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The Epidemiology of Staphylococcus aureus Transmission in the Anesthesia Work Area

Loftus, Randy W. MD*; Koff, Matthew D. MS, MD*; Brown, Jeremiah R. MS, PhD; Patel, Hetal M. BS*; Jensen, Jens T. MS*; Reddy, Sundara MD; Ruoff, Kathryn L. PhD§; Heard, Stephen O. MD; Yeager, Mark P. MD*; Dodds, Thomas M. MD*

doi: 10.1213/ANE.0b013e3182a8c16a
Patient Safety: Research Report

BACKGROUND: Little is known regarding the epidemiology of intraoperative Staphylococcus aureus transmission. The primary aim of this study was to examine the mode of transmission, reservoir of origin, transmission locations, and antibiotic susceptibility for frequently encountered S aureus strains (phenotypes) in the anesthesia work area. Our secondary aims were to examine phenotypic associations with 30-day postoperative patient cultures, phenotypic growth rates, and risk factors for phenotypic isolation.

METHODS: S aureus isolates previously identified as possible intraoperative bacterial transmission events by class of pathogen, temporal association, and analytical profile indexing were subjected to antibiotic disk diffusion sensitivity. The combination of these techniques was then used to confirm S aureus transmission events and to classify them as occurring within or between operative cases (mode). The origin of S aureus transmission events was determined via use of a previously validated experimental model and links to 30-day postoperative patient cultures confirmed via pulsed-field gel electrophoresis. Growth rates were assessed via time-to-positivity analysis, and risk factors for isolation were characterized via logistic regression.

RESULTS: One hundred seventy S aureus isolates previously implicated as possible intraoperative transmission events were further subdivided by analytical profile indexing phenotype. Two phenotypes, phenotype P (patients) and phenotype H (hands), accounted for 65% of isolates. Phenotype P and phenotype H contributed to at least 1 confirmed transmission event in 39% and 28% of cases, respectively. Patient skin surfaces (odds ratio [OR], 8.40; 95% confidence interval [CI], 2.30–30.73) and environmental (OR, 10.89; 95% CI, 1.29–92.13) samples were more likely than provider hands (referent) to have phenotype P positivity. Phenotype P was more likely than phenotype H to be resistant to methicillin (OR, 4.38; 95% CI, 1.59–12.06; P = 0.004) and to be linked to 30-day postoperative patient cultures (risk ratio, 36.63 [risk difference, 0.174; 95% CI, 0.019–0.328]; P < 0.001). Phenotype P exhibited a faster growth rate for methicillin resistant and for methicillin susceptible than phenotype H (phenotype P: median, 10.32H; interquartile range, 10.08–10.56; phenotype H: median, 10.56H; interquartile range, 10.32–10.8; P = 0.012). Risk factors for isolation of phenotype P included age (OR, 14.11; 95% CI, 3.12–63.5; P = 0.001) and patient exposure to the hospital ward (OR, 41.11; 95% CI, 5.30–318.78; P < 0.001).

CONCLUSIONS: Two S aureus phenotypes are frequently transmitted in the anesthesia work area. A patient and environmentally derived phenotype is associated with increased risk of antibiotic resistance and links to 30-day postoperative patient cultures as compared with a provider hand-derived phenotype. Future work should be directed toward improved screening and decolonization of patients entering the perioperative arena and improved intraoperative environmental cleaning to attenuate postoperative health care–associated infections.

Published ahead of print June 17, 2014

From the *Department of Anesthesiology, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire; The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire; Department of Anesthesiology, The University of Iowa Hospitals and Clinics, Iowa City, Iowa; §Department of Pathology, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire; and Departments of Anesthesiology and Surgery, University of Massachusetts Medical School and UMass Memorial Medical Center, Worcester, Massachusetts.

Accepted for publication July 31, 2013.

Published ahead of print June 17, 2014

Funding: Anesthesia Patient Safety Foundation.

Conflict of Interest: See Disclosures at the end of the article.

Reprints will not be available from the authors.

Address correspondence to Randy W. Loftus, MD, Department of Anesthesiology, Dartmouth-Hitchcock Medical Center, 1 Medical Center Dr., Lebanon, NH 03756. Address e-mail to randy.w.loftus@hitchcock.org.

Health care–associated infections (HCAIs) are a tremendous health care problem, associated with increased patient morbidity and mortality and an economic burden amounting to approximately 4.5 billion dollars annually.1–5 This problem certainly applies to the perioperative arena, where 8% to 16% of patients suffer from ≥1 HCAIs, including surgical site infections (SSIs), the number one surgical complication today.6–10 Surprisingly, little is known regarding the perioperative epidemiology of Staphylococcus aureus transmission, the number one bacterial pathogen known to cause SSIs. This lack of knowledge may partially explain the equivocal evidence surrounding the efficacy of preoperative patient decolonization strategies,11–13 the subsequent lack of compliance with preoperative preventive measures,9 and ultimately, the prevalence of HCAIs.1–5 The primary aims of this study were to characterize the mode of transmission (between and/or within case), reservoir of origin, transmission locations, and antibiotic susceptibility for S aureus strains and/or strain characteristics (phenotypes) commonly encountered in the intraoperative anesthesia work area. Our secondary aims were to examine phenotypic associations with 30-day postoperative patient cultures, phenotypic growth rates, and risk factors for isolation of a more virulent phenotype.

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METHODS

General Approach

We previously conducted a prospective, randomized, observational study at 3 major academic medical centers, Dartmouth-Hitchcock Medical Center in New Hampshire, the University of Iowa Hospitals and Clinics in Iowa, and the UMass Memorial Medical Center in Massachusetts to characterize the frequency and implications of bacterial transmission events to intravascular devices in the intraoperative arena.9 The study took place over 12 consecutive months (from March 2009 to February 2010). Approval was obtained at each study site from the respective IRB for the protection of human subjects, with a waiver for informed patient consent. For the current study, additional approval was obtained from the Committee for Protection of Human Subjects at Dartmouth-Hitchcock Medical Center.

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Reservoir Sampling

Sampling procedures were standardized at all 3 study sites with quality assurance monitoring of sampling techniques performed on 2 separate occasions.9

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Hand Sampling:

Using a previously validated, modified glove juice technique, provider hands were sampled before, during, and after patient care.

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Patient Sampling:

The patient’s nasopharynx was sampled to assess the patient reservoir because nasopharyngeal pathogens have been strongly associated with postoperative SSIs. The patient’s axilla was also sampled because the axilla harbors up to 30% of pathogens colonizing patient skin.

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Environmental Sampling:

Two sites on the anesthesia machine, the adjustable pressure-limiting valve and the agent dial, are proven representatives of the anesthesia environment and have been associated with an increase in the probability of bacterial contamination of the IV stopcock set. These sites were sampled at baseline (after active decontamination at case start for case 1 and routine decontamination at case start for case 2) and at end of the case via a standardized method. Active decontamination involved targeted cleaning of the study sites by the study investigators using a quaternary ammonium compound (Dimension III; Butcher’s, Sturtevant, WI) strictly according to the manufacturer’s protocol, while routine decontamination was performed by the usual operating room personnel according to their standard procedure applied to the environment between operative cases. Routine decontamination also involved use of the same quaternary ammonium compound, but personnel were not asked to specifically target the adjustable pressure-limiting and agent dial.

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Sampling of Peripheral IV Tubing 3-Way Stopcocks:

Bacterial cultures obtained from stopcock sets immediately on removal from the packaging (at case start) were shown to be invariably negative. A positive stopcock set at case end was defined as ≥1 colony forming unit (CFU) per culture plate, consistent with prior study protocols.

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Bacterial Isolates Previously Obtained and Archived

Using the sampling methodology described above, we previously examined 2170 environmental bacterial culture sites, 2640 health care provider hand cultures, and 1087 patient skin cultures in 274 case-pairs representing 548 operating rooms across the 3 major academic medical centers. From these reservoirs, >6000 potential and 2184 true bacterial pathogens were isolated and archived for later analysis. Each pathogen received a unique identification number linked to a specific date, operating room, reservoir, patient, and provider.9

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Reservoir Contribution to Intraoperative Transmission Events and Infection

We used a validated model for study of intraoperative bacterial cross-contamination (Fig. 1) in this prior study to prospectively evaluate the relative contribution of known intraoperative bacterial reservoirs to intraoperative bacterial transmission events to high-risk intravascular devices (stopcocks). These transmission events were identified via use of the experimental model (Fig. 1) and compared with the causative organism of 30-day postoperative infections via pulsed-field gel electrophoresis (PFGE) analysis. The potential association of each archived bacterial pathogen isolate with patient, provider and environmental characteristics and patient bacterial cultures, in those patients with 30-day postoperative infections, was assessed. Basic patient, procedural, and provider information was collected and linked to each frozen pathogen. This demographic information included the hospital site, age, sex, case 1 or case 2, ASA physical status classification, Study on the Efficacy of Nosocomial Infection Control (SENIC)14 score (an index predicting the probability of postoperative HCAI development for a given patient), case duration, patient comorbidities, patient origin, patient discharge location, and procedure type. We also assessed the intraoperative fraction of inspired oxygen concentration (FIO2) and temperature. Case duration of >2 hours was also assessed given the prior association with increased risk of postoperative HCAIs.9

Figure 1

Figure 1

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Prior Study Focus

Our focus in the initial study was on bacterial organisms transmitted to intravascular devices, and we used the combination of class of organism, phenotype defined by a series of biochemical reactions (analytical profile index [API]), temporal association given the timed sequence of bacterial culture acquisition during the process of patient care in each operating room (Fig. 1), and, in some cases, PFGE to examine the origin of device-related transmission events. Thus, our initial approach was not focused on 1 particular class of organisms, but on all transmission events.9

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Current Study Focus

Our current interest was in characterizing the epidemiology of all bacterial transmission events in the anesthesia work area involving major bacterial pathogens known to cause SSIs, including S aureus (methicillin-sensitive S aureus [MSSA] and methicillin-resistant S aureus [MRSA]), Enterococcus (vancomycin resistant and vancomycin sensitive), and a variety of Gram-negative pathogens. By examining the mode of transmission (between and/or within case), reservoir of origin, and antibiotic susceptibility, an approach consistent with the Centers for Disease Control investigations of outbreaks,15 we hypothesized that improvements in intraoperative infection control can be generated. As such, we have conducted a systematic analysis of frequently encountered strains and/or strain characteristics (phenotypes) of these pathogens in the anesthesia work area with focused analysis of more virulent phenotypes. We considered more virulent phenotypes as those more transmissible within and/or between operative cases, more often resistant to commonly used prophylactic antibiotics, and/or more often linked by PFGE to postoperative patient cultures in patients diagnosed with infection. We also sought to examine growth rates and risk factors for isolation of more common and more virulent strains, respectively.

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Systematic Epidemiological Approach

The sequence of this systematic analysis focused on S aureus is shown in Figure 2. As the first step in this analysis, we classified all previously archived major bacterial pathogens according to colony morphology, Gram stain, and simple rapid tests. Next, we reviewed 274 case-pairs (548 cases) for evidence of possible S aureus transmission defined by the presence of an S aureus isolate in ≥2 reservoir sites across the case-pair. We then used a commercially available bioMerieux API identification system (Marcy l’Etoile, France) to identify phenotype. The API system generates a 7-digit profile number based on positive or negative reactions in a minimum of 20 phenotypic tests. The profile number can be used for isolate identification by comparing it with profiles of known strains in API’s database, and profile numbers can also be used to compare the phenotypes of different strains.

Figure 2

Figure 2

Each API-derived phenotype represents observable characteristics of bacterial organisms in terms of uptake and utilization of elemental nutrients required for cell survival. Use of these elements is intimately related to the bacterial genome and allows differentiation between species.16 With identification of the class of organism and API phenotype, we then used temporal association (the same class of pathogen isolated in >1 site surveyed in the same operating room on the same day at the same time with the same API phenotype, Fig. 1) to identify possible transmission events occurring within and between operative cases. We identified 170 S aureus isolates that were involved in possible transmission events across the 274 case-pairs (548 operating rooms). We then used disk diffusion antibiotic susceptibility testing analysis (antibiotic susceptibility profiling same response to methicillin and 15 commonly used prophylactic antibiotics, Appendix) to confirm and to determine the origin (provider, environment, or patient) of S aureus transmission events. Bacterial sensitivity was recorded and subsequently analyzed as sensitive or resistant (intermediate resistance was considered resistant due to clinical relevance) except for vancomycin where the zone in millimeters of growth inhibition was recorded and analyzed.16 As susceptibility to antibiotics is another observable characteristic intimately related to the bacterial genome, a similar API phenotype combined with diffusion antibiotic susceptibility analysis (across 16 antibiotics) and an appropriate temporal exposure allowed us to identify with reasonable certainty intraoperative bacterial transmission events involving S aureus between and within operative cases, and to examine the origin of these events. Confirmed transmission events (identical S aureus isolates from ≥2 intraoperative locations) were defined as the isolation of ≥1 S aureus isolates with the same API phenotype, antibiotic susceptibility profile, and appropriate reservoir exposure (Fig. 1) from a patient, provider, or environmental site during or after patient care that was not present at case start.

Provider origin of contamination was assumed if the transmitted isolate was identical to an isolate from the hands of ≥1 anesthesia providers but not found in the patient or environmental reservoirs earlier in the sampling sequence. Environmental origin of contamination was assumed if the transmitted isolate was identical to an isolate from the environment sampled at baseline or at case end but not isolated either from the hands of providers or from the patient reservoirs earlier in the sampling sequence. The hands of all providers who would potentially interact with the anesthesia environment were sampled at baseline (Fig. 1). Patient origin of contamination was assumed if the transmitted isolate was identical to an isolate from the patient sampled at case start but was not isolated from provider hand or environmental reservoirs earlier in the sampling sequence.

Transmission events were then compared, by temporal association, biotype, and disk diffusion antibiotic susceptibility testing analysis, to all patient cultures obtained in the 30-day postoperative period. Probable patient bacterial culture links, defined by the same class of pathogen, the same biotype, the same response to 15 commonly used prophylactic antibiotics and methicillin, and an appropriate temporal exposure, were then confirmed with PFGE.17 We used PFGE to further verify links between intraoperative transmission events and patient cultures due to the loss of temporal resolution in the 30-day postoperative period. Probable patient bacterial culture links, defined by phenotypic similarities (the same class of pathogen, API phenotype, and an appropriate temporal exposure) were examined for genotypic similarities with PFGE. While a PFGE match to an intraoperative pathogen rules in a link, it does not exclude potential links defined by phenotype, temporal exposure, and antibiotic susceptibility. In a single S aureus cell, as many as 300 mutations could theoretically occur in a 2.8 million nucleotide base pair genome in 30 hours.18 These mutations could lead to genomic changes conferring differences in restriction endonuclease cutting sites, resulting in a different PFGE banding pattern and ultimately obscuring the relationship to the initial transmission event. However, our intent was to identify intraoperative S aureus transmission events and infection development with certainty.

As the primary assessment, this systematic analysis was used to characterize intraoperative S aureus isolates according to the frequency, mode, origin, and antibiotic susceptibility of intraoperative transmission events. As a secondary assessment, we analyzed links to 30-day postoperative patient cultures and growth rates for the 2 most prevalent S aureus phenotypes encountered in the intraoperative arena and risk factors for isolation of a high-risk S aureus phenotype.

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Phenotypic Growth Rate Analysis

The 2 most prevalent S aureus phenotypes encountered in the intraoperative arena were phenotype P (patients) and phenotype H (hands). Phenotype P and phenotype H were so called to signify the most common source of the phenotype. Each API-derived phenotype represented observable characteristics of bacterial organisms according to response to uptake and utilization of basic elemental nutrients, a process intimately related to the bacterial genome, and therefore is a differentiating factor between species and bacterial strains. Growth rate (incubation period) is another way to assess phenotype.19 Differences in API or growth rate phenotypes could manifest as a result of genomic differences that could, in theory, confer other survival advantages (evasion of current environmental cleaning practices, prolonged survival in the intraoperative environment) and virulence, such as predisposition for bacterial transmission and subsequent infection development.20

To examine growth rates for the 2 most prevalent phenotypes identified by API, 5 MRSA and 5 MSSA bacterial isolates were randomly selected from the available pool of S aureus isolates across the 3 major academic medical centers for each phenotype (10 organisms per phenotype with 5 MRSA and 5 MSSA). Thus, each experimental phenotype group comprised 10 samples for a total of 20 samples per experiment (this experiment was duplicated for validity, see below). Selected organisms were grown on blood agar plates for 24 hours at 36.5°C. A 0.5 McFarland standard dilution was then prepared for each organism and serially diluted to a final concentration of 50,000 CFU/mL. A BacT/Alert bottle containing aerobic culture media (BacT/Alert, bioMerieux Inc., Durham, NC) was then disinfected with an alcohol prep and 30 seconds allowed for air drying. One milliliter of the 50,000 CFU/1.0 mL test concentration for each sample was then drawn up using aseptic technique and injected into the aerobic culture media contained within the BacT/Alert bottle. A negative control was prepared for each study unit via injection of 1 mL sterile saline in place of the bacterial sample. All inoculated bottles were then placed into the BacT/Alert machine and incubated at 36.5°C for 5 days or until positive. The BacT/Alert incubator identified a positive bottle based on colorimetry; a color change in the media secondary to CO2 production from the growing bacteria was detected via spectroscopy occurring every 10 minutes. If the color change did not occur for 5 days, the bottle was determined to be negative. For positive samples, the time to positivity was recorded. For negative samples, the BacT/Alert bottle was removed from the incubator and sterility confirmed by aspirating 1 mL of the fluid using standard, aseptic technique, spreading the solution onto standard blood agar plates, and incubating for 72 hours at 36.5°C. The experiment was duplicated with a total sample size of 40 experimental units and 8 negative controls. Time to positivity in hours was then compared for each phenotype and antibiotic susceptibility (MRSA and MSSA).

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Statistical Analysis

The primary aims of this study were to examine the mode (within and/or between case transmission), reservoir of origin, transmission location, and antibiotic resistance patterns of S aureus stains/strain characteristics (phenotypes) frequently transmitted in the anesthesia work area of the operating room environment. Our secondary aims were to examine the phenotypic contributions to 30-day postoperative patient cultures, growth rates (incubation periods) for these same phenotypes, and risk factors for transmission of a more virulent phenotype.

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Primary Aims

The frequency of transmission between and within cases (mode) and the relative contributions of the patient, environmental, and provider hand bacterial reservoirs (reservoir of origin) to S aureus transmission events from the anesthesia work area were compared using the χ2 or Fisher exact test where appropriate for the 2 most prevalent S aureus phenotypes. Differences in antibiotic resistance profiles for S aureus phenotypes were also compared using the χ2 or Fisher exact test for categorical data and 2-tailed Student t test for comparison of continuous data (zone of inhibition in millimeters); for continuous variables, both Student t and Wilcoxon rank sum tests were conducted and confirmed the results (e.g., if the P value was <0.01 for the t test, the rank sum test was also <0.01 and vice versa).

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Secondary Aims

The relative contributions of S aureus phenotypes to 30-day postoperative PFGE links to patient cultures were compared using Fisher exact test. As phenotype H was not associated with a single PFGE link to subsequent bacterial cultures, the risk ratio for PFGE links was hand calculated, and substituted 0.5 for 0 in the case of phenotype H. An α level of P < 0.05 was defined as statistically significant.

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Growth Rate Analysis:

Kaplan–Meier time to event analysis was conducted to evaluate the difference between phenotypes in time to critical growth threshold (that required for detection) after injection. We used the log rank test for equality of time to critical threshold of contamination differences across the 2 phenotypes.

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Risk Factors for Phenotypic Isolation:

We used univariate logistic regression to compare x, y, z on the likelihood of phenotype P positivity. An α level of P < 0.05 was defined as statistically significant. Predictors for phenotype P were also assessed in a multivariable model including surgical procedure, case duration, FIO2, temperature, administered prophylactic antibiotics, age, ASA physical status, sex, SENIC score, urgency, and preoperative origin using forward and backward stepwise logistic regression analysis. Important factors identified by backward stepwise logistic regression analysis were included in the final model as shown in Table 1. We assessed and excluded all 1-way interactions (P < 0.05). An α level of P < 0.05 was defined as statistically significant.

Table 1

Table 1

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Power Analysis

This study was previously powered to detect a rate of between case stopcock bacterial transmission events of 5% with an alternative rate of 1%. Approximately 400 patients (200 pairs) were needed to be powered at 0.9 with a type 1 error rate of 0.05.9 No additional power analyses were conducted for analysis of S aureus transmission events, as all S aureus isolates were assessed for possible transmission events. Only those organisms implicated in transmission events were subjected to further analysis.

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RESULTS

The 170 S aureus isolates previously implicated in potential intraoperative transmission events were stratified by API phenotype. Two phenotypes, phenotype P and phenotype H, explained 65% (phenotype P = 13% [23/170] and phenotype H = 51% [87/170]) of all potentially transmissible isolates.

Table 2 details the mode of transmission, reservoir of origin, transmission locations, and antibiotic susceptibility profiles for S aureus phenotype P as compared with phenotype H. Phenotype H and phenotype P were implicated in a confirmed transmission event in 28% and 39% of cases, respectively. Both phenotypes were involved in similar rates of within and between-case transmissions (Table 2). The primary route of between-case transmission for phenotype P was environmental (66%), whereas the primary route for phenotype H between-case transmission was provider hands (80%). Patient skin surfaces (odds ratio [OR], 8.40; 95% confidence interval [CI], 2.30–30.73) and environmental (OR, 10.89; 95% CI, 1.29–92.13) samples were more likely than provider hands (referent) to have phenotype P positivity. Phenotype P was more likely to be resistant to methicillin (OR, 4.38; 95% CI, 1.59–12.06; P = 0.004) than phenotype H (OR, 0.208; 95% CI, 0.075–0.579; P = 0.003).

Table 2

Table 2

Phenotype P was more likely than phenotype H to be linked to 30-day postoperative patient cultures (risk ratio, 36.63 [risk difference, 0.173; 95% CI, 0.019–0.329]). As compared with phenotype H, phenotype P was more frequently isolated from abdominal (43.5% vs 27.6%), orthopedic (30.4% vs 19.5%), and plastic surgical cases (17.4% vs 2.3%), with a statistical difference in surgical type at P = 0.01, as well as from patients arriving from the hospital ward (26.1% vs 3.45%, P = 0.001) and/or intensive care unit (8.7% vs 2.3%, P = 0.001), and from patients undergoing urgent (17.4% vs 6.98%, P = 0.006) or emergent (8.7% vs 2.3%, P = 0.006) surgery. There was no statistically significant difference between phenotypes in terms of administered prophylactic antibiotics, inadequate antibiotic coverage, case duration, SENIC score, intraoperative temperature or FIO2, postoperative glucose (milligrams per deciliter), or ASA status (Table 3).

Table 3

Table 3

In multivariable logistic regression analysis, older patients (OR, 14.1; 95% CI, 3.12–63.5; P = 0.001) and patients with preoperative hospital ward exposure (OR, 41.11; 95% CI, 5.30–318.78; P < 0.001) were more likely to have phenotype P positivity (Table 1).

As shown in Figures 3 and 4, phenotype P exhibited a faster growth rate for MRSA and for MSSA as compared with phenotype H (phenotype P: median, 10.32H; interquartile range [IQR], 10.08–10.56; phenotype H: median, 10.56H; IQR, 10.32–10.8; P = 0.012). The greatest difference in growth rate, however, was seen for MRSA (P: median, 10.08H; IQR, 9.84–10.32; H: median, 10.8H; IQR, 10.8–11.4; P < 0.001), where phenotype P had a 43-minute growth advantage over phenotype H (Fig. 5).

Figure 3

Figure 3

Figure 4

Figure 4

Figure 5

Figure 5

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DISCUSSION

We examined the epidemiology of perioperative S aureus by systematically evaluating intraoperative bacterial transmission events in 3 different medical centers. Using a previously validated experimental model for study of intraoperative bacterial cross-contamination, we identified S aureus isolates frequently implicated in intraoperative bacterial transmission events and stratified them according to phenotype. We identified the 2 most prevalent S aureus phenotypes that are encountered within the anesthesia work area and have further characterized these organisms by examining their mode of transmission, most probable reservoir of origin, transmission locations, and antibiotic susceptibility. In addition, we examined phenotypic differences in contributions to 30-day postoperative patient cultures, growth rates, and risk factors for isolation. The findings of this study are important because they confirm the importance of addressing the patient reservoir while at the same time highlighting the importance of hand hygiene and environmental decontamination for S aureus control, and they provide information that might serve to guide targeted interventions in the complex perioperative arena.

Our prior studies led us to study 3 different potential reservoirs of bacterial pathogens (patients, anesthesia provider hands, and the surrounding environment) and downstream catheter care of high-risk intravascular devices.6–9,21 An important outcome of our research is the finding that patients frequently arrive to the intraoperative environment with skin surfaces colonized with major bacterial pathogens and <20% of patients are effectively decolonized preoperatively.9 Our data have led to the hypothesis that colonized patient skin surfaces serve as a major bacterial reservoir in the operative environment, one that often participates in vertical bacterial transmission leading to infection in the patient. Surprisingly, we have strong evidence suggesting that patient-derived strains were transmitted to subsequent patients who had procedures on the same day, again leading to HCAI development. Our most striking evidence supporting the role of the patient in bacterial transmission in operating rooms came from our multicenter study in which we systematically characterized the relative importance of intraoperative bacterial reservoirs in intraoperative bacterial transmission and 30-day postoperative infection development.9 This work clearly demonstrated that patient skin colonization is a major factor impacting other patients undergoing care in the same arena (i.e., between operative cases), is the main source of S aureus origin and transmission, and is the main source of 30-day postoperative infections (both between and within operative cases) from S aureus. While patient colonization contributed partially to bacterial transmission within the environment, it also significantly contributed to endogenous infection (in 83% of cases).

We have extended these findings in the current study by demonstrating that not only are patients most likely to harbor S aureus, but also they are more likely to harbor one of the more transmissible and more virulent S aureus phenotypes encountered in the intraoperative setting. Thus, taken together, these findings should help guide improvements in patient decolonization strategies.

Our work has led us to hypothesize that specific strains and/or strain characteristics of pathogenic organisms make them more likely to resist decontamination procedures or eradication by antibiotics that are administered during the perioperative period and thus are likely to be more transmissible to patients undergoing care in the same arena, and as a result, are more likely to lead to HCAIs, hospital readmission, and increases in the cost of patient care. If we can identify strains that are more likely to be transmitted and/or to cause infection, we can better identify patient carriers in the preoperative setting, identify environmental components that lead to transmission events during patient care, including health care provider characteristics, and identify factors that lead to transformation of less virulent to more virulent organisms. Such knowledge could lead to the development and implementation of new, patient-centered, and cost-effective screening strategies for patient decolonization or novel drug therapy. Thus, our focus is on understanding the biology of bacterial transmission in the operating room.

We have shown that there are 2 S aureus phenotypes frequently encountered in the anesthesia work area environment. Although these phenotypes are transmitted at similarly high rates between and within cases, they differ greatly in terms of their most probable reservoir of origin, transmission routes, and overall virulence. Phenotype P is patient and environmentally derived, transmits more frequently through the environment, and is more virulent (increased antibiotic resistance and linkage to 30-day postoperative cultures) than the provider-derived phenotype H. These findings are important, as they confirm the need for a multimodal program in handling S aureus, but also serve as a foundation for future development of improved patient screening and decolonization efforts.

Our findings also provide some insight into potential reasons for previously reported variability in efficacy of patient decolonization efforts. Previous work has suggested that patient nasopharyngeal colonization with S aureus is a strong risk factor for SSI development,12 and some evidence suggests that preoperative patient decolonization with nasal mupirocin and/or chlorhexidine is an effective SSI prevention strategy.13,22 However, the overall body of evidence pertaining to preoperative patient decolonization is equivocal.9,11 As a result, perioperative compliance with patient screening and decolonization efforts is suboptimal, and SSIs continue to affect 3% to 5% of surgical patients.1,4,5 We previously hypothesized that a better understanding of the contribution of patient skin colonization to intraoperative bacterial transmission events and subsequent infection development would provide the impetus for improvements in patient decolonization efforts.

The observation that the more virulent of these 2 highly transmissible S aureus phenotypes was often isolated from hospitalized patients may explain the success of Bode et al.13 in demonstrating efficacy for patient decolonization in SSI reduction. Bode et al.13 enrolled a particularly high-risk subgroup of patients that had been previously exposed to the surgical and/or medical wards before a subsequent surgical exposure. Given this increased risk, Bode et al.13 may have required a smaller sample size to demonstrate a statistically significant effect as compared with the work by Konvalinka et al.,11 in which patients undergoing elective cardiothoracic surgery, potentially of lower risk for S aureus SSIs, were enrolled and no effect was demonstrated.

Thus, as we have identified patients who are exposed to the hospital ward and/or the intensive care unit before surgery as a particularly high-risk subgroup for colonization with the more virulent S aureus phenotype, this may be an appropriate target for future improvements in patient decolonization strategies. These results also provide some insight as to whether antibiotic resistance impacts patterns of transmission. Here, we show that a particular phenotype, phenotype P, is more likely to be resistant to methicillin and is transmitted differently than a separate phenotype, phenotype H, which is more likely to be sensitive to this antibiotic. As opposed to using contaminated hands as a transmission vehicle between cases, it appears that this more virulent phenotype is able to transmit via residual contamination of the environment or nonanesthesiologist hands. The reasons for this finding are unclear and require further follow-up, but the implications are that operating rooms exposed to patients who are colonized with MRSA may need to be targeted with enhanced cleaning strategies. These results further support the argument for a multimodal approach to intraoperative infection control. While hand hygiene is important,23,24 it is a single pronged intervention targeting 1 pathway for transmission, and as we show here, a specific subset of dangerous bacterial pathogens.

The patient reservoir and environmental cleaning must also be addressed. For phenotype P, improvements to perioperative infection control practices might include preoperative bathing of hospitalized patients, better methods of confirming adequate cleaning of the operating room (e.g., adenosine triphosphate analysis), and abstaining from preparing for the next case while still caring for a patient in the operating room given the highly transmissible nature from patient to patient. While preoperative bathing/showering of outpatients does not appear to be effective,25 bathing in hospitalized patients does appear to be effective.26 Again, the results of this study may partially explain the difference in efficacy of these various approaches. For phenotype H, as we know that anesthesia provider hand hygiene is abysmal, efforts to improve intraoperative hand hygiene must be made. There is reasonable evidence for an improved intraoperative approach to hand hygiene.8

Our findings pertaining to phenotypic differences in S aureus growth rate are particularly important because these results suggest that there are important differences in behavior within MRSA and MSSA isolates. In this case, the phenotype P represents strain characteristics associated with enhanced doubling time. This may be very important in the operating room, as a 43-minute advantage for MRSA may be enough to establish growth and generate quorum sensing (a protective mechanism used by bacterial organisms that involves formation of a biofilm once a critical number of cells has been reached).27 A potential implication therefore is that biofilm formation may begin before the concentration of prophylactic antibiotics administered immediately before incision reaches an effective tissue concentration for bactericidal or static inhibition. Thus, we may need to refine preoperative screening strategies beyond MRSA versus MSSA alone to the type of MRSA or MSSA that is colonizing the surgical patient. This could lead to improvements in preoperative therapeutic interventions, such as timing of antibiotic administration.

It is important to note that phenotypic differences in virulence as demonstrated in this study cannot be explained by differences in prophylactic antibiotic exposure, inappropriate antibiotic coverage, intraoperative temperature or FIO2, or patient severity of illness as reflected by ASA status. The lack of difference in antibiotic exposure or inappropriate administration of antibiotics is important in that it suggests that there is more to intraoperative infection prevention than simple administration of antibiotics. As discussed above, timing may be an important feature and should be further refined, perhaps, but in addition, the type of organism may have developed virulence factors that facilitate evasion of the administered agent. In other words, the phenotypic differences in outcomes that we show in this study are organism specific; there is something unique about the bacterial organism that facilitates transmission, antibiotic resistance, and infection development. The data do not suggest that these differences can be simply explained by a lack of appropriate prophylactic antibiotic exposure or predisposition to infection.

Phenotypic differences are, however, independently associated with increasing age and preoperative exposure to the hospital ward, as these associations remain intact despite adjustment for potentially confounding variables in stepwise logistic regression analysis. These findings, in combination, highlight the importance of phenotype P virulence and may highlight the importance of potential host–pathogen relationships. In addition, preoperative exposure to the hospital ward appears to be a very important factor in isolation of phenotype P. As these were secondary outcomes, these results should be interpreted with caution.

The overall strength of this study resides in the fact that it involves an extensive investigation of S aureus transmission across 3 major academic medical centers, thus making the results rather generalizable.9 However, this study is limited by a lack of a genotypic explanation for phenotypic differences. Future work should address this deficit. Furthermore, although enhanced growth rates may be associated with increased virulence, we recognize that this relationship is not always true and that in this study, growth rates were analyzed under optimal in vitro conditions that may not reflect the in vivo state.

In conclusion, the results of this study provide important insight into the epidemiology of intraoperative S aureus transmission in the anesthesia work area environment. A prevalent, highly transmissible, virulent S aureus phenotype is more likely to be isolated from patient skin and environmental surfaces as compared with provider hands. Patients exposed to the hospital ward and/or the intensive care units are at particularly high risk for carriage of this important pathogen. Improvements in preoperative patient decolonization efforts, especially those targeting patients arriving to the operating room from high-risk environments, and improved environmental cleaning strategies are indicated as part of a multimodal strategy to reduce 30-day postoperative HCAIs.

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APPENDIX. Antibiotics Tested in Kirby-Bauer Analysis

Table

Table

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DISCLOSURES

Name: Randy W. Loftus, MD.

Contribution: This author helped design and conduct the study, analyze the data, and write the manuscript.

Attestation: Randy W. Loftus has seen the original study data, reviewed the analysis of the data, approved the final manuscript, and is the author responsible for archiving the study files.

Conflicts of Interest: The author has no conflicts of interest to declare.

Name: Matthew D. Koff, MS, MD.

Contribution: This author helped design and conduct the study, analyze the data, and write the manuscript.

Attestation: Matthew D. Koff has seen the original study data, reviewed the analysis of the data, and approved the final manuscript.

Conflicts of Interest: Matthew D. Koff received research funding from B. Braun Medical Inc. for a portion of the study.

Name: Jeremiah R. Brown, MS, PhD.

Contribution: This author helped analyze the data and write the manuscript.

Attestation: Jeremiah R. Brown has seen the original study data, reviewed the analysis of the data, and approved the final manuscript.

Conflicts of Interest: The author has no conflicts of interest to declare.

Name: Hetal M. Patel, BS.

Contribution: This author helped conduct the study.

Attestation: Hetal M. Patel has seen the original study data and approved the final manuscript.

Conflicts of Interest: Hetal M. Patel received research funding from B. Braun Medical Inc. for a portion of the study.

Name: Jens T. Jensen, MS.

Contribution: This author helped conduct the study, analyze the data, and write the manuscript.

Attestation: Jens T. Jensen has seen the original study data, reviewed the analysis of the data, and approved the final manuscript.

Conflicts of Interest: Jens T. Jensen received research funding from B. Braun Medical Inc. for a portion of the study.

Name: Sundara Reddy, MD.

Contribution: This author helped design and conduct the study, and write the manuscript.

Attestation: Sundara Reddy has seen the original study data and approved the final manuscript.

Conflicts of Interest: Sundara Reddy received research funding from B. Braun Medical Inc. for a portion of the study.

Name: Kathryn L. Ruoff, PhD.

Contribution: This author helped design and conduct the study, analyze the data, and write the manuscript.

Attestation: Kathryn L. Ruoff has seen the original study data and approved the final manuscript.

Conflicts of Interest: Kathryn L. Ruoff received research funding from B. Braun Medical Inc. for a portion of the study.

Name: Stephen O. Heard, MD.

Contribution: This author helped design and conduct the study, analyze the data, and write the manuscript.

Attestation: Stephen O. Heard has seen the original study data and approved the final manuscript.

Conflicts of Interest: The author has no conflicts of interest to declare.

Name: Mark P. Yeager, MD.

Contribution: This author helped design the study, analyze the data, and write the manuscript.

Attestation: Mark P. Yeager has seen the original study data and approved the final manuscript.

Conflicts of Interest: Mark P. Yeager received research funding from B. Braun Medical Inc. for a portion of the study.

Name: Thomas M. Dodds, MD.

Contribution: This author helped design and conduct the study, analyze the data, and write the manuscript.

Attestation: Thomas M. Dodds has seen the original study data, reviewed the analysis of the data, and approved the final manuscript.

Conflicts of Interest: Thomas M. Dodds received research funding from B. Braun Medical Inc. for a portion of the study.

This manuscript was handled by: Sorin J. Brull, MD, FCARCSI (Hon).

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REFERENCES

1. 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;32:470––85
2. Klevens RM, Morrison MA, Nadle J, Petit S, Gershman K, Ray S, Harrison LH, Lynfield R, Dumyati G, Townes JM, Craig AS, Zell ER, Fosheim GE, McDougal LK, Carey RB, Fridkin SK. Invasive methicillin-resistant Staphylococcus aureus infections in the United States. JAMA. 2007;298:1763–71
3. Department of Health and Human Services and Centers for Medicare & Medicaid Services. . Medicare Program; Proposed Changes to the Hospital Inpatient Prospective Payment Systems and Fiscal Year 2008 Rates; Proposed Rule Part II. 42 CFR Parts 411, 412, 413, and 489 [CMS-1533-P] RIN 0938-A070. Fed Regist. 2008;72:38–48
4. Dellinger E, Gordon S Surgical-Associated Infection in Today’s Operating Room. Special Report, Anesthesiology, General Surgery, and OB/GYN News. 2006 New York, NY McMahon Publishing Group:1–10
5. Kirkland KB, Briggs JP, Trivette SL, Wilkinson WE, Sexton DJ. The impact of surgical-site infections in the 1990s: attributable mortality, excess length of hospitalization, and extra costs. Infect Control Hosp Epidemiol. 1999;20:725–30
6. Loftus RW, Koff MD, Burchman CC, Schwartzman JD, Thorum V, Read ME, Wood TA, Beach ML. Transmission of pathogenic bacterial organisms in the anesthesia work area. Anesthesiology. 2008;109:399–407
7. Loftus RW, Muffly MK, Brown JR, Beach ML, Koff MD, Corwin HL, Surgenor SD, Kirkland KB, Yeager MP. Hand contamination of anesthesia providers is an important risk factor for intraoperative bacterial transmission. Anesth Analg. 2011;112:98–105
8. Koff MD, Loftus RW, Burchman CC, Schwartzman JD, Read ME, Henry ES, Beach ML. Reduction in intraoperative bacterial contamination of peripheral intravenous tubing through the use of a novel device. Anesthesiology. 2009;110:978–85
9. Loftus RW, Brown JR, Koff MD, Reddy S, Heard SO, Patel HM, Fernandez PG, Beach ML, Corwin HL, Jensen JT, Kispert D, Huysman B, Dodds TM, Ruoff KL, Yeager MP. Multiple reservoirs contribute to intraoperative bacterial transmission. Anesth Analg. 2012;114:1236–48
10. Vogel TR, Dombrovskiy VY, Lowry SF. Impact of infectious complications after elective surgery on hospital readmission and late deaths in the U.S. Medicare population. Surg Infect (Larchmt). 2012;13:307–11
11. Konvalinka A, Errett L, Fong IW. Impact of treating Staphylococcus aureus nasal carriers on wound infections in cardiac surgery. J Hosp Infect. 2006;64:162–8
12. von Eiff C, Becker K, Machka K, Stammer H, Peters G. Nasal carriage as a source of Staphylococcus aureus bacteremia. Study Group. N Engl J Med. 2001;344:11–6
13. Bode LG, Kluytmans JA, Wertheim HF, Bogaers D, Vandenbroucke-Grauls CM, Roosendaal R, Troelstra A, Box AT, Voss A, van der Tweel I, van Belkum A, Verbrugh HA, Vos MC. Preventing surgical-site infections in nasal carriers of Staphylococcus aureus. N Engl J Med. 2010;362:9–17
14. Haley RW, Quade D, Freeman HE, Bennett JVCDC SENIC Planning Committee. . Study on the efficacy of nosocomial infection control (SENIC project): summary of study design. Am J Epidemiol. 1980;111:472–85
15. Archibald LK, Jarvis WR. Health care-associated infection outbreak investigations by the Centers for Disease Control and Prevention, 1946–2005. Am J Epidemiol. 2011;174(Suppl):S47–64
16. Clinical Laboratory Standards Institute. Performance Standards for Antimicrobial Disk Susceptibility Tests; Approved Standard-9th ed. CLSI document M2-A9. 2006;26(1) Wayne, PA Clinical Laboratory Standards Institute
17. Tenover FC, Arbeit RD, Goering RV, Mickelsen PA, Murray BE, Persing DH, Swaminathan B. Interpreting chromosomal DNA restriction patterns produced by pulsed-field gel electrophoresis: criteria for bacterial strain typing. J Clin Microbiol. 1995;33:2233–9
18. Pray L. Antibiotic resistance, mutation rates and MRSA. Nature Education. 2008;1
19. Loftus RW, Patel HM, Huysman BC, Kispert DP, Koff MD, Gallagher JD, Jensen JT, Rowlands J, Reddy S, Dodds TM, Yeager MP, Ruoff KL, Surgenor SD, Brown JR. Prevention of intravenous bacterial injection from health care provider hands: the importance of catheter design and handling. Anesth Analg. 2012;115:1109–19
20. Bochner BR. Global phenotypic characterization of bacteria. FEMS Microbiol Rev. 2009;33:191–205
21. Loftus RW, Brindeiro BS, Kispert DP, Koff MD, Jensen JT, Dodds TM, Yeager MP, Ruoff KL, Gallagher JD, Beach ML, Brown JR. Reduction in intraoperative bacterial contamination of peripheral intravenous tubing through the use of a passive catheter care station. Anesth Analg. 2012;115:1315–23
22. Kluytmans JA, Mouton JW, VandenBergh MF, Manders MJ, Maat AP, Wagenvoort JH, Michel MF, Verbrugh HA. Reduction of surgical-site infections in cardiothoracic surgery by elimination of nasal carriage of Staphylococcus aureus. Infect Control Hosp Epidemiol. 1996;17:780–5
23. Boyce JM, Pittet DHealthcare Infection Control Practices Advisory Committee; HICPAC/SHEA/APIC/IDSA Hand Hygiene Task Force. . Guideline for hand hygiene in health-care settings. Recommendations of the Healthcare Infection Control Practices Advisory Committee and the HICPAC/SHEA/APIC/IDSA Hand Hygiene Task Force. Society for Healthcare Epidemiology of America/Association for Professionals in Infection Control/Infectious Diseases Society of America. MMWR Recomm Rep. 2002;51:1–45
24. World Health Organization. WHO Guidelines on Hand Hygiene in Health Care (Advanced Draft). World Health Organization Health System Policies and Operations Evidence and Information for Policy. 2005 Geneva, Switzerland World Health Organization 9-13-0007
25. Webster J, Osborne S. Preoperative bathing or showering with skin antiseptics to prevent surgical site infection. Cochrane Database Syst Rev. 2012;9:CD004985
26. Climo MW, Yokoe DS, Warren DK, Perl TM, Bolon M, Herwaldt LA, Weinstein RA, Sepkowitz KA, Jernigan JA, Sanogo K, Wong ES. Effect of daily chlorhexidine bathing on hospital-acquired infection. N Engl J Med. 2013;368:533–42
27. Waters CM, Bassler BL. Quorum sensing: cell-to-cell communication in bacteria. Annu Rev Cell Dev Biol. 2005;21:319–46
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