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Previous antibiotic exposure and evolution of antibiotic resistance in mechanically ventilated patients with nosocomial infections
Journal of Critical Care (2013) 28, 728–734
Previous antibiotic exposure and evolution of antibiotic resistance in mechanically ventilated patients with nosocomial infections☆,☆☆ Chun Hui MD a,⁎, Ming-Chih Lin MD, PhD b,c,d , Mei-Shin Jao RN e , Tu-Chen Liu MD a , Ren-Guang Wu MD a a
Department of Chest Medicine, Cheng-Ching General Hospital, Taichung, Taiwan Department of Pediatrics, Taichung Veterans General Hospital, Taichung, Taiwan c School of Medicine, National Yang-Ming University, Taipei, Taiwan d Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan e Infection Control Office, Cheng-Ching General Hospital, Taichung, Taiwan b
Keywords: Antibiotic resistance; Antibiotic exposure; Antibiotic selection pressure; Antibiotic stewardship; Nosocomial infection
Abstract Purpose: This study aimed to evaluate the impact of previous antibiotic exposure and the influence of time interval since exposure on the evolution of antibiotic-resistant infections. Methods: We retrospectively analyzed 167 mechanically ventilated patients with nosocomial infections over a 3-year period, with focus on infections in the bloodstream, urinary tract, lower respiratory tract, and surgical sites. Results: Of 167 patients, 62% were confirmed as antibiotic resistant. The most common isolated pathogen was extended-spectrum β-lactamase Enterobacteriaceae (43.9%), followed by methicillin-resistant Staphylococcus aureus (22.8%), and carbapenem-resistant Acinetobacter baumannii (17.5%). Multivariate analysis revealed that the association between resistance and the time interval increased within 10 days (odds ratio [OR], 2.45; P = .133) and peaked at 11 to 20 days (OR, 7.17; P = .012). The data were categorized into 2 groups: when the time interval was more than 20 days, there was a 23.9% reduction in resistance rate compared with when the time interval was 20 days or less (OR, 0.36; P = .002). Conclusions: Although antibiotic exposure increased resistance rate in nosocomial infections, this association decreased as time interval increased. Antibiotic stewardship should consider the significance of time interval while investigating the evolution of subsequent antibiotic-resistant infections. © 2013 Elsevier Inc. All rights reserved.
☆
All authors: no conflicts of interest to declare. Chun Hui designed the study, analyzed and interpreted the data, wrote the manuscript, and was the coordinator of the study. Ming-Chih Lin analyzed and interpreted the data and provided important critical revisions of the manuscript. Mei-Shin Jao, Tu-Chen Liu, and Ren-Guang Wu collected and analyzed the data. All authors have read and approved the final manuscript. ⁎ Corresponding author. Department of Chest Medicine, Cheng-Ching General Hospital, Taichung 407, Taiwan. Tel.: +886 4 24632000x53371; fax: + 886 4 24635961. E-mail address: [email protected] (C. Hui). ☆☆
0883-9441/$ – see front matter © 2013 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.jcrc.2013.04.008
Antibiotic exposure and evolution of resistance
1. Introduction Antibiotic-resistant nosocomial infections are increasing rapidly [1]. Patients with severe illness, poor functional status, or immune suppression or who undergo invasive medical procedures have been reported to be at an elevated risk for drug-resistant infections and many additional risk factors [2-4]. However, selection pressure caused by previous antibiotic exposure is the major cause of drug resistance [2,5-7]. Recent antibiotic exposure is also associated with increased hospital mortality in severe sepsis [8]. Most previous studies on the emergence of resistance have focused on class of antibiotics, rather than considering more complex patterns of use. This has led to a paucity of data on dose-cumulative effects of long-term antibiotic exposure on the emergence of subsequent resistant infections [6,9]. Although some studies have proposed that a short time interval (b 3-6 months) between antibiotic treatments is likely to result in the emergence of antibiotic-resistant bacteria [7,8,10-12] and the effects of antibiotic selection pressure can extend for up to 12 months [13], there is limited information regarding the time interval between antibiotic treatments and the evolution of resistance in individual patients. Because previous studies have relied upon patients' recall memory to determine the time between previous antibiotic exposure and the emergence of resistant infections [14,15], the possibility of patients' recall bias casts doubt upon the significance of the time interval. Fortunately, one prospective study [16], wherein pneumonia patients were treated, has provided reasonable evidence that reductions in previous antibiotic exposure coupled with formulary restriction could result in a reduction of drugresistant nosocomial infections. This study was designed to test the hypothesis that the antibiotic used to treat the initial infection and the time between antibiotic exposure and the onset of subsequent infection would have a significant correlation with the emergence of nosocomial drug-resistant infections. We used data collected over a 3-year period in our chronic respiratory care units. Patients in this unique group often experienced repeated infections, resulting in frequent antibiotic exposure and prolonged hospital stay. Identification of modifiable risk factors may allow health care professionals to avoid broadspectrum antibiotics overuse and improve clinical outcomes and antibiotic stewardship [17,18].
2. Methods 2.1. Study location and patient population In this retrospective study, we screened all patients with suspected nosocomial infections in the 10-bed subacute respiratory care center (RCC) and 30-bed chronic respiratory
729 care ward (RCW) of Cheng-Ching General Hospital, a community teaching hospital in Taiwan, during the period January 2008 through December 2010. Patients who fail weaning trials in the intensive care unit or are ventilator dependent are transferred to RCC or RCW [19]. When suspected nosocomial infections were compatible with the Centers for Disease Control and Prevention definition [20], the patients were enrolled in the study. Nosocomial infections were classified into 4 groups: primary site bloodstream (BSI), urinary tract infection (UTI), lower respiratory tract infection (LRTI), or surgical site infection (SSI). Only documented microbiological infections were included in the study; prospective screening was performed to increase specificity and reduce the possibility of colonization. Enrolled nosocomial infections were not confined to the first episode of infection during hospitalization; however, duplicate culture results in the same episode or in the early recurrence of the same infection were excluded. The hospital's institutional review board approved the study.
2.2. Data sources Baseline characteristics included age, sex, length of mechanical ventilation before nosocomial infection, type of artificial airway, comorbidities, cause of respiratory failure (leading to prolonged mechanical ventilation), and source of nosocomial infection. Previous antibiotic exposure was determined as any antibiotic, intravenous, or oral, administered for the last infection. Comorbidities other than prolonged mechanical ventilation were identified by reviewing charts and imaging reports. Recent use of systemic corticosteroids (ie, prednisolone N 0.5 mg kg− 1 d− 1 for N 2 weeks) was also recorded [15].
2.3. Definitions We defined antibiotic exposure using the study patients' prescription data. The antibiotics were grouped, as described elsewhere [9], into the following classes: carbapenems, β-lactam/ β-lactamase inhibitor combinations, fluoroquinolones, third- and fourth-generation cephalosporins, and glycopeptides or aminoglycosides. Antibioticresistant nosocomial pathogens were defined using previous reports [21-23] and categorized as carbapenem-resistant Pseudomonas aeruginosa (CRPA), carbapenem-resistant Acinetobacter baumannii (CRAB), methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant Enterococcus (VRE), and potential extended-spectrum βlactamase (ESBL)–producing Enterobacteriaceae. The time interval was defined as the time between the last day of antibiotic exposure and the occurrence of subsequent nosocomial infection. As described previously [7], we characterized exposure as occurring within either 10, 20, 40, or 60 days before the diagnosis of nosocomial infection. If patients had not received antibiotics within 60 days, they
730 were considered as not previously exposed. The incidence of antibiotic-resistant infection in this subgroup was regarded as the baseline level during the study period. Antimicrobial susceptibility testing was performed using the disk diffusion method according to the standards of Clinical and Laboratory Standards Institute. Polymicrobial infection was defined as isolation of more than 1 microorganism from the same sample.
2.4. Data analyses Continuous variables were expressed as the mean (SD) or median (interquartile range, or IQR). Student t test was used to compare data with a normal distribution, and the MannWhitney U test was used to analyze data with a skewed distribution. Categorical data were expressed as frequency distributions, and χ2 or Fisher test was used to identify statistically significant differences. To control potential confounding factors, we included variables other than antibiotic exposure associated with a drug-resistant infection at a significance level of P ≤ .2 in the univariate analysis, and variables of potential clinical importance were included in a multivariable logistic regression model. Antibiotic exposures were examined in 2 ways, as qualitative data (ie, exposed or not exposed) or quantitative data (ie, number of days of therapy with each antibiotic). Because several antibiotics are often concomitantly or sequentially used to treat infections, antibiotic groups showing a significant association with outcome were included in the multivariate analysis. All statistical tests were 2 tailed. A P value of less than .05 was considered statistically significant. All data analyses were performed using the SPSS software package (SPSS Inc, Chicago, Ill).
3. Results During the 3-year study period, 167 study subjects were diagnosed as having nosocomial infections (55.1% female; median age, 81 years; range, 19-101 years). The baseline characteristics are presented in Table 1. Although more patients were treated in the RCW (n = 96; 57.5%) than in the RCC and their median length of hospital stay was significantly longer (175 [IQR, 88-330] days vs 44 [IQR, 33-55] days, P b .001), the incidence of antibiotic-resistant infections was comparable (62.5% [RCW] vs 62.0% [RCC]; odds ratio (OR), 1.023; 95% confidence interval (CI), 0.5431.925; P = .944). Cross-transmission of epidemic strains was rare in the 2 units during the study. Both units had a matchedcontact isolation strategy, and infection control measures did not change significantly during the study. Furthermore, the most common factor leading to prolonged mechanical ventilation was sepsis-related organ dysfunction (46.7%), followed by pulmonary injury (23.4%), neurologic injury (22.2%), and cardiac injury (7.8%).
C. Hui et al. The most common infection source was BSI (n = 67; 40.1%), followed by UTI (n = 63; 37.7%), LRTI (n = 33; 19.8%), and SSI (n = 4; 2.4%). The resistance rate for BSI, UTI, LRTI, and SSI were 70.1%, 66.7%, 33.3%, and 100%, respectively (P = .001). Sixty-four (38.3%) nosocomial infections were polymicrobial and were significantly less common in BSI than other types of nosocomial infections (25.4% vs 47%, P = .008). Table 2 displays the antibiotic resistance patterns in different infection sites. A total of 114 (46.3%) of 246 isolates were classified as antibiotic resistant, with ESBLproducing Enterobacteriaceae being the most common antibiotic organism encountered (n = 50; 43.9%), followed by 26 (22.8%) strains of MRSA, 20 (17.5%) strains of CRAB, 8 (7.0%) strains of CRPA, and 2 (1.8%) strains of VRE. The microorganisms responsible for nosocomial infections from different sources are presented in Table 3. Of the 246 isolated pathogens, Enterobacteriaceae were once again the most frequently identified (117; 47.6%), followed by S aureus (32; 13.0%), P aeruginosa (26; 10.6%), and A baumannii (25; 10.2%). In univariate analysis using “no antibiotic exposure within 60 days” as reference (Table 4), the association between antibiotic resistance and time interval since the last day of previous antibiotic exposure increased rapidly within 10 days, significantly peaked at 11 to 20 days, dropped at 21 to 40 days, and decreased to almost baseline at 40 to 60 days. Patients with antibiotic-resistant infections were administered more classes of antibiotics in previous infections. Furthermore, administration of third- and fourth-generation cephalosporins for previous infections was associated with increased incidence of antibiotic-resistant infections; however, there was no significant association between the duration of previous antibiotic therapy and the emergence of antibiotic-resistant infections. Multivariate analysis, after adjustment, for patient disposition, length of prolonged mechanical ventilation, and Charlson score revealed no statistically significant association between history of antibiotic exposure (duration, number, and type) and antibiotic-resistant infections (Table 5). Time interval since the last day of previous antibiotic exposure alone was independently associated with the development of antibiotic-resistant infections. The association followed the same pattern as observed in univariate analysis; it increased within 10 days, peaked significantly at 11 to 20 days, subsequently dropped rapidly at 21 to 40 days and even more at 41 to 60 days. As presented in Fig. 1, the evolution of the resistance rate increased from baseline (43.5%) to peak at 11 to 20 days since the last day of antibiotic exposure and dropped to baseline when the interval was 41 to 60 days (45.5%). When data were combined and categorized into 2 groups, an absolute reduction of 23.9% in resistance rate was observed when the interval was more than 20 days compared with an interval of 20 days or less (Fig. 2).
Antibiotic exposure and evolution of resistance Table 1
731
Clinical characteristics associated with the development of antibiotic-resistant nosocomial infection
Variables
Resistant (n = 104)
Not resistant (n = 63)
OR
95% CI
P
Age (y) Sex Male Female Artificial airway Tracheostomy Endotracheal tube Residency RCC RCW Length of hospitalization (d) Comorbid diseases Chronic neurologic illness Chronic lung disease Congestive heart failure Cirrhosis of liver Hemodialysis Malignancy Diabetes mellitus Chronic use of corticosteroids Charlson comorbidity index
81 (73-86)
82 (75-88)
0.99
0.97-1.02
.605
46 (44.2) 58 (55.8)
29 (46) 34 (54)
0.93 1.0
0.50-1.74
.821
53 (51.0) 51 (49.0)
25 (39.7) 38 (60.3)
1.58 1.0
0.84-2.98
.158
44 (42.3) 60 (57.7) 70 (42-193)
27 (42.9) 36 (57.1) 65 (39-237)
0.98 1.0 1.00
0.52-1.84
.944
0.99-1.00
.645
83 40 46 6 29 18 38 35 3
46 27 22 2 18 15 16 19 3
1.46 0.83 1.48 1.87 0.97 0.67 1.69 1.18 1.07
0.70-3.04 0.44-1.58 0.77-2.82 0.37-9.88 0.48-1.94 0.31-1.45 0.85-3.38 0.60-2.31 0.87-1.32
.312 .574 .236 .453 .924 .308 .138 .640 .504
(79.8) (38.5) (44.2) (5.8) (27.9) (17.3) (36.5) (33.7) (2-4)
(73.0) (42.9) (34.9) (3.2) (28.6) (23.8) (25.4) (30.2) (2-4)
Data are expressed as number (%), mean ± SD, or median (IQR).
4. Discussion This study highlighted an important point, that is, environmental fitness, secondary to antibiotic selection pressure, was associated with the evolution of incidence of antibiotic-resistant nosocomial infections. Empirical broad-spectrum antibiotics have been recommended in previous studies [24-27] for patients with severe sepsis. Accordingly, clinicians have developed a low threshold to using broad-spectrum antibiotics more often to treat patients in nursing homes or chronic care facilities because they usually have a history of recurrent hospitalization because of infection and frequent antibiotic exposure in past 3 to 6 months. This recommendation may aggravate the overuse of antibiotics or unnecessary treatment in noninfected patients, thereby leading to more selection pressure on bacterial evolution [23,28]. Nevertheless, to elaborate the issue of selection pressure on bacterial ecology,
Table 2
only a small number of studies have evaluated the latency effect of previous antibiotic exposure. For example, Leone and colleagues [16] proposed an integrative approach to break the vicious circle of antibiotic overuse in the management of pneumonia. They suggested that patients with no history of hospital admission within 21 days or previous antibiotic exposure within 10 days should be treated with limited spectrum antibiotics. They found that using “10 days” since exposure as a reference time interval may reduce the subsequent emergence of antibiotic-resistant infections, which, in turn, reduces the need for broad-spectrum antibiotics. The present study provided additional evidence that antibiotic exposure raised the risk of resistance for a relatively short period and indicated that the time interval from previous antibiotic exposure be restricted to less than 3 weeks in clinical practice. Specific antibiotic classes correlate positively with the prevalence of specific antibiotic-resistant pathogens, with
Resistant patterns of the 167 nosocomial infections in different sites
Resistant pattern a
BSI b (n = 67)
SSI b (n = 4)
LRTI b (n = 33)
UTI b (n = 63)
CRAB (20/25; 80%) CRPA (8/26; 30.8%) ESBL Enterobacteriaceae (50/117; 42.7%) MRSA (26/32; 81.3%) VRE (2/14; 14.3%)
9 3 10 21 1
1 0 2 2 0
6 (18.2) 3 (9.1) 3 (9.1) 2 (6.1) 0 (0)
4 2 35 1 1
(13.4) (4.5) (14.9) (31.3) (1.5)
(25) (0) (50) (50) (0)
(6.3) (3.2) (55.6) (1.6) (1.6)
a Values of different resistant patterns are displayed as the number of resistant organisms in the numerator, overall isolated number in the denominator, and percentage of the same species in parentheses. b Data are expressed as the number of resistant strains and percentage of different sites of infection in parentheses.
732
C. Hui et al.
Table 3
Microorganisms isolated from different sites of infection
Pathogen Gram-negative organisms P aeruginosa A baumannii Other NFGNB Enterobacteriaceae Other GNB Gram-positive organisms S aureus CoNS Enterococcus species Streptococcus species Candida species
BSI (n = 85)
SSI (n = 9)
LRTI (n = 68)
UTI (n = 84)
7 10 3 25 2
(8.2) (11.8) (3.5) (29.4) (2.4)
0 1 0 3 0
(0) (11.1) (0) (33.3) (0)
14 (20.6) 10 (14.7) 0 (0) 25 (36.8) 6 (8.8)
5 4 0 64 1
(5.9) (4.8) (0) (76.2) (1.2)
22 2 7 2 5
(25.9) (2.4) (8.2) (2.4) (5.9)
3 0 0 1 1
(33.3) (0) (0) (11.1) (11.1)
6 (18.8) 0 (0) 0 (0) 7 (10.3) 0 (0)
1 0 7 0 2
(1.2) (0) (8.3) (0) (2.4)
Data are expressed as number and percentage of different sources of infection in parentheses. Other NFGNB indicates nonfermenting gram-negative bacilli, including 1 Stenotrophomonas maltophilia, 1 Burkholderia cepacia, and 1 Pseudomonas fluorescens; other GNB, gram-negative bacilli, including 3 Haemophilus influenza, 2 Haemophilus parainfluenzae, and 4 Chryseobacterium indologenes; CoNS, coagulase-negative Staphylococcus species.
higher level of exposure corresponding to greater risk [8,11,16]. In agreement with these studies, our study revealed that third- and fourth-generation cephalosporins were related to the emergence of antibiotic-resistant infections. In addition, patients who received more antibiotic classes had an increased risk of subsequent antibioticresistant infections. However, when the impact of all antibiotic-associated variables was evaluated in multivariate analysis (type, number, and duration of antibiotics), there was no effect of duration of antibiotic exposure or number of antibiotic classes on the risk of subsequent antibioticresistant infections. Collateral effects of previous antibiotic Table 4
exposure may play a key role in decreasing the susceptibility of pathogens to other antimicrobial classes [29,30] and doseaccumulating effect by considering the duration of previous exposure that diminished the interaction of qualitative (present/absent) antibiotic exposure variables [9]. Furthermore, the development of cross-resistance was a complex change in exposure over time, and different bacteria with variable physiology and genetics have different antibioticbacterium dynamics [31]. Although our study population was a unique patient group [19], it provided us with the additional opportunity to control confounding variables: long-term bed-ridden state, the
Comparison of previous antibiotic exposure for the development of antibiotic-resistant nosocomial infection
Variables
Resistant (n = 104)
Not resistant (n = 63)
OR
95% CI
P
Duration of previous infection (d) No. of antibiotic classes Time interval since previous antibiotic exposure (d) None 0-10 11-20 21-40 40-60 Antibiotic groups a Carbapenems Days of treatment, mean ± SD β-Lactam/β-lactamase inhibitor combinations Days of treatment, mean ± SD Fluoroquinolones Days of treatment, mean ± SD Cephalosporins, third and fourth generations Days of treatment, mean ± SD Glycopeptides Days of treatment, mean ± SD Aminoglycosides Days of treatment, mean ± SD
13 (7-25) 2 (1-3)
8 (3-19) 1 (1-2)
1.022 1.312
0.99-1.05 1.027-1.677
.066 .030
10 (9.6) 51 (49.0) 24 (23.1) 14 (13.5) 5 (4.8)
13 (20.6) 27 (42.9) 4 (6.3) 13 (20.6) 6 (9.5)
1.0 2.456 7.800 1.400 1.083
0.952-6.332 2.039-29.838 0.458-4.281 0.255-4.596
.063 .003 .555 .914
51 (49.0) 8.9 ± 8.6 27 (26.0) 9.9 ± 5.3 31 (29.8) 8.2 ± 4.5 39 (37.5) 7.2 ± 4.1 25 (24.0) 8 ± 6.1 32 (30.8) 6.2 ± 3.1
22 (34.9) 9.6 ± 7.6 12 (19.0) 7.3 ± 5.4 12 (19.0) 6.5 ± 4.4 14 (22.2) 5.2 ± 2.6 15 (23.8) 9.8 ± 7.0 18 (28.6) 5.5 ± 3.4
1.864 0.988 1.490 1.111 1.805 1.098 2.100 1.185 1.013 0.958 1.111 1.073
0.978-3.552 0.932-1.047 0.692-3.208 0.958-1.288 0.847-3.845 0.929-1.298 1.028-4.290 0.965-1.455 0.486-2.109 0.867-1.058 0.559-2.209 0.890-1.292
.059 .679 .308 .160 .126 .274 .042 .105 .973 .396 .764 .460
a
Data are expressed as the number (%) of patients treated with an antibiotic for a previous infection. The mean and SD for days of treatment were calculated for patients exposed to each antibiotic.
Antibiotic exposure and evolution of resistance
733
Table 5 Independent risk factors for antibiotic-resistant nosocomial infections Variables
AOR
95% CI
P
None (n = 23) 0-10 (n = 78) 11-20 (n = 28) 21-40 (n = 27) 41-60 (n = 11)
Reference 2.451 7.173 0.985 0.758
0.76-7.90 1.56-33.09 0.28-3.44 0.17-3.46
.133 .012 .981 .721
Variables in the multivariate logistic regression model included disposition, length of prolonged mechanical ventilation, Charlson score, antibiotic exposure (duration, number and type) in previous infection, and time interval since previous antibiotic use. AOR indicates adjusted OR; None, no antibiotic exposure within 60 days of nosocomial infection.
presence of artificial airways, a similar health care environment with the same infection control contact precautions, and multiple underlying diseases. In addition, this patient group provided us a clear record of antibiotic usage over periods of long-term hospitalization, allowing us to characterize the evolution of antibiotic resistance rates. In multivariate analysis, similar between-group, antibiotic selection pressure [5-8] may best explain our study results because history of antibiotic exposure was the most important independent factor associated with incidence rate of antibiotic-resistant infections. A recent study on ampicillin-resistant UTIs in children revealed that the magnitude of the association between antibiotic exposure and resistant infections peaked with time since exposure within 30 days [7]. In contrast to this study, our study showed that the incidence rate of antibiotic resistance peaked within 2 weeks after antibiotic exposure
Fig. 1 Antibiotic resistance rate of nosocomial infections categorized based on different time intervals since previous antibiotic exposure. **Data were combined and categorized into 2 groups. This revealed an absolute reduction of 23.9% in resistance rate when the time interval was more than 20 days compared with that when the time interval was 20 days or less (OR, 0.362; 95% CI, 0.189-0.694; P = .002). *P b .05, time interval of 11 to 20 days vs baseline.
Fig. 2 Antibiotic resistance rates for different pathogens before and after the 20-day interval since previous antibiotic exposure. A trend of decreasing resistance rate is apparent for all the pathogens, although the sample size was small and the difference was not statistically significant. AB indicates carbapenem-resistant A baumannii; PA, carbapenem-resistant P aeruginosa; EB, potential extended-spectrum β-lactamase–producing Enterobacteriaceae; SA, methicillin-resistant S aureus; ET, vancomycin-resistant Enterococcus spp.
and then declined rapidly in 3 weeks. The reason for this decline may be that the cost of maintaining resistance caused the resistant bacteria to lose their competitive benefit owing to the absence of selective pressure [32]. This discrepancy was probably caused by the different classes of antibiotics administered and the different definitions of time interval from previous antibiotic exposure. This retrospective study had several limitations. First, the study could not completely capture and control all confounding risk factors. Second, the possibility of anatomical abnormalities in patients with recurrent sites of infection could not be detected retrospectively. Moreover, structured and compartmentalized environments may hamper the complete eradication of bacteria and allow colonization by antibiotic-resistant bacteria for long periods [31,33], which may result in a false overestimation of the impact of antibiotic selection pressure, lasting an extended period. In addition, we could not evaluate bacterial surveillance data and genotyping results to distinguish between new colonization with less resistant bacteria and changed resistant patterns. However, we presented a “real-life situation” analyzing a unit-based population without patient selection. Therefore, our results may have an impact on the way physicians practice medicine and antimicrobial stewardship. Nevertheless, further large-scale multicenter studies are required to confirm our findings.
5. Conclusions This study initially confirmed the hypothesis that antibiotic choice was related to the emergence of antibiotic resistance, but this relationship was not confirmed by multivariate
734 analysis. However, the data did confirm the hypothesis that the time between antibiotic exposure and the onset of subsequent infection would have a significant correlation with the emergence of nosocomial drug–resistant infections. Our study supports the principles of antibiotic stewardship to prevent pathogens from developing resistance to even more antibiotics. In addition, we suggest that future research use the time interval since the last day of previous antibiotic exposure as an important reference while investigating the evolution of subsequent antibiotic-resistant infections.
C. Hui et al.
[13]
[14]
[15]
[16]
Acknowledgments This work was supported by the Intramural Clinical Research Program of Cheng-Ching General Hospital (CH9900131).
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ria in nonhospitalized patients. Eur J Clin Microbiol Infect Dis 2004;23:163-7. Costelloe C, Metcalfe C, Lovering A, Mant D, Hay AD. Effect of antibiotic prescribing in primary care on antimicrobial resistance in individual patients: systematic review and meta-analysis. BMJ 2010;340:c2096. Yu VL. Guidelines for hospital-acquired pneumonia and health-care– associated pneumonia: a vulnerability, a pitfall, and a fatal flaw. Lancet Infect Dis 2011;11:248-52. Saely S, Kaye KS, Fairfax MR, Chopra T, Pogue JM. Investigating the impact of the definition of previous antibiotic exposure related to isolation of extended spectrum β-lactamase–producing Klebsiella pneumoniae. Am J Infect Control 2011;39:390-5. Leone M, Garcin F, Bouvenot J, et al. Ventilator-associated pneumonia: breaking the vicious circle of antibiotic overuse. Crit Care Med 2007;35:379-85. Heenen S, Jacobs F, Vincent J-L. Antibiotic strategies in severe nosocomial sepsis: why do we not de-escalate more often?Crit Care Med 2012;40:1404-9 10.097/CCM.0b013e3182416ecf. Dellit TH, Owens RC, McGowan Jr JE, et al. Infectious Diseases Society of America and the Society for Healthcare Epidemiology of America guidelines for developing an institutional program to enhance antimicrobial stewardship. Clin Infect Dis 2007;44:159-77. Hui C, Lin MC, Liu TC, Wu RG. Mortality and readmission among ventilator-dependent patients after successful weaned discharge from a respiratory care ward. J Formos Med Assoc 2010;109:446-55. Horan TC, Andrus M, Dudeck MA. CDC/NHSN surveillance definition of health care-associated infection and criteria for specific types of infections in the acute care setting. Am J Infect Control 2008;36:309-32. Jung B, Sebbane M, Chanques G, et al. Previous endotracheal aspirate allows guiding the initial treatment of ventilator-associated pneumonia. Intensive Care Med 2009;35:101-7. Mnatzaganian G, Sprung CL, Zitser-Gurevich Y, et al. Effect of infections on 30-day mortality among critically ill patients hospitalized in and out of the intensive care unit. Crit Care Med 2008;36:1097-104. Arias CA, Murray BE. Antibiotic-resistant bugs in the 21st century—a clinical super-challenge. N Engl J Med 2009;360:439-43. Mandell LA, Wunderink RG, Anzueto A, et al. Infectious Diseases Society of America/American Thoracic Society consensus guidelines on the management of community-acquired pneumonia in adults. Clin Infect Dis 2007;44(Suppl 2):S27-72. Niederman MS, Craven DE, Bonten MJ, et al. Guidelines for the management of adults with hospital-acquired, ventilator-associated, and healthcare-associated pneumonia. Am J Respir Crit Care Med 2005;171:388-416. Woodhead M, Blasi F, Ewig S, et al. Guidelines for the management of adult lower respiratory tract infections. Eur Respir J 2005;26:1138-80. Textoris J, Wiramus S, Martin C, Leone M. Antibiotic therapy in patients with septic shock. Eur J Anaesthesiol 2011;28:318-24. Boucher HW, Talbot GH, Bradley JS, et al. Bad bugs, no drugs: no ESKAPE! An update from the Infectious Diseases Society of America. Clin Infect Dis 2009;48:1-12. Paterson DL. “Collateral damage” from cephalosporin or quinolone antibiotic therapy. Clin Infect Dis 2004;38(Suppl 4):S341-5. Ben-Ami R, Olshtain-Pops K, Krieger M, et al. Antibiotic exposure as a risk factor for fluconazole-resistant Candida bloodstream infection. Antimicrob Agents Chemother 2012;56:2518-23. Martinez JL, Baquero F. Mutation frequencies and antibiotic resistance. Antimicrob Agents Chemother 2000;44:1771-7. Guo B, Abdelraouf K, Ledesma KR, Nikolaou M, Tam VH. Predicting bacterial fitness cost associated with drug resistance. J Antimicrob Chemother 2012;67:928-32. Ha YE, Kang CI, Joo EJ, et al. Clinical implications of healthcareassociated infection in patients with community-onset acute pyelonephritis. Scand J Infect Dis 2011;43:587-95.
Previous antibiotic exposure and evolution of antibiotic resistance in mechanically ventilated patients with nosocomial infections☆,☆☆ Chun Hui MD a,⁎, Ming-Chih Lin MD, PhD b,c,d , Mei-Shin Jao RN e , Tu-Chen Liu MD a , Ren-Guang Wu MD a a
Department of Chest Medicine, Cheng-Ching General Hospital, Taichung, Taiwan Department of Pediatrics, Taichung Veterans General Hospital, Taichung, Taiwan c School of Medicine, National Yang-Ming University, Taipei, Taiwan d Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan e Infection Control Office, Cheng-Ching General Hospital, Taichung, Taiwan b
Keywords: Antibiotic resistance; Antibiotic exposure; Antibiotic selection pressure; Antibiotic stewardship; Nosocomial infection
Abstract Purpose: This study aimed to evaluate the impact of previous antibiotic exposure and the influence of time interval since exposure on the evolution of antibiotic-resistant infections. Methods: We retrospectively analyzed 167 mechanically ventilated patients with nosocomial infections over a 3-year period, with focus on infections in the bloodstream, urinary tract, lower respiratory tract, and surgical sites. Results: Of 167 patients, 62% were confirmed as antibiotic resistant. The most common isolated pathogen was extended-spectrum β-lactamase Enterobacteriaceae (43.9%), followed by methicillin-resistant Staphylococcus aureus (22.8%), and carbapenem-resistant Acinetobacter baumannii (17.5%). Multivariate analysis revealed that the association between resistance and the time interval increased within 10 days (odds ratio [OR], 2.45; P = .133) and peaked at 11 to 20 days (OR, 7.17; P = .012). The data were categorized into 2 groups: when the time interval was more than 20 days, there was a 23.9% reduction in resistance rate compared with when the time interval was 20 days or less (OR, 0.36; P = .002). Conclusions: Although antibiotic exposure increased resistance rate in nosocomial infections, this association decreased as time interval increased. Antibiotic stewardship should consider the significance of time interval while investigating the evolution of subsequent antibiotic-resistant infections. © 2013 Elsevier Inc. All rights reserved.
☆
All authors: no conflicts of interest to declare. Chun Hui designed the study, analyzed and interpreted the data, wrote the manuscript, and was the coordinator of the study. Ming-Chih Lin analyzed and interpreted the data and provided important critical revisions of the manuscript. Mei-Shin Jao, Tu-Chen Liu, and Ren-Guang Wu collected and analyzed the data. All authors have read and approved the final manuscript. ⁎ Corresponding author. Department of Chest Medicine, Cheng-Ching General Hospital, Taichung 407, Taiwan. Tel.: +886 4 24632000x53371; fax: + 886 4 24635961. E-mail address: [email protected] (C. Hui). ☆☆
0883-9441/$ – see front matter © 2013 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.jcrc.2013.04.008
Antibiotic exposure and evolution of resistance
1. Introduction Antibiotic-resistant nosocomial infections are increasing rapidly [1]. Patients with severe illness, poor functional status, or immune suppression or who undergo invasive medical procedures have been reported to be at an elevated risk for drug-resistant infections and many additional risk factors [2-4]. However, selection pressure caused by previous antibiotic exposure is the major cause of drug resistance [2,5-7]. Recent antibiotic exposure is also associated with increased hospital mortality in severe sepsis [8]. Most previous studies on the emergence of resistance have focused on class of antibiotics, rather than considering more complex patterns of use. This has led to a paucity of data on dose-cumulative effects of long-term antibiotic exposure on the emergence of subsequent resistant infections [6,9]. Although some studies have proposed that a short time interval (b 3-6 months) between antibiotic treatments is likely to result in the emergence of antibiotic-resistant bacteria [7,8,10-12] and the effects of antibiotic selection pressure can extend for up to 12 months [13], there is limited information regarding the time interval between antibiotic treatments and the evolution of resistance in individual patients. Because previous studies have relied upon patients' recall memory to determine the time between previous antibiotic exposure and the emergence of resistant infections [14,15], the possibility of patients' recall bias casts doubt upon the significance of the time interval. Fortunately, one prospective study [16], wherein pneumonia patients were treated, has provided reasonable evidence that reductions in previous antibiotic exposure coupled with formulary restriction could result in a reduction of drugresistant nosocomial infections. This study was designed to test the hypothesis that the antibiotic used to treat the initial infection and the time between antibiotic exposure and the onset of subsequent infection would have a significant correlation with the emergence of nosocomial drug-resistant infections. We used data collected over a 3-year period in our chronic respiratory care units. Patients in this unique group often experienced repeated infections, resulting in frequent antibiotic exposure and prolonged hospital stay. Identification of modifiable risk factors may allow health care professionals to avoid broadspectrum antibiotics overuse and improve clinical outcomes and antibiotic stewardship [17,18].
2. Methods 2.1. Study location and patient population In this retrospective study, we screened all patients with suspected nosocomial infections in the 10-bed subacute respiratory care center (RCC) and 30-bed chronic respiratory
729 care ward (RCW) of Cheng-Ching General Hospital, a community teaching hospital in Taiwan, during the period January 2008 through December 2010. Patients who fail weaning trials in the intensive care unit or are ventilator dependent are transferred to RCC or RCW [19]. When suspected nosocomial infections were compatible with the Centers for Disease Control and Prevention definition [20], the patients were enrolled in the study. Nosocomial infections were classified into 4 groups: primary site bloodstream (BSI), urinary tract infection (UTI), lower respiratory tract infection (LRTI), or surgical site infection (SSI). Only documented microbiological infections were included in the study; prospective screening was performed to increase specificity and reduce the possibility of colonization. Enrolled nosocomial infections were not confined to the first episode of infection during hospitalization; however, duplicate culture results in the same episode or in the early recurrence of the same infection were excluded. The hospital's institutional review board approved the study.
2.2. Data sources Baseline characteristics included age, sex, length of mechanical ventilation before nosocomial infection, type of artificial airway, comorbidities, cause of respiratory failure (leading to prolonged mechanical ventilation), and source of nosocomial infection. Previous antibiotic exposure was determined as any antibiotic, intravenous, or oral, administered for the last infection. Comorbidities other than prolonged mechanical ventilation were identified by reviewing charts and imaging reports. Recent use of systemic corticosteroids (ie, prednisolone N 0.5 mg kg− 1 d− 1 for N 2 weeks) was also recorded [15].
2.3. Definitions We defined antibiotic exposure using the study patients' prescription data. The antibiotics were grouped, as described elsewhere [9], into the following classes: carbapenems, β-lactam/ β-lactamase inhibitor combinations, fluoroquinolones, third- and fourth-generation cephalosporins, and glycopeptides or aminoglycosides. Antibioticresistant nosocomial pathogens were defined using previous reports [21-23] and categorized as carbapenem-resistant Pseudomonas aeruginosa (CRPA), carbapenem-resistant Acinetobacter baumannii (CRAB), methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant Enterococcus (VRE), and potential extended-spectrum βlactamase (ESBL)–producing Enterobacteriaceae. The time interval was defined as the time between the last day of antibiotic exposure and the occurrence of subsequent nosocomial infection. As described previously [7], we characterized exposure as occurring within either 10, 20, 40, or 60 days before the diagnosis of nosocomial infection. If patients had not received antibiotics within 60 days, they
730 were considered as not previously exposed. The incidence of antibiotic-resistant infection in this subgroup was regarded as the baseline level during the study period. Antimicrobial susceptibility testing was performed using the disk diffusion method according to the standards of Clinical and Laboratory Standards Institute. Polymicrobial infection was defined as isolation of more than 1 microorganism from the same sample.
2.4. Data analyses Continuous variables were expressed as the mean (SD) or median (interquartile range, or IQR). Student t test was used to compare data with a normal distribution, and the MannWhitney U test was used to analyze data with a skewed distribution. Categorical data were expressed as frequency distributions, and χ2 or Fisher test was used to identify statistically significant differences. To control potential confounding factors, we included variables other than antibiotic exposure associated with a drug-resistant infection at a significance level of P ≤ .2 in the univariate analysis, and variables of potential clinical importance were included in a multivariable logistic regression model. Antibiotic exposures were examined in 2 ways, as qualitative data (ie, exposed or not exposed) or quantitative data (ie, number of days of therapy with each antibiotic). Because several antibiotics are often concomitantly or sequentially used to treat infections, antibiotic groups showing a significant association with outcome were included in the multivariate analysis. All statistical tests were 2 tailed. A P value of less than .05 was considered statistically significant. All data analyses were performed using the SPSS software package (SPSS Inc, Chicago, Ill).
3. Results During the 3-year study period, 167 study subjects were diagnosed as having nosocomial infections (55.1% female; median age, 81 years; range, 19-101 years). The baseline characteristics are presented in Table 1. Although more patients were treated in the RCW (n = 96; 57.5%) than in the RCC and their median length of hospital stay was significantly longer (175 [IQR, 88-330] days vs 44 [IQR, 33-55] days, P b .001), the incidence of antibiotic-resistant infections was comparable (62.5% [RCW] vs 62.0% [RCC]; odds ratio (OR), 1.023; 95% confidence interval (CI), 0.5431.925; P = .944). Cross-transmission of epidemic strains was rare in the 2 units during the study. Both units had a matchedcontact isolation strategy, and infection control measures did not change significantly during the study. Furthermore, the most common factor leading to prolonged mechanical ventilation was sepsis-related organ dysfunction (46.7%), followed by pulmonary injury (23.4%), neurologic injury (22.2%), and cardiac injury (7.8%).
C. Hui et al. The most common infection source was BSI (n = 67; 40.1%), followed by UTI (n = 63; 37.7%), LRTI (n = 33; 19.8%), and SSI (n = 4; 2.4%). The resistance rate for BSI, UTI, LRTI, and SSI were 70.1%, 66.7%, 33.3%, and 100%, respectively (P = .001). Sixty-four (38.3%) nosocomial infections were polymicrobial and were significantly less common in BSI than other types of nosocomial infections (25.4% vs 47%, P = .008). Table 2 displays the antibiotic resistance patterns in different infection sites. A total of 114 (46.3%) of 246 isolates were classified as antibiotic resistant, with ESBLproducing Enterobacteriaceae being the most common antibiotic organism encountered (n = 50; 43.9%), followed by 26 (22.8%) strains of MRSA, 20 (17.5%) strains of CRAB, 8 (7.0%) strains of CRPA, and 2 (1.8%) strains of VRE. The microorganisms responsible for nosocomial infections from different sources are presented in Table 3. Of the 246 isolated pathogens, Enterobacteriaceae were once again the most frequently identified (117; 47.6%), followed by S aureus (32; 13.0%), P aeruginosa (26; 10.6%), and A baumannii (25; 10.2%). In univariate analysis using “no antibiotic exposure within 60 days” as reference (Table 4), the association between antibiotic resistance and time interval since the last day of previous antibiotic exposure increased rapidly within 10 days, significantly peaked at 11 to 20 days, dropped at 21 to 40 days, and decreased to almost baseline at 40 to 60 days. Patients with antibiotic-resistant infections were administered more classes of antibiotics in previous infections. Furthermore, administration of third- and fourth-generation cephalosporins for previous infections was associated with increased incidence of antibiotic-resistant infections; however, there was no significant association between the duration of previous antibiotic therapy and the emergence of antibiotic-resistant infections. Multivariate analysis, after adjustment, for patient disposition, length of prolonged mechanical ventilation, and Charlson score revealed no statistically significant association between history of antibiotic exposure (duration, number, and type) and antibiotic-resistant infections (Table 5). Time interval since the last day of previous antibiotic exposure alone was independently associated with the development of antibiotic-resistant infections. The association followed the same pattern as observed in univariate analysis; it increased within 10 days, peaked significantly at 11 to 20 days, subsequently dropped rapidly at 21 to 40 days and even more at 41 to 60 days. As presented in Fig. 1, the evolution of the resistance rate increased from baseline (43.5%) to peak at 11 to 20 days since the last day of antibiotic exposure and dropped to baseline when the interval was 41 to 60 days (45.5%). When data were combined and categorized into 2 groups, an absolute reduction of 23.9% in resistance rate was observed when the interval was more than 20 days compared with an interval of 20 days or less (Fig. 2).
Antibiotic exposure and evolution of resistance Table 1
731
Clinical characteristics associated with the development of antibiotic-resistant nosocomial infection
Variables
Resistant (n = 104)
Not resistant (n = 63)
OR
95% CI
P
Age (y) Sex Male Female Artificial airway Tracheostomy Endotracheal tube Residency RCC RCW Length of hospitalization (d) Comorbid diseases Chronic neurologic illness Chronic lung disease Congestive heart failure Cirrhosis of liver Hemodialysis Malignancy Diabetes mellitus Chronic use of corticosteroids Charlson comorbidity index
81 (73-86)
82 (75-88)
0.99
0.97-1.02
.605
46 (44.2) 58 (55.8)
29 (46) 34 (54)
0.93 1.0
0.50-1.74
.821
53 (51.0) 51 (49.0)
25 (39.7) 38 (60.3)
1.58 1.0
0.84-2.98
.158
44 (42.3) 60 (57.7) 70 (42-193)
27 (42.9) 36 (57.1) 65 (39-237)
0.98 1.0 1.00
0.52-1.84
.944
0.99-1.00
.645
83 40 46 6 29 18 38 35 3
46 27 22 2 18 15 16 19 3
1.46 0.83 1.48 1.87 0.97 0.67 1.69 1.18 1.07
0.70-3.04 0.44-1.58 0.77-2.82 0.37-9.88 0.48-1.94 0.31-1.45 0.85-3.38 0.60-2.31 0.87-1.32
.312 .574 .236 .453 .924 .308 .138 .640 .504
(79.8) (38.5) (44.2) (5.8) (27.9) (17.3) (36.5) (33.7) (2-4)
(73.0) (42.9) (34.9) (3.2) (28.6) (23.8) (25.4) (30.2) (2-4)
Data are expressed as number (%), mean ± SD, or median (IQR).
4. Discussion This study highlighted an important point, that is, environmental fitness, secondary to antibiotic selection pressure, was associated with the evolution of incidence of antibiotic-resistant nosocomial infections. Empirical broad-spectrum antibiotics have been recommended in previous studies [24-27] for patients with severe sepsis. Accordingly, clinicians have developed a low threshold to using broad-spectrum antibiotics more often to treat patients in nursing homes or chronic care facilities because they usually have a history of recurrent hospitalization because of infection and frequent antibiotic exposure in past 3 to 6 months. This recommendation may aggravate the overuse of antibiotics or unnecessary treatment in noninfected patients, thereby leading to more selection pressure on bacterial evolution [23,28]. Nevertheless, to elaborate the issue of selection pressure on bacterial ecology,
Table 2
only a small number of studies have evaluated the latency effect of previous antibiotic exposure. For example, Leone and colleagues [16] proposed an integrative approach to break the vicious circle of antibiotic overuse in the management of pneumonia. They suggested that patients with no history of hospital admission within 21 days or previous antibiotic exposure within 10 days should be treated with limited spectrum antibiotics. They found that using “10 days” since exposure as a reference time interval may reduce the subsequent emergence of antibiotic-resistant infections, which, in turn, reduces the need for broad-spectrum antibiotics. The present study provided additional evidence that antibiotic exposure raised the risk of resistance for a relatively short period and indicated that the time interval from previous antibiotic exposure be restricted to less than 3 weeks in clinical practice. Specific antibiotic classes correlate positively with the prevalence of specific antibiotic-resistant pathogens, with
Resistant patterns of the 167 nosocomial infections in different sites
Resistant pattern a
BSI b (n = 67)
SSI b (n = 4)
LRTI b (n = 33)
UTI b (n = 63)
CRAB (20/25; 80%) CRPA (8/26; 30.8%) ESBL Enterobacteriaceae (50/117; 42.7%) MRSA (26/32; 81.3%) VRE (2/14; 14.3%)
9 3 10 21 1
1 0 2 2 0
6 (18.2) 3 (9.1) 3 (9.1) 2 (6.1) 0 (0)
4 2 35 1 1
(13.4) (4.5) (14.9) (31.3) (1.5)
(25) (0) (50) (50) (0)
(6.3) (3.2) (55.6) (1.6) (1.6)
a Values of different resistant patterns are displayed as the number of resistant organisms in the numerator, overall isolated number in the denominator, and percentage of the same species in parentheses. b Data are expressed as the number of resistant strains and percentage of different sites of infection in parentheses.
732
C. Hui et al.
Table 3
Microorganisms isolated from different sites of infection
Pathogen Gram-negative organisms P aeruginosa A baumannii Other NFGNB Enterobacteriaceae Other GNB Gram-positive organisms S aureus CoNS Enterococcus species Streptococcus species Candida species
BSI (n = 85)
SSI (n = 9)
LRTI (n = 68)
UTI (n = 84)
7 10 3 25 2
(8.2) (11.8) (3.5) (29.4) (2.4)
0 1 0 3 0
(0) (11.1) (0) (33.3) (0)
14 (20.6) 10 (14.7) 0 (0) 25 (36.8) 6 (8.8)
5 4 0 64 1
(5.9) (4.8) (0) (76.2) (1.2)
22 2 7 2 5
(25.9) (2.4) (8.2) (2.4) (5.9)
3 0 0 1 1
(33.3) (0) (0) (11.1) (11.1)
6 (18.8) 0 (0) 0 (0) 7 (10.3) 0 (0)
1 0 7 0 2
(1.2) (0) (8.3) (0) (2.4)
Data are expressed as number and percentage of different sources of infection in parentheses. Other NFGNB indicates nonfermenting gram-negative bacilli, including 1 Stenotrophomonas maltophilia, 1 Burkholderia cepacia, and 1 Pseudomonas fluorescens; other GNB, gram-negative bacilli, including 3 Haemophilus influenza, 2 Haemophilus parainfluenzae, and 4 Chryseobacterium indologenes; CoNS, coagulase-negative Staphylococcus species.
higher level of exposure corresponding to greater risk [8,11,16]. In agreement with these studies, our study revealed that third- and fourth-generation cephalosporins were related to the emergence of antibiotic-resistant infections. In addition, patients who received more antibiotic classes had an increased risk of subsequent antibioticresistant infections. However, when the impact of all antibiotic-associated variables was evaluated in multivariate analysis (type, number, and duration of antibiotics), there was no effect of duration of antibiotic exposure or number of antibiotic classes on the risk of subsequent antibioticresistant infections. Collateral effects of previous antibiotic Table 4
exposure may play a key role in decreasing the susceptibility of pathogens to other antimicrobial classes [29,30] and doseaccumulating effect by considering the duration of previous exposure that diminished the interaction of qualitative (present/absent) antibiotic exposure variables [9]. Furthermore, the development of cross-resistance was a complex change in exposure over time, and different bacteria with variable physiology and genetics have different antibioticbacterium dynamics [31]. Although our study population was a unique patient group [19], it provided us with the additional opportunity to control confounding variables: long-term bed-ridden state, the
Comparison of previous antibiotic exposure for the development of antibiotic-resistant nosocomial infection
Variables
Resistant (n = 104)
Not resistant (n = 63)
OR
95% CI
P
Duration of previous infection (d) No. of antibiotic classes Time interval since previous antibiotic exposure (d) None 0-10 11-20 21-40 40-60 Antibiotic groups a Carbapenems Days of treatment, mean ± SD β-Lactam/β-lactamase inhibitor combinations Days of treatment, mean ± SD Fluoroquinolones Days of treatment, mean ± SD Cephalosporins, third and fourth generations Days of treatment, mean ± SD Glycopeptides Days of treatment, mean ± SD Aminoglycosides Days of treatment, mean ± SD
13 (7-25) 2 (1-3)
8 (3-19) 1 (1-2)
1.022 1.312
0.99-1.05 1.027-1.677
.066 .030
10 (9.6) 51 (49.0) 24 (23.1) 14 (13.5) 5 (4.8)
13 (20.6) 27 (42.9) 4 (6.3) 13 (20.6) 6 (9.5)
1.0 2.456 7.800 1.400 1.083
0.952-6.332 2.039-29.838 0.458-4.281 0.255-4.596
.063 .003 .555 .914
51 (49.0) 8.9 ± 8.6 27 (26.0) 9.9 ± 5.3 31 (29.8) 8.2 ± 4.5 39 (37.5) 7.2 ± 4.1 25 (24.0) 8 ± 6.1 32 (30.8) 6.2 ± 3.1
22 (34.9) 9.6 ± 7.6 12 (19.0) 7.3 ± 5.4 12 (19.0) 6.5 ± 4.4 14 (22.2) 5.2 ± 2.6 15 (23.8) 9.8 ± 7.0 18 (28.6) 5.5 ± 3.4
1.864 0.988 1.490 1.111 1.805 1.098 2.100 1.185 1.013 0.958 1.111 1.073
0.978-3.552 0.932-1.047 0.692-3.208 0.958-1.288 0.847-3.845 0.929-1.298 1.028-4.290 0.965-1.455 0.486-2.109 0.867-1.058 0.559-2.209 0.890-1.292
.059 .679 .308 .160 .126 .274 .042 .105 .973 .396 .764 .460
a
Data are expressed as the number (%) of patients treated with an antibiotic for a previous infection. The mean and SD for days of treatment were calculated for patients exposed to each antibiotic.
Antibiotic exposure and evolution of resistance
733
Table 5 Independent risk factors for antibiotic-resistant nosocomial infections Variables
AOR
95% CI
P
None (n = 23) 0-10 (n = 78) 11-20 (n = 28) 21-40 (n = 27) 41-60 (n = 11)
Reference 2.451 7.173 0.985 0.758
0.76-7.90 1.56-33.09 0.28-3.44 0.17-3.46
.133 .012 .981 .721
Variables in the multivariate logistic regression model included disposition, length of prolonged mechanical ventilation, Charlson score, antibiotic exposure (duration, number and type) in previous infection, and time interval since previous antibiotic use. AOR indicates adjusted OR; None, no antibiotic exposure within 60 days of nosocomial infection.
presence of artificial airways, a similar health care environment with the same infection control contact precautions, and multiple underlying diseases. In addition, this patient group provided us a clear record of antibiotic usage over periods of long-term hospitalization, allowing us to characterize the evolution of antibiotic resistance rates. In multivariate analysis, similar between-group, antibiotic selection pressure [5-8] may best explain our study results because history of antibiotic exposure was the most important independent factor associated with incidence rate of antibiotic-resistant infections. A recent study on ampicillin-resistant UTIs in children revealed that the magnitude of the association between antibiotic exposure and resistant infections peaked with time since exposure within 30 days [7]. In contrast to this study, our study showed that the incidence rate of antibiotic resistance peaked within 2 weeks after antibiotic exposure
Fig. 1 Antibiotic resistance rate of nosocomial infections categorized based on different time intervals since previous antibiotic exposure. **Data were combined and categorized into 2 groups. This revealed an absolute reduction of 23.9% in resistance rate when the time interval was more than 20 days compared with that when the time interval was 20 days or less (OR, 0.362; 95% CI, 0.189-0.694; P = .002). *P b .05, time interval of 11 to 20 days vs baseline.
Fig. 2 Antibiotic resistance rates for different pathogens before and after the 20-day interval since previous antibiotic exposure. A trend of decreasing resistance rate is apparent for all the pathogens, although the sample size was small and the difference was not statistically significant. AB indicates carbapenem-resistant A baumannii; PA, carbapenem-resistant P aeruginosa; EB, potential extended-spectrum β-lactamase–producing Enterobacteriaceae; SA, methicillin-resistant S aureus; ET, vancomycin-resistant Enterococcus spp.
and then declined rapidly in 3 weeks. The reason for this decline may be that the cost of maintaining resistance caused the resistant bacteria to lose their competitive benefit owing to the absence of selective pressure [32]. This discrepancy was probably caused by the different classes of antibiotics administered and the different definitions of time interval from previous antibiotic exposure. This retrospective study had several limitations. First, the study could not completely capture and control all confounding risk factors. Second, the possibility of anatomical abnormalities in patients with recurrent sites of infection could not be detected retrospectively. Moreover, structured and compartmentalized environments may hamper the complete eradication of bacteria and allow colonization by antibiotic-resistant bacteria for long periods [31,33], which may result in a false overestimation of the impact of antibiotic selection pressure, lasting an extended period. In addition, we could not evaluate bacterial surveillance data and genotyping results to distinguish between new colonization with less resistant bacteria and changed resistant patterns. However, we presented a “real-life situation” analyzing a unit-based population without patient selection. Therefore, our results may have an impact on the way physicians practice medicine and antimicrobial stewardship. Nevertheless, further large-scale multicenter studies are required to confirm our findings.
5. Conclusions This study initially confirmed the hypothesis that antibiotic choice was related to the emergence of antibiotic resistance, but this relationship was not confirmed by multivariate
734 analysis. However, the data did confirm the hypothesis that the time between antibiotic exposure and the onset of subsequent infection would have a significant correlation with the emergence of nosocomial drug–resistant infections. Our study supports the principles of antibiotic stewardship to prevent pathogens from developing resistance to even more antibiotics. In addition, we suggest that future research use the time interval since the last day of previous antibiotic exposure as an important reference while investigating the evolution of subsequent antibiotic-resistant infections.
C. Hui et al.
[13]
[14]
[15]
[16]
Acknowledgments This work was supported by the Intramural Clinical Research Program of Cheng-Ching General Hospital (CH9900131).
[17]
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