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Understanding the burden of multidrug-resistant organisms in neurosurgical care: Resistance trends and risk profiles
, Fatma Al Busaidi2, Yasmine Al Balushi2, Teeba Al Malki2, Marwa Al Hamdani2, Sultan AlSaidi3, Thuraya AlYazidi4, Rajwa Alkhatri5, Tariq Al-Saadi6,
*Corresponding author: Tariq Al-Saadi, Department of Neurosurgery, Cedars-Sinai Medical Center, California, United States. tariq.al-saadi@mail.mcgill.ca
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Received: ,
Accepted: ,
How to cite this article: Al-Mufargi Y, Al Busaidi F, Al Balushi Y, Al Malki T, Al Hamdani M, AlSaidi S, et al. Understanding the burden of multidrug-resistant organisms in neurosurgical care: Resistance trends and risk profiles. J Neurosci Rural Pract. 2025;16:501-8. doi: 10.25259/ JNRP_160_2025
Abstract
Objective:
This study aimed to assess the prevalence, resistance patterns, and clinical predictors of MDROs in neurosurgical patients at a tertiary hospital in Oman.
Materials and Methods:
A retrospective analysis was conducted on 238 patients admitted to the neurosurgical and neurology departments at Khoula Hospital from January to December 2019. Data included demographics, clinical diagnoses, hospital stays, and antimicrobial resistance profiles. Statistical analyses, including Chi-square tests and logistic regression, identified predictors of prolonged hospital stay and high-risk patient groups.
Results:
Methicillin-resistant Staphylococcus aureus (MRSA, 54.2%) and carbapenem-resistant Enterobacteriaceae (CRE, 34.5%) were the most prevalent MDROs. Resistance was highest against b-lactams (95.4%), sulfonamides (95.4%), and quinolones (91.6%), with no resistance observed to oxazolidinones and lipopeptides. Multidrug resistance and CRE were significant predictors of prolonged length of stay (LOS) (p < 0.001), and logistic regression identified a high-risk subgroup (10.1%) requiring focused interventions. The predominance of multi-source diagnostics (69%) underscored the importance of comprehensive testing in managing MDRO infections.
Conclusion:
The study highlights the substantial burden of MDROs in neurosurgical care in Oman, necessitating robust antimicrobial stewardship, targeted interventions, and ongoing surveillance. Future research should focus on multicenter studies to address the growing challenge of antimicrobial resistance.
Keywords
Antimicrobial resistance
Infection control
Multidrug-resistant organisms
Neurosurgery
Oman
INTRODUCTION
The widespread use of antibiotics, pre-operative surgical antibiotic prophylaxis, and antibiotic-impregnated devices has significantly influenced the prevalence and epidemiology of microorganisms responsible for post-neurosurgical infections, particularly meningitis. While gram-positive bacteria have conventionally been the primary culprits, gram-negative bacteria, many exhibiting multidrug resistance (MDR), are increasingly implicated in these infections.[1] MDR, defined as non-susceptibility to three or more antibiotic classes, represents a critical global challenge across medical fields, including neurosurgery.[2] Accurate diagnosis of central nervous system (CNS) infections and antibiotic susceptibility testing are pivotal for delivering effective therapy and mitigating the risks of heightened morbidity, mortality, prolonged hospital stays, and associated healthcare costs.
In Oman and the broader Arabian peninsula, limited published data exist on antimicrobial resistance patterns, especially in neurosurgical patients. A retrospective study from Oman reported a multidrug-resistant organism (MDROs) prevalence rate of 10.8 cases per 1,000 hospital admissions, with Acinetobacter baumannii and extended-spectrum beta-lactamase-producing Escherichia coli as the most prevalent organisms.[3] These findings highlight an urgent need for comprehensive national guidelines to address the rising burden of antimicrobial resistance in the region. In addition, evolving resistance trends in CNS infections emphasize the necessity for targeted surveillance, as demonstrated by studies revealing a shift from Gram-positive to gram-negative pathogens and notable variations in antibiotic sensitivities over time.[4]
This study aims to analyze the prevalence, resistance patterns, and clinical predictors of MDROs in neurosurgical patients at a tertiary hospital in Oman, providing crucial insights into regional resistance trends and identifying high-risk patient profiles to guide tailored interventions and resource allocation.
MATERIALS AND METHODS
Ethical approval for this study was obtained from the Hospital Research Ethics Committee, Ministry of Health, Muscat, Oman. Code: PRO0102019044. All methods were carried out in accordance with relevant guidelines and regulations. The Research Ethics Committee at the Ministry of Health waived the requirement of written informed consent for participation in the study MDR in Oman (PRO02202055).
Study design
A retrospective study was conducted at Khoula Hospital (KH), Muscat, Oman, involving 238 patients admitted to the neurology and neurosurgery and neurology departments between January 2019 and December 2019. Following the ethical approval, data were collected from the health information system-KH. All participants were identified by going through the electronic medical records. All methods were carried out in accordance with relevant guidelines and regulations.
Data collection and subjects
The patients from whom the data were collected included all patients who presented to KH with neurology or neurosurgery cases diagnosed with traumatic brain injuries, cerebrovascular diseases, neuro-oncology cases, seizure disorders, hydrocephalus and shunt-related issues, spinal condition, infection, demyelination, autoimmune conditions, and polyneuropathy, which were seen from January 2019 to December 2019, the patients were of all nationalities. In our research, the age group was classified into four categories: children 0–14 years, youth 15–24 years, adults 25–64 years, and geriatrics 65 years and over. The records of admissions were divided into a neurology ward, intensive care units (ICU), Golden Wing, and neurosurgery ward during the given period. Other collected data were the length of hospital stay, tests done on the patients, and the antibiotic medications patients received. In this study, the term MDROs refers broadly to any clinical bacterial isolates exhibiting resistant phenotypes such as methicillin-resistant Staphylococcus aureus (MRSA), carbapenem-resistant Enterobacteriaceae (CRE), vancomycin-resistant Enterococci (VRE), and others. In addition, the term MDR is used to designate isolates that are non-susceptible to at least one agent in ≥ antimicrobial classes, based on the interim standard definitions proposed by Magiorakos et al. (2012).[2] MDR isolates were included as a distinct subgroup within the broader MDRO classification. This distinction was applied consistently throughout the analysis to ensure clarity between overall organism categorization (MDRO) and specific resistance profiles (MDR). “Multi-source diagnostics” refers to testing two or more types of clinical samples (e.g., Cerebrospinal fluid, blood, urine, wound swabs, or endotracheal aspirates) from the same patient to detect MDRO.
Date availability
The datasets used in the current study are available from the corresponding author on reasonable request.
Data analysis
Statistical analyses were conducted using international business machines (IBM) Statistical Package for the Social Sciences Statistics version 25 (IBM Corporation, Armonk, New York). Descriptive statistics summarized demographic, clinical, and hospitalization characteristics, with continuous variables presented as means, standard deviation (SD), and ranges and categorical variables as frequencies and percentages. The length of stay (LOS) was classified into four categories based on a percentile-based approach: Short stay (≤25th percentile), moderate stay (26th–50th percentile), long stay (51st–75th percentile), and prolonged stay (>75th percentile; >36 days). The >36-day threshold corresponds to the upper quartile of LOS distribution and was selected to define “prolonged stay” based on both statistical and clinical relevance, as it captures patients with the most extended and resource-intensive hospitalizations. Chi-square tests assessed associations between categorical variables, including MDR, specific MDROs, and prolonged LOS. Logistic regression analysis was conducted to identify predictors of prolonged LOS, with MDR and CRE evaluated as significant predictors. Model performance was assessed using the Hosmer– Lemeshow test, with additional metrics including Cox and Snell R2, Nagelkerke R2, sensitivity, specificity, and overall classification accuracy. Patients were classified into high- and low-risk groups based on predicted probabilities derived from the logistic regression model. High-risk patients were defined as having a predicted probability (p) of ≥ 0.5, while low-risk patients were classified as having p < 0.5. Subgroup analyses explored demographic, clinical, and hospitalization characteristics within the high-risk group. Figures and tables illustrated resistance patterns across antimicrobial classes, diagnostic test distributions, and subgroup characteristics. A p < 0.05 was considered statistically significant.
RESULTS
Demographic and clinical characteristics
The demographic, clinical, and hospitalization characteristics of the study population are summarized in Table 1. The study population consisted of 238 participants, with a gender distribution of 66.0% males (n = 157) and 34.0% females (n = 81), and a mean age of 43.05 years (SD = 23.00), ranging from 0.1 to 91 years. Age group classifications revealed that adults aged 36–60 years comprised the largest proportion (58.4%, n = 139), followed by seniors over 60 years (20.6%, n = 49), children aged 0–18 years (14.3%, n = 34), and youth aged 19–35 years (6.7%, n = 16). Participants were admitted across various hospital wards, with the majority in general wards (45.8%, n = 109), followed by the neurosurgery ward (28.6%, n = 68) and the intensive care unit (25.6%, n = 61). Cerebrovascular diseases (28.6%, n = 68) and infections (27.7%, n = 66) were the most prevalent clinical diagnoses, with infections involving single MDROs in 81.5% (n = 194), dual MDRO infections in 16.0% (n = 38), and triple or complex MDRO infections in 2.5% (n = 6). The LOS ranged from 0 to 437 days, with a mean of 31.41 days (SD = 47.15), and was categorized as short (≤7 days, 27.7%, n = 65), moderate (8–15 days, 24.7%, n = 58), long (16–36 days, 24.3%, n = 57), and prolonged (>36 days, 23.4%, n = 55).
| Variable | Category | Mean±SD (min–max)/n(%) |
|---|---|---|
| Gender | Male | 157 (66.0) |
| Female | 81 (34.0) | |
| Age | - | 43.05±23.00 (0.1–91.0) |
| Age categories | Children (0–14 years) | 34 (14.3) |
| Youth (15–24 years) | 16 (6.7) | |
| Adults (25–64 years) | 139 (58.4) | |
| Seniors (≥65 years) | 49 (20.6) | |
| Admission wards | ICU ward | 61 (25.6) |
| Neurosurgery ward | 68 (28.6) | |
| Other wards | 109 (45.8) | |
| Diagnoses | Cerebrovascular disease | 68 (28.6) |
| Infection | 66 (27.7) | |
| Others | 26 (10.9) | |
| Neuro-oncology | 19 (8.0) | |
| Demyelination/autoimmune disease/polyneuropathy | 16 (6.7) | |
| Hydrocephalic/shunt-related | 12 (5.0) | |
| TBI | 11 (4.6) | |
| Seizure disorder | 10 (4.2) | |
| Spinal condition | 10 (4.2) | |
| MDRO | Single MDRO | 194 (81.5) |
| Dual MDRO | 38 (16.0) | |
| Triple/complex MDRO | 6 (2.5) | |
| Length of stay | - | 31.41±47.15 (0–437) |
| LOS categories | Short stay (≤7 days) | 65 (27.7) |
| Moderate stay (8–15 days) |
58 (24.7) | |
| Long stay (16–36 days) | 57 (24.3) | |
| Prolonged stay (>36 days) | 55 (23.4) |
SD: Standard deviation, MDRO: Multidrug-resistant organisms, TBI: Traumatic brain injury, ICU: Intensive care units, LOS: Length of stay
Prevalence of MDROs
The analysis of 238 cases revealed marked variability in the prevalence of the five MDROs studied, as summarized in Table 2. MRSA was the most prevalent organism (54.2%), followed by CRE (34.5%). In addition, 26.1% of isolates met the criteria for MDR, defined as resistance to ≥3 antimicrobial classes. These MDR isolates may overlap with organisms such as CRE or MRSA, depending on their resistance profiles. In contrast, Pseudomonas aeruginosa and VRE were identified in 5.5% and 0.8% of cases, respectively. These data suggest that MRSA and CRE constitute the primary contributors to the MDRO burden within this cohort, emphasizing the necessity for targeted antimicrobial stewardship and enhanced infection control measures. The relatively low prevalence of P. aeruginosa and VRE may reflect distinct regional epidemiological patterns or the effectiveness of existing preventive strategies. Collectively, these findings underscore the importance of ongoing surveillance and the implementation of tailored intervention programs to mitigate the healthcare challenges associated with MDROs.
| MDRO | Absence (n, %) | Presence (n, %) |
|---|---|---|
| MDR (≥3-class resistance phenotype) | 176 (73.9) | 62 (26.1) |
| Carbapenem-resistant Enterobacteriaceae | 156 (65.5) | 82 (34.5) |
| Methicillin-resistant Staphylococcus aureus | 109 (45.8) | 129 (54.2) |
| Pseudomonas aeruginosa | 225 (94.5) | 13 (5.5) |
| Vancomycin-resistant enterococci | 236 (99.2) | 2 (0.8) |
Note: “MDR” refers to organisms meeting the definition of resistance to ≥3 antibiotic classes and is presented here as a distinct phenotype within the broader MDRO category. MDRO: Multidrug-resistant organisms, MDR: Multidrug resistance
Diagnostic approaches for MDRO detection
Among the diagnostic tests utilized to detect multidrug-resistant organisms (MDROs) in neurosurgical patients, multi-source tests were the most performed, accounting for 69% of all assessments [Figure 1]. Single-source tests, including wound swab cultures (9%), ET secretion cultures (6%), and urine cultures (5%), were also utilized, along with targeted screenings such as MRSA and MDR (groin) screenings, each contributing 5%. Less frequently, biopsy cultures and infection control screening swabs were employed (4% or less). This distribution underscores the reliance on multi-source diagnostics to detect MDROs and the supplemental role of single-source approaches.
- Distribution of single and multi-source diagnostic tests in identifying multi-drug-resistant organisms. MDRO: Multidrug-Resistant Organism, MRSA: Methicillin-Resistant Staphylococcus aureus, ET: Endotracheal Tube, C: Culture, S: Sensitivity.
Antimicrobial resistance patterns
The distribution of resistant and susceptible bacterial isolates across various antimicrobial classes in a cohort of 238 neurosurgical patients with MDR infections is shown [Figure 2]. High resistance rates were observed among bacterial isolates to b-lactams and sulfonamides, with 95.4% (227 isolates) exhibiting resistance in each class. Resistance was also notably high in isolates against quinolones (91.6%) and aminoglycosides (90.3%). Moderate resistance levels were identified in isolates to glycylcyclines (77.7%), tetracyclines (78.6%), macrolides (61.8%), and glycopeptides (63.9%). By contrast, resistance to nitrofurans was lower, with 35.7% of isolates resistant and 64.3% susceptible. Notably, no resistance was observed among isolates against oxazolidinones, chloramphenicol, lipopeptides, and streptogramins, with all isolates being susceptible. These findings underscore the variability in bacterial resistance across antimicrobial classes and the complexities of managing infections caused by MDROs in neurosurgical patients.
- Resistance patterns of bacterial isolates in neurosurgical patients with multi-drug-resistant organisms: Trends across antimicrobial classes.
The distribution of antibiotic resistance patterns among bacterial isolates was analyzed based on clinical diagnoses in neurosurgical patients [Figure 3]. Traumatic brain injury (TBI) and cerebrovascular diseases represented the most common diagnoses associated with resistant isolates, with b-lactams, quinolones, and glycopeptides being the predominant antibiotic classes identified. Infections, hydrocephalus-related conditions, and neuro-oncology diagnoses also showed significant representation of resistant bacterial isolates, although with varied antimicrobial class involvement. Less frequently, spinal conditions, demyelination, autoimmune diseases, and seizure disorders were linked with antibiotic resistance. This classification highlights the varying burden of resistant organisms across distinct neurosurgical diagnoses, emphasizing the importance of tailored antimicrobial stewardship in these settings.
- Antimicrobial resistance profiles among multi-drug-resistant bacterial isolates: Insights by clinical diagnoses in neurosurgical patients. TBI: Traumatic brain injury, CSF: Cerebrospinal fluid
LOS and MDRO infections
The association of MDR, drug-resistant pathogens, and clinical factors associated with length of hospital stay is summarized in Table 3. Chi-square tests revealed significant associations between MDR and prolonged LOS (c2 [1] = 21.97, p < 0.001), with patients having MDR more likely to experience prolonged LOS. Among specific MDROs, CRE (c2 [1] = 28.99, p < 0.001) was significantly associated with prolonged LOS, while MRSA (c2 [1] = 25.13, p < 0.001) was associated with shorter stays. Moreover, P. aeruginosa (χ2 [1] = 1.08, p = 0.298) and VRE (c2 [1] = 0.62, p = 0.432) were not associated with prolonged LOS. No significant differences in LOS were found across demographic factors such as gender (c2 [1] = 0.007, p = 0.933) and age categories (c2 [3] = 0.880, p = 0.830). However, ICU admission was significantly associated with prolonged LOS (c2 [1] = 4.05, p = 0.044), with ICU patients more likely to have prolonged stays compared to those admitted to general wards.
| Variable | Category | LOS (n, %) | (χ2) | p-value | |
|---|---|---|---|---|---|
| Non-prolonged | Prolonged | ||||
| Gender | Male | 120 (76.4) | 37 (23.6) | 0.007 | 0.933 |
| 60 (76.9) | 18 (23.1) | ||||
| Age categories | Children | 27 (81.8) | 6 (18.2) | 0.880 | 0.830 |
| 13 (81.3) | 3 (18.8) | ||||
| 103 (75.2) | 34 (24.8) | ||||
| 37 (75.5) | 12 (24.5) | ||||
| Multidrug resistance | Absence | 148 (84.1) | 28 (15.9) | 21.97 | < 0.001 |
| 32 (54.2) | 27 (45.8) | ||||
| Carbapenem-resistant Enterobacteriaceae | Absence | 136 (87.2) | 20 (12.8) | 28.99 | < 0.001 |
| 44 (55.7) | 35 (44.3) | ||||
| Methicillin-resistant Staphylococcus aureus | Absence | 65 (61.3) | 41 (38.7) | 25.13 | < 0.001 |
| 115 (89.1) | 14 (10.9) | ||||
| Pseudomonas aeruginosa | Absence | 173 (77.2) | 51 (22.8) | 1.08 | 0.298 |
| 7 (63.6) | 4 (36.4) | ||||
| Vancomycin-resistant Enterococci | Absence | 178 (76.4) | 55 (23.6) | 0.62 | 0.432 |
| 2 (100.0) | 0 (0.0) | ||||
| Admission type | Floor | 139 (79.9) | 35 (20.1) | 4.05 | 0.044 |
| 41 (67.2) | 20 (32.8) | ||||
ICU: Intensive care units, P-value < 0.05, LOS: Length of stay
Risk factors for prolonged hospital stay
Logistic regression analysis was performed to identify factors associated with prolonged hospital stay among the study population [Table 4]. Out of 238 cases, 235 (98.7%) were analyzed, with 180 (76.6%) classified as non-prolonged hospital stays and 55 (23.4%) as prolonged stays. The logistic regression model was statistically significant (χ2 = 51.044, p < 0.001) and demonstrated good fit (Hosmer–Lemeshow χ2 = 0.991, p = 0.911), explaining 19.5% to 29.4% of the variance (Cox and Snell R2, Nagelkerke R2) and achieving an overall classification accuracy of 80.4%, with a sensitivity of 27.3% and specificity of 96.7%. MDR and CRE were significant predictors of prolonged hospital stay, with odds ratios of 5.576 (95% CI: [3.131, 9.929], p < 0.001) and 6.137 (95% CI: [3.030, 12.424], p < 0.001), respectively, indicating substantially increased risks. ICU admission showed no significant association (OR = 1.534, 95% CI: [0.721, 3.264], p = 0.267).
| Predictor | B (coefficient) | SE | Wald statistic | p-value | Exp (B) (odds ratio) | 95% CI (lower-upper) |
|---|---|---|---|---|---|---|
| MDR | 1.719 | 0.378 | 20.630 | <0.001 | 5.576 | (3.131–9.929) |
| CRE | 1.814 | 0.366 | 24.525 | <0.001 | 6.137 | (3.030–12.424) |
| ICU | 0.428 | 0.386 | 1.233 | 0.267 | 1.534 | (0.721–3.264) |
SE: Standard errors, CI: Confidence interval, MDR: Multidrug resistance, CRE: Carbapenem-resistant Enterobacteriaceae, ICU: Intensive care units, P-value < 0.05
Subgroup analysis of high-risk patients
The study population of 238 cases was classified into low- and high-risk groups based on predicted probabilities derived from significant predictors of prolonged LOS, including MDR and CRE [Table 5]. Among these, 24 cases (10.1%) were identified as high-risk (p ≥ 0.5), while the remaining 214 cases (89.9%) were categorized as low risk (p < 0.5). High-risk patients thus represent a small but clinically significant subset of the population. Within the high-risk group, males constituted the majority (58.3%), with females accounting for 41.7%. Age-wise, adults (25–64 years) predominated, forming 54.2% of the subgroup, followed by seniors (65+ years) at 25.0%. Children (0–12 years) and youth (13–24 years) were less represented, at 8.3% and 12.5%, respectively. In terms of clinical outcomes, 41.7% of high-risk cases required ICU admission, indicating the presence of critical conditions within this group. Conversely, the remaining 58.3% were managed with floor admissions. The high-risk group comprises a disproportionately male and adult population, with a notable proportion of cases necessitating intensive care. This demographic and clinical profile underscores the importance of targeted monitoring and intervention strategies for this subgroup. The overall prevalence of high-risk patients (10.1%) emphasizes the necessity for resource allocation to adequately manage these individuals within the broader study population.
| Category | Subcategory | Count (n) | Percentage |
|---|---|---|---|
| Gender distribution | Male | 14 | 58.3 |
| Female | 10 | 41.7 | |
| Age categories | Children (0–12 years) | 2 | 8.3 |
| Youth (13–24 years) | 3 | 12.5 | |
| Adults (25–64 years) | 13 | 54.2 | |
| Seniors (65+ years) | 6 | 25.0 | |
| ICU admission | Floor admission | 14 | 58.3 |
| ICU admission | 10 | 41.7 |
DISCUSSION
This study provides valuable insights into the prevalence, resistance patterns, and clinical implications of MDROs in neurosurgical patients. The findings underscore the significant burden posed by MDROs in this high-risk population, necessitating targeted interventions and robust infection control measures.
The high prevalence of MRSA (54.2%) and CRE (34.5%) observed in our cohort is consistent with global trends reported in similar neurosurgical populations. For instance, Wang et al. (2023) identified prolonged hospital stays, invasive procedures, and prior antibiotic use as critical risk factors for MDRO infections in neurosurgical intensive care units (NICUs), supporting our findings that prolonged LOS is significantly associated with MDRO presence.[5] Similarly, Akins et al. (2010) reported that MRSA poses a significant challenge in neurosurgical patients, contributing to increased morbidity and mortality.[6]
The resistance patterns observed in this study underscore significant challenges in antimicrobial management within neurosurgical settings. High resistance rates to b-lactams (95.4%), sulfonamides (95.4%), and quinolones (91.6%) are particularly concerning. These findings align with previous reports indicating substantial antimicrobial resistance in neurosurgical patients, especially those with external ventricular drains (EVDs). For instance, a study on ventriculitis due to MDR Gram-negative bacilli associated with EVDs highlighted the complexities of treating such infections, emphasizing the need for effective antimicrobial strategies.[7] Notably, our cohort exhibited no resistance to oxazolidinones and lipopeptides, suggesting potential therapeutic options. MRSA was significantly associated with LOS (p < 0.001); however, a lower proportion of MRSA-positive patients experienced prolonged stays. This contrasts with previous studies linking MRSA to increased LOS and may reflect earlier detection, effective treatment, or unadjusted confounding factors (6, 10).[6,8] This observation underscores the importance of tailored antimicrobial stewardship programs in NICUs. Implementing such programs has been shown to reduce antibiotic misuse and decrease MDRO infection rates, as demonstrated in a study evaluating a clinical pharmacist-led antimicrobial stewardship program in a neurosurgical ICU.[9]
The predominance of multi-source diagnostic tests (i.e., ≥2 samples per patient, such as cerebrospinal fluid (CSF), blood, or swabs) highlights the need for comprehensive testing strategies. This strategy is crucial for managing high-risk populations, as early detection is vital to mitigating severe outcomes. For instance, a study on extensively drug-resistant and MDR gram-negative pathogens in neurocritical intensive care units highlights the importance of early identification and targeted interventions to improve patient outcomes.[10] Furthermore, our logistic regression analysis identified MDR phenotype (resistance to ≥3 antimicrobial classes) and CRE as strong predictors of prolonged LOS. This finding aligns with the predictive model developed by Wang et al. (2023), which identified similar risk factors in NICU settings.[5] Although the model demonstrated high specificity (96.7%), its low sensitivity (27.3%) suggests that it is more effective at identifying a small high-risk subgroup than capturing all at-risk patients. Moreover, the wide confidence intervals for predictors such as CRE reflect variability that should be interpreted cautiously.
Our analysis identified a clinically significant high-risk subgroup (10.1%) with MDR and CRE infections. This group exhibited longer hospital stays and a higher likelihood of ICU admission. These findings align with previous studies indicating that hospital-acquired infections caused by resistant microorganisms increase morbidity, mortality, and length of hospital stay.[8]
The findings of this study reinforce the importance of robust antimicrobial stewardship programs and infection control strategies. Implementing such measures has been shown to reduce antibiotic misuse and MDRO infection rates, as demonstrated in a neurosurgical ICU by Yu et al. (2023).[9] In addition, continuous surveillance is crucial for identifying regional resistance trends, as highlighted by the work of Balkhair et al. (2014) in Oman, where increasing MDRO prevalence necessitated stricter preventive measures.[3]
This study has several limitations. Its retrospective nature limits control over data accuracy and introduces potential bias. Being a single-center study, the findings may not be generalizable to other settings. The inclusion of both neurology and neurosurgery patients introduces cohort heterogeneity that may influence outcomes. The absence of a non-MDRO control group limits causal inferences regarding the impact of MDRO infections on hospital stay. In addition, a lack of detailed data on prior antibiotic use and missing data may affect the interpretation of resistance trends.
Despite these limitations, the study offers important region-specific insights and highlights the need for multicenter prospective research. For instance, a European multicenter cohort study assessed the incidence, impact, and risk factors for MDROs in patients with major trauma, highlighting the value of multicenter approaches in understanding MDRO dynamics.[11] Evaluating the long-term impact of MDRO colonization on rehabilitation outcomes is also essential. Addressing the neurological complications of emerging infectious diseases, including those caused by MDROs, requires a multidisciplinary approach to develop individualized treatment plans and improve patient outcomes.[12]
CONCLUSION
This study underscores the significant burden of MDROs in neurosurgical patients, with MRSA and CRE as primary contributors. MDRO infections were strongly associated with prolonged hospital stays and exhibited high resistance to b-lactams, sulfonamides, and quinolones. The identification of high-risk subgroups highlights the need for targeted monitoring and resource allocation. The findings emphasize the critical role of antimicrobial stewardship, robust infection control measures, and ongoing surveillance in managing MDROs in neurosurgical settings. Future research should focus on multicenter studies and novel therapeutic strategies to address the growing challenge of antimicrobial resistance effectively.
Acknowledgment:
We would like to acknowledge the support of the information technology in retrieving all medical records of the study population.
Ethical approval:
The research/study was approved by the Institutional Review Board at Hospital Research Ethics Committee, Ministry of Health, Muscat, Oman, approval number PRO0102019044, dated 10th October 2019.
Declaration of patient consent:
Patient’s consent not required as patients identity is not disclosed or compromised.
Conflicts of interest:
There are no conflicts of interest.
Use of artificial intelligence (AI)-assisted technology for manuscript preparation:
The authors confirm that there was no use of artificial intelligence (AI)-assisted technology for assisting in the writing or editing of the manuscript and no images were manipulated using AI.
Financial support and sponsorship: Nil.
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