratory of The Karen Hospital for bacterial culture and sensitivity testing between 2012 and 2016. Records of specimens positive for Enterobacteriaceae, Staphylococcus aureus, or Pseudomonas spp. were included in the study. Only records of these organisms were included for further analysis because they were the most frequently isolated organisms and sample sizes of other organisms were not sufficient to enable meaningful analysis. The following data were extracted from the laboratory records of specimens selected for inclusion in the study: medical record number, sample identification, patient gender, age, specimen site, bacterial growth, bacterial species isolated, and results of antimicrobial susceptibility testing. Hospital records for both inpatient and outpatient visits were also obtained. The following data were extracted from the hospital records: medical record number, marital status, visit date, admission date, discharge date, and inpatient/outpatient designation.

Bacterial isolation and antimicrobial susceptibility testing

The hospital laboratory followed the European Committee on Antimicrobial Susceptibility testing (EUCAST) procedures for bacterial isolation and antimicrobial susceptibility testing (The European Committee on Antimicrobial Susceptibility Testing, 2012a; The European Committee on Antimicrobial Susceptibility Testing, 2013a; The European Committee on Antimicrobial Susceptibility Testing, 2014a; The European Committee on Antimicrobial Susceptibility Testing, 2015a). Antimicrobial susceptibility testing was performed using the Kirby-Bauer disk diffusion method (The European Committee on Antimicrobial Susceptibility Testing, 2012a; The European Committee on Antimicrobial Susceptibility Testing, 2013a; The European Committee on Antimicrobial Susceptibility Testing, 2014a; The European Committee on Antimicrobial Susceptibility Testing, 2015a). Isolates were classified as susceptible, intermediate or resistant based on breakpoints listed in the EUCAST guidelines (The European Committee on Antimicrobial Susceptibility Testing, 2012b; The European Committee on Antimicrobial Susceptibility Testing, 2013b; The European Committee on Antimicrobial Susceptibility Testing, 2014b; The European Committee on Antimicrobial Susceptibility Testing, 2015b; The European Committee on Antimicrobial Susceptibility Testing, 2016a). The laboratory followed EUCAST guidelines for routine quality control, using EUCAST-recommended quality control strains (The European Committee on Antimicrobial Susceptibility Testing, 2012c; The European Committee on Antimicrobial Susceptibility Testing, 2012d; The European Committee on Antimicrobial Susceptibility Testing, 2013c; The European Committee on Antimicrobial Susceptibility Testing, 2014c; The European Committee on Antimicrobial Susceptibility Testing, 2015c; The European Committee on Antimicrobial Susceptibility Testing, 2016b).

Data management

Data management was performed in SAS 9.4 (SAS Institute, Cary, NC, USA). The data were assessed for duplicate entries but none were found. Patient hospital records were joined to laboratory records based on medical record numbers and if the date of hospital visit, admission, or discharge occurred within 14 days of sample submission or laboratory report. Patient entries were coded as “inpatient” or “outpatient” based on a visit identification indicator when available. If a visit identification indicator was not available, hospital status (inpatient vs outpatient) was determined based upon the presence or absence of an admission date.

Records for non-duplicate specimens positive for Enterobacteriaceae, Staphylococcus aureus, or Pseudomonas spp. isolates were included for further analysis in the study because these were the most frequently isolated organisms. Specimen site was re-categorized to reflect the most common sites of sample collection. For enteric bacteria and Pseudomonas spp. this included stool, blood, and sputum. Isolates obtained from all other sites were categorized as “other”. The most common specimen sites for S. aureus isolates were blood, wounds and abscesses; isolates from all other sites were categorized as “other”.

The results of susceptibility testing were re-coded to two categories, “resistant” and “susceptible”. Isolates with “intermediate” susceptibility were re-coded as resistant (Magiorakos et al., 2012). In order to further classify isolates as antimicrobial or multidrug resistant, antimicrobial agents were placed into clinically significant antimicrobial categories listed in guidelines established by the European Center for Disease Prevention and Control (ECDC) and United States Centers for Disease Control and Prevention (CDC) (Magiorakos et al., 2012). Isolates resistant to one or more drugs in at least one category were considered to be antimicrobial resistant (AMR). Isolates resistant to one or more antimicrobial agents in at least three categories were classified as multidrug resistant (MDR) (Magiorakos et al., 2012). If a bacterial species was intrinsically resistant to an antimicrobial agent or category, results of susceptibility testing for isolates of that species were not reported, and the antimicrobial was excluded during classification of the isolate as AMR or MDR (Bouza & Cercenado, 2002; Pfeifer, Cullik & Witte, 2010; Magiorakos et al., 2012; Leclercq et al., 2013; Ruppé, Woerther & Barbier, 2015; Hughes et al., 2016; Goto et al., 2017; CLSI, 2020).

The following antimicrobial categories and agents were used in the classification of Enterobacteriaceae: aminoglycosides (gentamicin, amikacin, kanamycin, streptomycin), carbapenems (ertapenem, imipenem, imipenem/cilastatin, doripenem, meropenem), non-extended spectrum cephalosporins (cefuroxime, cefaclor, cephalexin), extended-spectrum cephalosporins (ceftriaxone, ceftazidime), quinolones and fluoroquinolones (ciprofloxacin, nalidixic acid, norfloxacin, ofloxacin), folate pathway inhibitors (sulfamethoxazole +/- trimethoprim), penicillins (ampicillin, cloxacillin, methicillin, piperacillin), penicillin/β-lactamase inhibitor combinations (amoxicillin/clavulanate), phenicols (chloramphenicol), and tetracyclines (tetracycline, minocycline).

Antimicrobial categories and agents used in the classification of S. aureus isolates included: aminoglycosides (gentamicin, amikacin, kanamycin, streptomycin), anti-staphylococcal β-lactams (oxacillin, methicillin, cloxacillin, flucloxacillin), fluoroquinolones (ciprofloxacin, norfloxacin, ofloxacin), folate pathway inhibitors (sulfamethoxazole +/- trimethoprim), glycopeptides (vancomycin), lincosamides (lincomycin), macrolides (erythromycin), phenicols (chloramphenicol), and tetracyclines (tetracycline, minocycline). For Pseudomonas spp. isolates, the following categories and agents were used: aminoglycosides (gentamicin, amikacin), antipseudomonal carbapenems (meropenem, doripenem), and antipseudomonal fluoroquinolones (ciprofloxacin, ofloxacin).

Statistical analysis

All statistical analyses were performed using SAS 9.4 (SAS Institute, Cary, NC, USA). The Shapiro–Wilk test was used to assess for normality of distribution of patient age. Since patient age was non-normally distributed, median and interquartile range were used as measures of central tendency and dispersion. The Cochran-Armitage trend test was used to assess for significant temporal trends in AMR and MDR. Univariable Firth logistic regression models were used to assess whether resistance to one antimicrobial class could be used to predict resistance to other antimicrobial classes among Enterobacteriaceae isolates. The Firth logistic regression models were necessary for these analyses because of the small number of isolates in some of the analyses. Ordinary logistic models, which use maximum likelihood estimation, are not appropriate for these models because they suffer from small-sample bias (Heinze & Schemper, 2002). To mitigate small-sample bias, the Firth models use penalized likelihood functions to obtain parameter estimates rather than maximum likelihood used by ordinary logistic models (Firth, 1993).

The following potential explanatory variables were investigated for associations with AMR and MDR: patient hospitalization status (inpatient vs outpatient), specimen site, patient gender, patient age category, patient marital status, and bacterial species isolated (for Enterobacteriaceae). Firth logistic regression models were used to investigate determinants of: (a) AMR and MDR among enteric bacteria, (b) AMR and MDR among S. aureus isolates, and (c) AMR among Pseudomonas spp. isolates. Model building was performed using a two-step process. First, univariable Firth logistic regression models were used to identify explanatory variables significantly associated with each of the outcomes. In the second step, variables that had significant univariable associations with the outcomes (using a relaxed p-value of <0.15) were considered in multivariable Firth logistic regression models. Model building was performed using manual backwards elimination, with a cutoff p- value of ≤0.05. Explanatory variables were considered to be confounders if their removal resulted in >20% change in the coefficients of the variables in the model. Model fit was assessed using the Hosmer-Lemeshow goodness-of-fit test.

Ethics approval

This study was approved by the Standards and Ethics Committee of The Karen Hospital and the University of Tennessee Institutional Review Board. UTK IRB16-03358-XM. The clinical specimens used in this study were collected as part of the routine diagnostic testing procedures used for patient care in the hospital. Since this was a retrospective study, no contact was made with patients and no attempt was made to link records to specific patients.

Descriptive statistics

Of the 1,217 specimens submitted for culture and sensitivity testing, 222 had bacterial growth of which 166 met the inclusion criteria and were included for further analysis. The most frequently isolated pathogens were enteric bacteria (108 specimens), followed by S. aureus (42), and Pseudomonas spp. (20). Other bacterial species isolated included: Acinetobacter spp., Enterococcus spp., Helicobacter spp., Moraxella catarrhalis, coagulase negative Staphylococcus spp., untyped Staphylococcus spp., Streptococcus pneumoniae, Streptococcus viridans, and untyped Streptococcus spp.

Among the Enterobacteriaceae, Klebsiella spp. were the most frequently isolated (28.7%), followed by Escherichia coli (26.9%), Proteus spp. (15.7%), and Salmonella spp. (14.8%) (Table 1). Bacterial species included in the “other” category were: Citrobacter spp., Enterobacter spp., Raoultella ornithinolytica, and Shigella spp. Most (64.4%) enteric bacterial isolates were from inpatients, and approximately half (51.1%) were obtained from stool specimens (Tables 1 and 2). The majority of specimens positive for Enterobacteriaceae were collected in 2015 (42) and 2016 (48). There was an approximately even gender distribution, with 47.2% of Enterobacteriaceae isolated from female patients and 52.8% from male patients. Age of patients from whom enteric bacteria were isolated ranged from 1 to 93 years, with a median of 46.5 years and an interquartile range of 23–70.

The majority (77.3%) of S. aureus isolates were also from inpatients, and blood was the most common specimen (48.3%) followed by wound or abscess (31.0%) (Table 1). Specimen sites in the “other” category included sputum (four), eye swab (one), and central venous catheter (one). Most (64.3%) S. aureus positive samples were from males. Patient age for S. aureus positive samples ranged from <1 year to 88 years, with a mean of 40.8 years. The majority of patients with S. aureus infections were married (67.7%). As with Enterobacteriaceae, most S. aureus positive specimens were collected in 2015 (17) and 2016 (17).

Almost half (45.5%) of Pseudomonas spp. isolates were obtained from sputum samples, followed by blood (18.2%) and stool (18.2%). The majority of specimens positive for Pseudomonas spp. were from male (80.0%) and married (64.3%) patients. The age of patients who were positive for Pseudomonas spp. ranged from 21 to 85 years, with a median of 71 years and an interquartile range of 43–82.

Patterns of antimicrobial and multidrug resistance

Overall, 75.9% of the isolates included in the current study were AMR, while 36.1% were MDR. There were no significant temporal trends in the overall burden of AMR (p = 0.2489) or MDR (p = 0.6340).

(a) Enterobacteriaceae

Susceptibility test results for one or more antimicrobial agents were available for 104 of the 108 enteric bacterial isolates. Among Enterobacteriaceae, the percentages of resistant isolates were highest for the following antimicrobial categories: penicillin/β-lactamase inhibitor combinations (91.2%), folate pathway inhibitors (83.7%), and penicillins (67.6%) (Table 3). Resistance to extended-spectrum cephalosporins was observed in about half (52.9%) of Enterobacteriaceae isolates. In addition, about one-third (30%) of the isolates exhibited resistance to at least one quinolone and/or fluoroquinolone, including 25.3% (22/87) that were resistant to one or more fluoroquinolones. Enteric bacteria had the lowest levels of resistance (6.5%) to carbapenems. As many as 88.5% and 51% of the Enterobacteriaceae isolates were AMR and MDR, respectively (Table 4). Moreover, 6.7% of the isolates exhibited MDR involving five antimicrobial categories (Table 4). While AMR exhibited a statistically significant temporal decrease between 2013 and 2016 (p = 0.0016), no significant temporal trends were observed for MDR (p = 0.4258).

Among Enterobacteriaceae, resistance to aminoglycosides was a significant predictor of resistance to carbapenems (Odds Ratio [OR] = 10.9; 95% confidence interval [CI] 1.3, 89.3; p = 0.027). In addition, resistance of enteric bacteria to extended-spectrum cephalosporins was a significant predictor (OR = 3.9; 95% CI 1.3, 11.6; p = 0.016) of resistance to quinolones and fluoroquinolones, as was resistance to penicillins (OR = 6.5; 95% CI 1.1, 40.3; p = 0.044). Resistance to extended-spectrum cephalosporins was also a predictor (OR = 17.9; 95% CI 3.7, 87.3; p = 0.0004) of resistance to penicillins.

(b) S. aureus

Approximately half (56.7%) of the S. aureus isolates were resistant to folate pathway inhibitors, while approximately one-third were resistant to tetracyclines (35%) and macrolides (29.5%) (Table 3). Among S. aureus isolates, 57.1% were AMR, while 16.7% were MDR (Table 4). There were no significant temporal trends in AMR (p = 0.9409) or MDR (p = 0.6819) among the S. aureus isolates.

(c) Pseudomonas spp

Among Pseudomonas spp. isolates, 42.1% were resistant to one or more aminoglycosides, and 15% were resistant to a fluoroquinolone (Table 3). Fifty percent of the Pseudomonas spp. isolates were AMR, and none were MDR (Table 4). Pseudomonas spp. isolates were only observed in 2015 and 2016, and there were no statistically significant differences (Fisher’s exact p = 0.6285) in the proportions of AMR isolates between the two years.

Distribution of AMR and MDR isolates

The distribution of patient and specimen characteristics among AMR and MDR isolates, as well as p- values for associations assessed using univariable Firth logistic regression models, are displayed in Table 5. While the majority of Klebsiella spp. (71%) and E. coli (58.6%) isolates were MDR, only 23.5% of Proteus spp. and 14.3% of Salmonella spp. isolates were MDR. Stool specimens had the lowest proportion of MDR Enterobacteriaceae (36.4%), while about half of the Enterobacteriaceae isolates from sputum samples (46.2%) were MDR. The highest percentage of MDR isolates among enteric bacteria were from blood samples (69.2%) and other sites (75.0%) (Table 5). Older patients had the highest percentage of MDR infections, with 73.3% of Enterobacteriaceae isolated from those over 65 years of age exhibiting multidrug resistance.

Determinants of MDR

Species of the infectious agent and gender of the patient were significant predictors of MDR (Table 6). Compared to E. coli isolates, Salmonella spp. (OR = 0.10, p =0.008) and Proteus spp. (OR = 0.15, p =0.010) isolates had significantly lower odds of being multidrug resistant. However, there was no difference in the odds of MDR between E. coli and Klebsiella spp. isolates. Enterobacteriaceae isolated from male patients had significantly higher odds (OR = 3.0, p =0.024) of being MDR than those from female patients. Results of the Hosmer-Lemeshow test showed no evidence of lack of model fit to the data (p = 0.994).

(a) Enterobacteriaceae

(b) S. aureus

S. aureus was the most frequently isolated Gram positive organism, mostly from bloodstream infections and wounds or abscesses, consistent with the findings of other studies (Omuse, Kabera & Revathi, 2015; Sangeda et al., 2017; Wangai et al., 2019). The reported prevalence of MRSA among S. aureus isolates has been quite variable across Africa (Kesah et al., 2003; Falagas et al., 2013), as well as between different facilities within Kenya (Omuse, Kabera & Revathi, 2015; Sangeda et al., 2017; Wangai et al., 2019; Iliya et al., 2020).

Consistent with findings of other studies in Kenya (Omuse, Kabera & Revathi, 2015; Sangeda et al., 2017; Wangai et al., 2019; Iliya et al., 2020), the majority (56.7%) of S. aureus isolates were resistant to folate pathway inhibitors. A somewhat lower but substantial proportion of S. aureus isolates (35%) were resistant to one or more tetracyclines, another commonly used class of antimicrobials. This is consistent with findings from other studies that reported levels ranging from 15.5% to 53.4% (Omuse, Kabera & Revathi, 2015; Sangeda et al., 2017; Wangai et al., 2019).

A much lower percentage of S. aureus isolates were MDR (16.7%) compared to Enterobacteriaceae (51%) in the current study. This is also much lower than findings from two other studies in the region, which reported that 63% and 64.8% of S. aureus isolates were MDR (Sangeda et al., 2017; Iliya et al., 2020). However, the criteria used to categorize isolates in the above studies as MDR were different from those used in the current study, limiting comparability. This calls for consistent use of a standardized classification scheme for identifying MDR isolates. This study used the framework recommended by the ECDC and CDC, which has the advantage of employing clinically and epidemiologically relevant categories of antimicrobials (Magiorakos et al., 2012).

(c) Pseudomonas spp.

Resistance to aminoglycosides was by far the most common (42.1%) among Pseudomonas spp. isolates in the current study. This is much higher than findings of two Kenyan studies, where lower levels of resistance to amikacin (11%) and gentamicin (9 and 18%) were observed among P. aeruginosa isolates (Bejon et al., 2005; Wangai et al., 2019). However, other studies have reported findings similar to those of the current study. For example, resistance to amikacin and gentamicin were observed in at least 40% of P. aeruginosa isolates from rural and urban hospitals in Kenya (Mukaya et al., 2018; Thuo et al., 2019). Similar findings were reported by a study in Tanzania, where 34.8% of Pseudomonas spp. isolated from hospital patients exhibited resistance to gentamicin (Mshana et al., 2009). Although monotherapy with aminoglycosides is not recommended for treatment of suspected P. aeruginosa infections, these agents play an important role in combination therapy (Bassetti et al., 2018). The substantial proportion of aminoglycoside-resistant isolates observed in our study and others in the region should be taken into consideration by clinicians formulating a plan for empirical treatment.

The percentage of Pseudomonas spp. isolates resistant to antipseudomonal fluoroquinolones (15%) was considerably lower than to aminoglycosides, a finding that is consistent with those of some other studies (Mshana et al., 2009; Thuo et al., 2019). Fluoroquinolones, which have a broad spectrum of activity and the advantage of oral administration, may be recommended for treatment of certain suspected P. aeruginosa infections, either alone or as part of combination therapy (Bassetti et al., 2018). Reports of fluoroquinolone resistance also appear to vary markedly between different facilities. While a 2005 study found that just 2% of P. aeruginosa isolates from inpatients at a Kenya hospital were resistant to ciprofloxacin (Bejon et al., 2005), this was as high as 52.7% in another more recent hospital-based study (Mukaya et al., 2018). Given the high proportion of aminoglycoside-resistant isolates and the limited number of useful antimicrobials due to natural resistance mechanisms of Pseudomonas, it is important to carefully monitor resistance to these agents (Bassetti et al., 2018).

Strengths and limitations

Firth logit models used in this study generate better estimates than ordinary logistic models, which use maximum likelihood estimation which suffer from small-sample bias (Heinze & Schemper, 2002). Parameter estimates of Firth models are generated using penalized likelihood, which reduces the small-sample bias associated with maximum likelihood estimation (Firth, 1993). Additionally, penalized likelihood estimations produce finite, consistent estimates of regression parameters in situations when maximum likelihood estimates do not exist due to complete or quasi-complete separation (Heinze, 2006; Williams, 2019). Therefore, although sample size was a statistical challenge, it was adequately addressed using this modeling approach.

Since this was a retrospective study that used secondary data, the investigators did not determine the antimicrobial agents included in the panel for susceptibility testing. The panels used by the diagnostic laboratory were not consistent for all isolates of the same bacterial species, highlighting the need for standardization of laboratory procedures and reporting in the region. Detailed patient medical history was not available. Therefore, the associations of AMR or MDR with clinical factors (such as current and prior antimicrobial use, underlying health conditions, primary diagnosis, and treatment outcomes) could not be investigated. Information about whether the isolated organisms were considered primary infectious agents was also not available. For instance, it is possible that some isolates from stool specimens were commensal organisms and may not have been responsible for clinical signs. However, commensal bacteria represent a potential reservoir for antimicrobial resistance genes that may be transferred to pathogens (Singh, Verma & Taneja, 2019). Therefore, monitoring patterns of AMR in these organisms provides valuable information.

Despite the above limitations, the findings of this study provide useful information for clinicians and contribute to literature on AMR patterns in Kenya specifically and sub-Saharan Africa in general. Moreover, the study focused on clinically and epidemiologically relevant antimicrobial categories as well as pathogens of public health importance. The study findings are therefore useful to guide future AMR surveillance programs and directions for future research.