Skip to main content
Download PDF

Risk factors for detection of Pseudomonas aeruginosa in clinical samples upon hospital admission

Antimicrobial Resistance & Infection Control volume 14, Article number: 17 (2025) Cite this article

Abstract

Background/introduction

Antipseudomonal antibiotics are frequently used in patients admitted to hospitals. Many of these substances are classified as a reserve or watch status by the WHO. Inappropriate risk assessment of invasive detection of P. aeruginosa (PAE) can be a reason for overuse of antipseudomonal antibiotics. Therefore it is important to define relevant and specific risk factors for invasive PAE detection.

Objective

The objective of this study was to identify risk factors for invasive detection of PAE in patients upon hospital admission.

Methods

All patients 18 years of age and older with a detection of PAE and/or Enterobacterales in clinical samples taken within 48 h of admission to one of the hospitals of Charité Universitätsmedizin Berlin between 2015 and 2020 were included into this retrospective cohort study.

Results

Overall, we included a total of 27,710 patients. In 3,764 (13.6%) patients PAE was detected in clinical samples taken within 48 h after admission. The most frequently detected Enterobacterales was E. coli in 14.142 (51%) patients followed by Klebsiella spp. in 4.432 (16%) patients. Multivariable regression analysis identified that prior colonisation with a multi drug resistant PAE or detection of a PAE in clinical samples during a previous hospitalisation increased the risk for invasive detection of PAE (OR 39.41; 95% CI 28.54–54.39) and OR 7.87 (95% CI 6.60–9.38) respectively. Admission to a specialised ward for patients with cystic fibrosis was associated with an increased risk (OR 26.99; 95% CI 20.48–35.54). Presence of chronic pulmonary disease (OR 2.05; 95% CI 1.85–2.26), hemiplegia (OR 2.16; 95% CI 1.90–2.45) and male gender (OR 1.60; 95% CI 1.46–1.75) were associated with a modest increase in risk for presence of PAE.

Conclusion

Patients with a prior detection of P. aeruginosa or admission to a cystic fibrosis ward had the highest risk for invasive detection of P. aeruginosa. Adherence to specific risk scores based on local risk factors could help to optimize prescription of anti-pseudomonal antibiotics that categorized as reserve and watch.

Introduction

The increase of antimicrobial resistance is an essential threat to human healthcare [1]. Cassini et al. estimate that more than 33,110 deaths in Europe are caused by multidrug resistant pathogens [2]. In Germany an increase of healthcare associated infection with multidrug resistant gram negative pathogens is observable [3]. Antimicrobial Stewardship is one of the most important strategies against antimicrobial resistance [4, 5]. Improved and rational use of antimicrobial substances could lower resistance rates [6]. In Europe most antibiotics in hospitals are prescribed for community acquired infections with a third of prescriptions potentially covering Pseudomonas aeruginosa (PAE) [7]. In German hospitals antibiotics with anti-pseudomonal coverage are also frequently used in patients with community and healthcare associated infections [8, 9]. Substances such as Piperacillin-Tazobactam, Meropenem and Ciprofloxacin account for a quarter of all prescriptions. In its aWaRe classification the WHO has categorised most of antipseudomonal antibiotics to the watch or reserve group [10]. An update of EUCAST criteria in 2020 regarding the meaning of the category „ Susceptible, increased Exposure“ might have further increased the use of these substances [11].

De-escalation of antimicrobial therapies is an essential and recommended tool of antimicrobial stewardship [12]. It is safe and effective even in cases with an increased risk for PAE lower respiratory tract infection [13] and suspected sepsis [14]. However, de-escalation is only performed in a fraction of antibiotic prescriptions [9, 15].

Despite the urgent need to improve prescription quality for antipseudomonal antibiotics, only little is known about the reasons for prescriptions of these broad-spectrum antibiotics.

In guidelines risk factors for PAE as a potential cause of lower respiratory tract infections are frequently mentioned, but infrequently reported and or weighted [16, 17]. For other types of infections, only few studies exist for specific patient populations [18, 19].

Therefore, we aim to identify risk factors for detection of PAE in clinical samples acquired within 48 h after hospital admission that are easy asses. In order to potentially improve selection of antipseudomonal antibiotics in this setting.

Methods

Participants

We identified all patients 18 years and older that were admitted to one of the three hospitals of Charité Universitätsmedizin Berlinwhich has all together account for about 3000 beds. Prior to the start of the study, we obtained a positive vote from the Charité Ethics Board (internal processing key EA1/264/21). This investigation was then performed as a retrospective cohort study. Cases were identifiedfrom the Institutions own database. We included all cases in patients 18 years of age or older with a finding of PAE and/or Enterobacterales in clinical samples obtained within 48 h of admission. Samples were stratified according to their collection site. In case of multiple samples from a single patient, the most invasive location was chosen.

Cases were defined as patients with the first invasive detection of PAE in a sample taken within 48 h of admission during the study period. Patients that had both invasive PAE and Enterobacterales were accounted to the PAE group. Colonization with PAE was defined as any detection of PAE in the patient during the 12 months prior to admission. Mortality was assessed based on discharge alive or in-hospital death.

For all of the patients enrolled, the following clinical and demographic characteristics were collected: age, sex, in-hospital death, length of hospital stay (LOS); type of initial ward admitted to, specialty of ward admitted to, stay on an intensive care unit (days)previous detection of any PAE prior to the admission and known colonisation or previous infection with multidrug resistant gram negative rod bacteria. Length of stay in total were defined as length of stay until death or discharge. The Charlson comorbidity index (CCI) was obtained based on the patients’ diagnosed comorbidities while using the method of Charlson et al. [20] and the adaptation for the ICD-10 by Thygesen et al. [21].

Microbiological methods

Microbiological sampling was performed at the treating physician’s discretion. The samples were at least cultured for forty-eight hours. MALDI TOF MS® and Vitek 2® automated system (Biomerieux, Marcy l’etoile, France) were used for identification and susceptibility testing of bacterial strains. Interpretation of susceptibility happened according to the updated EUCAST definitions at each point in time. Gram negative rods with non-intrinsic resistance against 3rd generation cephalosporins, fluorchinolone and/or carbapenems were defined as MDRGN according to the national recommendations.

Statistical methods

In the descriptive analysis, the median and the interquartile range (IQR) (25% percentile, 75% percentile) were calculated for continuous parameters and the number and percentage were calculated for binary parameters. Descriptive analysis was performed for the total cohort of patients with positive clinical sample caused by gram-negative pathogens within 48 h after hospital admission and stratified by the presence of PAE or not. Differences were tested using Wilcoxon rank-sum test for continuous variables or Chi-square test for binary variables respectively.

The following patient-based parameters were considered in the analysis: age, gender (male/female) and comorbidities. In addition, the following non-patient-based parameters were used in the analysis:

  • the type of unit, where the clinical sample was taken (ICU, IMC, regular ward; specialization).

  • the type of clinical sample (subgroup).

  • the year and the season, when the clinical sample was taken and.

  • detection of Pseudomonas aeruginosa in clinical samples prior to the admission.

  • colonization with multidrug resistant gram negative bacteria (MDRGN) before or simultaneously to the time point the clinical sample was taken.

Multivariable logistic regression analysis was performed to identify independent risk factors and confounders for the outcome “presence of PAE” in the positive clinical sample within 48 h after admission in the hospital. The multivariable model building strategy was performed in a stepwise backward approach. From a full model including all investigated parameters, non-significant parameters were removed with the significance level of p = 0.05 and above.

P-values less than 0.05 were considered significant. All analyses were exploratory in nature were performed using SPSS version 29 (IBM SPSS statistics, Somer, NY, USA) and SAS version 9.4 (SAS Institute, Cary, NC, USA).

Results

Of the 802,859 cases admitted to the hospital between 2015 and 2020 a total of 27,710 (3.45%) admitted cases had clinical samples with growth of GN-pathogens within 48 h from admission (Table 1). A total of 3764 (13.6%) patients had growth of PAE in a clinical sample. However, E. coli was the GN 14,142 (51%) most frequently detected followed by Klebsiella spp. 4432 (16%). Even though the frequency of GN-pathogens in clinical samples increased over time, their sequence did not (Fig. 1).

Table 1 Patients (N = 27,710) with positive clinical sample with Gram negative-pathogen during the first 48 h from admission
Full size table
Fig. 1
figure 1

Yearly development of admitted cases with positive sample with GN-pathogens within the first 48 h from the admission to the hospital

Full size image

Patients with growth of GN-pathogens in clinical samples had a median age of 67 (IQR:53–77). They had a median CCS of 5 (IQR:2–8). The Univariable logistic regression identified several potential risk factors for the “presence of PAE” in clinical samples (Table 2). Only for those deemed relevant and statitically significant multivariable analysis was performed.

Table 2 Results of univariable analysis: admissions (N = 27,710) with positive clinical sample with Gram negative-pathogen during the first 48 h from admission stratified by “presence of PAE” or not
Full size table

Multivariable logistic regression analysis identified a variety of independent risk factors for the outcome “presence of PAE” in clinical samples taken within the first 48 h from admission (Table 3).

Table 3 Results of multivariable logistic regression analysis for the outcome “presence of P. aeruginosa” in clinical sample with Gram negative-pathogen during the first 48 h from admission
Full size table

Male gender of patients increased the risk for presence of PAE OR 1.60 (95% CI 1.46–1.75). Prior colonisation with a multi drug resistant PAE or detection of a PAE in clinical samples during a previous hospitalisation increased the risk OR 39.41 (95% CI 28.54–54.39) and OR 7.869 (95% CI 6.60–9.38) respectively.

In addition, admission to a cystic fibrosis ward was associated with an increased risk of OR 26.99 (95% CI 20.48–35.54). Admission to other types of wards such as oncology, internal medicine, neurology and others were only associated with a modest but statistically significant increased odds.

Presence of chronic pulmonary disease OR 2.05 (95% CI 1.85–2.26), hemiplegia OR 2.16 (95% CI1.90-2.45), leukaemia OR 1.52 (95% CI 1.12–2.08), as well as the diagnosis of pneumonia OR 1.35 (95% CI 1.20–1.51) and infection OR 1.17 (95% CI 1.05–1.31) were associated with a modest increase in risk for presence of PAE.

Growth of GN-pathogens in samples derived from the lower respiratory tract and other sites were associated with a modest increased risk for detection of PAE with OR 1.53 (95% CI 1.24–1.90) and OR 1.34 (95% CI 1.11–1.62) respectively.

Interestingly enough the detection GN-pathogens in blood cultures and urine were associated with a statistically significant decreased risk for detection of PAE OR 0.42 (95% CI 0.33–0.53) and OR 0.53 (95% CI 0.43–0.67).

In addition, we identified that patients that were colonized with MDRGN Enterobacterales prior to admission, had a decreased risk for invasive detection of PAE.

Discussion

In this retrospective single centre cohort study, we were able to identify novel as well as well-established risk factors for the presence of PAE in clinical samples acquired immediately after admission. Our findings could potentially support physicians in evaluation if antipseudomonal coverage in calculated antimicrobial therapy is necessary.

Previous infection with PAE as well as colonisation are well known and internationally established risk factors for subsequent or re- infections with PAE [22]. This is especially true for bacterial infections of the lower respiratory tract such as pneumonia [20].

Patients with structural pulmonary disease have an increased risk for bacterial colonisation and infections with PAE [22]. This is based as much on the medical condition itself, as on the recurrent medical treatment for infection and exacerbation often times with antibiotics [23]. Similar observations are true for patients with hemiplegia. Those patients often spend prolonged durations of time in hospitals and rehabilitation facilities and potentially encounter repeated exposition to antibiotics as well as PAE.

In patients with cystic fibrosis the course of disease is in direct correlation to the presence and infection with PAE in the lung [24]. Cystic fibrosis is a known risk factor for PAE when compared to patients without. Due to this fact, studies investigating risk factors for PAE frequently exclude patients with cystic fibrosis in their analysis. However, we tried to achieve a full overview of risk factor for our setting. Only 994 adult patients (3.6%) of the cohort were admitted to the cystic fibrosis ward. Of whom 92 did not have PAE detected in their clinical samples.

In general, PAE plays an important role in LRTI. The detection in microbiological samples derived from other sites is less common and potentially associated with underlying immunosuppressive medical conditions [18]. Our data underline this fact with an increased independent risk for PAE if the sample is derived from the LRT and an independent risk reduction if samples such as blood cultures or even urine grew Gram-negative rods [25]. So, in alternative sites of infection outside the respiratory tract, PAE is less likely to be the cause of infection [26].

Colonization with MDRGN Enterobacterales was associated with significantly decreased risk for detection of PAE in clinical samples with Gram-negative rods upon admission. Underlying reasons could be based on differences ofcharacteristics in patients underlying conditions. Patients colonized with MDR E. coli are frequently less impaired and have a lower Charlson Comorbity Score compared to other Enterobacterales. Patients colonized with non-fermentative bacteria are known to even have more severe impairment and higher CharlsonComorbidity scores. In addition, prior antibiotic exposure, and differences in the susceptibility of pathogens could play an important role as well [27]. CRE are relatively rare in Germany. However, nonspecific carbapenem resistance in PAE are the most frequently identified Carbapenem-resistant organisms [28].

There are certain limitations that apply to this study. (i) The retrospective design of this study and the non-clinical definition for presence of PAE in clinical samples potentially affects our findings to a certain degree. Often patients that are colonized with MDRGN are believed to be similar to patients with predetected colonisation or infection with PAE However, in clinical medicine often the presence of a pathogen in a clinical sample is considered for the antibiotic treatment. (ii) Our findings might not be applicable for some other centers, since this cohort is derived from a single university hospital that consist of three independent hospitals and provides specialized care as including patient with cystic fibrosis as well as organ transplant. (iii) We were not able to differentiate between community acquired and healthcare associated infections with readmission. But often this not possible for the physicians evaluating the patients upon admission. iiii) Some of the variables used in this study rather represent surrogates than the directly measured information. iiiii) The definition used for MDRGN is specific to the German healthcare system and unfortunately cannot easily be extrapolated to international definitions.

Conclusion

PAE is frequently cultured from clinical samples taken from patients within 48 h after admission. The overall most important risk factors are prior detection or cystic fibrosis. Knowledge of these risk factors upon admission can significantly help shaping the use of anti-pseudomonal antibiotics. Future studies should investigate the potential of such risk factor analysis in clinical routine for antibiotic prescriptions.

Data availability

No datasets were generated or analysed during the current study.

References

  1. Antimicrobial Resistance Collaborators. Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. Lancet Lond Engl 12 Februar. 2022;399(10325):629–55.

    Article  Google Scholar 

  2. Cassini A, Högberg LD, Plachouras D, Quattrocchi A, Hoxha A, Simonsen GS. u. a. attributable deaths and disability-adjusted life-years caused by infections with antibiotic-resistant bacteria in the EU and the European Economic Area in 2015: a population-level modelling analysis. Lancet Infect Dis Januar. 2019;19(1):56–66.

    Article  Google Scholar 

  3. Remschmidt C, Schneider S, Meyer E, Schroeren-Boersch B, Gastmeier P, Schwab F. Surveillance of antibiotic use and resistance in Intensive Care Units (SARI). Dtsch Arzteblatt Int 15 Dezember. 2017;114(50):858–65.

    Google Scholar 

  4. Baur D, Gladstone BP, Burkert F, Carrara E, Foschi F, Döbele S. u. a. 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 September. 2017;17(9):990–1001.

    Article  Google Scholar 

  5. Kallen MC, Binda F, Ten Oever J, Tebano G, Pulcini C, Murri R. u. a. comparison of antimicrobial stewardship programmes in acute-care hospitals in four European countries: a cross-sectional survey. Int J Antimicrob Agents September. 2019;54(3):338–45.

    Article  CAS  Google Scholar 

  6. Mo Y, Oonsivilai M, Lim C, Niehus R, Cooper BS. Implications of reducing antibiotic treatment duration for antimicrobial resistance in hospital settings: a modelling study and meta-analysis. PLoS Med Juni. 2023;20(6):e1004013.

    Article  CAS  Google Scholar 

  7. Plachouras D, Kärki T, Hansen S, Hopkins S, Lyytikäinen O, Moro ML. November, Antimicrobial use in European acute care hospitals: results from the second point prevalence survey (PPS) of healthcare-associated infections and antimicrobial use, 2016 to 2017. Euro Surveill Bull Eur Sur Mal Transm Eur Commun Dis Bull. 2018;23(46):1800393.

  8. Aghdassi SJS, Gastmeier P, Piening BC, Behnke M, Peña Diaz LA, Gropmann A. u. a. antimicrobial usage in German acute care hospitals: results of the third national point prevalence survey and comparison with previous national point prevalence surveys. J Antimicrob Chemother 1 April. 2018;73(4):1077–83.

    Article  CAS  Google Scholar 

  9. Aghdassi SJS, Hansen S, Peña Diaz LA, Gropmann A, Saydan S, Geffers C. Healthcare-Associated infections and the Use of antibiotics in German hospitals—results of the Point Prevalence Survey of 2022 and comparison with earlier findings. Dtsch Arzteblatt Int. 3. Mai 2024;(Forthcoming):arztebl.m2024.0033.

  10. Adekoya I, Maraj D, Steiner L, Yaphe H, Moja L, Magrini N. u. a. comparison of antibiotics included in national essential medicines lists of 138 countries using the WHO Access, Watch, Reserve (AWaRe) classification: a cross-sectional study. Lancet Infect Dis Oktober. 2021;21(10):1429–40.

    Article  CAS  Google Scholar 

  11. Munting A, Regina J, Damas J, Lhopitallier L, Kritikos A, Guery B. u. a. impact of 2020 EUCAST criteria on meropenem prescription for the treatment of Pseudomonas aeruginosa infections: an observational study in a university hospital. Clin Microbiol Infect off Publ Eur Soc Clin Microbiol Infect Dis April. 2022;28(4):558–63.

    CAS  Google Scholar 

  12. de With K, Allerberger F, Amann S, Apfalter P, Brodt HR, Eckmanns T. u. a. strategies to enhance rational use of antibiotics in hospital: a guideline by the German Society for Infectious diseases. Infect Juni. 2016;44(3):395–439.

    Google Scholar 

  13. Deshpande A, Richter SS, Haessler S, Lindenauer PK, Yu PC, Zilberberg MD. u. a. de-escalation of empiric antibiotics following negative cultures in hospitalized patients with pneumonia: Rates and outcomes. Clin Infect Dis off Publ Infect Dis Soc Am 26 April. 2021;72(8):1314–22.

    Article  Google Scholar 

  14. Moehring RW, Yarrington ME, Warren BG, Lokhnygina Y, Atkinson E, Bankston A. u. a. evaluation of an opt-out protocol for antibiotic de-escalation in patients with suspected Sepsis: a Multicenter, Randomized, Controlled Trial. Clin Infect Dis off Publ Infect Dis Soc Am 8 Februar. 2023;76(3):433–42.

    Article  Google Scholar 

  15. Waagsbø B, Tranung M, Damås JK, Heggelund L. Antimicrobial therapy of community-acquired pneumonia during stewardship efforts and a coronavirus pandemic: an observational study. BMC Pulm Med 14 Oktober. 2022;22(1):379.

    Article  Google Scholar 

  16. Metlay JP, Waterer GW, Long AC, Anzueto A, Brozek J, Crothers K. u. a. diagnosis and treatment of adults with community-acquired Pneumonia. An Official Clinical Practice Guideline of the American Thoracic Society and Infectious Diseases Society of America. Am J Respir Crit Care Med 1 Oktober. 2019;200(7):e45–67.

    Article  Google Scholar 

  17. Ewig S, Kolditz M, Pletz M, Altiner A, Albrich W et al. Drömann D, [Management of Adult Community-Acquired Pneumonia and Prevention - Update 2021 - Guideline of the German Respiratory Society (DGP), the Paul-Ehrlich-Society for Chemotherapy (PEG), the German Society for Infectious Diseases (DGI), the German Society of Medical Intensive Care and Emergency Medicine (DGIIN), the German Viological Society (DGV), the Competence Network CAPNETZ, the German College of General Practitioneers and Family Physicians (DEGAM), the German Society for Geriatric Medicine (DGG), the German Palliative Society (DGP), the Austrian Society of Pneumology Society (ÖGP), the Austrian Society for Infectious and Tropical Diseases (ÖGIT), the Swiss Respiratory Society (SGP) and the Swiss Society for Infectious Diseases Society (SSI)]. Pneumol Stuttg Ger. September 2021;75(9):665–729.

  18. Royo-Cebrecos C, Laporte-Amargós J, Peña M, Ruiz-Camps I, Garcia-Vidal C, Abdala E. u. a. Pseudomonas aeruginosa Bloodstream infections presenting with septic shock in Neutropenic Cancer patients: impact of empirical antibiotic therapy. Microorganisms 30 März. 2024;12(4):705.

    Article  CAS  Google Scholar 

  19. Gudiol C, Albasanz-Puig A, Laporte-Amargós J, Pallarès N, Mussetti A, Ruiz-Camps. I, u. a. clinical predictive model of Multidrug Resistance in Neutropenic Cancer patients with bloodstream infection due to Pseudomonas aeruginosa. Antimicrob Agents Chemother 24 März. 2020;64(4):e02494–19.

    CAS  Google Scholar 

  20. Hyun H, Song JY, Yoon JG, Seong H, Noh JY, Cheong HJ. u. a. risk factor-based analysis of community-acquired pneumonia, healthcare-associated pneumonia and hospital-acquired pneumonia: microbiological distribution, antibiotic resistance, and clinical outcomes. PLoS ONE. 2022;17(6):e0270261.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Rojas A, Palacios-Baena ZR, López-Cortés LE, Rodríguez-Baño J. Rates, predictors and mortality of community-onset bloodstream infections due to Pseudomonas aeruginosa: systematic review and meta-analysis. Clin Microbiol Infect off Publ Eur Soc Clin Microbiol Infect Dis August. 2019;25(8):964–70.

    CAS  Google Scholar 

  22. Restrepo MI, Babu BL, Reyes LF, Chalmers JD, Soni NJ, Sibila O. u. a. Burden and risk factors for Pseudomonas aeruginosa community-acquired pneumonia: a multinational point prevalence study of hospitalised patients. Eur Respir J August. 2018;52(2):1701190.

    Article  Google Scholar 

  23. Fujitani S, Sun HY, Yu VL, Weingarten JA. Pneumonia due to Pseudomonas aeruginosa: part I: epidemiology, clinical diagnosis, and source. Chest April. 2011;139(4):909–19.

    Article  Google Scholar 

  24. Stapleton PJ, Izydorcyzk C, Clark S, Blanchard A, Wang PW, Yau Y. u. a. Pseudomonas aeruginosa strain-sharing in early infection among children with cystic fibrosis. Clin Infect Dis off Publ Infect Dis Soc Am 2 November. 2021;73(9):e2521–8.

    Article  CAS  Google Scholar 

  25. Herrera S, Bodro M, Soriano A. Predictors of multidrug resistant Pseudomonas aeruginosa involvement in bloodstream infections. Curr Opin Infect Dis 1 Dezember. 2021;34(6):686–92.

    Article  CAS  Google Scholar 

  26. Fernández-Barat L, Ferrer M, De Rosa F, Gabarrús A, Esperatti M, Terraneo S. u. a. intensive care unit-acquired pneumonia due to Pseudomonas aeruginosa with and without multidrug resistance. J Infect Februar. 2017;74(2):142–52.

    Article  Google Scholar 

  27. Cobos-Trigueros N, Solé M, Castro P, Torres JL, Hernández C, Rinaudo M. u. a. Acquisition of Pseudomonas aeruginosa and its resistance phenotypes in critically ill medical patients: role of colonization pressure and antibiotic exposure. Crit Care Lond Engl 4 Mai. 2015;19(1):218.

    Article  Google Scholar 

  28. Kresken M, Wohlfarth E, Wichelhaus TA, Gatermann SG, Pfennigwerth N, Eisfeld J. u. a. susceptibility of Multidrug-Resistant Escherichia coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa from Germany to Ceftolozane-Tazobactam, Ceftazidime-Avibactam, and Imipenem-Relebactam. Microb Drug Resist Larchmt N April. 2023;29(4):138–44.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We acknowledge support from the German Research Foundation (DFG) and the Open Access Publication Funds of Charité–Universitätsmedizin Berlin.

Funding

Open Access funding enabled and organized by Projekt DEAL.

This research received no external funding.

Author information

Authors and Affiliations

  1. Institute of Hygiene and Environmental Medicine, Charité Universitätsmedizin Berlin, Hindenburgdamm 27, 12203, Berlin, Germany

    Romeo Reyle, Frank Schwab, Selin Saydan, Michael Behnke, Petra Gastmeier, Christine Geffers & Tobias Siegfried Kramer

  2. National Reference Center for the Surveillance of Nosocomial Infections, 12203, Berlin, Germany

    Frank Schwab, Selin Saydan, Michael Behnke, Petra Gastmeier, Christine Geffers & Tobias Siegfried Kramer

  3. LADR der Laborverbund Dr. Kramer & Kollegen, 21502, Geesthacht, Germany

    Tobias Siegfried Kramer

  4. Department of Gastroenterology, Infectious Diseases and Rheumatology, Charité Universitätsmedizin Berlin, Hindenburgdamm 30, 12203, Berlin, Germany

    Rasmus Leistner

Authors
  1. Romeo Reyle
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Frank Schwab
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Selin Saydan
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Michael Behnke
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Rasmus Leistner
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Petra Gastmeier
    View author publications

    You can also search for this author inPubMed Google Scholar

  7. Christine Geffers
    View author publications

    You can also search for this author inPubMed Google Scholar

  8. Tobias Siegfried Kramer
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

Author Contributions: Conceptualization, T.S.K. and R.R.; methodology, T.S.K., R.R. and R.L.; software, M.B., F.S., P.G. and R.L.; validation, T.S.K., F.S. and R.R.; formal analysis, T.S.K., F.S., S.S.,B.S. and R.R.; investigation, T.S.K., R.R. and R.L.; resources, P.G. and C.G.; data curation, R.R., F.S., T.S.K.; writing—original draft preparation, T.S.K.,R.R. R.L. and P.G.; writing—review and editing, T.S.K., R.R., F.S., D.G., M.B.,P.G. and R.L.; visualization, S.S. and F.S.; supervision, P.G., T.S.K., C.G.; project administration, T.S.K. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Tobias Siegfried Kramer.

Ethics declarations

Competing interests

The authors declare no competing interests.

Conflict of interest

The authors declare no conflict of interest. Parts of this study were presented at ECCMID 2023 and DGHM annual meeting 2023 as part of a poster session.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Reyle, R., Schwab, F., Saydan, S. et al. Risk factors for detection of Pseudomonas aeruginosa in clinical samples upon hospital admission. Antimicrob Resist Infect Control 14, 17 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13756-025-01527-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13756-025-01527-4

Keywords

Antimicrobial Resistance & Infection Control

ISSN: 2047-2994

Contact us