Abstract
Background
Wartime conditions have exacerbated the importance of infection prevention and control (IPC) practices and their impact on patient prognosis. While microflora contamination occurs at the time of injury, multiple subsequent invasive procedures potentially place patients at a higher risk of acquiring nosocomial flora. This study aimed to evaluate whether the region of trauma, the length of the evacuation chain, or the previous healthcare facility impact the risk of acquiring ESKAPE pathogens upon admission to a rear hospital, and to assess the consequences of these pathogens on wound microflora and patient prognosis.
Methods
A retrospective cohort study was conducted at the Feofaniya Clinical Hospital of the State Administration of Affairs, which serves as a rear hospital in Ukraine’s medical evacuation chain. Combatants treated in the Surgical Intensive Care Unit (ICU) were selected for analysis. Medical records were reviewed to obtain geographical details of the trauma region, the approximate route and duration of evacuation, injury type, and microbiological results from wound swabs collected upon ICU admission. Binary logistic regression analysis was performed to identify factors associated with the primary outcome.
Results
A total of 264 male combatants admitted between February 2022 and December 2024 were included (mean age: 38.1 years). The median time from injury to admission at the rear hospital was 7 days. The most frequent regions of trauma were Donetsk (57.1%) and Kharkiv (12.2%), with patients primarily evacuated via hospitals in the Dnipro region and the Kharkiv Northern region military medical center. Blast injuries predominated (70.3%), followed by gunshot wounds (22.4%). Initial microbiological screening revealed ESKAPE pathogens in 63.8% of blast injury cases and 62.7% of gunshot wounds. While the prevalence of ESKAPE-positive swabs slightly decreased from 66.0% in 2022 to 58.2% in 2023, it rose to 59.3% in 2024, remaining the dominant finding. Logistic regression analysis indicated that the geographic region of trauma, evacuation route, and duration of evacuation did not significantly affect the risk of ESKAPE pathogen presence upon admission (\(p > 0.05\)). However, the presence of ESKAPE pathogens at admission was a significant independent predictor of mortality (aOR 22.39; 95% CI 2.97–168.55; \(p = 0.003\)).
Conclusions
Our study demonstrated that neither the specific geographical region of injury nor the medical evacuation route significantly influenced the microbiological profile of combat-related wounds upon admission to a rear hospital. ESKAPE pathogens are highly prevalent in the initial wound swabs of combatants and serve as a critical independent predictor of increased in-hospital mortality.
Clinical trial number
Not applicable.
Keywords: Primary contaminants, Military trauma, Combat-related injuries, Wound infection, ESKAPE pathogens, Multidrug-resistant bacteria, Infection prevention and control, Medical evacuation, In-hospital mortality, Ukraine
Background
Healthcare-associated infections (HAIs) represent a critical challenge for modern global healthcare systems [1]. Low- and middle-income countries face disproportionate difficulties in the prevention, diagnosis, surveillance, and reporting of HAIs due to limited infrastructure, insufficient infection control resources, and underdeveloped surveillance frameworks [2, 3]. Armed conflicts further exacerbate these vulnerabilities, severely impacting healthcare resilience and frequently leading to HAI outbreaks. In such environments, patient colonization with multidrug-resistant (MDR) pathogens significantly increases the risk of severe infectious complications.
The ongoing war in Ukraine serves as a profound example of conflict-driven transformations in medical services. Ukrainian clinicians have navigated unprecedented challenges, ranging from personal shifts in professional roles to managing massive structural reorganizations under direct physical threats. Such drastic systemic shifts have exposed critical weaknesses in the pre-existing healthcare infrastructure, most notably in HAI management and colonization control. Currently, comprehensive national HAI databases remain absent in Ukraine. However, the rising number of young, previously healthy patients without comorbidities in Intensive Care Units (ICUs) has highlighted the devastating impact of infectious complications on survival and long-term disability.
Wound infections remain a primary concern in the management of combatants. Combat wounds are inherently contaminated at the point of injury; as patients move through the evacuation chain, they undergo various invasive surgical interventions at multiple levels of care. At each stage, the initial microbial profile may be supplanted by nosocomial flora. Managing such trauma requires specialized approaches that are often difficult to maintain within civilian hospital settings. Furthermore, existing infection prevention and control (IPC) guidelines for combat trauma have proven insufficient under current wartime conditions and require substantial adaptation [4].
This study aims to evaluate 305 cases of combatants treated in the ICU at Feofaniya Clinical Hospital between 2022 and 2024. The objective is to determine the prevalence of admission-acquired nosocomial colonization and assess whether the evacuation route and duration impacted microbial patterns, the development of infectious complications, and overall survival rates.
Methods
We conducted a retrospective study at the Feofaniya Clinical Hospital State Administration of Affairs, which takes part in the evacuation chain facilities as a rare hospital. We investigated the population of ICU patients that were admitted to the surgical ICU starting from February 2022 (beginning of the full-scaled invasion) to December 2024.
Feofaniya Clinical Hospital has 550 beds, 40 of them are ICU beds divided by cardiac, neurological and surgical ICU departments. All three ICU are capable of admitting combat trauma patients. Dataset from the surgical ICU was analysed.
Overall during the time of observation 305 combatant patients were treated in the surgical ICU − 61 in 2022 year, 115 in 2023 year, 129 in 2024 year.
Inclusion criteria were admission with combatant trauma evacuated from the frontline hospitals.
Exclusion criteria were combatant admission due to non-trauma diagnosis or somatic illnesses, combatants admitted after the infectious complications of reconstructive surgery, long-term treatment (more than 100 days) on the previous stage of evacuation, cases with missing diagnosis available at time of analysis medical recordings.
After inclusion/exclusion criteria were applied the cases that became eligible for analysis were − 53 cases in 2022 year, 98 cases in 2023 year, 113 in 2024 year, totally 264 cases.
Medical recordings available on combatant admission were analysed, including the information initially fulfilled at first encounter with medical care.
The geographic region where the patient was injured was documented. The stages of further evacuation chains were documented (stabilisation point, district hospital, frontline hospital) with timing of evacuation. The data collected as the result gave the possibility to estimate the main directions of evacuation, from where patients were transported to the studied hospital; how long did the evacuation take to Feofaniya Clinical Hospital.
Among admitted combatant patients the mortality rate was estimated.
From medical recordings the first bacteriological investigations results on admission were extracted and analysed. Patients with nosocomial flora identified in the primary bacteriological wound swab results were included in the analysis. As nosocomial flora were suspected pathogens from the ESKAPE group [5] (Escherichia coli, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, Enterobacter spp.) [6, 7].
As the medical informational system available at the study hospital has its own limitations, unfortunately we considered impossible to prove the leading or most important role in the wound infectious complications. An existing way of documentation of the wound healing course made it impossible to distinguish retrospectively, whether the result meant contamination (the presence on the surface of the wound without a significant impact on the wound state), or this was a pathogen causing the infectious complication. Establishing the primary role of a specific pathogen was particularly challenging in cases where multiple microorganisms were present in high titers (> 10^6 CFU/mL). Consequently, all detected pathogens were initially categorized as ‘contaminants’ to evaluate the full microbial spectrum present upon admission to the rear hospital. This approach allowed for an estimation of the proportion of microorganisms that were potentially nosocomial.
Statistical computations were conducted using the jamovi statistical spreadsheet (version 2.6), which is built upon the R statistical infrastructure [8, 9]. The analysis was further extended using specialized jamovi modules, specifically ‘tableone’ for automated table generation and ‘ClinicoPath’ for integrated clinical data processing [10–12].
Binary logistic regression analysis was performed to identify factors associated with the primary outcome. Variables with clinical relevance and baseline significance were included in the model. Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated [13].
Ethical approval was obtained for data analysis.
Results
A total of 305 combatants treated in the Surgical ICU between February 2022 and December 2024 were assessed for eligibility (Fig. 1). Forty-one patients were excluded for the following reasons: non-trauma illness (n = 19), reconstructive surgery (n = 2), admission more than 100 days after injury (n = 10), elective surgery (n = 3), and missing data (n = 7). Consequently, 264 patients were included in the final analysis and assessed for the primary outcome (Fig. 1).
Fig. 1.
Flow diagram of patient selection
Baseline characteristics of the study population are presented in Table 1. A total of 264 combatants were included in the analysis, of whom 159 (60.2%) had at least one pathogen from the ESKAPE group identified in the initial microbiological wound swab analysis, while 105 (39.8%) had no ESKAPE pathogens detected on admission (Table 1). The median age of the cohort was 38.0 years [IQR 29.5–46.0], with no significant difference between groups (p = 0.227). Similarly, the time from injury to hospital admission did not differ significantly between the ESKAPE-positive and ESKAPE-negative groups (7.0 [5.0–11.0] vs. 6.5 [3.0–10.8] days, respectively; p = 0.081). Patients in the ESKAPE-positive group had a significantly longer ICU stay compared to the ESKAPE-negative group (6.0 [3.0–13.5] vs. 1.0 [1.0–4.0] days, p < 0.001). All included patients were male. Blast injuries represented the predominant mechanism of trauma (70.3%), followed by gunshot injuries (22.4%). The distribution of injury type differed significantly between groups (p = 0.004), whereas no significant differences were observed regarding year of admission (p = 0.618) or trauma region (p = 0.127) (Table 1).
Table 1.
Baseline characteristics of the study population
| Variable | Total (n = 264) | ESKAPE (+) | ESKAPE (–) | p-value |
|---|---|---|---|---|
| Age, years | 38.0 [29.5–46.0] | 39.0 [30.0–47.0] | 37.0 [29.0–43.0] | 0.227 |
| Injury to admission, days | 7.0 [4.0–11.0] | 7.0 [5.0–11.0] | 6.5 [3.0–10.8] | 0.081 |
| Days in ICU | 4.0 [1.0–8.0] | 6.0 [3.0–13.5] | 1.0 [1.0–4.0] | 0.000 |
| Sex | 1.000 | |||
|
Male Female |
264 (100.0%) - |
159 (100.0%) - |
105 (100.0%) - |
|
| Injury type | 0.004 | |||
| blast | 185 (70.3%) | 118 (63.78%) | 67 (36.21%) | |
| gunshot | 59 (22.4%) | 37 (62.72%) | 22 (37.28%) | |
| other | 15 (5.7%) | 3 (20%) | 12 (80%) | |
| traffic accident | 4 (1.5%) | 1 (25%) | 3 (75%) | |
| Year | 0.618 | |||
| 2022 | 53 (20,1%) | 35 (66.0%) | 18 (34.0%) | |
| 2023 | 98 (37,1%) | 57 (58.2%) | 41 (41.8%) | |
| 2024 | 113 (42,8%) | 67 (59.3%) | 46 (40.7%) | |
| Trauma region | 0.127 | |||
| Dnipro | 3 | 0 (0.0%) | 3 (2.0%) | |
| Donetsk | 84 | 56 (38.1%) | 28 (19.0%) | |
| Kharkiv | 18 | 9 (6.1%) | 9 (6.1%) | |
| Kherson | 9 | 5 (3.4%) | 4 (2.7%) | |
| Kursk | 7 | 2 (1.4%) | 5 (3.4%) | |
| Kyiv | 2 | 1 (0.7%) | 1 (0.7%) | |
| Luhansk | 4 | 3 (2.0%) | 1 (0.7%) | |
| Sumy | 7 | 2 (1.4%) | 5 (3.4%) | |
| Zaporizhzhіa | 12 | 8 (5.4%) | 4 (2.7%) | |
| Unknown | 1 | 1 (0.7%) | 0 (0.0%) |
Data are presented as median [IQR] for continuous variables and number (percentage) for categorical variables. Comparisons between groups were performed using the Mann–Whitney U test or χ² test, as appropriate. For year and injury type among ESKAPE(+) and ESKAPE (-) groups percentages are calculated row-wise (category total = 100%)
The duration from injury to admission to the study hospital is presented in Table 2. The shortest intervals were observed for patients evacuated from Kursk — 1 day [IQR 1], and from Sumy — 3 days [IQR 1]. The duration of evacuation from the most common directions was 7 days [IQR 5.50] from Donetsk, 7.5 days [IQR 4.50] from Kharkiv, and 10 days [IQR 7.75] from Zaporizhzhia. The duration of transfer between Kyiv clinics was the longest and was 31 days [IQR 3] (Table 2).
Table 2.
Duration of the period from injury to admission to the study hospital
| Trauma region | Median | IQR | |
|---|---|---|---|
| Duration of the period from injury to admission to the study hospital (days) | Dnipro | 12 | 26.00 |
| Donetsk | 7.00 | 5.50 | |
| Kharkiv | 7.50 | 4.50 | |
| Kherson | 3 | 2.00 | |
| Kursk | 1 | 1.00 | |
| Kyiv | 31.00 | 3.00 | |
| Luhansk | 7.50 | 2.00 | |
| Sumy | 3 | 1.00 | |
| Zaporizhzhіa | 10.00 | 7.75 |
Most combatants were transferred from the Dnipro Regional Hospital (63.9%) and the Kharkiv North Region Military Medical Center (8.3%) (Table 3).
Table 3.
Frequencies of previous stage of evacuation
| Previous hospital | Counts | % of Total |
|---|---|---|
| Dnipro East region military medical center | 7 | 3.9% |
| Dnipro city hospital 16 | 3 | 1.7% |
| Dnipro city hospital 4 | 2 | 1.1% |
| Dnipro city hospital 6 | 1 | 0.6% |
| Dnipro emergency hospital | 5 | 2.8% |
| Dnipro regional hospital | 115 | 63.9% |
| Kharkiv | 1 | 0.6% |
| Kharkiv North region military medical center | 15 | 8.3% |
| Kherson South region military medical center | 1 | 0.6% |
| Kryvyi Rih city hospital 2 | 5 | 2.8% |
| Kyiv city clinical hospital 1 | 1 | 0.6% |
| Kyiv military hospital | 3 | 1.7% |
| Military clinical center | 1 | 0.6% |
| Odesa South region military medical center | 1 | 0.6% |
| Samar city hospital | 1 | 0.6% |
| State Institution “V.T. Zaitsev Institute of General and Emergency Surgery of the National Academy of Medical Sciences of Ukraine” | 1 | 0.6% |
| Sumy Regional Clinical Hospital for War Veterans | 6 | 3.3% |
| Sumy St.Panteleymon clinical hospital | 1 | 0.6% |
| Sumy city hospital 5 | 1 | 0.6% |
| Sumy regional hospital | 3 | 1.7% |
| Zaporizhzhіa city hospital 9 | 1 | 0.6% |
| Zaporizhzhіa emergency hospital | 2 | 1.1% |
| Zaporizhzhіa regional hospital | 3 | 1.7% |
Mortality rates demonstrated a gradual decline over the study period and are represented in the Table 4. In 2022, mortality was observed in 8 out of 53 cases (15.0%), compared to 11 out of 98 cases (11.22%) in 2023 and 11 out of 113 cases (9.73%) in 2024. This trend suggests a progressive reduction in mortality across the analyzed years (Table 4).
Table 4.
Mortality rates across the study years
| Year | Mortality, n/N (%) |
|---|---|
| 2022 | 8/53 (15.0%) |
| 2023 | 11/98 (11.22%) |
| 2024 | 11/113 (9.73%) |
We performed a multivariable logistic regression to evaluate the impact of trauma region, the stage of medical evacuation (previous hospital), and evacuation time on the probability of identifying ESKAPE group pathogens upon admission. The overall model demonstrated a low pseudo-R-squared value (0.033) and a non-significant Likelihood Ratio test (p = 0.408), suggesting that the analyzed logistical factors were not primary drivers of ESKAPE contamination in this cohort (Table 5).
Table 5.
Logistic regression analysis of factors associated with ESKAPE pathogen contamination
| Predictor | Odds Ratio (OR) | 95% CI (Lower) | 95% CI (Upper) | p-value |
|---|---|---|---|---|
| Injury to admission (days) | 0.992 | 0.964 | 1.022 | 0.603 |
| Trauma Region | ||||
| Donetsk region | 1.055 | 0.542 | 2.055 | 0.874 |
| Kharkiv region | 0.529 | 0.173 | 1.616 | 0.263 |
| Kherson region | 0.820 | 0.186 | 3.623 | 0.794 |
| Zaporizhzhia region | 1.156 | 0.318 | 4.198 | 0.826 |
| Other regions | 0.307 | 0.095 | 0.988 | 0.048* |
| Previous Hospital | ||||
| Dnipro Regional Hospital | 1.338 | 0.677 | 2.647 | 0.402 |
| VMMC Northern Region (Kharkiv) | 1.874 | 0.523 | 6.712 | 0.335 |
| VMMC Eastern Region (Dnipro) | 1.668 | 0.301 | 9.234 | 0.558 |
| Sumy Reg. Hospital for War Veterans | 1.018 | 0.124 | 8.320 | 0.987 |
| Other facilities | 0.936 | 0.381 | 2.302 | 0.886 |
The duration of evacuation (injury-to-admission interval) did not show a statistically significant association with the risk of ESKAPE contamination (OR 0.992; 95% CI 0.964–1.022; p = 0.603). This suggests that the time elapsed from injury to definitive hospital admission, within the observed range, did not increase the likelihood of harboring resistant flora.
Regarding geographical factors, no significant differences were found between the main combat regions (Donetsk, Kharkiv, and Zaporizhzhia). However, patients injured in “Other regions” (representing less frequent trauma locations) showed a significantly lower risk of ESKAPE contamination (OR 0.307; 95% CI 0.095–0.988; p = 0.048).
The history of transfer from previous medical facilities also did not emerge as a significant predictor. While patients transferred from major regional military medical centers (Kharkiv and Dnipro) showed slightly higher odds of contamination compared to those admitted directly (e.g., Kharkiv VMMC OR 1.87), these findings did not reach statistical significance (all p > 0.05) (Table 5).
A multivariate logistic regression analysis was performed to determine the predictors of in-hospital mortality. After adjusting for potential confounders, including patient age and the duration of evacuation (injury-to-admission interval), the presence of ESKAPE pathogens was identified as a powerful independent predictor of mortality (Table 6).
Table 6.
Association between ESKAPE pathogen presence and patient mortality (Logistic Regression Model)
| Variable | Odds Ratio (OR) | 95% CI Lower | 95% CI Upper | p-value |
|---|---|---|---|---|
| Pathogen status (ESKAPE vs. None) | 22.385 | 2.973 | 168.545 | 0.003 |
| Age (years) | 1.038 | 0.997 | 1.082 | 0.069 |
| Injury to admission (days) | 1.016 | 0.967 | 1.067 | 0.530 |
Patients who tested positive for ESKAPE pathogens upon admission had significantly higher odds of mortality compared to those without such contamination (Adjusted Odds Ratio [aOR] 22.39; 95% CI 2.97–168.55; p = 0.003). While age showed a marginal trend toward increased mortality risk (aOR 1.038 per year; 95% CI 0.99–1.08; p = 0.069), the duration of evacuation did not significantly influence the survival outcome in this model (p = 0.530).
Discussion
The present study evaluated the microbiological profile of critically ill combatants with trauma admitted to the intensive care unit (ICU) of a rear hospital. Our findings demonstrate a high and consistent prevalence of ESKAPE pathogens (approximately 60%) in initial wound swabs upon admission throughout the 2022–2024 period. Notably, the presence of these pathogens was a powerful independent predictor of mortality and significantly extended the ICU length of stay.
A key finding of our multivariable analysis was the lack of correlation between logistical factors—such as the geographic region of injury, specific evacuation routes, or the duration of evacuation—and the probability of ESKAPE pathogen detection. This suggests that the primary microbiological profile is likely established very early in the care chain, potentially influenced by individual injury characteristics or environmental factors at the point of injury, rather than the subsequent logistics of the evacuation process. This aligns with the observation that blast injuries, which predominated in our cohort, often result in extensive tissue damage and significant environmental contamination from soil and debris, creating an ideal niche for opportunistic pathogens. Current data regarding blast injury frequency reflect a broader shift in military traumatology, consistent with the evolving threat landscape of drone-mediated combat [14]. Initial microbial colonizers are not static; rather, they demonstrate a marked capacity for evolving resistance profiles throughout the treatment period. This is particularly evident in conflict-related trauma, where Gram-negative pathogens have been shown to transition toward extensively drug-resistant (XDR) phenotypes during prolonged hospitalization [15].
Our data regarding the high prevalence of ESKAPE flora are consistent with other Ukrainian studies. For instance, research conducted in civilian hospitals in Sumy—where patients often bypassed the multi-stage evacuation chain typical of our cohort—showed a similar microbial appearance in wounds (56.16%) predominance of Gram-negative bacilli [16]. Furthermore, our results mirror findings from foreign combatants treated in Germany after injury in Ukraine, where a high burden of well-established global lineages of K. pneumoniae, A. baumannii, and P. aeruginosa was documented [17]. The ubiquity of these pathogens across different studies and care settings suggests that MDR contamination is a systemic challenge within the current conflict environment.
Interestingly, even when the evacuation duration was significantly shorter (e.g., 3 days for the Sumy region compared to 7 days for Donetsk), the risk of ESKAPE colonization remained high. The wide variability in trauma regions and the relatively small sample size per region may have limited our ability to detect subtle logistical correlations; however, it reinforces our hypothesis that contamination occurs almost immediately or during the very first stages of stabilization. The anomalously long interval for transfers between clinics within Kyiv city — 31 days [IQR 3] — could be related to the reason for transfer (Table 2). These cases did not follow the generally accepted evacuation pathway from geographically close frontline areas, but were instead associated with administrative decisions regarding the availability of more specialized treatment options at the study hospital.
Based on these findings, our facility has adopted a proactive “high-risk” assumption for all admitted combatants. We implemented a cohorting principle and contact precautions to mitigate the risk of horizontal MDR transmission in a setting with limited isolation wards [18–20]. Our standard operating procedure, which includes immediate device replacement and surgical debridement upon admission, was developed intuitively to combat the high baseline infection risk. While we have not yet formally validated the impact of these specific isolation strategies on overall prognosis, the significant mortality risk associated with ESKAPE pathogens (aOR 22.39) justifies the aggressive use of contact precautions and early empirical consideration of MDR-active antimicrobial therapy.
Limitations and future directions
A primary limitation of this study is its retrospective nature, which resulted in certain data gaps and limited the clinical variables available for analysis. The relatively small sample size, particularly regarding mortality events, may have limited the statistical power of the logistic regression to detect subtle associations. Furthermore, the high variability in injury-to-admission intervals contributed to wide confidence intervals in our predictive models.
Future research should focus on longitudinal, intra-hospital investigations to identify the exact “critical points” where wound microflora shifts during the hospitalization period. Additionally, the impact of infection control practices, such as cohorting and early device replacement, should be prospectively assessed for their direct impact on patient survival. Finally, incorporating advanced diagnostic tools—such as MALDI-TOF mass spectrometry and metagenomic sequencing—could overcome the limitations of classical culture methods, enabling the development of rapid point-of-care testing systems (e.g., CRISPR-Cas or LAMP-PCR) essential for the modern combat environment [21–24].
Conclusions
Our retrospective analysis demonstrates that the microbial landscape of combat-related wounds in the current Russian-Ukrainian war is characterized by a high and persistent prevalence of ESKAPE pathogens. Approximately 60% of combatants admitted to the rear hospital intensive care unit in Kyiv already harboured these multidrug-resistant organisms upon arrival.
In this study we appeared unable to prove that logistic details can have an impact on the microbial content of wounds, and potentially raise chances of wound infection complications. The specific geographical region of injury, the particular medical evacuation route, and the duration of evacuation period did not significantly influence the likelihood of a patient presenting with ESKAPE pathogen in wound swabs on admission. This suggests that contamination likely occurs at the point of injury or during the earliest stages of evacuation.
The presence of ESKAPE pathogens in initial wound swabs serves as a powerful, independent predictor of in-hospital mortality (aOR 22.39) and results in a significantly extended duration of ICU stay. The ubiquity of these pathogens across various years (2022–2024) and injury mechanisms - particularly blast injuries - highlights a systemic challenge within the wartime healthcare infrastructure.
These findings necessitate a proactive “high-risk” approach to all combat trauma admissions. To mitigate the impact of these highly resistant flora, rear hospitals should prioritize aggressive IPC measures, including immediate cohorting, contact precautions, and the early empirical consideration of targeted antimicrobial therapy especially among ICU population and in case of patient unstable condition. Future efforts must focus on identifying the precise early-care “critical points” where microflora shifts occur to develop more effective point-of-care diagnostics and intervention strategies.
Acknowledgements
We are grateful to the Armed Forces of Ukraine for providing security to perform this work.
Abbreviations
- CRISPR
Clustered Regularly Interspaced Short Palindromic Repeats
- ICU
Intensive care unit
- IPC
Infection prevention and control
- HAIs
Health care–associated infections
- LAMP-PCR
Loop-mediated isothermal amplification
- MALDI-TOF
Matrix-Assisted Laser Desorption/Ionization - Time-of-Flight
- PCR
Polymerase Chain Reaction
- SOPs
Standard operating procedures
- WHO
World Health Organization
Author contributions
OI and OP - conceptualized the study, OI, AS, VM and OP - contributed to the discussion of the results. OI and OP - were responsible for manuscript writing and preparation;OI, VM and AS- performed patient assessment, clinical data collection, data management and formal analysis. All authors participated in the study design, contributed to the interpretation and analysis of the data, reviewed and approved the final manuscript.
Funding
This work was carried out without grant funding.
Data availability
The datasets used and/or analyzed during the current study available from the corresponding and last authors (Olha Izmailova and Oksana Piven) and on reasonable request.
Declarations
Ethics approval and consent to participate
The study was conducted in accordance with the Declaration of Helsinki and approved by the Bioethics Committee of the Institute of Molecular Biology and Genetics of the National Academy of Sciences of Ukraine (150 Akademika Zabolotnoho Str., Kyiv, 03143, Ukraine; Protocol No. 47 dated July 15, 2025). Clinical data and biological materials were collected at the Feofaniya Clinical Hospital of the State Administration of Affairs (21 Akademika Zabolotnoho Str., Kyiv, 03143, Ukraine) within the framework of an official research cooperation agreement between both institutions. Due to the retrospective design of the study and the use of completely anonymized clinical data, the requirement for informed consent was waived by the Bioethics Committee of the Institute of Molecular Biology and Genetics.
Ethics, consent to participate, and consent to publish declarations
Not applicable. This manuscript does not contain any individual person's data, images, videos, or identifiable clinical details. All presentations of data are aggregated and fully anonymized.
Author information
OI is a PhD student at the Department of Human Genetics of the Institute of Molecular Biology and Genetics of National Academy of Sciences of Ukraine and a practicing clinician at Feofaniya Clinical Hospital of the State Administration of Affairs.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets used and/or analyzed during the current study available from the corresponding and last authors (Olha Izmailova and Oksana Piven) and on reasonable request.