Catheter-Associated Urinary Tract Infections, Bacteremia, and Infection Control Interventions in a Hospital: A Six-Year Time-Series Study
by
, , ,
Amalia Papanikolopoulou
1, 
Helena C. Maltezou
2,*
,
Athina Stoupis
3,
Dimitra Kalimeri
4,
Androula Pavli
5,
Fotini Boufidou
6
,
Maria Karalexi
7,
Nikos Pantazis
8,
Constantinos Pantos
9,
Yannis Tountas
8,
Vasiliki Koumaki
7,
Maria Kantzanou
7 and
Athanasios Tsakris
7
1
Clinical Pharmacology Department, Athens Medical Center, 5-7 Distomou Str., Marousi, 15125 Athens, Greece
2
Directorate of Research, Studies, and Documentation, National Public Health Organization, 3-5 AgrafonStr., Marousi, 15123 Athens, Greece
3
Clinical Infectious Diseases Department, Athens Medical Center, 58 Kifissias Avenue, Marousi, 15125 Athens, Greece
4
Nurse Department Athens Medical Center, 5-7 Distomou Str., Marousi, 15125 Athens, Greece
5
Department of Travel Medicine, National Public Health Organization, 3-5 Agrafon Str., Marousi, 15123 Athens, Greece
6
Neurochemistry and Biological Markers Unit, 1st Department of Neurology, School of Medicine, Eginition Hospital, National and Kapodistrian University of Athens, 11528 Athens, Greece
7
Department of Microbiology, Faculty of Medicine, School of Health Sciences, National and Kapodistrian University of Athens, 75 Mikras Asias Str., 15772 Athens, Greece
8
Department of Hygiene, Epidemiology and Medical Statistics, Faculty of Medicine, School of Health Sciences, National and Kapodistrian University of Athens, 75 Mikras Asias Str., 15772 Athens, Greece
9
Department of Pharmacology, Faculty of Medicine, School of Health Sciences, National and Kapodistrian University of Athens, 75 Mikras Asias Str., 15772 Athens, Greece
*
Author to whom correspondence should be addressed.
J. Clin. Med. 2022, 11(18), 5418; https://doi.org/10.3390/jcm11185418
Submission received: 30 July 2022
/
Revised: 1 September 2022
/
Accepted: 13 September 2022
/
Published: 15 September 2022
(This article belongs to the Special Issue Clinical Advances in Bacteremia: From Pathophysiology to New Therapeutic Possibilities)
Abstract
:Catheter-associated urinary tract infections (CAUTIs) are among the most common healthcare-associated infections. Urine catheters are often reservoirs of multidrug-resistant (MDR) bacteria and sources of pathogens transmission to other patients. The current study was conducted to investigate the correlation between CAUTIs, MDR bacteremia, and infection control interventions, in a tertiary-care hospital in Athens, from 2013 to 2018. The following data were analyzed per month: 1. CAUTI incidence; 2. consumption of hand hygiene disinfectants; 3. incidence of isolation of MDR carrier patients, and 4.incidence of bacteremia/1000 patient-days [total resistant a.Gram-negative: carbapenem-resistant Pseudomonas aeruginosa, Acinetobacter baumannii, and Klebsiella pneumoniae; b.Gram-positive: vancomycin-resistant Enterococci and methicillin-resistant Staphylococcus aureus]. The use of scrub disinfectant solutions was associated with decreased CAUTI rate in Total Hospital Clinics (OR: 0.97, 95% CI: 0.96–0.98, p-value: <0.001) and in Adults ICU (OR: 0.79, 95% CI: 0.65–0.96, p-value:0.018) while no correlation was found with isolation rate of MDR-carrier pathogens. Interestingly, an increase in total bacteremia (OR: 0.81, 95% CI: 0.75–0.87, p-value:<0.001) or carbapenem-resistant bacteremia correlated with decreased incidence of CAUTIs (OR: 0.96, 95% CI: 0.94–0.99, p-value: 0.008). Hand hygiene measures had a robust and constant effect on infection control, reducing the incidence of CAUTIs.
1. Introduction
Urinary tract infection (UTI) is one of the most common healthcare-associated infections (HAIs), and approximately two-thirds of these infections are attributed to an indwelling urethral catheter [1]. The first pan-European point prevalence survey of HAIs in 2011–2012 found that UTIs accounted for 19% of all HAIs in acute-care hospitals in Europe [2]. Catheter-associated urinary tract infection (CAUTI) has been associated with increased morbidity, mortality, hospital cost, and length of stay [3,4,5]. In addition, bacteriuria commonly leads to unnecessary antimicrobial use, and urinary drainage systems are often reservoirs of multidrug-resistant (MDR) bacteria and sources of transmission of pathogens to other patients [6,7]. The source of microorganisms causing CAUTI can be endogenous, typically via meatal, rectal, or vaginal colonization, or exogenous, such as via contaminated hands of healthcare personnel or equipment [8].
The most prevalent pathogen associated with CAUTI in hospital wards and ICUs are Escherichia coli, followed by Klebsiella spp., Enterococcus spp., Pseudomonas aeruginosa, and Enterobacter spp. [9]. In addition, approximately one third of E. coli isolates and a quarter of P. aeruginosa isolates from CAUTI cases are fluoroquinolone-resistant, while resistance to other advanced class antibiotics such as third-generation cephalosporins and carbapenems, and MDR pathogens are also substantial [10].
The correlation between HAIs and infection control measures has been studied n the last decade [11,12,13]. More recent studies using interrupted time series analysis, focused on evaluating educational and interventional bundles to reduce CAUTIs [14,15,16]. The aim of the current prospective study was to investigate the correlation between the incidence rate of CAUTIs, specific infection control measures, and the incidence rate of MDR bacteremia in a hospital in Greece.
2. Materials and Methods
2.1. Setting
The study was conducted prospectively from January 2013 to December 2018 in a 300-bed private tertiary-care hospital in Athens. The hospital consists of: 1. one Adults Clinic with Internal Medicine, Oncology, Hematology, Surgery Departments and one ICU; 2. one Obstetrics and Gynecology Clinic with one neonatal ICU, and 3. one Pediatrics Clinic with one pediatric ICU.
2.2. Infection Control Measures
As we already described [17,18], the following infection control measures were applied: 1. active surveillance of carbapenem-resistant A. baumannii, P. aeruginosa, and K. pneumoniae, and methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant Enterococci (VRE); 2. implementation of a CAUTI bundle, which consisted of aseptic insertion and maintenance techniques, catheter change guidelines, and discontinuation indications; 3. promotion of hand hygiene before and after providing healthcare to patients; 4. carriage screening (pharyngeal, axillary-rectal, nasal) cultures followed by isolation of MDR carrier patients; and 5. audit of CAUTI bundle and ASP on a monthly basis.
2.3. Data Collection
Data were collected prospectively on a monthly basis: 1. number of CAUTIs was collected from Clinical Infectious Diseases Department; 2. catheterization was detected and recorded manually by clinical visits from Nurse Department; 3. hand disinfectant solutions use was collected from Pharmacy Department; 4. number of bacteremia was collected from Microbiology Department. The medical/nurse hospital quality procedures and laboratory diagnostic procedures were supervised by Quality Assurance Department and did not change throughout the six year-study period.
2.4. Outcomes
Estimated outcomes included: 1. CAUTI rate (incidence/1000 catheter-days); 2. consumption of hand disinfectant solutions (L/1000 patient-days); and 3. incidence of bacteremia (incidence/1000 patient-days). The outcomes were estimated on a monthly basis.
2.5. Detection of Bacteremia
Bacteremia was detected through Gram stains and blood cultures. The automated VITEK 2 system (Biomerieux) was used for isolation, identification, and antibiotic susceptibility testing. The CLSI breakpoints were applied. The susceptibility of bacteria was determined by Kirby–Bauer test, MIC semi-automated testing, and/or E-test.
2.6. Definitions
CAUTI in a patient with an indwelling urethral catheter is defined as the onset of signs and/or symptoms compatible with UTI and no other source of infection along with ≥103 colony-forming units (cfu)/mL of ≥1 bacterial species in a single urine specimen or in a midstream voided urine specimen from a patient whose catheter was removed the past 48 h [19]. Signs and symptoms compatible with CAUTI include new onset or worsening of fever, rigors, altered mental status or lethargy with no other identified cause; costovertebral angle tenderness; acute hematuria; pelvic discomfort; and in those whose catheters have been removed, dysuria, urgent or frequent urination, or suprapubic pain or tenderness [19]. Isolation rate of MDR-carrier patients was expressed as a percentage of isolated patients per 100 admissions. Bacteremia was defined as a laboratory-confirmed bloodstream infection, either primary (not related to an infection at another body site) or secondary (thought to be seeded from a site-specific infection at another body site) [20,21]. New episode of bacteremia within a month period was defined as a new episode of bacteremia due to a different pathogen strain or due to the same pathogen strain but with different phenotype of resistance. The incidence of total bacteremia is the sum of total Gram-positive and Gram-negative bacteremia. The incidence of total carbapenem-resistant Gram-negative bacteremia refers to carbapenem-resistant A. baumannii, P. aeruginosa and K. pneumoniae bacteremia. The incidence of total resistant Gram-positive bacteremia refers to the incidence of MRSA and VRE bacteremia [21]. Hand hygiene concerns scrub disinfectant solutions with chlorhexidine, alcohol 70% disinfectant solutions with chlorhexidine, and/or simple soap [18].
2.7. Statistical Analysis
As already described [17,18], an analysis of time trends in the intervention and outcome variables during the study period was initially performed. The variable under investigation was the dependent variable. Time since the beginning of the study (in months) was the independent variable in the regression models and was entered through appropriate restricted cubic splines. In order to capture potential seasonality effects, Fourier series terms of time (1st and 2nd order) were also used in the models. Standard errors (SE) and corresponding 95% confidence intervals (CI) were estimated using the robust (sandwich) variance estimator to adjust for potential violations of models’ assumptions. Estimated values for start and end of the study period and corresponding 95% CIs were estimated through a simplification of the models. Spline time terms were replaced by a single linear time trend or two piecewise linear terms to capture average long-term trend. A linear regression model was applied for consumption of hand disinfectants. When the outcome of interest was CAUTI or bacteremia rates, Poisson regression models were applied with number of cases as dependent variable and the appropriate number of catheter-days or patient-days, respectively, used as an offset after logarithmic transformation [17]. When the percentage over total number of hospitalizations was the outcome of interest (e.g., isolations), binomial regression models were applied with the number of cases as the dependent variable and the appropriate number of hospitalizations as the binomial denominator [17]. Associations between outcomes and interventions were studied by introducing appropriate independent variables into the models [22]. The effects of the independent variables were initially tested separately for current (“month 0”) and lagged values (months −1, −2 and −3). In case of statistical significance (p-value < 0.05) or indicative significance (0.05 < p-value < 0.10) for more than one case (e.g., in month 0 and in month −1) and association direction was the same (e.g., positive for both), average value was used as independent variable. In cases where the direction of the association was different (e.g., positive for “month 0” and negative for “month −1”), the results of the respective models are presented separately [22]. All p-values reported throughout the manuscript have not been adjusted for multiple testing. Analyses were conducted using Stata version 14.2 (Stata Corporation, College Station, TX, USA).
3. Results
A total of 95,228 admissions occurred during the entire study period. Overall, 12.84% of hospitalized patients underwent catheterization. There were 379 CAUTI episodes among 12,228 catheterized patients; therefore, the CAUTI rate during the six-year study period was 5.28 episodes of CAUTI/1000 catheter days. The monthly CAUTI incidence in all Hospital Clinics and in Adults ICU are depicted in Figure 1. Results over time for each measure are shown in Table 1, Table 2, Table 3 and Table 4. The relationship between CAUTI rates and concurrent or lagged (1–3 months) values of each process measure are shown in Table 5 and Table 6.
The time trends of CAUTI rate during the six-year study period in the entire hospital and divisions are shown in Table 1. The incidence of CAUTI decreased significantly in all Hospital Clinics and Departments and also in the Adults ICU (p-value: <0.001 for all comparisons).
Table 2 shows time trends of incidence of isolations per 100 admissions. A significant increase in the rate of isolation of patients with MDR pathogens was observed in Total Hospital Departments and in Adults ICU (p-value = <0.001), while in Adults Clinic the increase was significant up to April 2015 only (p-value < 0.001) and in Adults Departments up to February 2015 only (p-value < 0.001).
Table 3 shows time trends in the consumption of hand disinfectant solutions per category of disinfectant. There was a statistically significant increase in the consumption of alcohol disinfectant solutions and all hand disinfectant solutions in all Departments and Clinics (p-value = <0.001). In Adults Clinic Departments separately the increase was observed only in alcohol disinfectant solutions (p-value = <0.001). In the ICU it is noticeable that there was a statistically significant increase in scrub and all hand disinfectant solutions (p-value = 0.001). Combining the results with Figure 1b for the ICU, the yearly increase of 24.4% from 2013 up to 8/2016 (95% CI: 16.8–30.1, p-value: <0.001) in the consumption of scrub disinfectant solutions agrees with the 1st trough value of CAUTI rate per month in September 2016, and the 6-year increase in the consumption of all hand disinfectant solutions with the 2nd trough value of CAUTI rate per month in February 2018.
Table 4 shows the time trends in the incidence of different bacteremia/1000 patient-days. The incidence of total bacteremia increased significantly in the entire hospital and divisions (p-value = <0.001), which is attributed to the increase in the number of blood cultures and admissions. However, there was no statistically significant difference in the incidence of total bacteremia from resistant pathogens. In total Hospital Clinics and Departments, the trend in the incidence of total carbapenem-resistant Gram-negative pathogens decreased not significantly, while in Adults ICU increased not significantly. The analysis per pathogen showed a significant decrease only for carbapenem-resistant P. aeruginosa in total Hospital Clinics (p-value = 0.027) and Departments (p-value = 0.042), and in Adults Clinic (p-value = 0.031) and Departments (p-value = 0.051). For carbapenem-resistant K. pneumoniae and A. baumannii the incidence did not change significantly. Interestingly the latter had zero incidence in the hospital departments. The incidence of resistant Gram-positive pathogens remained very low and stable throughout the entireperiod; for this reason, we did not apply a linear model. Finally, in the Adults ICU the analysis per pathogen did not show any statistical change for any of the carbapenem resistant Gram-negative bacteria.
The correlation of CAUTI with the incidence of different bacteremia is shown in Table 5. Every increase in the incidence of total bacteremia the current and the previous month correlated with a decreased CAUTI rate, in total Hospital Clinics and Departments and in Adults Clinics and Departments (p-value = <0.001). There was a negative correlation for total carbapenem-resistant Gram-negative bacteremia in total Hospital Clinics (p-value = 0.008) and Adults Clinics (p-value = 0.042) three months before, but in all Hospital Departments (p-value = 0.048) and Adults Departments (p-value = 0.060), we noticed a positive correlation for current month and negative three months before (p-value = 0.009 and 0.027, respectively). For each category of carbapenem-resistant Gram-negative pathogen, the negative correlation was constant in all Hospital Clinics/Departments and in Adults Clinics/Departments, and statistically significant especially for carbapenem-resistant K. pneumoniae bacteremia (p-value < 0.001). Every increase in their incidence two and three months earlier resulted in a decreased CAUTI rate. Every increase in the incidence of carbapenem-resistant P. aeruginosa bacteremia two and three months earlier significantly correlated with increased CAUTI rate (p-value < 0.001). This phenomenon was not repeated for the other Gram-negative resistant pathogens. However, there was a significant correlation between the incidence of carbapenem-resistant K. pneumoniae and a decrease in CAUTI incidence three months earlier (p-value = 0.018).
Table 6 shows the correlation between CAUTI and infection control measures. Every increase in the consumption of either scrub or all disinfectant solutions the previous months significantly correlated with decreased CAUTI rate in all Hospital Clinics (p-value = <0.001 and 0.004), Adults Clinic (p-value = <0.001 and 0.006), and Adults Departments (p-value = <0.001 and 0.005). In the Adults ICU, every increase in the consumption of alcohol and all disinfectant solutions current month correlated significantly with increased CAUTI (p-value = 0.012 and 0.016), while every increase the previous month in the consumption of scrub disinfectant solutions correlated significantly with decreased CAUTI rate (p-value = 0.018). Finally, the intervention of isolation of patients did not show a direct correlation with the CAUTI rate.
4. Discussion
In this six-year study we studied the relationship among infection control measures and outcomes with CAUTI in a tertiary-care hospital located in Athens. In our institution from 2013 to 2018 a CAUTI-bundle was implemented to promote the rational use of indwelling urinary catheters, resulting overall in a catheterization percentage of 12.84%, which stands within international references [23,24]. According to the 2011–2012 pan-European point prevalence survey of HAIs, the mean catheterization rate of hospitalized patients in acute care hospitals in European countries was 17.2%, while in Greece it was 30% [2]. In our study, the 6-year CAUTI rate was 5.27 infections per 1000 catheter days, which is within the range of pooled mean CAUTI rates (3.1–7.5 infections per 1000 catheter-days) found in acute care hospitals during 2015–2017, as reported by National Healthcare Safety Network [9].
During the last 15 years many guidelines for the prevention of CAUTI have been published [19,25,26,27], and many programs with evidence-based educational and interventional bundles to reduce the incidence of CAUTI have been evaluated [28,29,30,31,32,33,34,35,36]. In our study a CAUTI-bundle was implemented along with other infection control interventions, resulting in a significant reduction in the incidence of CAUTI in all hospital Clinics and Departments, and in Adults ICU.
In our hospital, the most significant infection control interventions were the increased isolation of MDR carrier patients and the increased consumption of hand disinfectant solutions indicating adherence to hand hygiene. Furthermore, the most significant outcome was the decrease in the CAUTI rate. The decrease in the incidence of carbapenem-resistant Gram-negative bacteremia, either in total or per studied pathogen, although it was indicative, reflects the effect of isolation of patients with MDR pathogens, while very low or zero incidence of resistant Gram-positive pathogens during the six-year study period, reflects the effect of implementation of hand hygiene [17,21]. Our findings, in different clinics and departments of the hospital, depict the continuous need of infection control interventions with tailored frequency, evaluation, and implementation.
Until recently, it was more feasible to design a study regarding bundled horizontal infection prevention strategies for the prevention of HAI [28,29,30,31,32,33,34]. In our study, for the first time in the literature, we have results regarding the association of CAUTI with different bacteremia and with consumption of hand disinfectant solutions for the entire hospital as well as its Clinics and Departments separately. For CAUTI and total bacteremia, the correlation was always negative and significant, indicating no cross-infection between blood and UTIs. In case of total carbapenem-resistant Gram-negative bacteremia, the correlation was positive only for the current month, in Total Hospital Departments, showing the severity of such infections. For each category of carbapenem-resistant Gram-negative pathogens, the correlation with CAUTI was negative, in the entire hospital and its divisions, two and three months earlier, showing a preserve time effect of the implemented interventions. For Adults ICU the results diverged from one carbapenem-resistant bacterium to another. Especially for carbapenem-resistant P. aeruginosa bacteremia the correlation with increased CAUTI rate, two and three months earlier, showed a disability of the implemented interventions to have prolonged effect for this type of pathogen, and the need for further tailored infection control interventions regarding this hydrophilic Gram-negative bacterium. Interestingly, the incidence of carbapenem-resistant A. baumannii and K. pneumoniae correlated with the decreased CAUTI rate, indicating different infection control behavior of these pathogens.
From the results of CAUTI and hand disinfectant solutions, the correlation was negative in the entire hospital and its divisions giving a 2 to 3 month-lasting effect of implementation of hand hygiene. While no actual changes in catheter indwelling rates and indwelling duration was noted (data not shown), CAUTI-bundle education regarding hand sanitizer consumption resulted in decreased CAUTI rate in the hospital setting. Especially in the ICU both trough values of CAUTI rate coincide with the increase consumption of scrub and all hand disinfectant solutions implying the importance of hand washing along with disinfection in controlling nosocomial infections such as CAUTI.
Moreover, for the first time in the literature, the correlation of CAUTI with the isolation of MDR-carrier patients was investigated and non-significance was found. Worthy to mention though that both decrease in CAUTI and increase in isolation of patient directly correlated with decrease in incidence of MDR bacteremia.
Our study has several strengths. For the first a time series analysis was used to study the potential association among CAUTI rate, infection control measures, and MDR bacteremia. A clear strength is the prospective study design and the prolonged study period. The analysis of findings per clinic and department gave us the opportunity to end up with more accurate conclusions. Catheter-associated asymptomatic bacteriuria was not included in the definition of CAUTI, which is a potential limitation. Lastly, since the analysis of data included the study of associations between various outcomes and potential predictors in several clinics, several hypotheses were investigated. Some inflation of the Type I error beyond the typical 0.05 level may be considered, since we selected to present unadjusted p-values [37].
5. Conclusions
We prospectively studied the association of infection control measures and CAUTI incidence. The consumption of all hand disinfectant solutions and scrub correlated with decreased CAUTI rate in total Hospital and its divisions, while no correlation was found with the intervention of isolation of MDR carrier patients. Moreover, the correlation of CAUTI with MDR bacteremia was investigated and was found always negative, which indicates a constant and robust effect of the infection control program. Time series analysis can be used to inform evidence-based interventions and infection control policies.
Author Contributions
Writing—original draft, Conceptualization, A.P. (Amalia Papanikolopoulou); Writing—original draft, Visualization, H.C.M.; Data curation, Validation, A.S.; Investigation; Validation, D.K.; Methodology, A.P. (Androula Pavli); Project administration, F.B.; Methodology, M.K. (Maria Karalexi); Formal analysis, N.P.; Writing—Review and Editing, C.P., Y.T. and M.K. (Maria Kantzanou); Visualization, V.K.; Supervision, A.T.; 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 according to the guidelines of the Declaration of Helsinki and approved by the Institutional Review Board and the Ethics Committee of Athens Medical Center (approval No: ΚΜ140678–26/07/2017) and by the Ethics Committee of the National and Kapodistrian University of Athens (approval No: 1718016673–25/01/2018). Since this study or data collection was based entirely on data abstraction from existing medical or laboratory record, with no interaction with the human subject concerned and with no collection of identifiable private information, an informed consent is not required from the patient.
Informed Consent Statement
Not applicable.
Data Availability Statement
The data that support the findings of this study are available on request from the corresponding author (H.C.M.). The data are not publicly available as data disclosure requires permission and ethical approval from Medical Ethical Committee of Athens Medical Center, Athens, Greece.
Acknowledgments
We thank the Infection Control Committee of the hospital for assistance in data collection. The opinions presented in this article are those of the authors, and do not necessarily represent those of their institutions.
Conflicts of Interest
The authors declare no conflict of interest.
References
- Weber, D.J.; Sickbert-Bennett, E.E.; Gould, C.V.; Brown, V.M.; Huslage, K.; Rutala, W.A. Incidence of catheter-associated and non-catheter-associated urinary tract infections in a healthcare system. Infect. Control Hosp. Epidemiol. 2011, 32, 822–823. [Google Scholar] [CrossRef] [PubMed]
- European Centre for Disease Prevention and Control. Point Prevalence Survey of Healthcare-Associated Infections and Antimicrobial use in European Acute Care Hospitals—Protocol Version 5.3; ECDC: Stockholm, Sweden, 2013. [Google Scholar]
- Saint, S. Clinical and economic consequences of nosocomial catheter-related bacteriuria. Am. J. Infect. Control 2000, 289, 68–75. [Google Scholar] [CrossRef]
- Foxman, B. The Epidemiology of urinary tract infections: Incidence, morbidity, and economic costs. Dis. Mon. 2003, 49, 53–70. [Google Scholar] [CrossRef] [PubMed]
- Umscheid, C.A.; Mitchell, M.D.; Doshi, J.A.; Agarwal, R.; Williams, K.; Brennan, P.J. Estimating the proportion of healthcare-associated infections that are reasonably preventable and the related mortality and costs. Infect. Control Hosp. Epidemiol. 2011, 32, 101–114. [Google Scholar] [CrossRef]
- Shuman, E.K.; Chenoweth, C.E. Urinary Catheter-Associated Infections. Infect. Dis. Clin. N. Am. 2018, 32, 885–897. [Google Scholar] [CrossRef]
- Pinto, H.; Simões, M.; Borges, A. Prevalence and Impact of Biofilms on Bloodstream and Urinary Tract Infections: A Systematic Review and Meta-Analysis. Antibiotics 2021, 10, 825. [Google Scholar] [CrossRef]
- Lo, E.; Nicolle, L.E.; Coffin, S.E.; Gould, C.; Maragakis, L.L.; Meddings, J.; Pegue, D.A.; Pettis, A.M.; Saint, S.; Yokoe, D.S. Strategies to prevent catheter-associated urinary tract infections in acute care hospitals: 2014 update. Infect. Control Hosp. Epidemiol. 2014, 35, 464–479. [Google Scholar] [CrossRef]
- Weiner-Lastinger, L.M.; Abner, S.; Edwards, J.R.; Kallen, A.J.; Karlsson, M.; Magill, S.S.; Pollock, D.; See, I.; Soe, M.M.; Walters, M.S.; et al. Antimicrobial-resistant pathogens associated with adult healthcare-associated infections: Summary of data reported to the National Healthcare Safety Network, 2015–2017. Infect. Control Hosp. Epidemiol. 2020, 41, 1–18. [Google Scholar] [CrossRef]
- De Oliveira, D.M.P.; Forde, B.M.; Kidd, T.J.; Harris, P.N.A.; Schembri, M.A.; Beatson, S.A.; Paterson, D.L.; Walker, M.J. Antimicrobial Resistance in ESKAPE Pathogens. Clin. Microbiol. Rev. 2020, 33, e00181-19. [Google Scholar] [CrossRef]
- Schuts, E.C.; Hulscher, M.E.J.L.; Mouton, J.W.; Verduin, C.M.; Stuart, J.W.T.C.; Overdiek, H.W.P.M.; van der Linden, P.D.; Natsch, S.; Hertogh, C.M.P.M.; Wolfs, T.F.W.; et al. Current evidence on hospital antimicrobial stewardship objectives: A systematic review and meta-analysis. Lancet Infect. Dis. 2016, 16, 847–856. [Google Scholar] [CrossRef]
- Baur, D.; Gladstone, B.P.; Burkert, F.; Carrara, E.; Foschi, F.; Döbele, S.; Tacconelli, E. Effect of antibiotic stewardship on the incidence of infection and colonisation with antibiotic-resistant bacteria and Clostridium difficile infection: A systematic review and meta-analysis. Lancet Infect. Dis. 2017, 17, 990–1001. [Google Scholar] [CrossRef]
- Chandramohan, S.; Navalkele, B.; Mushtaq, A.; Krishna, A.; Kacir, J.; Chopra, T. Impact of a Multidisciplinary Infection Prevention Initiative on Central Line and Urinary Catheter Utilization in a Long-term Acute Care Hospital. Open Forum Infect. Dis. 2018, 5, ofy156. [Google Scholar] [CrossRef]
- Sadeghi, M.; Leis, J.A.; Laflamme, C.; Sparkes, D.; Ditrani, W.; Watamaniuk, A.; Taggar, R.; Jinnah, F.; Avaness, M.; Vearncombe, M.; et al. Standardisation of perioperative urinary catheter use to reduce postsurgical urinary tract infection: An interrupted time series study. BMJ Qual. Saf. 2019, 28, 32–38. [Google Scholar] [CrossRef]
- Zhong, X.; Xiao, L.H.; Wang, D.L.; Yang, S.W.; Mo, L.F.; He, L.F.; Wu, Q.F.; Chen, Y.W.; Luo, X.F. Impact of a quality control circle on the incidence of catheter-associated urinary tract infection: An interrupted time series analysis. Am. J. Infect. Control 2020, 48, 1184–1188. [Google Scholar] [CrossRef]
- Laan, B.J.; Maaskant, J.M.; Spijkerman, I.J.B.; Borgert, M.J.; Godfried, M.H.; Pasmooij, B.C.; Opmeer, B.C.; Vos, M.C.; Geerlings, S.E. De-implementation strategy to reduce inappropriate use of intravenous and urinary catheters (RICAT): A multicentre, prospective, interrupted time-series and before and after study. Lancet Infect. Dis. 2020, 20, 864–872. [Google Scholar] [CrossRef]
- Papanikolopoulou, A.; Maltezou, H.C.; Gargalianos-Kakolyris, P.; Michou, I.; Kalofissoudis, Y.; Moussas, N.; Pantazis, N.; Kotteas, E.; Syrigos, K.N.; Pantos, C.; et al. Central line-associated bloodstream infections (CLABSI), multidrug-resistant bacteremias and infection control interventions: A six-year time-series analysis in a tertiary-care hospital in Greece. J. Hosp. Infect. 2022, 123, 27–33. [Google Scholar] [CrossRef]
- Papanikolopoulou, A.; Maltezou, H.C.; Stoupis, A.; Pangalis, A.; Kouroumpetsis, C.; Chronopoulou, G.; Kalofissoudis, Y.; Kostares, E.; Boufidou, F.; Karalexi, M.; et al. Ventilator associated pneumonia (VAP), multidrug-resistant bacteremia and infection control interventions in an intensive care unit: Analysis of six-year time-series data. Antibiotics 2022, 11, 1128. [Google Scholar] [CrossRef]
- Hooton, T.M.; Bradley, S.F.; Cardenas, D.D.; Colgan, R.; Geerlings, S.E.; Rice, J.C.; Saint, S.; Schaeffer, A.J.; Tambayh, P.A.; Tenke, P.; et al. Infectious Diseases Society of America. Diagnosis, prevention, and treatment of catheter-associated urinary tract infection in adults: 2009 International Clinical Practice Guidelines from the Infectious Diseases Society of America. Clin. Infect. Dis. 2010, 50, 625–663. [Google Scholar] [CrossRef]
- National Healthcare Safety Network. Bloodstream Infection Event (Central Line-Associated Bloodstream Infection and Non-central Line Associated Bloodstream Infection). Available online: https://www.cdc.gov/nhsn/pdfs/pscmanual/4psc_clabscurrent.pdf (accessed on 26 June 2022).
- Papanikolopoulou, A.; Maltezou, H.C.; Kritikou, H.; Papadopoulos, T.; Kandalepas, G.; Pentzouris, A.; Kartsonakis, I.; Chronopoulou, G.; Gargalianos-Kakolyris, P.; Pantazis, N.; et al. Six-year time-series data on multidrug-resistant bacteremia, antibiotic consumption and infection control interventions in a hospital. Microb. Drug Resist. 2022, 28, 806–818. [Google Scholar] [CrossRef]
- Papanikolopoulou, A.; Maltezou, H.M.; Gargalianos-Kakolyris, P.; Pangalis, A.; Pantazis, N.; Pantos, C.; Tountas, Y.; Tsakris, A.; Kantzanou, M. Association between consumption of antibiotics, infection control interventions and Clostridioides difficile infections: Analysis of six-year time-series data in a tertiary-care hospital in Greece. Infect.Dis. Health 2022, 27, 119–128. [Google Scholar] [CrossRef]
- Chen, Y.Y.; Chi, M.M.; Chen, Y.C.; Chan, Y.J.; Chou, S.S.; Wang, F.D. Using a criteria-based reminder to reduce use of indwelling urinary catheters and decrease urinary tract infections. Am. J. Crit. Care 2013, 22, 105–114. [Google Scholar] [CrossRef] [PubMed]
- Safdar, N.; Codispoti, N.; Purvis, S.; Knobloch, M.J. Patient perspectives on indwelling urinary catheter use in the hospital. Am. J. Infect. Control 2016, 44, e23–e24. [Google Scholar] [CrossRef] [PubMed]
- Gould, C.V.; Umscheid, C.A.; Agarwal, R.K.; Kuntz, G.; Pegues, D.A. Healthcare Infection Control Practices Advisory Committee. Guideline for prevention of catheter-associated urinary tract infections 2009. Infect. Control Hosp. Epidemiol. 2010, 31, 319–326. [Google Scholar] [CrossRef] [PubMed]
- Tenke, P.; Kovacs, B.; Bjerklund Johansen, T.E.; Matsumoto, T.; Tambyah, P.A.; Naber, K.G. European and Asian guidelines on management and prevention of catheter-associated urinary tract infections. Int. J.Antimicrob. Agents 2008, 31, S68–S78. [Google Scholar] [CrossRef]
- Yokoe, D.S.; Anderson, D.J.; Berenholtz, S.M.; Calfee, D.P.; Dubberke, E.R.; Ellingson, K.D.; Gerding, D.N.; Haas, J.P.; Kaye, K.S.; Klompas, M.; et al. Society for Healthcare Epidemiology of America (SHEA). A compendium of strategies to prevent healthcare-associated infections in acute care hospitals: 2014 updates. Infect. Control Hosp. Epidemiol. 2014, 35, 967–977. [Google Scholar] [CrossRef]
- Knoll, B.M.; Wright, D.; Ellingson, L.; Kraemer, L.; Patire, R.; Kuskowski, M.A.; Johnson, J.R. Reduction of inappropriate urinary catheter use at a Veterans Affairs hospital through a multifaceted quality improvement project. Clin. Infect. Dis. 2011, 52, 1283–1290. [Google Scholar] [CrossRef]
- Rosenthal, V.D.; Todi, S.K.; Álvarez-Moreno, C.; Pawar, M.; Karlekar, A.; Zeggwagh, A.A.; Mitrev, Z.; Udwadia, F.E.; Navoa-Ng, J.A.; Chakravarthy, M.; et al. INICC Members. Impact of a multidimensional infection control strategy on catheter-associated urinary tract infection rates in the adult intensive care units of 15 developing countries: Findings of the International Nosocomial Infection Control Consortium (INICC). Infection 2012, 40, 517–526. [Google Scholar]
- Saint, S.; Greene, M.T.; Kowalski, C.P.; Watson, S.R.; Hofer, T.P.; Krein, S.L. Preventing catheter-associated urinary tract infection in the United States: A national comparative study. JAMA Intern. Med. 2013, 173, 874–879. [Google Scholar] [CrossRef]
- Parker, V.; Giles, M.; Graham, L.; Suthers, B.; Watts, W.; O’Brien, T.; Searles, A. Avoiding inappropriate urinary catheter use and catheter-associated urinary tract infection (CAUTI): A pre-post control intervention study. BMC Health Serv. Res. 2017, 17, 314. [Google Scholar] [CrossRef]
- Patel, P.K.; Gupta, A.; Vaughn, V.M.; Mann, J.D.; Ameling, J.M.; Meddings, J. Review of Strategies to Reduce Central Line-Associated Bloodstream Infection (CLABSI) and Catheter-Associated Urinary Tract Infection (CAUTI) in Adult ICUs. J. Hosp. Med. 2018, 13, 105–116. [Google Scholar] [CrossRef]
- Atkins, L.; Sallis, A.; Chadborn, T.; Shaw, K.; Schneider, A.; Hopkins, S.; Bunten, A.; Michie, S.; Lorencatto, F. Reducing catheter-associated urinary tract infections: A systematic review of barriers and facilitators and strategic behavioural analysis of interventions. Implement. Sci. 2020, 15, 44. [Google Scholar] [CrossRef]
- Kuy, S.; Gupta, R.; Roy, C.; Awad, S. Incidence of Catheter-Associated Urinary Tract Infections with Compliance With Preventive Guidelines. JAMA Surg. 2020, 155, 661–662. [Google Scholar] [CrossRef]
- Wanat, M.; Borek, A.J.; Atkins, L.; Sallis, A.; Ashiru-Oredope, D.; Beech, E.; Butler, C.C.; Chadborn, T.; Hopkins, S.; Jones, L.; et al. Optimising Interventions for Catheter-Associated Urinary Tract Infections (CAUTI) in Primary, Secondary and Care Home Settings. Antibiotics 2020, 9, 419. [Google Scholar] [CrossRef]
- Costa, B.; Mota, R.; Tamagnini, P.; Martins, M.C.L.; Costa, F. Natural Cyanobacterial Polymer-Based Coating as a Preventive Strategy to Avoid Catheter-Associated Urinary Tract Infections. Mar. Drugs 2020, 18, 279. [Google Scholar] [CrossRef]
- Perneger, T.V. What’s wrong with Bonferroni adjustments. BMJ 1998, 316, 1236–1238. [Google Scholar] [CrossRef]
Figure 1.
Observed values and estimated time trends for CAUTI rate in (a) total Hospital Clinics, (b) Adults Intensive Care Unit from January 2013 to December 2018.CAUTI: catheter-associated urinary tract infection; trends shown with dashed lines derived from Poisson regression models with robust standard errors, seasonality terms and linear or piecewise linear long term trend: log(N) = β0 +β1t− +β2t + +β3 × sin(2πt/12) + β4 × cos(2πt/12) + β5 × sin(4πt/12) + β6 × cos(4πt/12) + log(ventilator-days) with N being the number of cases and t being time since study start in months (t− and t+ piecewise linear time terms). Trends shown with grey area derived similarly but spline terms of time were used for long term trend instead of piecewise linear terms.
Figure 1.
Observed values and estimated time trends for CAUTI rate in (a) total Hospital Clinics, (b) Adults Intensive Care Unit from January 2013 to December 2018.CAUTI: catheter-associated urinary tract infection; trends shown with dashed lines derived from Poisson regression models with robust standard errors, seasonality terms and linear or piecewise linear long term trend: log(N) = β0 +β1t− +β2t + +β3 × sin(2πt/12) + β4 × cos(2πt/12) + β5 × sin(4πt/12) + β6 × cos(4πt/12) + log(ventilator-days) with N being the number of cases and t being time since study start in months (t− and t+ piecewise linear time terms). Trends shown with grey area derived similarly but spline terms of time were used for long term trend instead of piecewise linear terms.
Table 1.
Time trend of CAUTI rate in a hospital from January 2013 to December 2018.
| Time Trend | |||||
|---|---|---|---|---|---|
| CAUTI Rate Per Month | EVSP Jan 2013 (95% CI) | EVEP Dec 2018 (95% CI) | p-Value | % Relative Change/Year (95% CI) | p-Value |
| Total Hospital Clinics | 8.2 (7.4 to 9.0) | 4.4 (3.5 to 5.5) | <0.001 | 3.5 (−9.0 to 17.6) up to September 2014 | 0.604 |
| −15.5 (−21.1 to −9.5) after September 2014 | <0.001 | ||||
| Total Hospital Departments | 6.8 (5.1 to 9.0) | 5.4 (5.0 to 5.9) | <0.001 | 13.9 (−0.9 to 30.9) up to September 2015 | 0.067 |
| −16.9 (−21.5 to −12.0) after September 2015 | <0.001 | ||||
| Adults Clinic | 8.5 (7.7 to 9.4) | 4.4 (3.5 to 5.5) | <0.001 | 3.0 (−9.5 to 17.0) up to September 2014 | 0.657 |
| −18.2 (−24.3 to −12.5) after September 2014 | <0.001 | ||||
| Adults Clinic Departments | 7.1 (5.3 to 9.4) | 5.4 (5.0 to 5.9) | <0.001 | 13.2 (−1.9 to 30.6) up to September 2015 | 0.090 |
| −17.5 (−22.3 to −12.4) after September 2015 | <0.001 | ||||
| Adults ICU | 12.5 (9.1 to 17.0) | 1.3 (0.5 to 3.5) | <0.001 | −33.8(−46.8 to −17.7) | <0.001 |
CAUTI: catheter-associated urinary tract infection; ICU: intensive care unit; EVSP: estimated value start period; EVEP: estimated value end period; CI: confidence interval. All estimates derived from Poisson regression models with robust standard errors, seasonality terms and linear or piecewise linear long-term trend: log(N) = β0 +β1t− +β2t + +β3 × sin(2πt/12) + β4 × cos(2πt/12) + β5 × sin(4πt/12) + β6 × cos(4πt/12) + log(catheter-days) with N being the number of cases and t being time since study start in months (t− and t+ piecewise linear time terms; when piecewise linear long term trend was not required a single time term was used). % Relative changes/year derived as [exp(12 × β1,2)−1] × 100%.
Table 2.
Time trend of isolations/100 hospital admissions in a hospital from January 2013 to December 2018.
Table 2.
Time trend of isolations/100 hospital admissions in a hospital from January 2013 to December 2018.
| Time Trend | |||||
|---|---|---|---|---|---|
| % Isolations/Admissions | EVSP Jan 2013 (95% CI) | EVEP Dec 2018 (95% CI) | p-Value | % Relative Change/Year (95% CI) | p-Value |
| Total Hospital Clinics | 8.0 (7.3 to 8.8) | 5.6 (5.2 to 6.0) | <0.001 | −2.2 (−6.2 to 1.8) up to April 2016 | 0.276 |
| −11.0 (−14.7 to −7.1) after April 2016 | <0.001 | ||||
| Total Hospital Departments | 1.0 (0.6 to 1.6) | 2.5 (2.1 to 3.0) | <0.001 | 86.6 (46.5 to 137.7) up to March 2015 | <0.001 |
| −9.6 (−15.7 to −3.1) after March 2015 | 0.005 | ||||
| Adults Clinic | 4.1 (3.4 to 4.9) | 5.0 (4.5 to 5.6) | 0.059 | 24.0 (12.3 to 36.9) up to April 2015 | <0.001 |
| −6.9 (−11.3 to −2.4) after April 2015 | 0.003 | ||||
| Adults Clinic Departments | 1.7 (1.1 to 2.5) | 2.6 (2.2 to 3.0) | 0.053 | 59.8 (28.7 to 98.5) up to February 2015 | <0.001 |
| −13.1 (−18.5 to −7.4) after February 2015 | <0.001 | ||||
| Adults ICU | 20.4 (19.2 to 21.8) | 27.6 (24.1 to 31.4) | <0.001 | 14.4 (10.6 to 18.2) up to June 2017 | <0.001 |
| −12.3 (−24.8 to 2.1) after June 2017 | 0.091 | ||||
ICU: intensive care unit; EVSP: estimated value start period; EVEP: estimated value end period; CI: confidence interval1; All estimates derived from binomial logistic regression models with robust standard errors, seasonality terms and piecewise linear long term trend: logit(π) = β0 +β1t− +β2t + +β3 × sin(2πt/12) + β4 × cos(2πt/12) + β5 × sin(4πt/12) + β6 × cos(4πt/12) with π being theprobability of isolation and t being time since study start in months (t− and t+ piecewise linear time terms). % Relative changes/year derived as [exp(12 × β1,2)−1] × 100%.
Table 3.
Time trend of hand disinfectant solutions use in a hospital from January 2013 to December 2018.
Table 3.
Time trend of hand disinfectant solutions use in a hospital from January 2013 to December 2018.
| Time Trend | |||||
|---|---|---|---|---|---|
| Hand Disinfectant Sol Consumption L/1000 Patient-Days | EVSP Jan 2013 (95% CI) | EVEP Dec 2018 (95% CI) | p-Value | % Relative Change/Year (95% CI) | p-Value |
| Total Hospital Clinics | |||||
| Alcohol disinfectant sol | 26.0 | 48.7 | <0.001 | 16.2 (12.4 to 20.0) up to September 2014 | <0.001 |
| (21.7 to 30.2) | (46.8 to 50.6) | −1.0 (−2.2 to 0.2) after September 2014 | 0.100 | ||
| Scrub disinfectant sol | 29.7 | 27.9 | 0.321 | 3.4 (0.6 to 6.2) up to October 2014 | 0.020 |
| (25.9 to 33.4) | (26.4 to 29.3) | −1.9 (−2.6 to −1.1) after October 2014 | <0.001 | ||
| Simple soap sol | 15.8 | 9.7 | <0.001 | −3.9 (−5.0 to −2.7) up to October 2014 | <0.001 |
| (14.5 to 17.2) | (8.3 to 11.1) | 0.2 (−0.4 to 0.7) after October 2014 | 0.542 | ||
| All hand disinfectant sol | 71.5 | 86.4 | 0.001 | 15.7 (8.8 to 22.6) up to September 2014 | <0.001 |
| (62.6 to 80.3) | (83.6 to 89.2) | −2.7 (−4.3 to −1.0) after September 2014 | 0.002 | ||
| Total Hospital Departments | |||||
| Alcohol disinfectant sol | 16.0 | 64.6 | <0.001 | 26.4 (22.8 to 30.0) up to October 2014 | <0.001 |
| (12.2 to 19.8) | (60.8 to 68.4) | 0.6 (−1.1 to 2.3) after October 2014 | 0.486 | ||
| Scrub disinfectant sol | 27.7 | 27.1 | 0.733 | 1.1 (0.4 to 1.8) up to December 2017 | 0.002 |
| (25.5 to 29.8) | (24.5 to 29.7) | −6.0 (−9.8 to −2.3) after December 2017 | 0.002 | ||
| Simple soap sol | 19.0 | 12.9 | <0.001 | −3.7 (−5.0 to −2.5) up to October 2014 | <0.001 |
| (17.7 to 20.2) | (11.1 to 14.6) | 0.1 (−0.5 to 0.7) after October 2014 | 0.749 | ||
| All hand disinfectant sol | 62.3 | 109.5 | <0.001 | 25.0 (19.4 to 30.6) up to September 2014 | <0.001 |
| (57.2 to 67.5) | (103.3 to 115.6) | 1.3 (−1.2 to 3.8) after September 2014 | 0.309 | ||
| Adults Clinic | |||||
| Alcohol disinfectant sol | 34.6 | 34.3 | 0.870 | 15.4 (11.1 to 19.7) up to July 2014 | <0.001 |
| (32.1 to 37.2) | (31.2 to 37.5) | −5.3 (−6.9 to −3.7) after July 2014 | <0.001 | ||
| Scrub disinfectant sol | 37.3 | 33.2 | 0.057 | −0.7 (−1.4 to 0.0) | 0.057 |
| (34.3 to 40.3) | (31.3 to 35.0) | ||||
| Simple soap sol | 22.6 | 13.9 | <0.001 | −5.4 (−6.9 to −3.9) up to November 2014 | <0.001 |
| (21.2 to 24.0) | (11.5 to 16.2) | 0.3 (−0.6 to 1.2) after November 2014 | 0.528 | ||
| All hand disinfectant sol | 91.0 | 79.7 | <0.001 | 12.3 (5.7 to 18.8) up to July 2014 | <0.001 |
| (85.3 to 96.7) | (75.5 to 83.8) | −6.7 (−8.8 to −4.7) after July 2014 | <0.001 | ||
| Adults Clinic Departments | |||||
| Alcohol disinfectant sol | 25.7 | 46.3 | <0.001 | 20.2 (16.5 to 24.0) up to August 2014 | <0.001 |
| (22.3 to 29.2) | (43.9 to 48.6) | −2.7 (−3.9 to −1.4) after August 2014 | <0.001 | ||
| Scrub disinfectant sol | 40.1 | 32.9 | 0.004 | −2.5 (−3.7 to −1.2) up to September 2016 | <0.001 |
| (36.5 to 43.7) | (29.7 to 36.1) | 0.8 (−1.4 to 3.1) after September 2016 | 0.463 | ||
| Simple soap sol | 25.5 | 13.1 | <0.001 | −6.3 (−8.1 to −4.5) up to October 2014 | <0.001 |
| (23.9 to 27.1) | (10.5 to 15.6) | −0.3 (−1.3 to 0.6) after October 2014 | 0.468 | ||
| All hand disinfectant sol | 88.1 | 88.6 | 0.901 | 13.7 (6.8 to 20.5) up to August 2014 | <0.001 |
| (80.4 to 95.8) | (84.5 to 92.7) | −4.9 (−6.8 to −3.0) after August 2014 | <0.001 | ||
| Adults ICU | |||||
| Alcohol disinfectant sol | 98.0 | 83.2 | 0.286 | −2.5 (−7.2 to 2.1) | 0.286 |
| (80.9 to 115.1) | (68.5 to 97.9) | ||||
| Scrub disinfectant sol | 1.9 | 35.9 | 0.001 | 24.4 (18.6 to 30.1) up to August 2016 | <0.001 |
| (7.4 to 11.3) | (17.7 to 54.2) | −22.9 (−34.4 to −11.4) after August 2016 | <0.001 | ||
| All hand disinfectant sol | 117.0 | 179.3 | 0.001 | 10.5 (4.6 to 16.5) | 0.001 |
| (97.3 to 136.7) | (157.8 to 200.9) | ||||
ICU: intensive care unit; L: liter; sol: solution; EVSP: estimated value start period; EVEP: estimated value end period; CI: confidence interval; All estimates derived from linear regression models with robust standard errors, seasonality terms and piecewise linear long term trend: E[Y] = β0 + β1t− +β2t + +β3 × sin(2πt/12) + β4 × cos(2πt/12) + β5 × sin(4πt/12) + β6 × cos(4πt/12) with E[Y] being theexpected consumption value and t being time since study start in months (t− and t+ piecewise linear time terms). Absolute changes/year derived as β1,2 × 12.
Table 4.
Time trend of the incidence of bacteremia in a hospital from January 2013 to December 2018.
Table 4.
Time trend of the incidence of bacteremia in a hospital from January 2013 to December 2018.
| Time Trend | |||||
|---|---|---|---|---|---|
| Incidence of Bacteremia/1000 Patient-Days | EVSP Jan 2013 (95% CI) | EVEP Dec 2018 (95% CI) | p-Value | % Relative Change/(95% CI) | p-Value |
| Total Hospital Clinics | |||||
| Total Bacteremia | 3.4 (3.0 to 3.8) | 5.0 (4.5 to 5.5) | <0.001 | 2.4 (−2.2 to 7.1) up to December 2016 | 0.311 |
| 15.9 (6.7 to 25.9) after December 2016 | <0.001 | ||||
| Total Bacteremia MDR Gram(+) and (−) | 0.4 (0.3 to 0.5) | 0.2 (0.2 to 0.3) | 0.093 | −8.0 (−16.5 to 1.4) | 0.093 |
| Total Bacteremia CR Gram(−) | 0.3 (0.2 to 0.5) | 0.2 (0.1 to 0.3) | 0.099 | −7.8 (−16.2 to 1.5) | 0.099 |
| Total Bacteremia MDR Gram(+) | 0.0 (0.0 to 0.1) | 0.0 (0.0 to 0.1) | 0.467 | −8.5 (−28.0 to 16.3) | 0.467 |
| Total Bacteremia CR-Ac | 0.1 | 0.1 | 0.935 | −46.7 (−67.6 to −12.3) up to March 2016 | 0.013 |
| (0.0 to 0.2) | (0.0 to 0.2) | 102.3 (10.9 to 269.0) after March 2016 | 0.022 | ||
| Total Bacteremia CR-KlPn | 0.1 | 0.0 | 0.545 | 51.2 (−10.0 to 153.8) up to February 2015 | 0.118 |
| (0.0 to 0.2) | (0.0 to 0.1) | −27.1 (−43.0 to −6.8) after February 2015 | 0.012 | ||
| Total Bacteremia CR-PsA | 0.2 | 0.1 | 0.027 | −13.5 (−24.0 to −1.6) | 0.027 |
| (0.1 to 0.2) | (0.0 to 0.1) | ||||
| Total Hospital Departments | |||||
| Total Bacteremia | 2.7 | 4.4 | <0.001 | 2.4 (−1.7 to 6.6) up to December 2017 | 0.260 |
| (2.4 to 3.1) | (3.5 to 5.5) | 43.2 (10.3 to 86.0) after December 2017 | 0.007 | ||
| Total Bacteremia MDR Gram(+) and (−) | 0.2 (0.1 to 0.4) | 0.1 (0.1 to 0.2) | 0.207 | −10.3 (−24.2 to 6.2) | 0.207 |
| Total Bacteremia MDR Gram(−) | 0.2 (0.1 to 0.3) | 0.1 (0.1 to 0.2) | 0.165 | −11.1 (−24.8 to 5.0) | 0.165 |
| Total Bacteremia MDR Gram(+) | N/A | N/A | N/A | N/A | N/A |
| Total Bacteremia CR-Ac | N/A | N/A | N/A | N/A | N/A |
| Total Bacteremia CR-KlPn | 0.1 | 0.0 | 0.445 | −9.4 (−29.7 to 16.8) | 0.445 |
| (0.0 to 0.2) | (0.0 to 0.1) | ||||
| Total Bacteremia CR-PsA | 0.1 | 0.0 | 0.042 | −22.9 (−39.9 to 0.9) | 0.042 |
| (0.1 to 0.2) | (0.0 to 0.1) | ||||
| Adults Clinic | |||||
| Total Bacteremia | 4.8 | 6.3 | 0.001 | −0.6 (−5.4 to 4.5) up to November 2016 | 0.821 |
| (4.2 to 5.5) | (5.6 to 7.2) | 15.5 (5.1 to 26.9) after November 2016 | 0.003 | ||
| Total Bacteremia MDR Gram(+) and (−) | 0.7 (0.5 to 0.9) | 0.4 (0.3 to 0.6) | 0.157 | −7.0 (−15.8 to 2.8) | 0.157 |
| Total Bacteremia MDR Gram(−) | 0.6 (0.4 to 0.8) | 0.4 (0.3 to 0.5) | 0.119 | −7.4 (−15.8 to 2.0) | 0.119 |
| Total Bacteremia MDR Gram(+) | 0.1 (0.0 to 0.2) | 0.1 (0.0 to 0.1) | 0.742 | −4.3 (−26.3 to 24.3) | 0.742 |
| Total Bacteremia CR-Ac | 0.1 | 0.1 | 0.980 | −45.4 (−66.8 to −10.2) up to March 2016 | 0.017 |
| (0.1 to 0.3) | (0.1 to 0.4) | 99.6 (9.4 to 264.2) after 0.024 | 0.024 | ||
| Total Bacteremia CR-KlPn | 0.1 | 0.1 | 0.614 | 56.3 (−6.7 to 161.7) up to February 2015 | 0.090 |
| (0.0 to 0.3) | (0.0 to 0.2) | −27.3 (−43.0 to −7.3) after February 2015 | 0.010 | ||
| Total Bacteremia CR-PsA | 0.3 (0.2 to 0.4) | 0.1 (0.1 to 0.2) | 0.031 | −13.5 (−24.2 to −1.3) | 0.031 |
| Adults Clinic Departments | |||||
| Total Bacteremia | 2.9 (2.6 to 3.4) | 4.1 (3.6 to 4.7) | 0.004 | 5.9 (1.9 to 10.1) | 0.004 |
| Total Bacteremia MDR Gram(+) and (−) | 0.3 (0.2 to 0.6) | 0.2 (0.1 to 0.4) | 0.343 | −8.0 (−22.6 to 9.3) | 0.343 |
| Total Bacteremia MDR Gram(−) | 0.3 (0.2 to 0.5) | 0.2 (0.1 to 0.3) | 0.205 | −10.0 (−23.6 to 5.9) | 0.205 |
| Total Bacteremia MDR Gram(+) | N/A | N/A | N/A | N/A | N/A |
| Total Bacteremia CR-Ac | N/A | N/A | N/A | N/A | N/A |
| Total Bacteremia CR-KlPn | 0.1 (0.0 to 0.2) | 0.1 (0.0 to 0.1) | 0.495 | −8.3 (−28.4 to 17.5) | 0.495 |
| Total Bacteremia CR-PsA | 0.2 (0.1 to 0.4) | 0.0 (0.0 to 0.1) | 0.051 | −21.8 (−38.9 to 0.1) | 0.051 |
| Adults ICU | |||||
| Total Bacteremia | 18.2 | 32.8 | <0.001 | −3.8 (−15.2 to 9.1) up to February 2016 | 0.545 |
| (13.9 to 23.7) | (27.5 to 39.2) | 28.6 (14.9 to 43.9) after February 2016 | <0.001 | ||
| Total Bacteremia MDR Gram(+) and (−) | 1.9 | 2.3 | 0.678 | 34.5 (3.3 to 75.1) up to October 2015 | 0.028 |
| (1.2 to 3.0) | (1.2 to 4.2) | −18.9 (−38.1 to 6.3) after October 2015 | 0.130 | ||
| Total Bacteremia MDR Gram(−) | 2.5 (1.7 to 3.5) | 3.3 (2.1 to 5.1) | 0.392 | 4.9 (−6.0 to 17.1) | 0.392 |
| Total Bacteremia MDR Gram(+) | N/A | N/A | N/A | N/A | N/A |
| Total Bacteremia CR-Ac | 0.8 (0.4 to 2.0) | 1.8 (0.6 to 5.6) | 0.256 | −28.9 (−51.9 to 5.0) up to January 2017 | 0.086 |
| 209.3 (46.7 to 552.3) after January 2018 | 0.003 | ||||
| Total Bacteremia CR-KlPn | 0.3 (0.1 to 1.2) | 0.5 (0.1 to 1.7) | 0.635 | 137.8 (8.6 to 420.6) up to January 2015 | 0.030 |
| −28.4 (−51.8 to 6.3) after January 2015 | 0.098 | ||||
| Total Bacteremia CR-PsA | 0.9 (0.4 to 1.8) | 0.9 (0.4 to 2.1) | 0.909 | 1.3 (−18.1 to 25.2) | 0.909 |
ICU: intensive care unit; MDR: multidrug-resistant; CR: carbapenem-resistant; CR-Ac: carbapenem-resistant Acinetobacter baumannii; CR-KlPn: carbapenem-resistant Klebsiella pneumoniae; CR-PsA: carbapenem-resistant Pseudomonas aeruginosa; N/A: not applicable; EVSP: estimated value start period; EVEP: estimated value end period; CI: confidence interval; All estimates derived from Poisson regression models with robust standard errors, seasonality terms and linear or piecewise linear long-term trend: log(N) = β0 +β1t− +β2t + +β3 × sin(2πt/12) + β4 × cos(2πt/12) + β5 × sin(4πt/12) + β6 × cos(4πt/12) + log(patient-days) with N being the number of cases and t being time since study start in months (t− and t+ piecewise linear time terms; when piecewise linear long term trend was not required a single time term was used). % Relative changes/year derived as [exp(12 × β1,2)−1] × 100%.
Table 5.
Correlation of CAUTI and incidence of bacteremia in a hospital from January 2013 to December 2018.
Table 5.
Correlation of CAUTI and incidence of bacteremia in a hospital from January 2013 to December 2018.
| CAUTI: Correlation with BACTEREMIA | ||||||||
|---|---|---|---|---|---|---|---|---|
| Incidence of Bacteremia/1000 Patient-Days | Per (n) Unit | Month 0 | Month −1 | Month −2 | Month −3 | IRR | 95% CI | p-Value |
| Total Hospital Clinics | ||||||||
| Total Gram(+) and (−) | 1 | ◊ | 0.81 | 0.75–0.87 | <0.001 | |||
| Total CR Gram(−) | 0.1 | ◊ | 0.96 | 0.94–0.99 | 0.008 | |||
| Total CR-Ac | 0.1 | ◊ | 0.93 | 0.86–1.01 | 0.079 | |||
| Total CR-KlPn | 0.1 | ◊ | 0.87 | 0.82–0.92 | <0.001 | |||
| Total CR-PsA | 0.1 | ◊ | 0.94 | 0.89–1.00 | 0.067 | |||
| Total hospital departments | ||||||||
| Total Gram(+) and (−) | 1 | ◊ | 0.78 | 0.71–0.86 | <0.001 | |||
| Total CR Gram(−) | 0.1 | ◊ | 1.06 | 1.00–1.13 | 0.048 | |||
| Total CR Gram(−) | 0.1 | ◊ | 0.97 | 0.94–0.99 | 0.009 | |||
| Total CR-KlPn | 0.1 | ◊ | 0.90 | 0.85–0.95 | <0.001 | |||
| Total CR-PsA | 0.1 | ◊ | 0.96 | 0.93–0.99 | 0.013 | |||
| Adults Clinic | ||||||||
| Total Gram(+) and (−) | 1 | ◊ | ◊ | 0.86 | 0.80–0.91 | <0.001 | ||
| Total CR Gram(−) | 0.1 | ◊ | 0.98 | 0.96–1.00 | 0.042 | |||
| Total CR-Ac | 0.1 | ◊ | 0.95 | 0.91–1.00 | 0.029 | |||
| Total CR-KlPn | 0.1 | ◊ | ◊ | 0.85 | 0.80–0.90 | <0.001 | ||
| Total CR-PsA | 0.1 | ◊ | 0.96 | 0.92–1.00 | 0.032 | |||
| Total CR-PsA | 0.1 | ◊ | 1.10 | 1.00–1.20 | 0.043 | |||
| Adults Clinic Departments | ||||||||
| Total Gram(+) and (−) | 1 | ◊ | 0.86 | 0.82–0.90 | <0.001 | |||
| Total CR Gram(−) | 0.1 | ◊ | 1.04 | 1.00–1.08 | 0.060 | |||
| Total CR Gram(−) | 0.1 | ◊ | 0.98 | 0.96–1.00 | 0.027 | |||
| Total CR-KlPn | 0.1 | ◊ | 0.93 | 0.89–0.96 | <0.001 | |||
| Total CR-PsA | 0.1 | ◊ | 0.97 | 0.95–1.00 | 0.038 | |||
| Adults ICU | ||||||||
| Total Gram(+) and (−) | 10 | ◊ | 1.09 | 0.99–1.20 | 0.086 | |||
| Total CR Gram(−) | 1 | ◊ | ◊ | ◊ | ◊ | 1.07 | 0.80–1.45 | 0.637 |
| Total CR-Ac | 1 | ◊ | 0.77 | 0.57–1.04 | 0.089 | |||
| Total CR-Kl.Pn | 1 | ◊ | 0.79 | 0.65–0.96 | 0.018 | |||
| Total CR-PsA | 1 | ◊ | 0.75 | 0.55–1.03 | 0.075 | |||
| Total CR-PsA | 1 | ◊ | 1.08 | 1.04–1.12 | <0.001 | |||
| Total CR-PsA | 1 | ◊ | 1.24 | 1.11–1.38 | <0.001 | |||
CAUTI: catheter-associated urinary tract infection; IRR: Incidence rate ratio; CI: Confidence Interval; ICU: intensive care unit; CR: carbapenem-resistant; CR-Ac: carbapenem-resistant A.baumannii; CR-KlPn: carbapenem-resistant K. pneumoniae; CR-PsA: carbapenem-resistant P. aeruginosa; ns: not-significant; Symbol ◊ denotes whether the association refers to the current month (month 0) value incidence of bacteremia, lagged values (months −1, −2, −3) or averaged values over more than one month. Incidence Rate Ratio (IRR) refers to increases incidence of bacteremia, denoted in column labeled “per (n) unit”. All estimates derived from Poisson regression models with robust standard errors, seasonality effects and spline terms of time: log(N) = β0 + β1V + β2S1(t) +β3S2(t) +β4S3(t) + β5 × sin(2πt/12) + β6 × cos(2πt/12) + β7 × sin(4πt/12) + β8 × cos(4πt/12) +log(catheter-days)with N being the number of cases, t being time since study start in months, S(t) being spline terms of t and V referring to the current month covariate (month 0) value, lagged values (months −1, −2, −3) or averaged values over more than one month. Incidence Rate Ratios derived as exp(n × β1) with n given in column labeled “per (n)”.
Table 6.
Correlation of CAUTI and infection control interventions in a hospital from January 2013 to December 2018.
Table 6.
Correlation of CAUTI and infection control interventions in a hospital from January 2013 to December 2018.
| CAUTI: Correlation with Infection Control Interventions | ||||||||
|---|---|---|---|---|---|---|---|---|
| Infection Control Interventions | Per (n) Unit | Month 0 | Month −1 | Month −2 | Month −3 | OR | 95% CI | p-Value |
| Total Hospital Clinics | ||||||||
| % isolations/admissions | ns | |||||||
| L of alcohol disinfectant sol/1000 patient-days | ns | |||||||
| L of scrub disinfectant sol/1000 patient-days | 1 | ◊ | ◊ | 0.97 | 0.96–0.98 | <0.001 | ||
| L of all hand disinfectant sol/1000 patient-days | 10 | ◊ | 0.85 | 0.76–0.95 | 0.004 | |||
| Adults Clinic | ||||||||
| % isolations/admissions | ns | |||||||
| L of alcohol disinfectant sol/1000 patient-days | ns | |||||||
| L of scrub disinfectant sol/1000 patient-days | 10 | ◊ | ◊ | 0.81 | 0.78–0.84 | <0.001 | ||
| L of all hand disinfectant sol/1000 patient-days | 10 | ◊ | 1.05 | 0.99–1.11 | 0.075 | |||
| L of all hand disinfectant sol/1000 patient-days | 10 | ◊ | 0.89 | 0.82–0.97 | 0.006 | |||
| Adults Clinic Departments | ||||||||
| % isolations/admissions | ns | |||||||
| L of alcohol disinfectant sol/1000 patient-days | ns | |||||||
| L of scrub disinfectant sol/1000 patient-days | 10 | ◊ | ◊ | 0.89 | 0.86–0.93 | <0.001 | ||
| L of all hand disinfectant sol/1000 patient-days | 10 | ◊ | 0.91 | 0.85–0.97 | 0.005 | |||
| Adults ICU | ||||||||
| % isolations/admissions | ns | |||||||
| L of alcohol disinfectant sol/1000 patient-days | 10 | ◊ | 1.10 | 1.02–1.19 | 0.012 | |||
| L of scrub disinfectant sol/1000 patient-days | 10 | ◊ | 0.79 | 0.65–0.96 | 0.018 | |||
| L of all hand disinfectant sol/1000 patient-days | 100 | ◊ | 1.72 | 1.11–2.66 | 0.016 | |||
CAUTI: catheter-associated urinary tract infection; IRR: incidence rate ratio; CI: Confidence Interval; ICU: intensive care unit; L: liter; sol: solution; ns: not-significant; Symbol ◊ denotes whether the association refers to the current month (month 0) value, lagged values (months −1, −2, −3) or averaged values over more than one month. Incidence Rate Ratios (IRR) refers to increases denoted in column labeled “per (n) units”. All estimates derived from Poisson regression models with robust standard errors, seasonality effects and spline terms of time: log(N) = β0 + β1V + β2S1(t) +β3S2(t) +β4S3(t) + β5 × sin(2πt/12) + β6 × cos(2πt/12) + β7 × sin(4πt/12) + β8 × cos(4πt/12) +log(catheter-days) with N being the number of cases, t being time since study start in months, S(t) being spline terms of t and V referring to the current month covariate (month 0) value, lagged values (months −1, −2, −3) or averaged values over more than one month. Incidence Rate Ratios derived as exp(n × β1) with n given in column labeled “per (n)”.
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Papanikolopoulou, A.; Maltezou, H.C.; Stoupis, A.; Kalimeri, D.; Pavli, A.; Boufidou, F.; Karalexi, M.; Pantazis, N.; Pantos, C.; Tountas, Y.; et al. Catheter-Associated Urinary Tract Infections, Bacteremia, and Infection Control Interventions in a Hospital: A Six-Year Time-Series Study. J. Clin. Med. 2022, 11, 5418. https://doi.org/10.3390/jcm11185418
AMA Style
Papanikolopoulou A, Maltezou HC, Stoupis A, Kalimeri D, Pavli A, Boufidou F, Karalexi M, Pantazis N, Pantos C, Tountas Y, et al. Catheter-Associated Urinary Tract Infections, Bacteremia, and Infection Control Interventions in a Hospital: A Six-Year Time-Series Study. Journal of Clinical Medicine. 2022; 11(18):5418. https://doi.org/10.3390/jcm11185418
Chicago/Turabian StylePapanikolopoulou, Amalia, Helena C. Maltezou, Athina Stoupis, Dimitra Kalimeri, Androula Pavli, Fotini Boufidou, Maria Karalexi, Nikos Pantazis, Constantinos Pantos, Yannis Tountas, and et al. 2022. "Catheter-Associated Urinary Tract Infections, Bacteremia, and Infection Control Interventions in a Hospital: A Six-Year Time-Series Study" Journal of Clinical Medicine 11, no. 18: 5418. https://doi.org/10.3390/jcm11185418
APA StylePapanikolopoulou, A., Maltezou, H. C., Stoupis, A., Kalimeri, D., Pavli, A., Boufidou, F., Karalexi, M., Pantazis, N., Pantos, C., Tountas, Y., Koumaki, V., Kantzanou, M., & Tsakris, A. (2022). Catheter-Associated Urinary Tract Infections, Bacteremia, and Infection Control Interventions in a Hospital: A Six-Year Time-Series Study. Journal of Clinical Medicine, 11(18), 5418. https://doi.org/10.3390/jcm11185418
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