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Article

Device-Associated Infections in Adult Intensive Care Units: A Prospective Surveillance Study

by
Alkmena Kafazi
1,*,
Eleni Apostolopoulou
1,
Eymorfia Andreou
1,
Alexandra Gavala
2,
Evagelos Stefanidis
3,
Fwteini Antwniadou
3,
Christos Stylianou
4,
Theodoros Katsoulas
1 and
Pavlos Myrianthefs
1
1
Department of Nursing, National and Kapodistrian University of Athens, 11527 Athens, Greece
2
Intensive Care Unit, General and Oncologic Hospital of Kifisia “Oi Agioi Anargyroi”, 14564 Athens, Greece
3
Intensive Care Unit, General Hospital of Athens “Sotiria”, 11521 Athens, Greece
4
Cardiac Intensive Care Unit, The 417 Army Equity Fund Hospital, 11521 Athens, Greece
*
Author to whom correspondence should be addressed.
Acta Microbiol. Hell. 2025, 70(2), 15; https://doi.org/10.3390/amh70020015 (registering DOI)
Submission received: 3 March 2025 / Revised: 22 April 2025 / Accepted: 25 April 2025 / Published: 27 April 2025
Versions Notes

Abstract

:
Device-associated infections (DAIs) are a significant public health concern because of their attributable mortality, along with the extra length of stay and cost. This two- year prospective surveillance study aimed to assess the incidence of DAIs and their clinical impact on four Greek adult medical-surgical Intensive Care Units (ICUs). Centers for Disease Control and Prevention (CDC) definitions were used to diagnose DAIs. Of the 500 patients hospitalized for 12,624 days, 254 (50.8%) experienced 346 episodes of DAIs. The incidence of DAIs was 27.4 episodes per 1000 bed-days. The incidence of ventilator-associated events (VAEs), central line-associated bloodstream infections (CLABSIs), and catheter-associated urinary tract infections (CAUTIs) was 20.5 episodes per 1000 ventilator-days, 8.6 episodes per 1000 central line-days, and 2.5 episodes per 1000 catheter-days, respectively. The most common pathogens isolated were Acinetobacter baumannii (35.7%) and Klebsiella pneumoniae (29.9%). All gram-negative pathogens were carbapenem-resistant. The ICU’s mortality was 44.9% for patients with DAIs and 24.8% for patients without a DAI (attributable mortality 20.1%, p < 0.001), while the mean ICU length of stay was 34.5 days for patients with DAIs and 15.6 days for patients without a DAI (attributable length of stay 18.9 days, p < 0.001). The high incidence of multidrug-resistant pathogens and the attributable length of stay and mortality of DAIs emphasize the need to establish an organized antimicrobial surveillance program and implement a care bundle for DAI prevention in ICUs with personnel educational training, monitoring, and feedback.

1. Introduction

Healthcare-associated infections (HCAIs) are one of the most serious patient safety issues in healthcare settings, affecting over 1.4 million people worldwide [1]. Patients in intensive care units (ICUs) are at higher risk for HCAIs due to the high prevalence of invasive devices and procedures, induced immunosuppression, comorbidity, frailty and increased age [2].
According to the Centers for Disease Control and Prevention (CDC), the COVID-19 pandemic has had a negative impact on the incidence of HCAIs, with a substantial increase in the rates of central line-associated bloodstream infections (CLABSIs), ventilator-associated events (VAEs), and catheter-associated urinary tract infections (CAUTIs) recorded through 2020 [3]. Similarly, the International Nosocomial Infection Control Consortium (INICC) study found significant increases in overall mortality, mean length of stay, and CLABSI and VAE rates in ICUs in low- and middle-income countries [4].
HCAIs are associated with prolonged hospital stays, long-term disability, increased antimicrobial resistance, a huge additional financial burden on health systems, high costs for patients and their families, and additional deaths [5,6]. Notably, according to studies conducted in high-income countries, device-associated infections (DAIs), such as CLABSIs and VAEs, have a more serious impact than other HCAIs [7]. Of the approximately 250,000 CLABSIs that occur each year in the United States, approximately 28,000 result in death in the ICU, with an annual cost of up to $2.3 billion [8]. Furthermore, in a study conducted in four European countries, the attributable length of stay per CLABSI episode ranged between 4 and 14 days [9]. The attributable cost per CLABSI episode ranged from €4200 to €13,030, with the annual cost to healthcare systems ranging between €53.9 million in the UK and €130 million in France [10]. For VAEs, the attributable mortality was estimated between 7% and 30% and the attributable cost between €3227 and $6775 per case [11,12]. For CAUTIs, mortality ranges between 3 and 28%, length of stay is 0.5 to 2.5 days, and costs range from $876 to $10,197 per episode [13,14].
This prospective, observational study aimed to assess DAI rates, microbiological profiles, and DAI-attributable mortality and length of stay in ICU patients in Athens, Greece.

2. Materials and Methods

2.1. Research Design

A prospective surveillance was conducted in four medical-surgical ICUs in Athens for a two-year period. The sample consisted of all adult patients hospitalized in the ICUs for more than 2 days during the surveillance period. The sample size was not pre-determined. A total of 120 patients who were hospitalized for ≤2 days in the ICU were excluded from this study. The nurse to patient ratio was 1 to 3. In all four ICUs, hematological tests were performed daily, chest X-rays were performed 2–4 times per week, and blood, bronchial secretions, urine, or wound cultures were performed when clinically indicated. Patients were actively monitored from admission to discharge from the ICU or until death. Exclusion criteria from this study were age under 18 years and ICU length of stay less than 3 days.
Standard definitions from the CDC’s National Healthcare Safety Network were used to diagnose VAEs, CLABSIs, and CAUTIs [15]. There are three levels of definition of VAEs: 1. Ventilator-associated conditions (VACs), when respiratory deterioration meets certain criteria for the detection of hypoxemia defined as an increase in daily minimum PEEP ≥ 3 cm H2O or FiO2 ≥ 0.20 maintained for at least 2 calendar days after a baseline period (2 calendar days) of stability or improvement, 2. Infections attributed to ventilator-associated conditions (IVACs), if considering the above and the presence of general signs of infection/inflammation, defined as a white blood cell count ≥ 12,000 cells/mm3 or ≤4000 cells/mm3 and/or temperature > 38 °C or <36 °C, a new antimicrobial agent has been started and continues for at least 4 calendar days and 3. Possible ventilator-associated pneumonia (PVAP), if in addition to the above, there is microbiological confirmation of a lower respiratory tract infection, defined as purulent respiratory secretions or positive culture (qualitative, semiquantitative, or quantitative) or more stringent microbiological criteria, where purulent secretions plus quantitative criteria are mandatory in addition to positive lung histopathology, positive pleural fluid culture and other tests such as Legionella spp. [15].
The definition of CLABSIs refers to laboratory-confirmed bacteremia in which a selected pathogen that meets the criteria for bacteremia has been isolated and a central line, which has been in place for more than two calendar days, is present on the day of the event or the previous day [15].
To define CAUTIs, the patient must have had an indwelling urinary catheter for more than 2 days on the day of the event and the catheter must be present on the day of the event or removed the day before the event and have at least 1 of the following signs or symptoms: fever (>38 °C), suprapubic tenderness, pain or tenderness in the costovertebral angle with no other recognized cause, increased urinary frequency, urgency, dysuria, and a positive urine culture with ≥105 colonies/mL without more than 2 species of organisms isolated and at least one of which is bacterial. The symptoms of increased urinary frequency, urgency, and dysuria cannot be used when the catheter is in place [15].
Data were collected daily from patient records, nursing staff forms, and the microbiological laboratory. The data collected included patient demographics, severity of disease at admission as indicated by the Acute Physiology and Chronic Health Evaluation (APACHE) II score [16], comorbidities as indicated by the Charlson Comorbidity Index [17], the type of DAI (CLABSI, VAE, CAUTI), duration of exposure to invasive devices, pathogens isolated, and patient outcome (discharge or death in the ICU). Data collection was carried out by trained health professionals and subsequent validation of infection diagnoses was carried out by an infection prevention specialist.
This study was conducted according to the guidelines of the Declaration of Helsinki and approved by the hospital Ethics Committee. Written informed consent was obtained from all the patients before their participation in this study.

2.2. Incidence and Impact of Device-Associated Infections

During the surveillance period, the incidence-density of DAIs was calculated. The incidence of VAEs was obtained by dividing the number of VAEs by the number of days on the ventilator and multiplying by 1000. The incidence of CLABSIs was obtained by dividing the number of CLABSIs by the number of days with a central line and multiplying by 1000. The incidence of CAUTIs was obtained by dividing the number of CAUTIs by the number of days with a bladder catheter and multiplying by 1000. The DAI rates were expressed as DAI episodes per 1000 days at risk [15].
The device utilization ratio was calculated by dividing the number of days with a device by the number of days in the ICU and multiplying by 100. The number of days with a device is the total number of days of exposure to each device (ventilator, central line, bladder catheter) for all patients during the surveillance period. The number of days in the ICU is the total number of days of hospitalization of patients in the ICU during the surveillance period [15].
To estimate extra mortality and length of stay, comparisons were made between patients with DAIs and patients admitted without HCAIs and who did not develop DAIs during their ICU stay. Crude excess mortality was defined as the difference between the mortality of patients with DAIs and the mortality of patients without DAIs, while crude excess length of stay was defined as the difference between the mean length of stay of patients with DAIs and the mean length of stay of patients without DAIs.

2.3. Statistical Analysis

Categorical variables were expressed as absolute (N) and relative frequencies (%) and differences between the two groups were compared using the χ2 test or Fisher exact test, where appropriate. Continuous variables were expressed as median and interquartile range and differences between groups were compared using the non-parametric Mann–Whitney U-test. The Kolmogorov–Smirnov test was used to test for normality. Data analysis was performed using the IBM SPSS Statistics package, version 22.0, and EpiInfo, version 6.04b. All tests were two-sided and statistical significance was defined as p < 0.05, with a power of 95%.

3. Results

During the two-year study period, 500 patients were hospitalized for at least three days in four Greek medical-surgical ICUs for 12,624 ICU days. A total of 254 (50.8%) of the 500 patients developed 346 DAI episodes in ICUs. A total of 179 patients developed one DAI, 58 patients developed two DAIs, and 17 patients developed three DAIs. Table 1 shows the distribution of demographic and clinical characteristics studied in patients with and without DAIs. Medical admission category was more frequent in patients with DAIs (p = 0.003), who had a higher APACHE II score at ICU admission (p = 0.004), compared to patients without DAIs. In patients with DAIs, infection was a more frequent reason for ICU admission compared to patients without DAIs (p = 0.022), who were admitted more often due to post operative monitoring (p < 0.001). Patients with DAIs had a significantly longer ICU length of stay (29 days versus 11 days, p < 0.001), as well as a longer duration of mechanical ventilation (23 days versus 8 days, p < 0.001), central line (29 days versus 11 days, p < 0.001), and urinary catheter (29 days versus 11 days, p < 0.001), compared to patients without DAIs. Also, patients with DAIs received antibiotics for more days, compared to patients without DAIs (28 days versus 10 days, p < 0.001) (Table 1).
The overall rate of DAIs was 27.4 episodes per 1000 ICU days (95% CI, 24.6–30.4). Table 2 shows DAI rates by infection types. VAEs were the most common type of DAIs, with an incidence of 20.5 episodes per 1000 ventilator-days, followed by CLABSIs, with 8.6 episodes per 1000 central line-days, and CAUTIs, with 2.5 episodes per 1000 catheter- days. Of the 207 VAE episodes, the majority were VACs (51.2%), followed by IVACs with a rate of 31.4% and PVAPs with a rate of 17.4%. The device utilization ratio was 80.1% for mechanical ventilation, 99.6% for central catheters, and 100% for urinary catheters (Table 2).
The median (IQR) time from the start of ventilation to the onset of VAEs was 7 (4–14) days. The median (IQR) time from the central line insertion to the onset of CLABSIs was 7 (5–10) days, while the median (IQR) time from the urinary catheter insertion to the onset of CAUTIs was 10 (5–15) days.
The distribution of pathogens by the type of DAI varied. A total of 56 (16.2%) of the 346 DAIs were polymicrobial. The most common pathogens isolated were Acinetobacter baumannii (35.7%), Klebsiella pneumoniae (29.9%), and Pseudomonas aeruginosa (19.5%). Acinetobacter baumannii was the most common pathogen isolated in patients with VAEs (44.4%) and in patients with CLABSIs (44.4%), and Klebsiella pneumoniae was the most common pathogen isolated in patients with CAUTIs (48.4%). Overall, 100% of Acinetobacter baumannii, Klebsiella pneumoniae, and Pseudomonas aeruginosa were carbapenem-resistant.
Table 3 shows the impact of DAIs in ICU mortality. The ICU mortality was 44.9% for patients who acquired a DAI and 24.8% for patients without a DAI, yielding an overall crude extra mortality of 20.1% (p < 0.001). The ICU mortality rates for patients with VAEs, CLABSIs, and both VAEs and CLABSIs were significantly higher than the mortality rate for patients without DAIs, yielding crude extra mortality rates of 22.1% (p < 0.001), 20.6% (p = 0.012), and 25.2% (p = 0.011), respectively (Table 3).
Table 4 provides data on ICU length of stay in patients hospitalized during the surveillance period, without DAIs and with DAIs. The mean ICU length of stay was 34.5 days for patients who acquired a DAI and 15.6 days for patients without a DAI, yielding a crude extra length of stay of 18.9 days (p < 0.001). The extra length of stay for patients with VAEs, CLABSIs, and CAUTIs was 19, 24.1, and 38.5 days (p < 0.001), respectively. In patients with more than one DAI the crude extra length of stay ranges between 27.2 and 51.4 days (p < 0.001) (Table 4).

4. Discussion

Our results highlight the serious problem of DAIs in Greek ICUs, since approximately 50% of patients experienced at least one episode of DAI, with an incidence-density of 27.4 DAIs per 1000 bed-days. According to the 2022–2023 point prevalence survey (PPS) of HCAIs in European hospitals, the prevalence of ICU patients with at least one HCAI was 20.5%. While a higher prevalence, this result is similar to our study, which was observed in Greek ICUs (45.7%) [18]. However, our rates differ from those reported by two previous studies in Greece, which estimated that DAI rates in ICU patients were 18.3 [19] and 34.1 episodes per 1000 bed-days [20]. In the present study, the frequency of DAIs was significantly higher compared to studies from Cyprus (19 DAIs per 1000 bed-days) [21], the United States CDC’s National Healthcare Safety Network (NHSN) report (0.9–4.4 DAIs per 1000 bed-days) [1], and the INICC reports from 45 countries in Latin America, Europe, the Eastern Mediterranean, Southeast Asia, and the Western Pacific (9.0–10.1 DAIs per 1000 bed-days) [22,23]. There are some reasons that could explain this difference, such as the socio-economic level of the country, the lack of an organized surveillance system, the reduced resources for infection prevention and control, and the low compliance with infection prevention measures [24,25,26].
Device utilization ratio constitutes a necessary measurement when combined with the measurement of DAI rates due to its important role in the HCAI surveillance process. Measuring the device utilization ratio can provide information on the risk of device-associated events, such as CLABSIs, VAEs, and CAUTIs [27]. Device utilization ratios were found to be 80.1%, 99.6%, and 100% for mechanical ventilation, central lines, and urinary catheters, respectively. Notably, all the device utilization ratios in this investigation were higher than those reported by the NHSN for the year 2013 [1], as well as higher than data reported by the INICC for the periods 2013–2018 (37.4%, 51.1%, and 58.7%, respectively) [22] and 2015–2020 (43%, 63%, and 67%, respectively) [23]. The elevated rate of DAIs may indicate the increased utilization of devices. The frequency of DAIs increases with the duration of device use. Additionally, the device use may increase the risk for the colonization of multidrug-resistant pathogens. Previous researchers found that the most important risk factors for the acquisition of multidrug resistance were prior antibiotic use, ICU admission, and the presence of indwelling medical devices [28]. Scanning electron microscopy has shown that bacteria embedded in a biofilm matrix invade most indwelling catheters [29]. The formation and development of biofilms involve several phases; the five primary stages of bacterial attachment are dispersion/detachment, bacterial aggregation, microcolony formation, maturation, and single bacterial attachment [30]. One of the main causes of DAIs is the growth of biofilms on their surfaces [31,32]. By creating a barrier that protects bacteria from antimicrobial treatments, these biofilms raise the risk of infection. To combat the threat of multidrug-resistant microorganisms, there is an urgent need for multilevel infection control measures, including reduction in device usage time [27,33], hand hygiene [34,35,36], environmental cleaning and disinfection [35], identification of risk factors for multidrug-resistant colonization [35,37], and educating healthcare workers on antibiotic stewardship principles, emphasizing the correct dosing, antibiotic, duration, and route of administration [35,38,39].
The most common pathogens isolated in the four ICUs were Acinetobacter baumannii and Klebsiella pneumoniae. Our results agree with a previous systematic review published in 2022 [40]. Prospective studies show that the rates of gram-negative pathogens resistant to carbapenems have increased in Europe [41]. According to the ECDC’s PPS of HCAIs, carried out in European acute care hospitals, Greece was one of the countries with the highest levels of antimicrobial resistance [18]. In our country, the percentage of carbapenem resistance among Pseudomonas aeruginosa, Klebsiella pneumoniae, and Acinetobacter baumannii isolates was 61.5%, 81.0%, and about 90%, respectively [18]. In another Greek study, the frequency of carbapenem-resistant Acinetobacter baumannii strains reached 98%, followed by Klebsiella pneumoniae strains with a rate of 75%, and Pseudomonas aeruginosa strains with a rate of 46% [42]. The resistance rates in our study were 100%, highlighting the endemic situation prevailing in Greek ICUs. The presence of multidrug-resistant pathogens can be attributed to the lack of appropriate policies regarding antibiotic use in most Greek hospitals. In the ECDC PPS, Greece recorded the highest standardized antimicrobial use ratio in European acute care hospitals. Overall, 55.3% of patients observed received at least one antibiotic, with an average of 1.67 antibiotics per patient. The indication was “medical prophylaxis” for 10.8% of patients receiving antimicrobials. The percentage of antimicrobials for which the route of administration was parenteral was more than 90% in Greece [18]. There is a significant prevalence of carbapenem utilization in the empirical management of non-septic hospital infections, leaving few alternatives for treatment escalation aside from colisitin or newer antibiotics. It is mandated by law that infectious disease physicians must be present at every hospital in Greece. The law mandates the formation of antimicrobial stewardship (AMS) teams for the proper management of antimicrobial agents in hospital environments. In hospitals we observed disparate staffing levels of infectious disease physicians, and more significantly, differing degrees of engagement in AMS, which frequently constitutes an additional obligation beyond their standard responsibilities without designated time particularly allocated for this purpose [43]. Therefore, more efforts are needed to adopt AMS, and to prevent and control DAIs and antimicrobial resistance in Greek ICUs [44]. AMS programs encompass three essential phases. The initial phase involves a thorough assessment of the patient’s health prior to the therapy, considering infection characteristics, physical examination findings, and laboratory results. To initiate the appropriate antibiotic, clinicians must evaluate the patient and the environmental factors. The next stage is to maintain a brief treatment duration, which can be regarded as a post-therapy criterion. The reduction of broad-spectrum antibiotic usage constitutes the basis of AMS. AMS team formation, restricting the use of broad-spectrum antibiotics, early treatment termination, early warning system use, infection management, and education and feedback are some of the parameters employed for this goal [45].
Also, this study presents data on the clinical impact of DAIs on ICU mortality rates and length of stay during the surveillance period. From our results it seems that DAI acquisition increases ICU mortality approximately two times. If the patient presents two or three DAIs simultaneously, the extra mortality increases more significantly, reaching over 25%. Also, the mean length of stay increases two times when a DAI is present, while in patients with two or three DAIs simultaneously, it increases three to four times compared to patients without DAIs. Although there is no current data at a national level, some Greek studies suggest that DAIs increase ICU mortality between 13.8% and 20.7% [12,20,46,47] and length of stay between 17 and 21 days [12,20,46,47]. Additionally, our results are confirmed by previous multi-center surveillance studies, which report that DAIs increase mortality and length of stay in adult and pediatric ICUs [22,23]. However, it is important to note that we did not proceed with further analysis of our data in order to identify if DAIs are an independent risk factor for ICU mortality and length of stay, after adjusting by several other variables. Nevertheless, other researchers have demonstrated that some unlikely to change risk factors are: country income level, hospitalization type, sex, and age [23,48,49], while some modifiable risk factors are: DAIs, length of stay, and device utilization ratio [23,48,50]. Additionally, low nurse-to-patient ratios have been identified as barriers for infection prevention and control [51,52,53]. In each of the four ICUs, the nurse-to-patient ratio was 1:3. Although there are several risk factors for the occurrence of DAIs, suboptimal nurse staffing levels may be a barrier to its elimination in ICUs [51,52,53]. The high crude attributable mortality rates of DAIs emphasize the implementation of active outcome surveillance programs and procedures to identify patients at risk, as well as to identify gaps in DAI control practice, provide staff feedback, and target performance improvement activities that will contribute to the reduction of DAIs.
Our results should be interpreted in the context of some potential limitations. First, as the present study was conducted in four ICUs in Athens, our sample may not represent the typical characteristics of patients in other ICUs in our country, which likely affects the rates of DAIs and attributable mortality. Second, due to the study design, we may not have considered some potential confounding factors, which could have affected the magnitude of our findings. For example, critically ill patients are more likely to remain in the ICU for prolonged periods and to die due to the severity of their illness rather than due to DAIs. In addition, it is important to emphasize that the present research was conducted from a hospital perspective. The consequences of mortality related to DAIs from a societal perspective (e.g., loss of productivity) were not considered. Due to this perspective, the time horizon of the analysis is limited to the ICU period. However, DAIs impose a significant burden in other settings as well. After discharge from the ICU, patients are transferred to hospital clinics. Further analysis could therefore be considered to extend this perspective beyond ICUs.
Despite limitations, this study presents an accurate mapping of the incidence of DAIs, based on the CDC’s standard definitions and protocols for diagnosing DAIs and monitoring ICU patients [15].

5. Conclusions

Our findings highlight the significance of monitoring DAIs in ICU patients. The elevated prevalence of DAIs, the frequency of device usage, and the levels of antimicrobial resistance among the pathogens found in this study emphasize the need for implementing multilevel infection control measures. The importance of antimicrobial surveillance and AMS along with bundles for ventilator, central line, and urinary catheter care is critical. Educational training, audit, feedback and culture of safety initiatives with emphasis on comprehensive hand hygiene education programs, device utilization, ratio reduction, and antibiotic rotation strategies can minimize the impact of DAIs, enhancing the safety and quality of care of ICU patients.

Author Contributions

Conceptualization, A.K., E.A. (Eleni Apostolopoulou), and P.M.; methodology, A.K.; software, A.K. and E.A. (Eymorfia Andreou); validation, E.A. (Eleni Apostolopoulou), T.K., and P.M.; formal analysis, A.K.; investigation, A.K., E.A. (Eymorfia Andreou), E.S., A.G., C.S., and F.A.; resources, A.K.; data curation, A.K.; writing—original draft preparation, A.K.; writing—review and editing, A.K. and C.S.; visualization, A.K.; supervision, A.K.; project administration, E.A. (Eleni Apostolopoulou), T.K., and P.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committees of the General Hospital of Athens “Evangelismos” (No. 247/10 October 2017).

Informed Consent Statement

Informed consent was obtained from all patients involved in the study.

Data Availability Statement

The data presented in this study are available on reasonable request from the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
AMSAntimicrobial Stewardship
APACHEAcute Physiology and Chronic Health Evaluation
CAUTIsCatheter-Associated Urinary Tract Infections
CDCCenters for Disease Control and Prevention
CLABSIsCentral Line-Associated Bloodstream Infections
DAIsDevice-Associated Infections
HCAIsHealthcare-Associated Infections
ICUsIntensive Care Units
IVACsInfections Attributed to Ventilator-Associated Conditions
INICCInternational Nosocomial Infection Control Consortium
NHSNNational Healthcare Safety Network
PPSPoint Prevalence Survey
PVAPPossible Ventilator-Associated Pneumonia
VACsVentilator-Associated Conditions
VAEsVentilator-Associated Events

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Table 1. Characteristics of patients with and without device-associated infections.
Table 1. Characteristics of patients with and without device-associated infections.
VariableAll (n = 500)DAIs (n = 254)No DAIs (n = 246)pvalue 
Male sex, n (%)297 (59.4)160 (63)137 (55.7)0.102
Age, median (interquartile range)61.5 (47–74)61.5 (47–74)61.5 (46.7–75.2)0.813
Admission category, n (%)
      Medical300 (60)169 (66.5)131 (53.3)0.003
      Surgical200 (40)85 (33.5)115 (46.7)
Reason for ICU admission, n (%)
      Infection122 (24.4)73 (28.7)49 (19.9)0.022
      Post operative monitoring87 (17.4)23 (9.1)64 (26)<0.001
      Trauma83 (16.6)49 (19.3)34 (13.8)0.118
      Neurological disease72 (14.4)38 (15)34 (13.8)0.799
      Pulmonary disease68 (13.6)42 (16.5)26 (10.6)0.067
      Cardiovascular disease36 (7.2)16 (6.3)20 (8.1)0.490
      Malignancy22 (4.4)8 (3.1)14 (5.7)0.194
      Other (burn, poisoning)10 (2.2)5 (2)5 (2)1.000
APACHE II score, median (interquartile range)18 (15–21)19 (15–22)17 (14–21)0.004
Charlson comorbidity index, median (interquartile range)3 (1–5)3 (1–5)3 (1–5)0.279
Invasive procedures, n (%)
      Central venous catheter500 (100)254 (100)246 (100)NA
      Urinary catheter500 (100)254 (100)246 (100)NA
      Endotracheal tube500 (100)254 (100)246 (100)NA
      Tracheostomy182 (36.4)130 (51.2)52 (21.1)<0.001
      Tube thoracostomy39 (7.8)21 (8.3)18 (7.3)0.741
      Hemodialysis131 (26.2)58 (22.8)73 (29.7)0.085
Days in ICU, median (interquartile range)19 (10–32)29 (20–44)11 (7–19)<0.001
Days of mechanical ventilation, median (interquartile range)15 (7–27)23 (15–35.2)8 (5–15)<0.001
Days with central line, median (interquartile range)19 (10–32.7)29 (19–44.2)11 (7–19)<0.001
Days with urinary catheter, median (interquartile range)19 (10–34)29 (20–46.2)11 (7–19)<0.001
Days with antibiotics, median (interquartile range)17 (9–30)28 (17–42)10 (6–16)<0.001
DAIs = Device-associated infections; ICU = Intensive care unit; NA = Not applicable.
Table 2. Device-associated infection rates.
Table 2. Device-associated infection rates.
Type of DAINo of Bed DaysDevice DaysDevice Utilization RatioNo of InfectionsDAI Rates (95% CI)
VAEs12,62410,11280.1%20720.5 (17.8–23.4)
VACs12,62410,11280.1%10610.5 (8.6–12.6)
IVACs12,62410,11280.1%656.4 (5.0–8.1)
PVAPs12,62410,11280.1%363.6 (2.5–4.9)
CLABSIs12,62412,56899.6%1088.6 (7.1–10.3)
CAUTIs12,62412,624100%312.5 (1.7–3.4)
DAIs = Device-associated infections; VAEs = Ventilator-associated events; VACs =Ventilator-associated conditions; IVACs = Infections attributed to ventilator-associated conditions; PVAPs = Possible ventilator-associated pneumonia; CLABSIs = Central line-associated bloodstream infections; CAUTIs = Catheter-associated urinary tract infections; CI = Confidence intervals.
Table 3. Device-associated infection attributable mortality.
Table 3. Device-associated infection attributable mortality.
Type of DAINo of DeathsMortality (%)Crude Extra Mortality (%)pvalue 
None6124.8--
DAIs11444.920.1<0.001
VAEs9746.922.1<0.001
CLABSIs4945.420.60.012
CAUTIs1445.220.40.245
VAEs and CLABSIs325025.20.011
VAEs and CAUTIs1147.8230.188
CLABSIs and CAUTIs642.918.10.575
VAEs, CLABSIs, and CAUTIs337.512.71.000
DAIs = Device-associated infections; VAEs = Ventilator-associated events; CLABSIs = Central line-associated bloodstream infections; CAUTIs = Catheter-associated urinary tract infections.
Table 4. Device-associated infection attributable length of stay.
Table 4. Device-associated infection attributable length of stay.
Type of DAIMean Length of Stay, DaysCrude Extra Length of Stay, Dayspvalue 
None15.6--
DAIs34.518.9<0.001
VAEs34.619<0.001
CLABSIs39.724.1<0.001
CAUTIs54.138.5<0.001
VAEs and CLABSIs42.827.2<0.001
VAEs and CAUTIs54.138.5<0.001
CLABSIs and CAUTIs66.450.8<0.001
VAEs, CLABSIs, and CAUTIs6751.4<0.001
DAIs = Device-associated infections; VAEs = Ventilator-associated events; CLABSIs = Central line- associated bloodstream infections; CAUTIs = Catheter-associated urinary tract infections.
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Kafazi, A.; Apostolopoulou, E.; Andreou, E.; Gavala, A.; Stefanidis, E.; Antwniadou, F.; Stylianou, C.; Katsoulas, T.; Myrianthefs, P. Device-Associated Infections in Adult Intensive Care Units: A Prospective Surveillance Study. Acta Microbiol. Hell. 2025, 70, 15. https://doi.org/10.3390/amh70020015

AMA Style

Kafazi A, Apostolopoulou E, Andreou E, Gavala A, Stefanidis E, Antwniadou F, Stylianou C, Katsoulas T, Myrianthefs P. Device-Associated Infections in Adult Intensive Care Units: A Prospective Surveillance Study. Acta Microbiologica Hellenica. 2025; 70(2):15. https://doi.org/10.3390/amh70020015

Chicago/Turabian Style

Kafazi, Alkmena, Eleni Apostolopoulou, Eymorfia Andreou, Alexandra Gavala, Evagelos Stefanidis, Fwteini Antwniadou, Christos Stylianou, Theodoros Katsoulas, and Pavlos Myrianthefs. 2025. "Device-Associated Infections in Adult Intensive Care Units: A Prospective Surveillance Study" Acta Microbiologica Hellenica 70, no. 2: 15. https://doi.org/10.3390/amh70020015

APA Style

Kafazi, A., Apostolopoulou, E., Andreou, E., Gavala, A., Stefanidis, E., Antwniadou, F., Stylianou, C., Katsoulas, T., & Myrianthefs, P. (2025). Device-Associated Infections in Adult Intensive Care Units: A Prospective Surveillance Study. Acta Microbiologica Hellenica, 70(2), 15. https://doi.org/10.3390/amh70020015

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