Introduction

Two million people in India die each year due to infectious diseases [1]. Septicaemia or sepsis results when circulating bacteria in blood multiply at a rate that surpasses their elimination by phagocytes [2]. Blood infections are a substantial reason for morbidity and mortality of patients, particularly in developing countries [3]. If left untreated, bloodstream infections may lead to more dangerous infections, involving all organs and ultimately death [4]. Bloodstream infection (BSI) is a leading cause of mortality in critically ill patients. Clinical microbiology laboratory performs identification (ID) and antimicrobial susceptibility testing (AST) to guide antibiotic therapy and possible drug resistance. Rapid bacterial identification and susceptibility testing improve patient therapy and outcome, decreases emergence of resistance [5]. There is a need to provide rapid, efficient and accurate system for identification and antimicrobial susceptibility testing of pathogens. In this regard the automated identification/AST systems aid in rapid diagnosis/treatment of bacterial pathogens [6]. Automated blood culture systems and automated identification and susceptibility testing of bacteria have been in the market for a number of years however application of automated systems in Microbiology is different than other clinical laboratories [7].

Although classical identification methods are still considered the gold standard, these methods are slow, time consuming and prone to subjective interpretations. On the other hand, the Vitek-2 compact system reduces the time necessary for identification and permits the standardization of inter- and intra-laboratory results, the storage of results, issuing rapid epidemiological reports, and simultaneous identification and antimicrobial susceptibility testing; however, the system is poorly efficient in identifying certain species of Gram-positive cocci [8].

Automated systems use sophisticated software to analyse the growth rates and determine the antibiotic minimum inhibitory concentration (MIC) for the organism by using specialized decision technology. Although there are differences among each system the general process of identification is almost same. The Vitek-2 compact system is the second generation of Vitek-2 compact system and offers a more sophisticated model of data analysis as well as a fully automated process for card identification, organism suspension dilution, and card filling [9]. Nonetheless a reduction in labour, faster reporting of results especially in blood stream infections and identification of uncommon or rare organisms are some of the reasons as to why automation in microbiology has gained popularity in recent years [10]. The current study was thus designed to evaluate the Vitek-2 compact system with conventional blood culture method from flagged BacT/ALERT blood culture bottles causing neonatal septicaemia in a tertiary care hospital.

Materials and Methods

This retrospective study was conducted in the Department of Microbiology at University College of Medical Sciences and GTBH, New Delhi over a period of one year from January 2023 to January 2024. A total of 170 non duplicate isolates of both Gram positive and Gram-negative organisms recovered from blood samples were included.

Sample Processing: After receiving the blood sample of the neonate of suspected septicaemia in the laboratory, the bottles were checked for adequacy of volume and labelling errors. The conventional bottles were processed accordingly as described below.

(a) Identification by conventional method: Phenotypic identification consisted of Gram staining for the observation of morphology and specific staining, followed by a series of biochemical tests specific for each group of microorganisms. Gram-positive cocci were submitted to the catalase test for differentiation between Staphylococcus and Enterococcus. The following biochemical test battery was used for the identification of species of the genus Staphylococcus: coagulase, sugar fermentation (sucrose, maltose, trehalose, xylose, and mannitol), anaerobic growth on semi-solid sodium thioglycolate medium and, if necessary, ornithine and urease production and novobiocin susceptibility [11]. Isolates previously identified as Gram-positive, catalase-negative, bile esculin-positive, NaCl-positive (growth in brain heart infusion broth with 6.5 % NaCl) and pyrrolidonyl aminopeptidase test-positive cocci were submitted to biochemical tests of fermentation of mannitol, arabinose, arginine and sorbitol, motility, and presence or absence of a pigment on sheep blood agar. Gram-negative bacilli were first tested for glucose fermentation. Glucose-fermenting bacilli were submitted to manual biochemical tests known as Citrate, an identification system based on the following tests: production of H2S, urease and l-tryptophan desaminase; motility; Indol production; lysine decarboxylase production, and the ability to use citrate as a single carbon source. Non-glucose-fermenting Gram-negative bacilli were identified based on motility, growth at a temperature of 42 °C, and production of DNAase.

(b) Automated identification: Samples exhibiting microbial growth were submitted to Gram staining and cultured on solid media directly from the blood culture bottles. After initial growth on blood agar (for Gram-positive cocci, Gram-positive bacilli, and yeast) and MacConkey agar (for Gram-negative bacilli), colonies were sub-cultured to ensure purity and then inoculated into specific identification cards for the VITEK-2 system. Gram-positive cocci, Gram-positive bacilli and yeast were inoculated into the cards from colonies grown on blood agar and Gram-negative bacilli from colonies grown on MacConkey agar, all diluted in saline (0.9 % NaCl) to a 0.5 McFarland standard. The VITEK-2 compact system automatically processes the inoculated cards. Each card contains a variety of biochemical tests that help identify the species. The system interprets the results based on the growth and biochemical reactions observed, providing a detailed report that includes the identification of the microorganism.

Quality Control: Staphylococcus aureus ATCC 25923, Enterococcus faecalis ATCC 29212, Escherichia coli ATCC 25922, Pseudomonas aeruginosa ATCC 27853, and Klebsiella pneumoniae ATCC 700603 were used as quality control (QC) standard strains for both methods.

Statistical Analysis: The results were compared by entering the data on excel sheets and simple statistical calculations were made and recorded.

Results

A total of 170 non duplicate isolates of Gram positive and Gram-negative bacteria recovered from the blood samples of the patients admitted at GTBH were included in the study. Gram negative organisms were recovered from 97 isolates (57.1%) and 73 isolates (42.9%) were gram positive organisms which included all the samples received from the neonatal age group [Figure 1]. Patients from whom Gram negative isolates (97) were recovered included 52 (53.6%) males and 45 (46.3%) females. Most of the Gram-Negative bacteria identified included Klebsiella pneumoniae (n=47; 48.4%) followed by Acinetobacter baumannii (n=15; 15.4%), Enterobacter cloacae (n=14; 14.4%), Citrobacter species (n=10; 10.3%), Pseudomonas aeruginosa (n=05; 5.1%), Escherichia coli (n=04; 4.4%) and Burkholderia cepacia (n=02; 2.0%) [Table 1].

Figure 1: Distribution of isolates of GPC and GNB

Concordant identification (ID) results of Vitek-2 when compared to the manual methods were seen with all the isolates of Enterobacter cloacae, Pseudomonas aeruginosa and Burkholderia cepacia. However, discrepancy in results of Vitek-2 compact system versus conventional methods was seen for 2 isolates of E. coli which were Acinetobacter baumannii complex by Vitek-2. Likewise, 3 isolates of Klebsiella pneumoniae were identified by Vitek-2 as Klebsiella oxytoca. Three (3) isolates of Citrobacter species were identified as E. coli by Vitek-2 compact system. In addition, 2 isolates of Acinetobacter baumannii were identified as Enterobacter cloacae in the Vitek-2 compact system. In case of gram-positive organisms (73), mostly were male patients. They are identified as Coagulase negative staphylococcus (n=38; 52.05%) followed by Staphylococcus aureus (n=27; 36.9%), Enterococcus faecalis (n=05; 6.8%) and Enterococcus faecium (n=03; 4.1%). Concordance between the isolates obtained from Vitek-2 compact system and manual ID was seen with all the isolates of Enterococcus faecium and Enterococcus faecalis. However discordant results were obtained for CONS and Staphylococcus aureus, as 3 isolates of CONS and 6 isolates of Staphylococcus aureus were identified as Enterococcus faecalis [Table 2].

Discussion

The need for the rapid and efficient identification of microorganisms isolated from blood cultures has encouraged studies that investigated automated identification systems to reduce the time of identification. The early identification of bloodstream infections allows for the early modification of antimicrobial treatment and a diminished need for other diagnostic tests. Thus, patients’ hospital stay can be shortened, and expenditures on patients can be reduced. Several of these studies have used direct inoculation from blood culture bottles, but the results were not as efficient as those obtained in studies using standard inocula from subcultures of microorganisms grown for 24 hours on solid media. Rapid bacterial identification and susceptibility testing improve patient therapy and outcome, decreases emergence of resistance and also reduces costs [10].

The present study included a total of 170 isolates from blood samples, out of which 97 isolates were gram negative and 73 were gram positive. In another comparative study done by Donay JL et al [12], evaluating the identification and antimicrobial susceptibility testing performances of the BD Phoenix Automated Microbiology System, a total of 305 clinical isolates were included, out of these 187 were Gram negative and 118 were Gram positive. Samples were received more from male patients in our study, similar to the previous study done by Nadheema et al [13].

Among the 73 isolates of gram-positive cocci obtained, Staphylococcus epidermidis (Coagulase negative staphylococcus) followed by Staphylococcus aureus were the most common Gram-positive organism in our study. Similar results were seen in previous studies done by Akgun et al [14] that reported 71 (61.2%) as coagulase-negative Staphylococcus (CoNS), 7 (6.0%) as Staphylococcus aureus while 20 (17.2%) were Enterococcus species. Also, another study done by Lupetti et al [15]. reported the CoNS to be slightly lower than S. aureus (62%). Another study by Chen et al [16] reported that out of 197 (79.4%) isolates of 248 Gram-positive organisms, 95 isolates were CoNS and 58 isolates were S. aureus.

Out of the 97 gram-negative rods studied, 75 (77.5%) corresponded to the family Enterobacteriaceae (47 Klebsiella species, 14 Enterobacter cloacae, 10 Citrobacter species and 04 Escherichia coli) and 20 (20.4%) were identified as non-fermentative gram-negative rods (15 Acinetobacter baumannii and 5 Pseudomonas aeruginosa). In our study Klebsiella species followed by Acinetobacter baumannii were the most common Gram-negative pathogen. Similar results were seen in a study done by Jagadish et al. [17]

In this study concordance between the ID results of Vitek-2 compact system and manual methods for all the isolates of Enterobacter cloacae, Pseudomonas aeruginosa, Enterococcus faecalis and Enterococcus faecium was seen. However discordant results majorly were seen for Klebsiella species, Citrobacter species, CONS and Staphylococcus aureus by Vitek-2 compact system. Furthermore, appropriate incubation conditions and duration should be specified for organisms having slow metabolic rates and late lactose fermenters like S. hominis that are prone to be misidentified by the Vitek-2 system. The identification part of vitek-2 compact system has flaws which need to be worked upon especially for the organisms which cause serious life-threatening infections (Salmonella spp, E. coli). The treatment modality of the patients changes if the identification of the organism is compromised because separate group of antibiotics need to be employed for treatment. The organisms having slow metabolic rates are prone to errors by the Vitek-2 compact system. Incorporation of additional biochemical tests like indole into the Vitek-2 cards can improve the identification and resolve errors where Indole aids in identification (Salmonella spp, E. coli, K. pneumonia, K. oxytoca).

Limitation of the study: The small number of samples collected may not adequately represent the diversity of pathogens and resistance patterns encountered in neonatal septicemia. While identification is important, the accuracy and reliability of antimicrobial susceptibility testing (AST) results from the VITEK-2 system should also be evaluated. Errors in AST can lead to inappropriate antibiotic therapy choices. To address these limitations, future studies could focus on increasing sample size to improve the robustness and generalizability of findings. Additionally, incorporating AST evaluation using the VITEK-2 system would enhance understanding of its performance in antimicrobial susceptibility testing, thereby providing more comprehensive data for clinical decision-making.

Conclusion

In our study overall concordance is 88.2% and discordance is 11.8%. It is necessary to implement identification methods that facilitate access to fast and reliable results, but at the same time, help to optimize the economic resources once those are implemented in the daily routine. By implementing identification methods that balance speed, reliability, and economic efficiency, one can enhance diagnostic capabilities in clinical microbiology. This approach not only improves patient care through timely and accurate treatment decisions but also optimizes resource allocation in the laboratory setting.

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