Abstract
Purpose
Wound swab cultures are frequently requested from patients suspected of having a wound infection. The quality of the sample should also be evaluated by performing a Gram-stained microscopic examination. “Q-scoring system” is not widely used and the literature on the subject is limited.
Methods
A total of 4648 wound swab samples were evaluated. Samples with a Q-score of “0” were considered as “poor quality samples”, and those with a score of “ ≥ 1” were classified as “good quality samples”. Microorganisms grown in the culture of samples that scored above one were identified by mass spectrometry, and antimicrobial susceptibility testing was performed.
Results
Gram stain results were found to be consistent with the culture result in 57.10% (n = 1078) of and inconsistent with the culture result in 42.90% (n = 813) of the samples. The number of samples with Q-scores one, two, and three among the 813 samples was 62, 29, and 722, respectively. The value observed in Q3 was found to be statistically significantly higher than the values observed in Q1 and Q2 (p < 0.05). Samples sent from surgical departments (61.92%) with a Q-score of ≥ 1, were statistically significant compared to internal medicine departments (p < 0.0001). There was no significant difference between samples sent from intensive care units and those sent from other inpatient services. For both groups with Q-scores ≥ 1 and “0” similar microorganisms were identified.
Conclusion
As a conclusion, the Q-scoring system will provide a common language between the laboratory and the clinic, especially by standardizing the evaluation of wound swab samples.
Keywords: Wound infection, Wound culture, q-score, Gram stain
Introduction
The skin is the most important physiological barrier that prevents microorganisms from invading the subcutaneous tissues and causing infection [1]. As a result of trauma such as burns, injury, and surgical intervention, bacteria can settle on the skin and even invade the lower layers of the skin, leading to the development of infection. Approximately 7–10% of hospitalized patients are affected by skin and soft tissue infections. This complicates patient management by inhibiting wound healing or prolonging wound healing time [2].
If a wound infection is suspected, a culture should be obtained from the wound site to detect the causative microorganism and to perform an antibiotic susceptibility test. Tissue biopsy and aspiration samples are the gold standard samples for diagnosing wound infection by culture. However, swab samples collected from the wound site are commonly sent to the laboratory since they are non-invasive and convenient to collect [3].
If proper sampling procedures are followed, swab samples can be as effective as gold standard techniques in determining the causative agent [4, 5]. However, it is not always possible to obtain a good quality wound swab sample with the appropriate method. In this case, the microorganisms grown may not reflect the true pathogen. So it is very important to evaluate the quality of the sample. When obtaining swab samples for microbiological examination, the sample’s quality can be evaluated by performing a Gram stain on the specimen in addition to the culture [6].
The Q-score is a simple and validated screening method that has the potential to assess the quality of wound swab specimens in a reliable manner after Gram staining. This approach scores the clinical sample based on the number of polymorphonuclear leukocytes (PMNL) and squamous epithelium present. This system determines the number and morphotype of microorganisms estimated to be present in the sample, allowing for a more uniform and rational approach to swab samples and preventing unnecessary further identification, antibiotic susceptibility testing (AST), and as a consequence antimicrobial administration [6]. However, this scoring system is not widely used in most laboratories, and the literature on the issue is limited [7].
The aim of this study is to evaluate the results achieved by utilizing the Q-scoring system in wound swab samples according to clinics and to compare the Q-scores based on the clinics, as well as the compatibility of Gram stain and culture results.
Materials and Methods
The study included 4648 wound swab samples received from different clinics and submitted to the Ankara University School of Medicine, Ibn-i Sina Hospital Central Microbiology Laboratory between January 2019 and December 2022.
The swab samples were collected properly and sent to the central medical microbiology laboratory in liquid Amies transport medium (ESwab™ COPAN Diagnostics, USA). The smears prepared from the swab samples were stained on the Previcolor Gram Stain (bioMérieux, France) device and evaluated within the same day.
The Q-score was determined according to the stained smears by counting the number of squamous epithelial cells and PMNL found in each small magnification field of the smears with a 10X objective (Table 1) [8]. In the Q-scoring system, the presence of PMNL suggests inflammation or infection, while the presence of squamous epithelial cells indicates the presence of the upper layer of the skin. The increase in the number of squamous epithelial cells suggests that the microorganisms grown in the culture may be colonizing or contaminating rather than being etiological agents. The presence of squamous epithelial cells, rather than the absence of PMNL, is used to assess sample quality in this scoring system. The morphologies of microorganisms in the samples were examined under a high magnification field with a 100X objective. Samples having a Q-score of “0” were considered “poor quality samples”, while those with a Q-score of ≥ 1 were considered “good quality samples”. The samples' Q-scores were also evaluated and compared based on the clinics from which they were sent, as well as the compatibility of Gram stain-culture results.
Table 1.
Q-scoring system
| Squamous epithelial cell | |||||
|---|---|---|---|---|---|
| Absent | 1–9/LMF | 10–24/LMF | > = 25/LMF | ||
| PMNL | Absent | 3 | 0 | 0 | 0 |
| 1–9/LMA | 3 | 0 | 0 | 0 | |
| 10–24/LMA | 3 | 1 | 0 | 0 | |
| > = 25/LMA | 3 | 2 | 1 | 0 | |
LMF, Low magnification field
Simultaneous cultures were also carried out. For the culture of the samples, 5% sheep blood agar (SBA) (Becton Dickinson, USA), eosin methylene blue agar (EMB) (Becton Dickinson, USA), and chocolate agar (CA) (Becton Dickinson, USA) were used. SBA and EMB agar were incubated at 37 °C under aerobic conditions, and CA at 37 °C in an environment with 5% CO2 for 18–24 h. At the end of incubation, for non-growing plates incubation was extended to 48 h.
Microorganisms grown in the culture of samples with a Q-score of ≥ 1 were identified by “matrix-assisted laser desorption ionization-time of flight” mass spectrometry (MALDI-TOF MS, Bruker Daltonics, Germany), and antimicrobial susceptibility testing (AST) was performed according to the recommendations of the “European Committee on Antimicrobial Susceptibility Testing” (EUCAST) [9]. For the samples with a Q-score of “0”, limited identification was made for the growing microorganisms, AST was not applied.
Statistical Analysis
The difference between the groups was determined using the “chi-square test” and the difference between the ratios was determined using the “two ratio comparison z-test”. A p value of < 0.05 was considered statistically significant.
Results
A total of 4648 superficial wound swab samples were evaluated in the study. Of the patients 58% were men and 42% were women. While growth was observed in 3287 (70.71%) of the samples, there were no growth in 1361 (29.29%) wound swab samples. The distribution of the Q-scores determined according to the clinics from which the swab sample was sent is given in Table 2. General surgery (16.24%), dermatology (14.58%), and orthopedics (13.44%) are the most common clinics wound swab samples are sent. The number of samples with a Q-score of “0” was 1892 (40.70%), while the number of samples with a Q-score of ≥ 1was 2756 (59.30%). Clinics with a Q-score of “0” were determined as cardiology (56.52%), transplantation (55%), rheumatology (50.64%), and endocrinology (50%), respectively. Clinics with a Q-score of ≥ 1 were urology (70.46%), hand surgery (66.09%), general surgery (65.83%), thoracic surgery (65.34%), and neurosurgery (65.06%), respectively (Table 2). It was determined that the Q-score of the samples sent from the inpatient clinics was statistically significantly higher than that of the samples sent from the outpatient clinics (p = 0.0001). 61.92% of samples sent from surgical departments with a Q-score of ≥ 1, was statistically significantly higher compared to internal medicine departments (p = 0.0001). Additionally, there was no statistically significant difference between samples sent from intensive care units and those sent from other inpatient services (p > 0.05) (Table 3).
Table 2.
Clinic-specific sample size and Q-scores
| Clinics | Sample size | Q = 0 [n(%)] | Q ≥ 1 [n(%)] |
|---|---|---|---|
| General surgery | 755 | 258 (34.17) | 497 (65.83) |
| Dermatology | 678 | 335 (49.41) | 343 (50.59) |
| Orthopedics | 625 | 245 (39.20) | 380 (60.80) |
| Ears, nose and throat (ENT) | 310 | 120 (38.70) | 190 (61.30) |
| Peripheral vascular surgery | 297 | 126 (42.42) | 171 (57.58) |
| Infectious diseases and clinical microbiology | 292 | 111 (38.01) | 181 (61.99) |
| Brain surgery | 289 | 101 (34.94) | 188 (65.06) |
| Anesthesiology and reanimation | 196 | 93 (47.44) | 103 (52.56) |
| Internal diseases | 118 | 51 (43.22) | 67 (56.78) |
| Hand surgery | 115 | 39 (33.91) | 76 (66.09) |
| Nephrology | 109 | 42 (38.53) | 67 (61.47) |
| Endocrinology | 92 | 46 (50.00) | 46 (50.00) |
| Cardiovascular surgery | 82 | 33 (40.24) | 49 (59.76) |
| Neurology | 77 | 30 (38.96) | 47 (61.04) |
| Rheumatology | 77 | 39 (50.64) | 38 (49.36) |
| Emergency medicine | 76 | 34 (44.73) | 42 (55.27) |
| Thoracic surgery | 75 | 26 (34.66) | 49 (65.34) |
| Postoperative clinic | 68 | 29 (42.64) | 39 (57.36) |
| Geriatrics | 59 | 22 (37.28) | 37 (62.72) |
| Cardiology | 46 | 26 (56.52) | 20 (43.48) |
| Medical oncology | 46 | 20 (43.47) | 26 (56.53) |
| Urology | 44 | 13 (29.54) | 31 (70.46) |
| Gastroenterology | 37 | 16 (43.24) | 21 (56.76) |
| Transplantation unit | 20 | 11 (55.00) | 9 (45.00) |
| Other | 65 | 26 (40.00) | 39 (60.00) |
| Total | 4648 | 1892 (40.70) | 2756 (59.30) |
Table 3.
Q-score comparison by divisions
| Q-Score | Total | p | ||
|---|---|---|---|---|
| 0 n(%) | ≥ 1 n(%) | |||
| Policlinic | 629 (44.99) | 769 (55.01) | 1398 | = 0.0001 |
| Service | 1263 (38.86) | 1987 (61.14) | 3250 | |
| Surgical branches | 1117 (38.08) | 1816 (61.92) | 2933 | < 0.0001 |
| Internal branches | 775 (45.18) | 940 (54.82) | 1715 | |
| Intensive care units (ICU) | 178 (39.55) | 272 (60.45) | 450 | > 0.05 |
| Services other than ICUs | 1085 (38.75) | 1715 (61.25) | 2800 | |
The compatibility of Gram stain-culture results based on Q-scores in the samples is shown in Table 4. The Q-score was ≥ 1 in 57.52% (n = 1891) of 3287 samples with culture growth. Gram stain results were found to be consistent with the culture result in 57.10% (n = 1078) of the samples (p = 0.04). Gram stain results were found to be incompatible with the culture result in 42.90% (n = 813). For incompatible results, the number of samples with Q-scores one, two, and three among the 813 samples was 62, 29, and 722, respectively. Although Gram staining revealed no bacteria in 613 of 813 discordant samples, growth was observed in culture. The value observed in Q3 was found to be statistically significantly higher than the values observed in Q1 and Q2 (p < 0.05). The microorganisms grown in Gram staining-culture incompatible results are shown in Table 5. Despite the absence of microorganisms in Gram staining, growth was noted in 41 of the samples with Q1 score, 22 of the samples with Q2 score, and 617 of the samples with Q3 score. Gram staining demonstrating Gram-negative bacilli, revealed growth other than Gram-negative bacilli in 16 of the samples with Q1 score, 2 of the samples with Q2 score, and 56 of the samples with Q3 score. Gram staining demonstrating Gram-positive cocci, revealed growth other than Gram-positive cocci in 3 of the samples with Q1 score, 20 of the samples with Q3 score. Gram staining demonstrating Gram-positive bacilli, revealed growth other than Gram- positive bacilli in 2 of the samples with Q1 score, 2 of the samples with Q2 score, and 22 of the samples with Q3 score. Gram staining demonstrating yeast, revealed growth other than yeast, 2 of the samples with Q2 score and 6 of the samples with Q3 score. Gram staining demonstrating Gram-negative cocci, revealed growth other than Gram-negative cocci, one of the samples with Q2 score and 2 of the samples with Q3 score.
Table 4.
Gram stain-culture result compatibility based on Q-scores in samples with culture growth
| Q-scores in samples with culture growth | Gram staining-Culture result | Total | |
|---|---|---|---|
| Compatible (n/%) | Incompatible (n/%) | ||
| 0 | 895 (64.11) | 501 (35.89) | 1396(42.48) |
| ≥ 1 | 1078 (57.10) | 813 (42.90) | 1891 (57.52) |
| Total | 1973 (60.02) | 1314 (39.98) | 3287 |
Table 5.
The microorganisms grown in Gram staining-culture incompatible results
| Q-Score | Gram staining | ||||||
|---|---|---|---|---|---|---|---|
| None | GNB | GPC | GPB | Yeast | GNC | ||
| Gram stain culture result incompatibility |
Q = 1 (n = 62) |
n = 41 | n = 16 | n = 3 | n = 2 | ||
| CoNS (n = 16) | CoNS (n = 6) | Candida spp. (n = 1) | Acinetobacter spp. (n = 2) | ||||
| Klebsiella spp. (n = 9) | Candida spp. (n = 4) | Klebsiella spp. (n = 1) | CoNS(n = 1) | ||||
| Enterococcus spp. (n = 7) | S.aureus (n = 4) | Citrobacter spp. (n = 1) | Klebsiella spp. (n = 1) | ||||
| Candida spp. (n = 6) | Cornebacterium spp. (n = 3) | ||||||
| Cornebacterium spp. (n = 6) | Enterococcus spp. (n = 2) | ||||||
| Acinetobacter spp. (n = 5) | |||||||
| Pseudomonas spp. (n = 4) | |||||||
| Enterobacter spp. (n = 3) | |||||||
| S.aureus (n = 3) | |||||||
| E.coli (n = 2) | |||||||
| S. maltophilia (n = 1) | |||||||
|
(n = 29) Q = 2 |
n = 22 | n = 2 | n = 2 | n = 2 | n = 1 | ||
| CoNS (n = 6) | S.aureus (n = 1) | Pseudomonas spp. (n = 1) | S.aureus (n = 1) | α haemolytic Streptococcus (n = 1) | |||
| S.aureus (n = 4) | CoNS (n = 1) | Serratia spp. (n = 1) | S. maltophilia (n = 1) | Candida spp. (n = 1) | |||
| Klebsiella spp. (n = 3) | Klebsiella spp. (n = 1) | ||||||
| Pseudomonas spp. (n = 3) | Candida spp. (n = 1) | ||||||
| E.coli (n = 3) | |||||||
| Candida spp. (n = 1) | |||||||
| Corynebacterium spp. (n = 1) | |||||||
| Acinetobacter spp. (n = 2) | |||||||
| Enterobacter spp. (n = 1) | |||||||
| Enterococcus spp. (n = 1) | |||||||
|
n = 722 Q = 3 |
n = 617 | n = 56 | n = 20 | n = 22 | n = 6 | n = 1 | |
| CoNS (n = 180) | CoNS (n = 32) | Corynebacterium spp. (n = 6) | CoNS (n = 11) | Enterococcus spp. (n = 2) | CoNS (n = 1) | ||
| S.aureus (n = 103) | S.aureus (n = 14) | Pseudomonas spp. (n = 4) | E.coli (n = 4) | Corynebacterium spp. (n = 2) | |||
| Klebsiella spp. (n = 67) | Corynebacterium spp. (n = 8) | Klebsiella spp. (n = 4) | Klebsiella spp. (n = 4) | Klebsiella spp. (n = 2) | |||
| Acinetobacter spp. (n = 60) | Enterococcus spp. (n = 4) | Candida spp. (n = 3) | Enterococcus spp. (n = 4) | CoNS (n = 1) | |||
| Enterococcus spp. (n = 59) | Candida spp. (n = 3) | Proteus spp. (n = 3) | Pseudomonas spp. (n = 3) | Proteus spp. (n = 1) | |||
| Corynebacterium spp. (n = 59) | S.agalactia (n = 2) | E.coli (n = 2) | Acinetobacter spp. (n = 2) | Pseudomonas spp. (n = 1) | |||
| Pseudomonas spp. (n = 50) | α haemolytic Streptococcus (n = 1) | S.marcescens (n = 2) | Enterobacter spp. (n = 1) | Citrobacter spp. (n = 1) | |||
| E.coli (n = 45) | Enterobacter spp. (n = 1) | S.aureus (n = 1) | Proteus spp. (n = 1) | ||||
| Candida spp. (n = 43) | Micrococcus spp. (n = 1) | Morganella spp. (n = 1) | |||||
| Proteus spp. (n = 19) | Clostridium novyi (n = 1) | ||||||
| Enterobacter spp. (n = 17) | Brucella spp. (n = 1) | ||||||
| S. marcescens (n = 10) | |||||||
| α haemolytic Streptococcus (n = 6) | |||||||
| Dermobacter spp. (n = 6) | |||||||
| S.pyogenes (n = 5) | |||||||
| S.agalactiae (n = 5) | |||||||
| Morganella spp. (n = 5) | |||||||
| Citrobacter spp. (n = 4) | |||||||
| Achromobacter spp. (n = 3) | |||||||
| S. maltophilia (n = 3) | |||||||
| Helcococcus spp. (n = 1) | |||||||
| Aggregatibacter spp. (n = 1) | |||||||
| Salmonella spp. (n = 1) | |||||||
| Issatchenkia orientalis (n = 1) | |||||||
| Fusarium spp. (n = 1) | |||||||
| Micrococcus spp. (n = 1) | |||||||
| Alcaligenes spp. (n = 1) | |||||||
| Providencia spp. (n = 1) | |||||||
| Bacillus spp. (n = 1) | |||||||
GNB: Gram negative bacilli, GPC: Gram positive cocci, GPB: Gram positive bacilli, GNC: Gram negative cocci, CoNS:coagulase negative staphylococci
For the samples with a Q-score ≥ 1 with no culture growth, antimicrobial therapy was prescribed to the patients before culture was taken. Microorganisms identified in Gram stain-culture compatible samples were Acinetobacter spp. (n = 152), Staphylococcus aureus (n = 137), Enterococcus spp. (n = 132), Corynebacterium spp. (n = 123), Escherichia coli (n = 100), coagulase-negative staphylococci (CoNS) (n = 79), Klebsiella spp. (n = 71), Pseudomonas spp. (n = 58), and Candida spp. (n = 46).
The Q-score was “0” in 1396 (42.48%) of 3287 wound swab specimens with culture growth. Gram stain results were found to be consistent with the culture result in 64.11% (n = 895) of the samples (p = 0.04). Microorganisms identified in Gram stain-culture compatible samples were Acinetobacter spp. (n = 131), Corynebacterium spp. (n = 123), S. aureus (n = 102), CoNS (n = 100), Enterococcus spp. (n = 91), E. coli (n = 70), Candida spp. (n = 50), Pseudomonas spp.(n = 50), and Klebsiella spp. (n = 44). For both groups with Q-scores ≥ 1 and “0” similar microorganisms were identified.
Discussion
In this study, growth was detected in 70% of the 4648 wound swab samples, and the Q-score was at least one in 60% of the samples and zero in 40%. Clinics with low sample quality were cardiology, transplantation unit, rheumatology, and endocrinology, respectively. When the Q-scores of the wound swab samples were compared, there was no statistically significant difference between the Q-scores of the ICU and non-ICU services (p > 0.05), but there was a statistically significant difference between the Q-scores of the samples taken from polyclinic-service and surgery-internal branches (p = 0.05). According to our study swab samples are taken more efficiently in inpatient service conditions and surgical departments. Sample collection and sample quality can be affected by interindividual skills such as hand hygiene adherence, field of expertise such as surgery, internal medicine and the varying length of time for medical care that each patient receive, lack of adherence to clinical guidelines and the environmental conditions [10–12].
Wound infections are one of the most common causes of hospital-acquired infections and cause significant morbidity and mortality [13]. The most common samples sent to microbiology laboratories are wound swab samples to determine the infectious agent. The skin surface is colonized by flora elements and microorganisms found in the hospital environment, especially for hospitalized patients [14, 15]. Therefore, microorganisms identified in the culture of wound swab samples may be the causative agent, as well as bacteria colonizing the surface. Due to polymicrobial commensal flora members originating from the mucosa and skin surface, taking wound swab samples without proper wound cleaning makes it difficult to interpret the culture results in the clinical microbiology laboratory. For improperly obtained samples, the identification of colonizing bacteria and the reporting of AST, result in unnecessary antimicrobial therapy in patients, leading to increased treatment costs, side effects, and the emergence of resistant bacterial populations [16].
Although medical microbiology specialists and clinicians frequently agree on which microorganisms are considered pathogens in wound cultures such as Staphylococcus aureus and Pseudomonas aeruginosa, they disagree on which microorganisms should be tested for antimicrobial susceptibility such as CoNS, yeast particularly if multiple microorganisms are isolated in swab samples. However, it is widely accepted that not all microorganisms grown in culture should be reported [16, 17]. For this reason, the Q-scoring system is an important method for determining which microorganisms should be identified and tested for antimicrobial susceptibility. However, this standardized laboratory method, which will determine the quality of the samples, has not yet been widely adopted for use in clinical microbiology laboratories [7, 18]. Potential pathogens for wound specimens include beta-hemolytic streptococci, Enterococcus spp., gram-negative bacilli (e.g., P. aeruginosa, Proteus spp., E. coli, Klebsiella spp., etc.), Bacillus anthracis, S. aureus, and yeasts [16]. Consistent with the literature, the most common microorganisms identified as etiological agents in our study were Acinetobacter spp., S. aureus, Enterococcus spp., E. coli, Klebsiella spp., Pseudomonas spp., and Candida spp. However, it should be noted that it is difficult to determine whether these microorganisms are infectious agents, colonizers, or flora elements (Corynebacterium spp. and CoNS) without using this scoring system.
Squamous epithelial cells are the most important variable in the Q scoring system's evaluation of superficial swab samples. In the absence of squamous epithelial cells in the wound swab, the score is three with the presence of PMNL[8]. However, we believe that evaluating the score as three in the absence of both squamous epithelial cells and PMNL simultaneously limits the use of the Q scoring system. We presume that examining samples in which both variables are zero with a larger number of sample series will enable the scoring system to be interpreted more effectively.
Conclusion
As a conclusion, the Q-scoring system will provide a common language between the laboratory and the clinic, especially by standardizing the evaluation of wound swab samples. With the routine use of the Q-scoring system in clinical microbiology laboratories, unnecessary AST will not be performed, thus preventing the development of antimicrobial resistance and high patient hospital costs. Furthermore, using this method, feedback will be given to the clinics about the quality of wound swab samples. According to our data, it was determined that the Q-score of the samples sent from the inpatient clinics was statistically significantly higher than the samples sent from the outpatient clinics. It has been concluded that there may be difficulties in obtaining wound swab samples with the proper method in outpatient clinic conditions.
Q1, Q2, and Q3 scores reveal the microorganism number to be assessed further, however, in our study we have determined that Q3 especially including any squamous epithelial cells and PMNL have discrepant results. Additionally, for sputum samples that are widely accepted to clinical microbiology laboratories, the quality of the sample determines whether further processing is required. It is necessary to discuss whether such an application for wound swab samples is feasible.
The insufficiency of studies on this subject in the literature suggests that the Q-scoring system is not widely used in most laboratories yet or that data on the subject has not yet been published. Further studies will shed light on the subject.
Author contributions
All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by EE, EO, EU, and ZCK. The first draft of the manuscript was written by EE and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
Funding
No funding was received for conducting this study.
Declarations
Conflict of interest
The authors have no competing interests to declare that are relevant to the content of this article.
Ethical approval
Ethical approval was waived by the local Ethics Committee of Ankara University School of Medicine in view of the retrospective nature of the study (I04-161-22).
Footnotes
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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