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End-of-Life Treatment and Bacterial Antibiotic Resistance
CHEST
Original Research CRITICAL CARE MEDICINE
End-of-Life Treatment and Bacterial Antibiotic Resistance A Potential Association Phillip D. Levin, MB, BChir; Andrew E. Simor, MD; Allon E. Moses, MD; and Charles L. Sprung, MD, FCCP
Background: Great variability exists in the occurrence of antibiotic-resistant bacteria in ICUs around the world. The contribution of specific ICU care variables to these geographic variations is unknown. Methods: ICU patients from two ICUs (in Jerusalem and Toronto) who were admitted for . 48 h and who grew a resistant bacteria in any culture during ICU admission were compared with those without resistant organisms across a range of demographic and ICU care interventions. Significant variables were investigated with logistic regression to identify factors predictive of infection/ colonization with a resistant organism. Results: Resistant organisms were acquired by 82/423 (19%) patients. Patients acquiring a resistant organism had a higher incidence of diabetes mellitus (21/82, 26% vs 52/341, 15%; P 5 .026), were more frequently admitted from another ICU (17/82, 21% vs 33/341, 10%; P 5 .005), received more antibiotics in the ICU (19 6 17 vs 14 6 14 days; P 5 .005), and had more ventilator (10 6 10 vs 7 6 8; P 5 .031) and central line days (10 6 8 vs 7 6 8; P , .001). These patients had a lower incidence of limitation-of-therapy orders (9/82, 11% vs 78/341, 23%; P 5 .015). Only the absence of a limitation-of-therapy order (odds ratio, 2.62; 95% CI, 1.21-5.68; P 5 .014) was independently associated with the acquisition of resistant organisms. Further, among ICU fatalities, 5/45 (11%) patients acquired a resistant organism prior to withdrawal vs 17/44 (39%) nonwithdrawal fatalities (P 5 .003). Nonwithdrawal fatalities received significantly more third-line antibiotics (7 6 14 vs 2 6 4; P 5 .031) despite similar ICU lengths of stay (15 6 21 days for nonwithdrawal fatalities vs 10 6 11 for withdraw fatalities; P 5 .210) Conclusions: End-of-life treatment is independently associated with acquisition of resistant bacteria. Patients dying without withdraw orders receive more antibiotics and develop more resistant organisms. These patients may represent a reservoir of resistant bacteria in the ICU. CHEST 2010; 138(3):588–594 Abbreviations: APACHE II 5 Acute Physiology and Chronic Health Evaluation II; DDD 5 defined daily dosages; DNR 5 do not resuscitate; IQR 5 interquartile range; MRSA 5 methicillin-resistant Staphylococcus aureus
are developing antimicrobial resistance Bacteria faster than drug companies are able to develop
new antibiotics. This is happening precisely at a time when there is a lack of new drug development, and the pharmaceutical industry is withdrawing from anti-infective drug development.1 Despite this, up to 20% of ICU patients2 will develop a nosocomial infection, often caused by highly resistant bacteria, and some ICU patients are now dying from bacterial infections for which there is no effective antibiotic therapy. Although bacterial resistance is increasing overall, great variability exists in the prevalence of resistant bacteria over geographic areas.3 For example, in south-
ern Europe (Greece, Portugal, Israel), methicillinresistant Staphylococcus aureus (MRSA) accounts for approximately 40% of all S aureus isolates, whereas in Scandinavia MRSA accounts for , 2% of S aureus isolates.3,4 Similar trends exist for multidrug-resistant gram-negative bacteria3,5 and can also be found across North6 and South America.7 Canadian ICUs have a relatively low incidence of resistant bacteria, with MRSA accounting for 22% of all S aureus isolates in ICU patients and multidrug-resistant gram-negative bacteria accounting for 13% of Pseudomonas aeruginosa isolates and 0% to 1% of Escherichia coli, Enterobacter cloacae, and Klebsiella pneumoniae.8 Resistance rates
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in Israel are similar to Southern Europe,3 considerably higher than in Canada.9 Descriptions of geographic variability in antibiotic resistance come from population-based studies including data on thousands of clinical cultures with little or no patient data. Thus, patient care-based explanations for the variability in resistance are difficult to determine. In contrast, studies focusing on specific nosocomial infections (defining risk factors for conditions such as ventilator-associated pneumonia, for example), do not address the range of ICU interventions that could contribute to differences in bacterial epidemiology in an ICU. The current study attempts to address this gap. It compares the acquisition of resistant bacteria in ICU patients admitted in two geographically distinct locations (Toronto, ON, Canada, and Jerusalem, Israel). Empirically, these ICUs admitted similar patient populations and had similar standards of ICU care with two major differences: prevalence of resistant bacteria (lower in Toronto), and end-of-life care. In Jerusalem, do-not-resuscitate (DNR) orders and withholding therapy were acceptable but withdrawal of therapy was not performed, whereas in Toronto all these were acceptable. The study examined whether end-of-life care or other care-related parameters were significant predictors of the acquisition of resistant organisms in the ICU. This is of importance as it may identify possible areas of change to reduce the risks associated with colonization or infection with highly resistant bacteria.
Materials and Methods Data were collected from two ICUs: the 20-bed medicalsurgical ICU of the 1,140-bed Sunnybrook Health Sciences Centre, Toronto, ON, Canada (“Toronto” ICU), and the 12-bed medical-surgical ICU of the 750-bed Hadassah Hebrew University Medical Center, Jerusalem, Israel (“Jerusalem” ICU). Both Manuscript received November 21, 2009; revision accepted March 23, 2009. Affiliations: From the Department of Anesthesiology and Critical Care Medicine (Drs Levin and Sprung), and the Department of Clinical Microbiology and Infectious Diseases (Dr Moses), Hadassah Hebrew University Medical Center, Jerusalem, Israel; and the Department of Microbiology (Dr Simor), Sunnybrook Health Sciences Center and Division of Infectious Diseases, University of Toronto, Toronto, ON, Canada. Presented in part at The Society of Critical Care Medicine 38th Critical Care Congress, Nashville, Tennessee, February 2009. Funding/Support: This study was funded in part by an internal Hadassah Medical Organization research grant. Correspondence to: Phillip Levin, MB, BChir, Department of Anesthesiology and Critical Care Medicine, POB 12000, Jerusalem 91120, Israel; e-mail: [email protected] © 2010 American College of Chest Physicians. Reproduction of this article is prohibited without written permission from the American College of Chest Physicians (http://www.chestpubs.org/ site/misc/reprints.xhtml). DOI: 10.1378/chest.09-2757 www.chestpubs.org
hospitals are university-affiliated tertiary-care referral teaching hospitals. Data were collected in Toronto from January 2003 to December 2003 and in Jerusalem from July 2005 to January 2006. Data were collected prospectively on consecutive patients admitted to the ICU for . 48 h. The ethics review board of each hospital approved the protocol and, as this study was observational in nature, waived the requirements for informed consent. Data Collection Demographics: Basic demographic data included admission and discharge dates, ICU and hospital outcome, hospital admission prior to ICU admission, prehospital functional capacity, operative status, antibiotic therapy during 14 days prior to ICU admission (whether in or out of hospital), and any limitations of end-of-life therapy. The presence of severe ischemic heart disease or congestive heart failure, severe lung disease (as defined in the Acute Physiology and Chronic Health Evaluation II [APACHE II] score10), diabetes mellitus, oral steroid use, cirrhosis, chronic dialysis, organ transplantation, immunosuppression, and malignancy was recorded (Table 1). The APACHE II score was calculated from data collected during the first 24 h in the ICU. ICU Course Data and Antibiotic Therapy: Data were collected daily in the ICU regarding the presence of mechanical ventilation, all intravascular catheters, and the presence of a urinary catheter. The dates antibiotics were started and stopped were collected. Antibiotics were divided prospectively into three broad groups (first, second, and third line) according to empirical usage in the ICU. First-line antibiotics were defined as amoxicillin, ampicillin, amoxicillin/clavulanic acid, azactam, azithromycin, cefamazine, cefuroxime, ciprofloxacin, clindamycin, cloxacillin, metronidazole, gentamicin, levofloxacin, penicillin, piperacillin, rifampicin, co-trimoxazole, and tobramycin. Second-line antibiotics were defined as amikacin, cefepime, cefotaxime, cefotetan, ceftazidime, ceftriaxone, and piperacillin/tazobactam. Third-line antibiotics were defined as colistin, ertapenem, imipenem, linezolid, meropenem, ampicillin/sulbactam, and vancomycin. Nosocomial Infections: Nosocomial pneumonia, bacteremia (primary and secondary), and urinary tract infections were sought prospectively according to the National Nosocomial Infection Surveillance system criteria.11 The rates of occurrence of ventilatorassociated pneumonia per 1,000 days of mechanical ventilation, bloodstream infections per 1,000 central venous catheter days, and urinary tract infections per 1,000 catheter days were calculated. Microbiology Data: Results of all microbiologic studies, whether clinically indicated (as ordered by the physician) or surveillance studies, were included. The following criteria were used to define resistant bacteria: MRSA, vancomycin-resistant enterococci, and gram-negative bacteria resistant to any one or more of the following: third-generation cephalosporins (ceftazidime only for P aeruginosa, any third-generation cephalosporin for other gram-negative bacteria), fluoroquinolones, or carbapenem antibiotics. Microbiology was performed according to standard techniques.12 For each patient, unique isolates were defined as the first occurrence of a particular isolate from a specific culture site with a single resistance pattern. Only unique isolates were included in the statistical analyses. Data Analysis The study is a cohort analysis analyzing risk factors for the acquisition of resistant bacteria in ICU patients. Each patient was CHEST / 138 / 3 / SEPTEMBER, 2010
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Table 1—Comparison of Patients With and Without Resistant Organisms Characteristic Age Sex, male On ICU admission Not functionally independent Presence of severe heart disease Diabetes mellitusa Presence of severe lung disease Cirrhosis Malignancy Steroid therapy Immunosuppressed Dialysis Antibiotic therapy prior to ICU admissiona Surgery in 24 h prior to admission Trauma etiology Admission prior to ICU admissiona Chronic care facility Other hospital Other ward Other ICU Length of hospital stay before ICU Diagnosed with nosocomial infection ICU length of stay prior to acquisition of resistant organisma Ventilation daysa Central venous catheter daysa Urinary catheter days Antibiotic days (in ICU)a First-line antibiotic days Second-line antibiotic days Third-line antibiotic days Limitation of therapya No CPR Withhold Withdraw Outcomes ICU length of stay ICU mortality Hospital mortality
No Resistant Organism (n 5 341)
Resistant Organism (n 5 82)
56 6 22 205 (60)
56 6 22 50 (61)
.997 .887
43 (13) 28 (8) 52 (15) 7 (2) 6 (2) 71 (21) 19 (6) 11 (3) 8 (2) 111 (33) 169 (50) 139 (41) 120 (35) 7 (2) 27 (8) 81 (24) 33 (10) 4 6 20 82 (24) 969
9 (11) 6 (7) 21 (26) 3 (4) 4 (5) 11 (13) 6 (7) 2 (2) 1 (1) 35 (43) 39 (48) 32 (39) 43 (52) 4 (5) 8 (10) 25 (30) 17 (21) 8 6 22 20 (24) 16 6 32
.706 .789 .026 .416 .108 .128 .601 1 1 .066 .727 .773 .066 .236 .587 .206 .005 .117 .948 .07
768 768 968 14 6 14 10 6 11 263 163 78 (23) 24 (7) 14 (4) 40 (12)
10 6 10 10 6 8 11 6 12 19 6 17 13 6 14 465 365 9 (11) 4 (5) 1 (1) 4 (5)
.031 , .001 .172 .005 .136 .002 , .001 .015 .48 .322 .072
9 (3) 67 (20) 99 (29)
27 (33) 11 (13) 22 (27)
, .001 .191 .67
P Value
Data are presented as No. (%) or mean 6 SD. Data from period prior to acquisition of resistant organism. Included in multivariate logistic regression analysis as potential predictors of acquisition of resistant organism.
a
defined according to the presence or absence of a resistant organism appearing in any culture, either surveillance or clinical. Patients for whom a resistant organism was detected on ICU admission or within 48 h of ICU admission were excluded from the analyses of acquisition of resistant organisms. Patients for whom a resistant organism was not detected on ICU admission were divided into two groups according to acquisition (or not) of a resistant organism after 48 h in the ICU. These two groups were compared for demographic variables, ICU admission cause and background illnesses, severity of illness score, and ICU care variables (including the presence of a nosocomial infection, ICU length of stay, antibiotic therapy divided according to first-, second-, and third-line drugs, and a limitation-of-therapy order) present prior to the discovery of a resistant organism. ICU and hospital outcome were also compared. Significant variables in this univariate analysis (P , .1) were included in a multivariate logistic regression analysis with the presence of a resistant organism used as the outcome variable. In order to validate inclusion of patients from the two ICUs into a single analysis, the two populations were compared for the same variables as above. In addition, nosocomial infection rates and antibiotic use were compared. As limitation of life support
was significantly negatively associated with the development of a resistant organism, a comparison was performed of ICU fatalities according to the presence of a limitation-of-therapy order. Incidence of resistant organisms and the mean number of antibiotics administered per day (total number of antibiotic days/ICU length of stay) were calculated and compared. Categorical variables were compared using the x2 or Pearson exact test and continuous variables using Student t test (for normally distributed variables) or the Wilcoxon rank test (for non-normally distributed variables). Normality of distribution was determined graphically. Significance was defined as P , .05, and all tests were two tailed. Statistics were generated using SAS, version 8.02 (SAS Institute Inc.; Cary, NC).
Results Resistant organisms were isolated from 44/337 (13%) Toronto ICU patients and 65/113 (57%) Jerusalem ICU patients ( P , .001). Resistant bacteria were
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Table 2—Incidence of Unique Bacterial Isolates and Resistance Profile According to ICU of Admission Jerusalem Bacteria Third-generation cephalosporin resistance Acinetobacter baumannii Enterobacter cloacae Escherichia coli Klebsiella pneumoniae Pseudomonas aeruginosa Fluoroquinolone resistance Acinetobacter baumannii Enterobacter cloacae Escherichia coli Klebsiella pneumoniae Pseudomonas aeruginosa Carbapenem resistance Acinetobacter baumannii Enterobacter cloacae Escherichia coli Klebsiella pneumoniae Pseudomonas aeruginosa VRE and MRSA Enterococcus faecium Staphylococcus aureus
Toronto
Total Number of Isolates
Resistant Isolates
%
Total Number of Isolates
Resistant Isolates
94 13 48 47 150
90 4 25 24 31
96 31 52 51 21
8 28 43 17 63
6 n/a 3 0 24
94 13 48 47 150
91 0 28 19 86
97 0 58 40 57
8 28 43 17 63
94 13 48 47 150
73 1 0 1 45
78 8 0 2 30
12 25
9 11
75 44
%
P Value
75 7 0 35
.049 ... , .001 , .001 .032
2 4 12 0 37
25 14 28 0 59
, .001 .297 .002 .002 .933
8 28 43 17 63
0 0 0 0 14
0 0 0 0 23
, .001 .3 ... 1 .219
4 97
0 20
0 21
.019 .017
For Pseudomonas aeruginosa, resistance to ceftazidime only considered under third-generation cephalosporins. MRSA 5 methicillin-resistant Staphylococcus aureus; n/a 5 not available; VRE 5 vancomycin-resistant enterococci.
significantly more common in the Jerusalem ICU over all five classes of antibiotic resistance (Table 2). To identify factors associated with the development of resistant organisms, data derived from the ICU admission prior to the discovery of a resistant organism were combined for all ICU patients and compared according to the presence of a resistant bacterial isolate (Table 1). Patients who were infected/ colonized with a resistant organism on ICU admission (27/450, 6%) were not at risk for acquiring a resistant organism during their ICU stay and were therefore excluded from this analysis. Overall, resistant bacteria were acquired by 82 of the remaining 423 (19%) patients in the cohort. A logistic regression analysis (including the variables marked in Table 1) showed that only the presence of a DNR order was significantly associated with developing a resistant bacteria, but in a protective manner (ie, the absence of a DNR order was associated with a higher risk of acquiring resistant bacteria; odds ratio, 2.62; 95% CI, 1.21-5.68; P 5 .014). To validate inclusion of patients from the two ICUs into a single analysis, the two populations were compared. Although the two populations displayed similarity in many variables, the Jerusalem ICU admitted significantly more patients with diabetes mellitus (33/113, 29% in Jerusalem vs 50/337, 15% in Toronto; P , .001), chronic steroid therapy (13/113, 11% vs 16/337; 5%, P 5 .011), poorer functional www.chestpubs.org
capacity (28/113, 25% not functionally independent in Jerusalem vs 38/337, 11% in Toronto; P , .001), and significantly fewer trauma patients (25/113, 22% vs 150/337, 44%; P , .001). More Jerusalem patients were admitted from other care environments (61/113, 54% vs 126/337, 37%; P 5 .002) and had previously received antibiotic therapy (53/113, 49% vs 113/337, 34%; P 5 .005). In contrast, limitation of end-of-life therapy (11/113, 10% vs 88/337, 26%; P , .001) and particularly treatment withdrawal (0/113 vs 45/337, 13%; P , .001) was significantly more common in Toronto. Despite these differences, the overall severity of illness score (APACHE II score, 20 6 9 vs 19 6 7; P 5 .935) and length of ICU stay (14 6 14 vs 13 6 16 days; P 5 .943) were similar, whereas ICU mortality (14/113, 12% vs 75/337, 22%; P 5 .023) and hospital mortality (25/113, 22% vs 112/337, 33%; P 5 .024) were lower for the Jerusalem ICU. Further, there were no significant differences between the ICUs in the incidence of nosocomial pneumonia (32 vs 32 pneumonias/1,000 ventilator days), blood stream infections (4 vs 6 bacteremias/1,000 central venous catheter days), or urinary tract infections, (8 vs 7 urinary tract infections/1,000 urinary catheter days). Use of second- and third-line antibiotics prior to the discovery of a resistant organism was higher in the Jerusalem ICU. There were 1,176 days of antibiotic therapy given in the Jerusalem ICU, of which CHEST / 138 / 3 / SEPTEMBER, 2010
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658 (56%) were first-line antibiotics, 236 (20%) second-line antibiotics, and 282 (24%) third-line antibiotics. In contrast, in Toronto 4,727 days of antibiotic therapy were administered, including 3,657 (77%) first-line antibiotics ( P , .001 compared with Jerusalem first-line antibiotic administration), 700 (15%, P , .001 vs Jerusalem) second-line antibiotics, and 370 (8%, P , .001 vs Jerusalem) thirdline antibiotics. To further assess the effect of limitation orders on the incidence of resistant organisms, antibiotic use for ICU fatalities in whom therapy was or was not withdrawn was compared. Resistant organisms were found in 17/44 (39%) patients dying without withdrawal of therapy vs in 5/45 (11%, P 5 .003) of those for whom a withdrawal order was applied. Nonwithdrawal fatalities also received significantly more third-line antibiotics (7 6 14 vs 2 6 4 days; P 5 .031) despite similar ICU lengths of stay (15 6 21 days for non-withdrawal fatalities vs 10 6 11 for withdraw fatalities; P 5 .210).
Discussion To our knowledge, this is the first study to identify end-of-life treatment as a factor potentially affecting the acquisition of resistant bacteria in the ICU. In this cohort, the absence of a DNR order was the only independent and significant risk factor examined that was associated with the acquisition of a resistant organism. Examining the effect of limitation-of-therapy orders on acquisition of resistant bacteria is complex. First, the different limitation orders have different therapeutic implications. For example, an essentially healthy elderly patient undergoing major surgery might sign a “living will” and have a DNR order placed. He could go through his entire postoperative ICU admission with this order having no practical effect if active resuscitation were not required. Conversely, withdrawal of ventilation is almost universally followed by death within a short time.13 Although both are limitation orders, the effect of DNR and withdraw decisions is very different, making comparison of patients complex. Further, patients with a limitation order are generally sicker than full-care patients (APACHE II score, 24 6 7 vs 18 6 7; P , .001) and have a higher mortality (76/99, 77% vs 12/348, 3% for full-care patients, P , .001). This may limit the validity of comparing patients with a limitation order to the remainder of the ICU population as a whole. So which patients should be used as the control group to investigate the effects of limitation orders? We chose to examine only ICU fatalities and compare the 45/88, 50% ICU fatalities with a withdrawal order to 44/88, 50% ICU fatalities without a withdrawal
order. Withdrawal of therapy represented the most extreme form of therapy limitation practiced in the cohort and was associated with 100% mortality. Comparing these patients to other patients who died might accentuate differences to explain the higher incidence of resistant organisms in nonlimitation patients, and indeed, the use of third-line antibiotics was significantly higher in non-withdrawal fatalities (7 6 14 antibiotic days for non-withdraw fatalities vs 2 6 4 days for withdraw fatalities; P 5 .032) despite similar length of ICU stay (14 6 21 vs 10 6 11 days; P 5 .210). So it is suggested that among ICU patients who ultimately die, absence of a limitation-of-therapy order is associated with increased antibiotic use that, in addition to a high severity of illness and intensity of ICU interventions, could lead to a higher prevalence of resistant bacteria. It should be noted that withdrawal of ventilation is illegal in Jerusalem, whereas it is common practice in Toronto, which may introduce bias into this analysis. Is there any other evidence of a connection between end-of-life therapy and antibiotic resistance in bacteria? The Ethicus study13 investigated end-of-life practices in different regions across Europe. The study showed that the incidence of withdrawal of life-supporting therapy prior to ICU death decreases from Northern Europe to Southern Europe (withdrawal occurring before ICU death in 47% vs 18% of cases, respectively). This trend parallels the increase in the incidence of resistant bacteria from Northern to Southern Europe described in the introduction. These findings also parallel the observation in this study that the presence of a limitation-of-therapy order was associated with decreased risk for the acquisition of a resistant organism. This study describes an association between end-of-life care and the risk for acquiring a resistant organism in the ICU. However, other geographic variation in treatments of infectious disease might be significant. For example, antibiotic prescriptions vary from 33 defined daily dosages (DDD) per 1,000 inhabitants per day in Greece to 9.7 DDD per 1,000 inhabitants per day in The Netherlands, whereas prescription of hospital-specific antibiotics ranges from 0.43 DDD/1,000 inhabitants per day in Greece to 0.08 DDD/1,000 inhabitants per day in Sweden.14-16 Antibiotic course lengths also range widely,17,18 as do hand hygiene practices.19 Even bacterial resistance in animals demonstrates geographic variation (also increasing from Northern to Southern Europe).20,21 Thus it is possible that end-of-life care represents only a surrogate marker for other geographic or cultural differences between Jerusalem and Toronto.22 This study has limitations. As only two centers were involved, the study could not exclude other influences (including climate difference, ICU layout, nurse-to-patient ratio, infection control practices, and
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general differences in bacterial resistance) that may have affected the acquisition of resistant bacteria. The data were collected sequentially (rather than concurrently) from the two study centers, and as antibiotic resistance is in general increasing, the differences between the two centers may have been exaggerated (although data comparisons not presented show similar resistance rates in each hospital across the two collection periods). The thresholds for obtaining clinical or surveillance cultures were not formally assessed and may have been different in the two ICUs, and molecular typing of isolates was not routinely performed in order to assess the degree of patient-topatient transmission of resistant organisms. As the study investigated acquisition of resistant bacteria, no distinction was made between bacteria causing colonization or infection. Finally, the statistical basis of the analysis is based on the assumption that the patients from the two ICUs are similar and can be combined into one homogeneous population. As noted, there were differences in the patient populations between the two centers. This study is the first to identify that choices in endof-life therapy may be a risk factor for the acquisition of resistant bacteria in the ICU. A suggested mechanism is that non-withdrawal of therapy in very sick ICU patients who ultimately die leads to increased use of antibiotics and thus an increased incidence and prevalence of resistant bacteria in the ICU. The study is observational, small, and hypothesis generating. Its findings require further investigation and confirmation in a multicenter, prospective observational study that is planned that will compare bacterial resistance patterns and end-of-life practices in different centers within homogeneous geographic regions. Acknowledgments Author contributions: Dr Levin: contributed as principal investigator to study concept, data collection, data analysis, and manuscript composition. Dr Simor: contributed to study design, study supervision at Toronto the site, and provision of all microbiologic data at the Toronto site. Dr Moses: contributed to provision of all microbiologic data at the Jerusalem site. Dr Sprung: contributed to mentorship, study design, data analysis, and manuscript review. Financial/nonfinancial disclosures: The authors have reported to CHEST that no potential conflicts of interest exist with any companies/organizations whose products or services may be discussed in this article. Other contributions: This study was performed at Hadassah Hebrew University Medical Center, Jerusalem, Israel, and Sunnybrook Health Sciences Center, Toronto, Ontario, Canada.
References 1. 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):1-12. www.chestpubs.org
2. Alberti C, Brun-Buisson C, Burchardi H, et al. Epidemiology of sepsis and infection in ICU patients from an international multicentre cohort study. Intensive Care Med. 2002;28(2):108-121. 3. European Antimicrobial Resistance Surveillance System 2008 annual report. http://www.rivm.nl/earss/Images/EARSS%20 2008_final_tcm61-65020.pdf. Accessed March 31, 2010. 4. Sader HS, Streit JM, Fritsche TR, Jones RN. Antimicrobial susceptibility of gram-positive bacteria isolated from European medical centres: results of the Daptomycin Surveillance Programme (2002-2004). Clin Microbiol Infect. 2006;12(9): 844-852. 5. Hanberger H, Diekema D, Fluit A, et al. Surveillance of antibiotic resistance in European ICUs. J Hosp Infect. 2001; 48(3):161-176. 6. Zilberberg MD, Shorr AF, Kollef MH. Growth and geographic variation in hospitalizations with resistant infections, United States, 2000-2005. Emerg Infect Dis. 2008;14(11): 1756-1758. 7. Hortal M, Lovgren M, de la Hoz F, et al; PAHO SIREVA-Vigía Study Group. Antibiotic resistance in Streptococcus pneumoniae in six Latin American countries: 1993-1999 surveillance. Microb Drug Resist. 2001;7(4):391-401. 8. Zhanel GG, DeCorby M, Laing N, et al; Canadian Antimicrobial Resistance Alliance (CARA). Antimicrobial-resistant pathogens in intensive care units in Canada: results of the Canadian National Intensive Care Unit (CAN-ICU) study, 2005-2006. Antimicrob Agents Chemother. 2008;52(4):1430-1437. 9. National Nosocomial Infections Surveillance System. National Nosocomial Infections Surveillance (NNIS) System Report, data summary from January 1992 through June 2004, issued October 2004. Am J Infect Control. 2004;32(8):470-485. 10. Knaus WA, Draper EA, Wagner DP, Zimmerman JE. APACHE II: a severity of disease classification system. Crit Care Med. 1985;13(10):818-829. 11. Blot S, Depuydt P, Vandewoude K, De Bacquer D. Measuring the impact of multidrug resistance in nosocomial infection. Curr Opin Infect Dis. 2007;20(4):391-396. 12. Clinical and Laboratory Standards Institute. Performance Standards for Antimicrobial Susceptibility Testing; 17th Informational Supplement. Wayne, PA: Clinical and Laboratory Standards Institute; 2007. CLSI document M100-S17. 13. Sprung CL, Cohen SL, Sjokvist P, et al; Ethicus Study Group. End-of-life practices in European intensive care units: the Ethicus Study. JAMA. 2003;290(6):790-797. 14. Vander Stichele RH, Elseviers MM, Ferech M, Blot S, Goossens H; European Survaillance of Antibiotic Consumption (ESAC) Project Group. Hospital consumption of antibiotics in 15 European countries: results of the ESAC Retrospective Data Collection (1997-2002). J Antimicrob Chemother. 2006; 58(1):159-167. 15. McDonnell Norms Group. Antibiotic overuse: the influence of social norms. J Am Coll Surg. 2008;207(2):265-275. 16. Deschepper R, Grigoryan L, Lundborg CS, et al. Are cultural dimensions relevant for explaining cross-national differences in antibiotic use in Europe? BMC Health Serv Res. 2008;8:123. 17. Corona A, Cislaghi F, Raimondi F, Singer M. Italy versus the United Kingdom: differing styles for treating bacteremia in the critically ill patient, but who’s right? Minerva Anestesiol. 2008;74(5):155-161. 18. Corona A, Bertolini G, Ricotta AM, Wilson APR, Singer M. Variability of treatment duration for bacteraemia in the critically ill: a multinational survey. J Antimicrob Chemother. 2003;52(5):849-852. 19. Allegranzi B, Memish ZA, Donaldson L, Pittet D; World Health Organization Global Patient Safety Challenge Task Force on Religious and Cultural Aspects of Hand Hygiene; World Alliance for Patient Safety. Religion and culture: CHEST / 138 / 3 / SEPTEMBER, 2010
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potential undercurrents influencing hand hygiene promotion in health care. Am J Infect Control. 2009;37(1):28-34. 20. Parveen S, Lukasik J, Scott TM, et al. Geographical variation in antibiotic resistance profiles of Escherichia coli isolated from swine, poultry, beef and dairy cattle farm water retention ponds in Florida. J Appl Microbiol. 2006;100(1):50-57.
21. Hendriksen RS, Mevius DJ, Schroeter A, et al. Prevalence of antimicrobial resistance among bacterial pathogens isolated from cattle in different European countries: 2002-2004. Acta Vet Scand. 2008;50:28. 22. Levin PD, Sprung CL. Cultural differences at the end of life. Crit Care Med. 2003;31(5)(Suppl):S354-S357.
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Original Research CRITICAL CARE MEDICINE
End-of-Life Treatment and Bacterial Antibiotic Resistance A Potential Association Phillip D. Levin, MB, BChir; Andrew E. Simor, MD; Allon E. Moses, MD; and Charles L. Sprung, MD, FCCP
Background: Great variability exists in the occurrence of antibiotic-resistant bacteria in ICUs around the world. The contribution of specific ICU care variables to these geographic variations is unknown. Methods: ICU patients from two ICUs (in Jerusalem and Toronto) who were admitted for . 48 h and who grew a resistant bacteria in any culture during ICU admission were compared with those without resistant organisms across a range of demographic and ICU care interventions. Significant variables were investigated with logistic regression to identify factors predictive of infection/ colonization with a resistant organism. Results: Resistant organisms were acquired by 82/423 (19%) patients. Patients acquiring a resistant organism had a higher incidence of diabetes mellitus (21/82, 26% vs 52/341, 15%; P 5 .026), were more frequently admitted from another ICU (17/82, 21% vs 33/341, 10%; P 5 .005), received more antibiotics in the ICU (19 6 17 vs 14 6 14 days; P 5 .005), and had more ventilator (10 6 10 vs 7 6 8; P 5 .031) and central line days (10 6 8 vs 7 6 8; P , .001). These patients had a lower incidence of limitation-of-therapy orders (9/82, 11% vs 78/341, 23%; P 5 .015). Only the absence of a limitation-of-therapy order (odds ratio, 2.62; 95% CI, 1.21-5.68; P 5 .014) was independently associated with the acquisition of resistant organisms. Further, among ICU fatalities, 5/45 (11%) patients acquired a resistant organism prior to withdrawal vs 17/44 (39%) nonwithdrawal fatalities (P 5 .003). Nonwithdrawal fatalities received significantly more third-line antibiotics (7 6 14 vs 2 6 4; P 5 .031) despite similar ICU lengths of stay (15 6 21 days for nonwithdrawal fatalities vs 10 6 11 for withdraw fatalities; P 5 .210) Conclusions: End-of-life treatment is independently associated with acquisition of resistant bacteria. Patients dying without withdraw orders receive more antibiotics and develop more resistant organisms. These patients may represent a reservoir of resistant bacteria in the ICU. CHEST 2010; 138(3):588–594 Abbreviations: APACHE II 5 Acute Physiology and Chronic Health Evaluation II; DDD 5 defined daily dosages; DNR 5 do not resuscitate; IQR 5 interquartile range; MRSA 5 methicillin-resistant Staphylococcus aureus
are developing antimicrobial resistance Bacteria faster than drug companies are able to develop
new antibiotics. This is happening precisely at a time when there is a lack of new drug development, and the pharmaceutical industry is withdrawing from anti-infective drug development.1 Despite this, up to 20% of ICU patients2 will develop a nosocomial infection, often caused by highly resistant bacteria, and some ICU patients are now dying from bacterial infections for which there is no effective antibiotic therapy. Although bacterial resistance is increasing overall, great variability exists in the prevalence of resistant bacteria over geographic areas.3 For example, in south-
ern Europe (Greece, Portugal, Israel), methicillinresistant Staphylococcus aureus (MRSA) accounts for approximately 40% of all S aureus isolates, whereas in Scandinavia MRSA accounts for , 2% of S aureus isolates.3,4 Similar trends exist for multidrug-resistant gram-negative bacteria3,5 and can also be found across North6 and South America.7 Canadian ICUs have a relatively low incidence of resistant bacteria, with MRSA accounting for 22% of all S aureus isolates in ICU patients and multidrug-resistant gram-negative bacteria accounting for 13% of Pseudomonas aeruginosa isolates and 0% to 1% of Escherichia coli, Enterobacter cloacae, and Klebsiella pneumoniae.8 Resistance rates
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in Israel are similar to Southern Europe,3 considerably higher than in Canada.9 Descriptions of geographic variability in antibiotic resistance come from population-based studies including data on thousands of clinical cultures with little or no patient data. Thus, patient care-based explanations for the variability in resistance are difficult to determine. In contrast, studies focusing on specific nosocomial infections (defining risk factors for conditions such as ventilator-associated pneumonia, for example), do not address the range of ICU interventions that could contribute to differences in bacterial epidemiology in an ICU. The current study attempts to address this gap. It compares the acquisition of resistant bacteria in ICU patients admitted in two geographically distinct locations (Toronto, ON, Canada, and Jerusalem, Israel). Empirically, these ICUs admitted similar patient populations and had similar standards of ICU care with two major differences: prevalence of resistant bacteria (lower in Toronto), and end-of-life care. In Jerusalem, do-not-resuscitate (DNR) orders and withholding therapy were acceptable but withdrawal of therapy was not performed, whereas in Toronto all these were acceptable. The study examined whether end-of-life care or other care-related parameters were significant predictors of the acquisition of resistant organisms in the ICU. This is of importance as it may identify possible areas of change to reduce the risks associated with colonization or infection with highly resistant bacteria.
Materials and Methods Data were collected from two ICUs: the 20-bed medicalsurgical ICU of the 1,140-bed Sunnybrook Health Sciences Centre, Toronto, ON, Canada (“Toronto” ICU), and the 12-bed medical-surgical ICU of the 750-bed Hadassah Hebrew University Medical Center, Jerusalem, Israel (“Jerusalem” ICU). Both Manuscript received November 21, 2009; revision accepted March 23, 2009. Affiliations: From the Department of Anesthesiology and Critical Care Medicine (Drs Levin and Sprung), and the Department of Clinical Microbiology and Infectious Diseases (Dr Moses), Hadassah Hebrew University Medical Center, Jerusalem, Israel; and the Department of Microbiology (Dr Simor), Sunnybrook Health Sciences Center and Division of Infectious Diseases, University of Toronto, Toronto, ON, Canada. Presented in part at The Society of Critical Care Medicine 38th Critical Care Congress, Nashville, Tennessee, February 2009. Funding/Support: This study was funded in part by an internal Hadassah Medical Organization research grant. Correspondence to: Phillip Levin, MB, BChir, Department of Anesthesiology and Critical Care Medicine, POB 12000, Jerusalem 91120, Israel; e-mail: [email protected] © 2010 American College of Chest Physicians. Reproduction of this article is prohibited without written permission from the American College of Chest Physicians (http://www.chestpubs.org/ site/misc/reprints.xhtml). DOI: 10.1378/chest.09-2757 www.chestpubs.org
hospitals are university-affiliated tertiary-care referral teaching hospitals. Data were collected in Toronto from January 2003 to December 2003 and in Jerusalem from July 2005 to January 2006. Data were collected prospectively on consecutive patients admitted to the ICU for . 48 h. The ethics review board of each hospital approved the protocol and, as this study was observational in nature, waived the requirements for informed consent. Data Collection Demographics: Basic demographic data included admission and discharge dates, ICU and hospital outcome, hospital admission prior to ICU admission, prehospital functional capacity, operative status, antibiotic therapy during 14 days prior to ICU admission (whether in or out of hospital), and any limitations of end-of-life therapy. The presence of severe ischemic heart disease or congestive heart failure, severe lung disease (as defined in the Acute Physiology and Chronic Health Evaluation II [APACHE II] score10), diabetes mellitus, oral steroid use, cirrhosis, chronic dialysis, organ transplantation, immunosuppression, and malignancy was recorded (Table 1). The APACHE II score was calculated from data collected during the first 24 h in the ICU. ICU Course Data and Antibiotic Therapy: Data were collected daily in the ICU regarding the presence of mechanical ventilation, all intravascular catheters, and the presence of a urinary catheter. The dates antibiotics were started and stopped were collected. Antibiotics were divided prospectively into three broad groups (first, second, and third line) according to empirical usage in the ICU. First-line antibiotics were defined as amoxicillin, ampicillin, amoxicillin/clavulanic acid, azactam, azithromycin, cefamazine, cefuroxime, ciprofloxacin, clindamycin, cloxacillin, metronidazole, gentamicin, levofloxacin, penicillin, piperacillin, rifampicin, co-trimoxazole, and tobramycin. Second-line antibiotics were defined as amikacin, cefepime, cefotaxime, cefotetan, ceftazidime, ceftriaxone, and piperacillin/tazobactam. Third-line antibiotics were defined as colistin, ertapenem, imipenem, linezolid, meropenem, ampicillin/sulbactam, and vancomycin. Nosocomial Infections: Nosocomial pneumonia, bacteremia (primary and secondary), and urinary tract infections were sought prospectively according to the National Nosocomial Infection Surveillance system criteria.11 The rates of occurrence of ventilatorassociated pneumonia per 1,000 days of mechanical ventilation, bloodstream infections per 1,000 central venous catheter days, and urinary tract infections per 1,000 catheter days were calculated. Microbiology Data: Results of all microbiologic studies, whether clinically indicated (as ordered by the physician) or surveillance studies, were included. The following criteria were used to define resistant bacteria: MRSA, vancomycin-resistant enterococci, and gram-negative bacteria resistant to any one or more of the following: third-generation cephalosporins (ceftazidime only for P aeruginosa, any third-generation cephalosporin for other gram-negative bacteria), fluoroquinolones, or carbapenem antibiotics. Microbiology was performed according to standard techniques.12 For each patient, unique isolates were defined as the first occurrence of a particular isolate from a specific culture site with a single resistance pattern. Only unique isolates were included in the statistical analyses. Data Analysis The study is a cohort analysis analyzing risk factors for the acquisition of resistant bacteria in ICU patients. Each patient was CHEST / 138 / 3 / SEPTEMBER, 2010
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Table 1—Comparison of Patients With and Without Resistant Organisms Characteristic Age Sex, male On ICU admission Not functionally independent Presence of severe heart disease Diabetes mellitusa Presence of severe lung disease Cirrhosis Malignancy Steroid therapy Immunosuppressed Dialysis Antibiotic therapy prior to ICU admissiona Surgery in 24 h prior to admission Trauma etiology Admission prior to ICU admissiona Chronic care facility Other hospital Other ward Other ICU Length of hospital stay before ICU Diagnosed with nosocomial infection ICU length of stay prior to acquisition of resistant organisma Ventilation daysa Central venous catheter daysa Urinary catheter days Antibiotic days (in ICU)a First-line antibiotic days Second-line antibiotic days Third-line antibiotic days Limitation of therapya No CPR Withhold Withdraw Outcomes ICU length of stay ICU mortality Hospital mortality
No Resistant Organism (n 5 341)
Resistant Organism (n 5 82)
56 6 22 205 (60)
56 6 22 50 (61)
.997 .887
43 (13) 28 (8) 52 (15) 7 (2) 6 (2) 71 (21) 19 (6) 11 (3) 8 (2) 111 (33) 169 (50) 139 (41) 120 (35) 7 (2) 27 (8) 81 (24) 33 (10) 4 6 20 82 (24) 969
9 (11) 6 (7) 21 (26) 3 (4) 4 (5) 11 (13) 6 (7) 2 (2) 1 (1) 35 (43) 39 (48) 32 (39) 43 (52) 4 (5) 8 (10) 25 (30) 17 (21) 8 6 22 20 (24) 16 6 32
.706 .789 .026 .416 .108 .128 .601 1 1 .066 .727 .773 .066 .236 .587 .206 .005 .117 .948 .07
768 768 968 14 6 14 10 6 11 263 163 78 (23) 24 (7) 14 (4) 40 (12)
10 6 10 10 6 8 11 6 12 19 6 17 13 6 14 465 365 9 (11) 4 (5) 1 (1) 4 (5)
.031 , .001 .172 .005 .136 .002 , .001 .015 .48 .322 .072
9 (3) 67 (20) 99 (29)
27 (33) 11 (13) 22 (27)
, .001 .191 .67
P Value
Data are presented as No. (%) or mean 6 SD. Data from period prior to acquisition of resistant organism. Included in multivariate logistic regression analysis as potential predictors of acquisition of resistant organism.
a
defined according to the presence or absence of a resistant organism appearing in any culture, either surveillance or clinical. Patients for whom a resistant organism was detected on ICU admission or within 48 h of ICU admission were excluded from the analyses of acquisition of resistant organisms. Patients for whom a resistant organism was not detected on ICU admission were divided into two groups according to acquisition (or not) of a resistant organism after 48 h in the ICU. These two groups were compared for demographic variables, ICU admission cause and background illnesses, severity of illness score, and ICU care variables (including the presence of a nosocomial infection, ICU length of stay, antibiotic therapy divided according to first-, second-, and third-line drugs, and a limitation-of-therapy order) present prior to the discovery of a resistant organism. ICU and hospital outcome were also compared. Significant variables in this univariate analysis (P , .1) were included in a multivariate logistic regression analysis with the presence of a resistant organism used as the outcome variable. In order to validate inclusion of patients from the two ICUs into a single analysis, the two populations were compared for the same variables as above. In addition, nosocomial infection rates and antibiotic use were compared. As limitation of life support
was significantly negatively associated with the development of a resistant organism, a comparison was performed of ICU fatalities according to the presence of a limitation-of-therapy order. Incidence of resistant organisms and the mean number of antibiotics administered per day (total number of antibiotic days/ICU length of stay) were calculated and compared. Categorical variables were compared using the x2 or Pearson exact test and continuous variables using Student t test (for normally distributed variables) or the Wilcoxon rank test (for non-normally distributed variables). Normality of distribution was determined graphically. Significance was defined as P , .05, and all tests were two tailed. Statistics were generated using SAS, version 8.02 (SAS Institute Inc.; Cary, NC).
Results Resistant organisms were isolated from 44/337 (13%) Toronto ICU patients and 65/113 (57%) Jerusalem ICU patients ( P , .001). Resistant bacteria were
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Table 2—Incidence of Unique Bacterial Isolates and Resistance Profile According to ICU of Admission Jerusalem Bacteria Third-generation cephalosporin resistance Acinetobacter baumannii Enterobacter cloacae Escherichia coli Klebsiella pneumoniae Pseudomonas aeruginosa Fluoroquinolone resistance Acinetobacter baumannii Enterobacter cloacae Escherichia coli Klebsiella pneumoniae Pseudomonas aeruginosa Carbapenem resistance Acinetobacter baumannii Enterobacter cloacae Escherichia coli Klebsiella pneumoniae Pseudomonas aeruginosa VRE and MRSA Enterococcus faecium Staphylococcus aureus
Toronto
Total Number of Isolates
Resistant Isolates
%
Total Number of Isolates
Resistant Isolates
94 13 48 47 150
90 4 25 24 31
96 31 52 51 21
8 28 43 17 63
6 n/a 3 0 24
94 13 48 47 150
91 0 28 19 86
97 0 58 40 57
8 28 43 17 63
94 13 48 47 150
73 1 0 1 45
78 8 0 2 30
12 25
9 11
75 44
%
P Value
75 7 0 35
.049 ... , .001 , .001 .032
2 4 12 0 37
25 14 28 0 59
, .001 .297 .002 .002 .933
8 28 43 17 63
0 0 0 0 14
0 0 0 0 23
, .001 .3 ... 1 .219
4 97
0 20
0 21
.019 .017
For Pseudomonas aeruginosa, resistance to ceftazidime only considered under third-generation cephalosporins. MRSA 5 methicillin-resistant Staphylococcus aureus; n/a 5 not available; VRE 5 vancomycin-resistant enterococci.
significantly more common in the Jerusalem ICU over all five classes of antibiotic resistance (Table 2). To identify factors associated with the development of resistant organisms, data derived from the ICU admission prior to the discovery of a resistant organism were combined for all ICU patients and compared according to the presence of a resistant bacterial isolate (Table 1). Patients who were infected/ colonized with a resistant organism on ICU admission (27/450, 6%) were not at risk for acquiring a resistant organism during their ICU stay and were therefore excluded from this analysis. Overall, resistant bacteria were acquired by 82 of the remaining 423 (19%) patients in the cohort. A logistic regression analysis (including the variables marked in Table 1) showed that only the presence of a DNR order was significantly associated with developing a resistant bacteria, but in a protective manner (ie, the absence of a DNR order was associated with a higher risk of acquiring resistant bacteria; odds ratio, 2.62; 95% CI, 1.21-5.68; P 5 .014). To validate inclusion of patients from the two ICUs into a single analysis, the two populations were compared. Although the two populations displayed similarity in many variables, the Jerusalem ICU admitted significantly more patients with diabetes mellitus (33/113, 29% in Jerusalem vs 50/337, 15% in Toronto; P , .001), chronic steroid therapy (13/113, 11% vs 16/337; 5%, P 5 .011), poorer functional www.chestpubs.org
capacity (28/113, 25% not functionally independent in Jerusalem vs 38/337, 11% in Toronto; P , .001), and significantly fewer trauma patients (25/113, 22% vs 150/337, 44%; P , .001). More Jerusalem patients were admitted from other care environments (61/113, 54% vs 126/337, 37%; P 5 .002) and had previously received antibiotic therapy (53/113, 49% vs 113/337, 34%; P 5 .005). In contrast, limitation of end-of-life therapy (11/113, 10% vs 88/337, 26%; P , .001) and particularly treatment withdrawal (0/113 vs 45/337, 13%; P , .001) was significantly more common in Toronto. Despite these differences, the overall severity of illness score (APACHE II score, 20 6 9 vs 19 6 7; P 5 .935) and length of ICU stay (14 6 14 vs 13 6 16 days; P 5 .943) were similar, whereas ICU mortality (14/113, 12% vs 75/337, 22%; P 5 .023) and hospital mortality (25/113, 22% vs 112/337, 33%; P 5 .024) were lower for the Jerusalem ICU. Further, there were no significant differences between the ICUs in the incidence of nosocomial pneumonia (32 vs 32 pneumonias/1,000 ventilator days), blood stream infections (4 vs 6 bacteremias/1,000 central venous catheter days), or urinary tract infections, (8 vs 7 urinary tract infections/1,000 urinary catheter days). Use of second- and third-line antibiotics prior to the discovery of a resistant organism was higher in the Jerusalem ICU. There were 1,176 days of antibiotic therapy given in the Jerusalem ICU, of which CHEST / 138 / 3 / SEPTEMBER, 2010
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658 (56%) were first-line antibiotics, 236 (20%) second-line antibiotics, and 282 (24%) third-line antibiotics. In contrast, in Toronto 4,727 days of antibiotic therapy were administered, including 3,657 (77%) first-line antibiotics ( P , .001 compared with Jerusalem first-line antibiotic administration), 700 (15%, P , .001 vs Jerusalem) second-line antibiotics, and 370 (8%, P , .001 vs Jerusalem) thirdline antibiotics. To further assess the effect of limitation orders on the incidence of resistant organisms, antibiotic use for ICU fatalities in whom therapy was or was not withdrawn was compared. Resistant organisms were found in 17/44 (39%) patients dying without withdrawal of therapy vs in 5/45 (11%, P 5 .003) of those for whom a withdrawal order was applied. Nonwithdrawal fatalities also received significantly more third-line antibiotics (7 6 14 vs 2 6 4 days; P 5 .031) despite similar ICU lengths of stay (15 6 21 days for non-withdrawal fatalities vs 10 6 11 for withdraw fatalities; P 5 .210).
Discussion To our knowledge, this is the first study to identify end-of-life treatment as a factor potentially affecting the acquisition of resistant bacteria in the ICU. In this cohort, the absence of a DNR order was the only independent and significant risk factor examined that was associated with the acquisition of a resistant organism. Examining the effect of limitation-of-therapy orders on acquisition of resistant bacteria is complex. First, the different limitation orders have different therapeutic implications. For example, an essentially healthy elderly patient undergoing major surgery might sign a “living will” and have a DNR order placed. He could go through his entire postoperative ICU admission with this order having no practical effect if active resuscitation were not required. Conversely, withdrawal of ventilation is almost universally followed by death within a short time.13 Although both are limitation orders, the effect of DNR and withdraw decisions is very different, making comparison of patients complex. Further, patients with a limitation order are generally sicker than full-care patients (APACHE II score, 24 6 7 vs 18 6 7; P , .001) and have a higher mortality (76/99, 77% vs 12/348, 3% for full-care patients, P , .001). This may limit the validity of comparing patients with a limitation order to the remainder of the ICU population as a whole. So which patients should be used as the control group to investigate the effects of limitation orders? We chose to examine only ICU fatalities and compare the 45/88, 50% ICU fatalities with a withdrawal order to 44/88, 50% ICU fatalities without a withdrawal
order. Withdrawal of therapy represented the most extreme form of therapy limitation practiced in the cohort and was associated with 100% mortality. Comparing these patients to other patients who died might accentuate differences to explain the higher incidence of resistant organisms in nonlimitation patients, and indeed, the use of third-line antibiotics was significantly higher in non-withdrawal fatalities (7 6 14 antibiotic days for non-withdraw fatalities vs 2 6 4 days for withdraw fatalities; P 5 .032) despite similar length of ICU stay (14 6 21 vs 10 6 11 days; P 5 .210). So it is suggested that among ICU patients who ultimately die, absence of a limitation-of-therapy order is associated with increased antibiotic use that, in addition to a high severity of illness and intensity of ICU interventions, could lead to a higher prevalence of resistant bacteria. It should be noted that withdrawal of ventilation is illegal in Jerusalem, whereas it is common practice in Toronto, which may introduce bias into this analysis. Is there any other evidence of a connection between end-of-life therapy and antibiotic resistance in bacteria? The Ethicus study13 investigated end-of-life practices in different regions across Europe. The study showed that the incidence of withdrawal of life-supporting therapy prior to ICU death decreases from Northern Europe to Southern Europe (withdrawal occurring before ICU death in 47% vs 18% of cases, respectively). This trend parallels the increase in the incidence of resistant bacteria from Northern to Southern Europe described in the introduction. These findings also parallel the observation in this study that the presence of a limitation-of-therapy order was associated with decreased risk for the acquisition of a resistant organism. This study describes an association between end-of-life care and the risk for acquiring a resistant organism in the ICU. However, other geographic variation in treatments of infectious disease might be significant. For example, antibiotic prescriptions vary from 33 defined daily dosages (DDD) per 1,000 inhabitants per day in Greece to 9.7 DDD per 1,000 inhabitants per day in The Netherlands, whereas prescription of hospital-specific antibiotics ranges from 0.43 DDD/1,000 inhabitants per day in Greece to 0.08 DDD/1,000 inhabitants per day in Sweden.14-16 Antibiotic course lengths also range widely,17,18 as do hand hygiene practices.19 Even bacterial resistance in animals demonstrates geographic variation (also increasing from Northern to Southern Europe).20,21 Thus it is possible that end-of-life care represents only a surrogate marker for other geographic or cultural differences between Jerusalem and Toronto.22 This study has limitations. As only two centers were involved, the study could not exclude other influences (including climate difference, ICU layout, nurse-to-patient ratio, infection control practices, and
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general differences in bacterial resistance) that may have affected the acquisition of resistant bacteria. The data were collected sequentially (rather than concurrently) from the two study centers, and as antibiotic resistance is in general increasing, the differences between the two centers may have been exaggerated (although data comparisons not presented show similar resistance rates in each hospital across the two collection periods). The thresholds for obtaining clinical or surveillance cultures were not formally assessed and may have been different in the two ICUs, and molecular typing of isolates was not routinely performed in order to assess the degree of patient-topatient transmission of resistant organisms. As the study investigated acquisition of resistant bacteria, no distinction was made between bacteria causing colonization or infection. Finally, the statistical basis of the analysis is based on the assumption that the patients from the two ICUs are similar and can be combined into one homogeneous population. As noted, there were differences in the patient populations between the two centers. This study is the first to identify that choices in endof-life therapy may be a risk factor for the acquisition of resistant bacteria in the ICU. A suggested mechanism is that non-withdrawal of therapy in very sick ICU patients who ultimately die leads to increased use of antibiotics and thus an increased incidence and prevalence of resistant bacteria in the ICU. The study is observational, small, and hypothesis generating. Its findings require further investigation and confirmation in a multicenter, prospective observational study that is planned that will compare bacterial resistance patterns and end-of-life practices in different centers within homogeneous geographic regions. Acknowledgments Author contributions: Dr Levin: contributed as principal investigator to study concept, data collection, data analysis, and manuscript composition. Dr Simor: contributed to study design, study supervision at Toronto the site, and provision of all microbiologic data at the Toronto site. Dr Moses: contributed to provision of all microbiologic data at the Jerusalem site. Dr Sprung: contributed to mentorship, study design, data analysis, and manuscript review. Financial/nonfinancial disclosures: The authors have reported to CHEST that no potential conflicts of interest exist with any companies/organizations whose products or services may be discussed in this article. Other contributions: This study was performed at Hadassah Hebrew University Medical Center, Jerusalem, Israel, and Sunnybrook Health Sciences Center, Toronto, Ontario, Canada.
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