Identification of selected primary bloodstream infection pathogens in patients attending Kisii level five and Homa Bay county hospitals

Study sites

Stored blood culture samples received from Kisii Teaching and Referral Hospital and Homa Bay County Referral hospital were analyzed at Microbiology Hub Kericho. These collection sites were selected because they do not have the capacity to identify BSI pathogens to the species level. Kisii Teaching and Referral Hospital is a public hospital located in Kitutu Chache Constituency, Kisii County. It is located in Kisii town Central Business, District Hospital Road, and serves a population of over 1 million. Homa Bay County Referral Hospital is a government health center located in Homa Bay Township Sub-location, Homa-Bay Location, Asego Division, and Rangwe Constituency in Homa Bay County. It has a population of over 1 million. The common diseases in these areas are malaria, upper respiratory tract infections, typhoid, pneumonia, tuberculosis and HIV¹¹.

Study design

This study analyzed BSI isolates data tested using FA, MS technologies and culture followed by API strip analysis. A total of 152 isolates data (122 blood culture isolates and 30 control isolates were analyzed. The study focused on the identification of BSI, accuracy, sensitivity, TAT as well as the cost of the items. Isolate analysis was performed at the Microbiology Hub Kericho (MHK), a United States Army Medical Research Directorate-Africa (USAMRD-A) facility working in collaboration with Kenya Medical Research Institute (KEMRI). The MHK performs surveillance and clinical research diagnosis of enteric pathogens causing acute diarrhea across Kenya in addition to screening blood culture samples for BSI agents.

Sample size determination

Formula n = Z² × P (1-P)/d², n = Sample size, Prevalence (p) = 10.4%

95% confidence interval (z) = 1.96

Precision d² = 0.05

n = 1.96² × 0.104(1-0.104)/0.05²

Calculated sample size (n) = 143 isolates.

The prevalence was taken from the study by Maze et al. (2018)¹² “The epidemiology of febrile illness in sub-Saharan Africa: implications for diagnosis and management”. The prevalence rate is based on East Africa data which is part of Kenya, the ranges in prevalence is between 10–20% in Kenya.

Blood culture

Blood samples were collected into BACTEC Plus Aerobic/F, Peds Plus Aerobic/F, Anaerobic/F and Lytic/10 Anaerobic/F vials (BD, United States) and incubated using the BacTec 9050 instrument (BD, United States) for 5 days to account for slow-growing pathogens. A positive signal was indicated by an increased fluorescence caused by the carbon dioxide released by an organism reacting with the vial dye. Positive blood culture samples were removed and processed to identify the organism. Samples were processed directly using the FA without need for prior subculture. As for the MS and the API strip method, samples were first gram-stained and sub-cultured on MacConkey agar, Blood agar plate, Sabouraud Dextrose agar, Hektoen enteric agar and Tryptic soy agar (Becton Dickson).

Microbial identification of the positive blood culture samples

For identification by FA, samples were tested per the manufacturer’s instructions. First, the blood culture identification panel was inserted in the loading chamber, then hydration solution was added to the sample and then the panel was placed into the FA. The results were checked after 1 hour.

Pure colonies were used for the MS identification procedure. An inoculum of 0.5 McFarland standard equivalents was prepared by selecting 1 to 3 discrete colonies from pure culture on MacConkey agar (MAC) or blood agar plate (BAP) or Sabouraud dextrose agar (SDA) and suspended in 3 ml of the MS inoculum water (Beckman Coulter). From the solution, 100 μl was transferred and mixed with 25 ml of the MS inoculum water with pluronic and then poured into the sterile inoculator D set tray. The solution (140 μl) was transferred into a gram-negative or a gram-positive panel (Beckman Coulter), and then loaded into the MS and results checked after 18–24 hours and 4 hours for yeast organisms.

For manual biochemical analysis method, the API strips (BioMerieux, United States) and media (MacConkey agar, blood agar, Hektoen enteric agar, triple sugar irons and Sabouraud dextrose agar plates) were brought to room temperature. After this, 5 mL of deionized water was added to the tray along with the strips. The API ampules or equivalent suspension medium was inoculated with a single colony. The inoculation of wells for gram negative, gram positive and yeast organisms was done as per manufacturer’s instruction. The incubation box was closed and incubated at 36°C for 18–24 hours for bacterial while for the yeast it was incubated at 29°C for 48 to 72 hours. A positive signal was indicated by a colorimetric change that was interpreted using the API guidelines.

Statistical analysis

Overall accuracy was calculated as the (number of individual isolate identified using evaluated technique) / (total number of same isolates identified) x 100%), sensitivity values and the 95% confidence intervals (CI) for both of these metrics were analyzed using the Graphpad Prism 8.2.1 software. The TAT was defined as the time taken from sample preparation to identification¹³. The average cost per samples included consumable reagents and disposable supplies and was defined as the total cost of each assay per sample run.

The indeterminate results were resolved by comparing the two techniques FA and MS, where the two techniques agreed was taken as a true positive, the discordant results were repeated using the API technique (gold standard).

Ethics approval and consent to participate

Ethical review for this work was obtained from the Kenya Medical Research Institute Scientific and Ethical review Unit-Scientific Steering Committee (KEMRI SERU-SSC) #3686 and Walter Reed Army Institute Research (WRAIR) Institutional Review Boards (IRBs) #2513. Consent was not sought for this study since it was determined that there was no interaction with human subjects.

The sensitivity and accuracy of the FA, MS, and API

The overall accuracy and sensitivity of each platform is shown in Table 1. For the FA, the calculated specificity was 99.04% (95% CI, 96.59-99.88%). Out of the total 152 isolates identified, FA technology was able to correctly identify 150 isolates resulting in a calculated sensitivity of 98.68% (95% CI: 95.33-99.84%). Similar to the specificity computation for the FA instrument, the specificity of the MS instrument was determined based on the number of isolates correctly identified by the MS out of the total number of isolates correctly identified. This yielded a specificity of 98.56% (95% CI: 95.86-99.70%). The MS technology identified 149 true isolates, which produced a calculated sensitivity of 98.68% (95% CI: 95.30-99.84%). The API strip analysis identified 150 isolates with specificity of 99.04% (95% CI: 96.59-99.88%) with no significant difference in overall specificity to the FA and MS. The sensitivity of API method was 98.68% (95% CI: 95.33-99.84%).

Pathogen identification by FA, MS and API

Next, we decided to breakdown which microbes that each platform correctly identified (Table 2). The FA and API were able to identify 150/152 BSI pathogens. Interestingly, the FA platform was able to identify 4/4 of Enterobacter cloacae isolates as opposed to API technique, which detected only 2/4. However, the FA only recognized Staphylococcus at the genus level. In addition, the system could not characterize Streptococcus anginosus and Streptococcus bovis at the species level as these bacteria were not in its database, and therefore, were identified as Streptococcus spp. The FA identified Salmonella isolates as Enterobactericiae while the API and the MS were able to identify these isolates as Salmonella spp. The MS was able to accurately identify the presence of all other isolates similar to the API method except with Staphylococcus spp. (93.75%) and Acinetobacter baumanii (85.71%), while the API method was 100% accurate for both. In addition, the MS correctly identified all other Streptococcus isolates besides S. pneumoniae, whereas the FA and API standard had accuracies of 83.3% and 85.7%, respectively. Results of identification using each technique are available (see Underlying data¹⁴).

The mean turn-around time for the FA, MS and API

We limited the running time for each platform to the length of a normal working day (8 hours) (Table 3). Under this condition, the FA could only run eight samples. We therefore established this limit to ensure an equal number of samples per run across all the techniques. The turnaround time (TAT) for the FA per sample run was 1 hour 6 minutes, with an average of 5 minutes processing time and 1 hour identification and results analysis. The average time for FA was 8 hours 40 minutes and significantly less (p < 0.0001) compared to the MS and API. The MS had a mean TAT of 42 hours per sample run, with 27 hours processing time and the mean TAT was significantly more (p < 0.0001) compared to the FA. Breaking this down, the culture process and incubation required 24 hours, which could be more depending on the bacteria and purity of culture. The API method had a mean TAT of 53 hours per sample run for bacterial isolates while for yeast was 103 Hrs. The mean TAT was significantly more (p < 0.0001) compared to the FA. Sample processing data are available for each technique (see Underlying data¹⁴).

The average cost of running one sample per technique

The total average cost of processing one sample using the FA technique was 140.11 USD (Table 4). The total average cost of running one isolate per sample using the MS instrument was 38.75 USD while API technique was 29.17 USD. Extra equipment required but not included were biosafety cabinet, incubators, autoclaves, hot plates, conical flasks, stirrers and spatulas as well as the annual preventive maintenance for this equipment. Only the costs of the consumable items needed for each method were evaluated. As expected, the cost of the items used for the API technique was lower than for those used by the MS and FA.

Recommendation

The FA and MS have not been evaluated at Kenya hospitals and further evaluation using a larger sample size is recommended in order to have more data on BSI pathogens and their antimicrobial susceptibilities in different localities. However, these preliminary results clearly suggest that both the FA and MS platforms are valuable tools in rapid identification of BSI. Each technology has its advantages and disadvantages, which must be considered. Still, implementation of either platform could result in reduction of hospital stays, lower cost, better patient management and more appropriate use of antibiotics by clinicians.