Prevalence of Multidrug-Resistant Pseudomonas aeruginosa and Risk Factors for their Infections at Intensive Care Units of a Tertiary Hospital in Southern China

  

    
Variable
    
Inappropriate therapy (n=35)
    
Appropriate therapy (n=35)
    
P
  
  

    
Age (Year) (mean ± standard deviation)
    
57.29±19.62
    
54.34±20.50
    
0.542
  
  

    
Sex (Male/Female)
    
27/8
    
28/7
    
0.771
  
  

    
Charlson Comorbidity Index (median, IQR)
    
2.46±2.01
    
2.63±2.29
    
0.319
  
  

    
Underlying disease
    
21(60.00%)
    
20(57.14%)
    
0.808
  
  

    
Cardiac disease
    
19(54.29%)
    
16(45.71%)
    
0.473
  
  

    
Liver disease
    
1
    
1
    
-
  
  

    
Renal disease
    
1
    
1
    
-
  
  

    
Respiratory disease
    
2(5.71%)
    
6(17.14%)
    
0.133
  
  

    
Diabetes mellitus
    
7(20.0%)
    
3(8.57%)
    
0.172
  
  

    
Comorbid    conditions
    

    

    

  
  

    
Receipt of recent operation
    
8(22.86%)
    
9(25.71%)
    
0.78
  
  

    
Prior hospitalization within 30 days
    
21(60.00%)
    
4(11.43%)
    
0
  
  

    
Antibiotic use prior to separation
    
26(74.29%)
    
16(45.71%)
    
0.015
  
  

    
Polymicrobial infection
    
22(62.86%)
    
16(45.72%)
    
0.15
  
  

    
Indwelling urinary catheters
    
-
    
-
    
-
  
  

    
Central venous catheterization
    
-
    
-
    
-
  
  

    
Invasive procedure
    
27 (77.14%)
    
30 (85.71%)
    
0.356
  
  

    
Onset    of infection
    

    

    

  
  

    
Community-acquired
    
8(22.86%)
    
17(48.57%)
    
0.025
  
  

    
HCAI
    
27/35(77.14%)
    
18/35(51.43%)
    
0.025
  
  

    
VAP
    
18/35(51.43%)
    
14/35(40.0%)
    
0.337
  
  

    
Severity of illness
    

    

    

  
  

    
APACHE II Score
    
21.06±8.61
    
20.66±11.11
    
0.387
  

Table 5: Statistical analysis of clinical factors between inappropriate and appropriate antimicrobials therapy groups.

In our study, carbapenems was the priority in the use of inappropriate treatment accounting for 51.43%, especially for MEM, followed by quinolones (45.71%) Β-lactamase inhibitors (34.29%), amide enzyme inhibitors (17.14%), cephalosporins (5.71%). The average LOS Length of hospital stay (mean days) in the group receiving an inappropriate therapy (68.86 days±54.60 SD) was greater than the one in the group that received an appropriate therapy (45.06 days±40.07 SD), and the difference has statistically significant (p = 0.041).

Discussion

PA is an opportunistic human pathogen capable in causing severe infections, especially in ICUs [2,13]. CHINET surveillance of bacterial resistance across China in 2016 showed that the drug resistance rates of PA to imipenem and meropenem were 28.7% and 25.3%, respectively; the drug resistance rates to polymyxin B and amikacin were 0.5% and 8.1%, respectively; the drug resistance rate of the two enzyme inhibitor mixture, gentamicin, ciprofloxacin, ceftazidime, cefepime and piperacillin were less than 20% [18]. Compared with the results in our study, the resistance rates of most drugs were greater than the 2016 China CHINET bacterial resistance monitoring results, except for the slightly lower resistance rate of imipenem and polymyxin B. Of course, this may be related to the fact that the strains we isolated were from the patients in ICUs. It indicated the severity of the antimicrobial resistance rate of PA in ICUs of our study.

The data emphasized the importance of establishing local monitoring for local antimicrobials guide and supported the best treatment. The polymyxin B had the lowest resistance rate in our study, followed by imipenem and aminoglycosides. However, because of the limited clinical application of polymyxin B, imipenem or aminoglycosides were preferred for empirical use, but for patients with severe infections or XDRPA infections, polymyxin B may be considered.

Our results showed considerable genetic variability among the 70 strains, with the detection of 37 distinct genetypes (0.946 of polymorphisms). The genotype cluster 4 contained IntI, OXA-1, IMP and aac(6’)-Ib, ant(2’’)-Ia, which suggested that this specific clone was endemic to the hospital. As a result of persistence over sustained periods, it had become more resistant to antimicrobials. It was speculated that OXA-1+IMP+ant (2 ‘) -Ia+aac (6’) -Ib+IntI was the main resistance gene pattern in 2016. To the best of our knowledge, IntI was closely related to a variety of drug resistance, carrying related drug-resistant gene cassettes that might lead to transmission of resistance as often detected in clinical isolates of PA [12]. Based on previous reports that IntI carried related resistance gene cassettes [20], we speculated that the main resistance gene pattern (OXA- 1+IMP+ant (2 ‘) -Ia+aac (6’) -Ib+IntI) in this study might also be related to the relevant resistance gene cassette carried by the IntI, which had not been reported yet. However, the lack of sequencing of related gene cassettes and IntI was not enough, thus despite this speculation, there remains questions to be clarified in future studies. The IntI might be the main genetic elements of the global resistance transmission of PA. If there is no proper and timely monitoring, the spread of the IntI gene, which may be the main genetic elements of resistance global communication about PA [21], will inevitably complicate treatment of HAIs by MDR PA. Therefore, there is an urgent need not only to rationalize the use of antimicrobials, but also to monitor the dissemination and possible mutation of related resistance genes at the same time. In addition, it is known that the mutation of OprD2 inactivation has become the main mechanism of imipenem resistance [8], but the detection of OprD2 gene deletion strains in this study did not suggest significant relationship with the imipenem resistance.

In the 70 strains of PA clinical isolates, compared with the prevelance of the exoS gene (49/70, 70.00%), the detection rate of exoU gene was lower (18/70, 25.71%), which was similar to the previous report [22]. However, previous studies had shown that not all clinical isolates had the ability to produce ExoS [23], with the overall prevalence of exoS gene in clinical isolates being only approximately 70%. In addition, virulence gene detection rate had obvious difference from the different specimens. The production of ExoS can provide the advantages of PA isolates in respiratory tract colonization or persistent existence [23,24]. In the ICU, there were many cases of colonization of colonized bacteria, which might lead to a significant increase in the detection rate of exoS, which was also important in the mechanism of PA colonization and infection in the respiratory tract.

Interestingly, almost every isolate contained exoS without harboring exoU and vice versa. Besides, only 1 isolate carrying both genes while 4 isolates harboring none. Feltman and coworkers [22] reported these similar findings, indicating it was a nearly universal characteristic of PA isolates, which might owe to the gene altered under the corresponding environmental pressure or related to the enhancement of virulence.

Studies on PA in European and American populations had suggested was the exoU genotype is significantly associated with multidrug resistance and fluoroquinolones resistance compared with the exoS genotype [25,26]. However, there was no significant difference in the resistance between exoU+ genotype with exoS+ genotype in our study, which might be related to the different distribution of population and area. Nevertheless, comparing with the exoU+ genotype, exoS+ genotype strains carried a significantly higher rate of drug resistance, which was different from the previous funding reports. Previous fundings had shown that from a clinical point of view, detection of toxA and eoxS genes in PA clinical isolates might be more significant in drug resistance [11,27]. In our study, 27 strains of MDR (27/35, 77.14%) contained both toxA and eoxS genes at the same time. Compared with non-MDR (22/35, 62.86%), there was not any significant differences could be found in gene prevalence, which was different from other studies [27]. Therefore, we should consider that pathogenicity of PA was multifactorial.

Multi-factor analysis showed that the use of antimicrobials before separation, prolonged hospitalization and mechanical ventilation were independent risk factors for HAIs. At the same time, some studies suggested that the use time of antimicrobials, hospitalization time and tracheal intubation were the main risk factors of healthcare associated infection in PA [26]. These risk factors were interacted to each other. The results of this study showed that the use of antimicrobials before separation, prolonged hospitalization, and mechanical ventilation were risk factors for the occurrence of PA infection in the hospital. The prolonged hospitalization time, the use of antimicrobials, the screening of PA lacking routine ESBL production and the colonization of multidrug-resistant strains in the environment might be the reason of the high resistance rate of PA.

Inappropriate antimicrobials therapy was significantly associated with increased mortality, morbidity, and length of hospital stay [28]. Furthermore, inappropriate empirical antimicrobials therapy was independently associated with higher mortality in patient and inappropriate initial antimicrobials therapy had an adverse effect on survival in patients with gram-negative sepsis [29], however, our study did not find a clear association with mortality. But the results showed that inappropriate treatment significantly increased the mean length of hospital stay compared with those who started receiving appropriate treatment. We observed that 50% of the strains isolated in the ICUs were treated inappropriately. And in contrast to community infections, patients with healthcare associated infections receiving inappropriate treatment was significantly higher with a high increasing incidence of VAP. Multifactor analysis showed a significant correlation between VAP and mortality within 30 days, and VAP was an independent risk factor for increased mortality in 30 of the patients. The use of Mechanical Ventilation as a means of rescue in ICU had also greatly increased the use of ventilator, thus got a high risk of patients with VAP. Current literature suggests that when the infection was effectively controlled along with improved ventilation, the time to use the ventilator should be reduced as much as possible, and the use of non-invasive ventilation can reduce the occurrence of VAP [30].

It was pointed out that the impact of inappropriate empirical antimicrobials therapy depended on the site of the infection. For patients with high risk of infection, inappropriate empiric antimicrobials therapy was identified as an independent risk factor for death [31]. Although most of the specimens were sputum from lower respiratory tract, no mortality was found to be associated with inappropriate treatment. In our study, the most inappropriate empirical antimicrobials was meropenem, which was consistent with a report from Italy showed the highest rate of drug use of meropenem for inappropriate therapy [19]. We suggested that the use of carbapenems and aminoglycoside antimicrobials should be used with caution in the empirical treatment of antimicrobials, especially in the consideration of patients with HCAIs or MDRPA infections. However, no significant statistical significance was found in the use of different kinds of drugs between the inappropriate drug treatment group and the appropriate group which indicated that we still need to guide the application of clinical antimicrobials strictly according to the drug sensitivity report. The appropriateness of empirical treatment cannot be guaranteed. Based on the results of the study, we did not recommend the use of quinolones as a priority in empiric therapy in the study area, which can easily lead to inappropriate therapeutic use. Especially when infected with MDRPA, inappropriate empirical treatment may aggravate the change of drug resistance.

A lack of significant association between inappropriate initial antimicrobial therapy and the outcome of patients with a low-risk source of bacteremia may have been due to a high proportion of catheter removal or early intervention for decompression of biliary or urinary obstruction in the majority of patients. It suggested that nonmedical interventions such as decompression of obstruction or the removal of infection foci were also important aspects of the treatment of infection.

Conclusion

The resistance rates of most drugs in our research were higher than the 2016 China CHINET bacterial resistance monitoring results, except for the slightly lower resistance rate of imipenem and polymyxin B. The drug resistance in this area was severe, and there seems to be a long-term process of colonization or transmission leading to infections at the ICUs with an extensively drug resistant clone type. The resistance genes like OXA-1 IMP, ant(2”)-Ia, aac(6’)- Ib and IntI were correlated with MLVA gene cluster. It is speculated that in 2016, at the ICUs, the related resistance gene island (OXA- 1+IMP+ant(2’’)-Ia+aac(6’)-Ib) carried in IntI was the predominant pattern of drug resistance genes. In the clinical data analysis, multifactor analysis shows that the VAP was an independent risk factor for increasing the mortality rate of 30 days in patients. The use of antimicrobials, length of hospitalization and the use of mechanical ventilation before isolation were independent risk factors for HAIs.

Inappropriate antimicrobials therapy seemed to substantially influence the empiric therapy of PA infections; In particular, use of carbapenems was notably frequent. At the same time, our research showed that patients who usually hospitalized for more than 30 days often did not receive proper treatment. This indicated that clinicians must strictly comply with the drug sensitivity test results in the use of antimicrobials, and use carbapenems with caution in empirical treatment, especially for patients with PA infection who have been hospitalized more than 30 days in the study area.

References

1. Keith Poole. Pseudomonas aeruginosa: Resistance to the Max. Frontiers in Microbiology. 2011; 2: 1-13.
2. Ribeiro ÁCDS, Crozatti MTL, Silva AAD, Macedo RS, Machado AMDO, Silva ATDA. Pseudomonas aeruginosa in the ICU: prevalence, resistance profile, and antimicrobial consumption. Rev Soc Bras Med Tro. 2020; 53: e20180498.
3. Raman G, Avendano EE, Chan J, Merchant S, Puzniak L. Risk factors for hospitalized patients with resistant or multidrug-resistant Pseudomonas aeruginosa infections: a systematic review and meta-analysis. Antimicrobial Resistance & Infection Control. 2018; 7: 79-93.
4. Horcajada JP, Montero M, Oliver A, Sorlí L, Luque S, Gómez-Zorrilla S, et al. Epidemiology and Treatment of Multidrug-Resistant and Extensively Drug-Resistant Pseudomonas aeruginosa Infections. Clinical Microbiology Reviews. 2019; 32: 19-31.
5. Andrew Walkty PLHA, Zhanel GG. Antimicrobial susceptibility of 2906 Pseudomonas aeruginosa clinical isolates obtained from patients in Canadian hospitals over a period of 8 years_ Results of the Canadian Ward surveillance study (CANWARD), 2008-2015. Diagnostic Microbiology and Infectious Disease. 2017; 87: 60-63.
6. Holbrook SYL, Garneau-Tsodikova S. Evaluation of Aminoglycoside and Carbapenem Resistance in a Collection of Drug-Resistant Pseudomonas aeruginosa Clinical Isolates. Microbial Drug Resistance. 2018; 24: 1020- 1030.
7. Tawfik AF, Shibl AM, Aljohi MA, Altammami MA, Al-Agamy MH. Distribution of Ambler class A, B and D Β-lactamases among Pseudomonas aeruginosa isolates. Burns. 2012; 38: 855-860.
8. Cai S, Yiqiang C, Dezhi S, Jinliang K, Yanbin W, Lu AH. Study on the resistance mechanism via outer membrane protein OprD2 and metal Β-lactamase expression in the cell wall of Pseudomonas aeruginosa. Exp Ther Med. 2016; 5: 2869-2872.
9. Wong-Beringer A, Wiener-Kronish J, Lynch S, Flanagan J. Comparison of type III secretion system virulence among fluoroquinolone-susceptible and -resistant clinical isolates of Pseudomonas aeruginosa. Clin Microbiol Infec. 2008; 14: 330-336.
10. Ullah W, Qasim M, Rahman H, Jie Y, Muhammad N. Beta-lactamaseproducing Pseudomonas aeruginosa: Phenotypic characteristics and molecular identification of virulence genes. J Chin Med Assoc. 2017; 80: 173- 177.
11. Khosravi AD, Shafie F, Abbasi Montazeri E, Rostami S. The frequency of genes encoding exotoxin A and exoenzyme S in Pseudomonas aeruginosa strains isolated from burn patients. Burns. 2016; 42: 1116-1120.
12. Liu M, Ma J, Jia W, Li W. Antimicrobial Resistance and Molecular Characterization of Gene Cassettes from Class 1 Integrons in Pseudomonas aeruginosa Strains. Microb Drug Resist. 2020; 26: 670-676.
13. Folic MM, Djordjevic Z, Folic N, Radojevic MZ, Jankovic SM. Epidemiology and risk factors for healthcare-associated infections caused by Pseudomonas aeruginosa. Journal of chemotherapy (Florence). 2020; 2: 1-8.
14. 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 Mocrobiol Infec. 2019; 25: 964-970.
15. Helio S Sader MDHM. Pseudomonas aeruginosa Antimicrobial Susceptibility Results from Four Years (2012 to 2015) of the International Network for Optimal Resistance Monitoring Program in the United States. Antimicrob Agents Chemother. 2017; 3: 2216-2252.
16. Levy Hara G, Kanj SS, Pagani L, Abbo L, Endimiani A, Wertheim HFL, et al. Ten key points for the appropriate use of antibiotics in hospitalised patients: a consensus from the Antimicrobial Stewardship and Resistance Working Groups of the International Society of Chemotherapy. Int J Antimicrobial Ag. 2016; 48: 239-246.
17. Vu-Thien H, Corbineau G, Hormigos K, Fauroux B, Corvol H, Clement A, et al. Multiple-Locus Variable-Number Tandem-Repeat Analysis for Longitudinal Survey of Sources of Pseudomonas aeruginosa Infection in Cystic Fibrosis Patients. J Clin Microbiol. 2017; 45: 3175-3183.
18. HU Fupin GYZD, Ping XYKM, NI Yuxing SJCY, GUO Sufang WLZF, SU Danhong WRFH, Wenen LYJY, et al. Institute Of Antibiotics, CHINET surveillance of bacterial resistance across China: report of the results in 2016. Chin J Infect Chemother. 2017; 17: 481-491.
19. L Ruiz-Martínez. Class 1 integrons in environmental and clinical isolates of Pseudomonas aeruginosa. International Journal of Antimicrobial Agents. 2011; 38: 398-402.
20. Ahmadian L, Haghshenas MR, Mirzaei B, Norouzi Bazgir Z, Goli HR. Distribution and Molecular Characterization of Resistance Gene Cassettes Containing Class 1 Integrons in Multi-Drug Resistant (MDR) Clinical Isolates of Pseudomonas aeruginosa. Infect Drug Resist. 2020; 13: 2773-2781.
21. Goli HR, Nahaei MR, Rezaee MA, Hasani A, Kafil HS, Aghazadeh M, et al. Role of MexAB-OprM and MexXY-OprM efflux pumps and class 1 integrons in resistance to antibiotics in burn and Intensive Care Unit isolates of Pseudomonas aeruginosa. J Infect Public Heal. 2018; 11: 364-372.
22. Feltman H, Schulert G, Khan S, Jain M, Peterson L, Hauser AR. Prevalence of type III secretion genes in clinical and environmental isolates of Pseudomonas aeruginosa, Microbiology. 2001; 147: 2659-2669.
23. Hauser CMSA. Relative Contributions of Pseudomonas aeruginosa ExoU, ExoS, and ExoT to Virulence in the Lung. Infect Immun. 2004; 12: 6969-6977.
24. Suzanne MJ, Fleiszig JPWH, Keith E, Mostov DKTS, Frank ADW. Pseudomonas aeruginosa-Mediated Cytotoxicity and Invasion Correlate with Distinct Genotypes at the Loci Encoding Exoenzyme S. Infect Immun. 1997; 65: 579-586.
25. Melissa Agnello AW. Differentiation in Quinolone Resistance by Virulence Genotype in Pseudomonas aeruginosa. Plos One. 2012; 7: 42973-42980.
26. Makaoui Maatallah JCAB. Population Structure of Pseudomonas aeruginosa from Five Mediterranean Countries: Evidence for Frequent Recombination and Epidemic Occurrence of CC235. Plos One. 2011; 10: 25617-25628.
27. Ivan Mitov TSBM. Prevalence of virulence genes among Bulgarian nosocomial and systic fibrosis isolates of Pseudomonas aeruginosa. Braz J Microbiol. 2010; 41: 588-595.
28. Hirsch EB, Tam VH. Impact of multidrug-resistant Pseudomonas aeruginosa infection on patient outcomes. Expert Rev Pharm Out. 2014; 10: 441-451.
29. Cheol-In Kang SKWB, Eui-Chong Kim MOAK. Bloodstream Infections Caused by Antibiotic-Resistant Gram-Negative Bacilli: Risk Factors for Mortality and Impact of Inappropriate Initial Antimicrobial Therapy on Outcome. Antimicrob Agents Ch. 2004; 2: 760-766.
30. Kos VN, Déraspe M, McLaughlin RE, Whiteaker JD, Roy PH, Alm RA, et al. The Resistome of Pseudomonas aeruginosa in Relationship to Phenotypic Susceptibility. Antimicrob Agents Ch. 2014; 59: 427-436.
31. Joo EJ, Kang CI, Ha YE, Park SY, Kang SJ, Wi YM, et al. Impact of inappropriate empiric antimicrobial therapy on outcome in Pseudomonas aeruginosa bacteraemia: a stratified analysis according to sites of infection. Infection. 2011; 39: 309-318.