SARS-CoV-2 surface and air contamination in an acute healthcare setting during the first and second pandemic waves.

BACKGROUND: Surfaces and air in healthcare facilities can be contaminated with severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Previously, the authors identified SARS-CoV-2 RNA on surfaces and air in their hospital during the first wave of the coronavirus disease 2019 pandemic (April 2020). AIM: To explore whether the profile of SARS-CoV-2 surface and air contamination had changed between April 2020 and January 2021. METHODS: This was a prospective, cross-sectional, observational study in a multi-site London hospital. In January 2021, surface and air samples were collected from comparable areas to those sampled in April 2020, comprising six clinical areas and a public area. SARS-CoV-2 was detected using reverse transcription polymerase chain reaction and viral culture. Sampling was also undertaken in two wards with natural ventilation alone. The ability of the prevalent variants at the time of the study to survive on dry surfaces was evaluated. FINDINGS: No viable virus was recovered from surfaces or air. Five percent (N=14) of 270 surface samples and 4% (N=1) of 27 air samples were positive for SARS-CoV-2, which was significantly lower than in April 2020 [52% (N=114) of 218 surface samples and 48% (N=13) of 27 air samples (P<0.001, Fisher's exact test)]. There was no clear difference in the proportion of surface and air samples positive for SARS-CoV-2 RNA based on the type of ventilation in the ward. All variants tested survived on dry surfaces for >72 h, with a <3-log10 reduction in viable count. CONCLUSION: This study suggests that enhanced infection prevention measures have reduced the burden of SARS-CoV-2 RNA on surfaces and air in healthcare facilities.

Hospital-onset COVID-19 infection surveillance systems: a systematic review.

Hospital-onset COVID-19 infections (HOCIs) are associated with excess morbidity and mortality in patients and healthcare workers. The aim of this review was to explore and describe the current literature in HOCI surveillance. Medline, EMBASE, the Cochrane Database of Systematic Reviews, the Cochrane Register of Controlled Trials, and MedRxiv were searched up to 30 November 2020 using broad search criteria. Articles of HOCI surveillance systems were included. Data describing HOCI definitions, HOCI incidence, types of HOCI identification surveillance systems, and level of system implementation were extracted. A total of 292 citations were identified. Nine studies on HOCI surveillance were included. Six studies reported on the proportion of HOCI among hospitalized COVID-19 patients, which ranged from 0 to 15.2%. Six studies provided HOCI case definitions. Standardized national definitions provided by the UK and US governments were identified. Four studies included healthcare workers in the surveillance. One study articulated a multimodal strategy of infection prevention and control practices including HOCI surveillance. All identified HOCI surveillance systems were implemented at institutional level, with eight studies focusing on all hospital inpatients and one study focusing on patients in the emergency department. Multiple types of surveillance were identified. Four studies reported automated surveillance, of which one included real-time analysis, and one included genomic data. Overall, the study quality was limited by the observational nature with short follow-up periods. In conclusion, HOCI case definitions and surveillance methods were developed pragmatically. Whilst standardized case definitions and surveillance systems are ideal for integration with existing routine surveillance activities and adoption in different settings, we acknowledged the difficulties in establishing such standards in the short-term.

Machine learning for clinical decision support in infectious diseases: a narrative review of current applications.

BACKGROUND: Machine learning (ML) is a growing field in medicine. This narrative review describes the current body of literature on ML for clinical decision support in infectious diseases (ID). OBJECTIVES: We aim to inform clinicians about the use of ML for diagnosis, classification, outcome prediction and antimicrobial management in ID. SOURCES: References for this review were identified through searches of MEDLINE/PubMed, EMBASE, Google Scholar, biorXiv, ACM Digital Library, arXiV and IEEE Xplore Digital Library up to July 2019. CONTENT: We found 60 unique ML-clinical decision support systems (ML-CDSS) aiming to assist ID clinicians. Overall, 37 (62%) focused on bacterial infections, 10 (17%) on viral infections, nine (15%) on tuberculosis and four (7%) on any kind of infection. Among them, 20 (33%) addressed the diagnosis of infection, 18 (30%) the prediction, early detection or stratification of sepsis, 13 (22%) the prediction of treatment response, four (7%) the prediction of antibiotic resistance, three (5%) the choice of antibiotic regimen and two (3%) the choice of a combination antiretroviral therapy. The ML-CDSS were developed for intensive care units (n = 24, 40%), ID consultation (n = 15, 25%), medical or surgical wards (n = 13, 20%), emergency department (n = 4, 7%), primary care (n = 3, 5%) and antimicrobial stewardship (n = 1, 2%). Fifty-three ML-CDSS (88%) were developed using data from high-income countries and seven (12%) with data from low- and middle-income countries (LMIC). The evaluation of ML-CDSS was limited to measures of performance (e.g. sensitivity, specificity) for 57 ML-CDSS (95%) and included data in clinical practice for three (5%). IMPLICATIONS: Considering comprehensive patient data from socioeconomically diverse healthcare settings, including primary care and LMICs, may improve the ability of ML-CDSS to suggest decisions adapted to various clinical contexts. Currents gaps identified in the evaluation of ML-CDSS must also be addressed in order to know the potential impact of such tools for clinicians and patients.

Antibiotic prescribing in general medical and surgical specialties: a prospective cohort study.

Background: Qualitative work has described the differences in prescribing practice across medical and surgical specialties. This study aimed to understand if specialty impacts quantitative measures of prescribing practice. Methods: We prospectively analysed the antibiotic prescribing across general medical and surgical teams for acutely admitted patients. Over a 12-month period (June 2016 - May 2017) 659 patients (362 medical, 297 surgical) were followed for the duration of their hospital stay. Antibiotic prescribing across these cohorts was assessed using Chi-squared or Wilcoxon rank-sum, depending on normality of data. The t-test was used to compare age and length of stay. A logistic regression model was used to predict escalation of antibiotic therapy. Results: Surgical patients were younger (p < 0.001) with lower Charlson Comorbidity Index scores (p < 0.001). Antibiotics were prescribed for 45% (162/362) medical and 55% (164/297) surgical patients. Microbiological results were available for 26% (42/164) medical and 29% (48/162) surgical patients, of which 55% (23/42) and 48% (23/48) were positive respectively. There was no difference in the spectrum of antibiotics prescribed between surgery and medicine (p = 0.507). In surgery antibiotics were 1) prescribed more frequently (p = 0.001); 2) for longer (p = 0.016); 3) more likely to be escalated (p = 0.004); 4) less likely to be compliant with local policy (p < 0.001) than medicine. Conclusions: Across both specialties, microbiology investigation results are not adequately used to diagnose infections and optimise their management. There is significant variation in antibiotic decision-making (including escalation patterns) between general surgical and medical teams. Antibiotic stewardship interventions targeting surgical specialties need to go beyond surgical prophylaxis. It is critical to focus on of review the patients initiated on therapeutic antibiotics in surgical specialties to ensure that escalation and continuation of therapy is justified.

The Differences in Antibiotic Decision-making Between Acute Surgical and Acute Medical Teams: An Ethnographic Study of Culture and Team Dynamics.

BACKGROUND: Cultural and social determinants influence antibiotic decision-making in hospitals. We investigated and compared cultural determinants of antibiotic decision-making in acute medical and surgical specialties. METHODS: An ethnographic observational study of antibiotic decision-making in acute medical and surgical teams at a London teaching hospital was conducted (August 2015-May 2017). Data collection included 500 hours of direct observations, and face-to-face interviews with 23 key informants. A grounded theory approach, aided by Nvivo 11 software, analyzed the emerging themes. An iterative and recursive process of analysis ensured saturation of the themes. The multiple modes of enquiry enabled cross-validation and triangulation of the findings. RESULTS: In medicine, accepted norms of the decision-making process are characterized as collectivist (input from pharmacists, infectious disease, and medical microbiology teams), rationalized, and policy-informed, with emphasis on de-escalation of therapy. The gaps in antibiotic decision-making in acute medicine occur chiefly in the transition between the emergency department and inpatient teams, where ownership of the antibiotic prescription is lost. In surgery, team priorities are split between 3 settings: operating room, outpatient clinic, and ward. Senior surgeons are often absent from the ward, leaving junior staff to make complex medical decisions. This results in defensive antibiotic decision-making, leading to prolonged and inappropriate antibiotic use. CONCLUSIONS: In medicine, the legacy of infection diagnosis made in the emergency department determines antibiotic decision-making. In surgery, antibiotic decision-making is perceived as a nonsurgical intervention that can be delegated to junior staff or other specialties. Different, bespoke approaches to optimize antibiotic prescribing are therefore needed to address these specific challenges.

Factors that impact on the burden of Escherichia coli bacteraemia: multivariable regression analysis of 2011-2015 data from West London.

BACKGROUND: The incidence of Escherichia coli bacteraemia in England is increasing amid concern regarding the roles of antimicrobial resistance and nosocomial acquisition on burden of disease. AIM: To determine the relative contributions of hospital-onset E. coli bloodstream infection and specific E. coli antimicrobial resistance patterns to the burden and severity of E. coli bacteraemia in West London. METHODS: Patient and antimicrobial susceptibility data were collected for all cases of E. coli bacteraemia between 2011 and 2015. Multivariable logistic regression was used to determine the association between the category of infection (hospital or community-onset) and length of stay, intensive care unit admission, and 30-day all-cause mortality. FINDINGS: E. coli bacteraemia incidence increased by 76% during the study period, predominantly due to community-onset cases. Resistance to quinolones, third-generation cephalosporins, and aminoglycosides also increased over the study period, occurring in both community- and hospital-onset cases. Hospital-onset and non-susceptibility to either quinolones or third-generation cephalosporins were significant risk factors for prolonged length of stay, as was older age. Rates of mortality were 7% and 12% at 7 and 30 days, respectively. Older age, a higher comorbidity score, and bacteraemia caused by strains resistant to three antibiotic classes were all significant risk factors for mortality at 30 days. CONCLUSION: Multidrug resistance, increased age, and comorbidities were the main drivers of adverse outcome. The rise in E. coli bacteraemia was predominantly driven by community-onset infections, and initiatives to prevent community-onset cases should be a major focus to reduce the quantitative burden of E. coli infection.

Planning to halve Gram-negative bloodstream infection: getting to grips with healthcare-associated Escherichia coli bloodstream infection sources.

BACKGROUND: A thorough understanding of the local sources, risks, and antibiotic resistance for Escherichia coli bloodstream infection (BSI) is required to focus prevention initiatives and therapy. AIM: To review the sources and antibiotic resistance of healthcare-associated E. coli BSI. METHODS: Sources and antibiotic resistance profiles of all 250 healthcare-associated (post 48 h) E. coli BSIs that occurred within our secondary and tertiary care hospital group from April 2014 to March 2017 were reviewed. Epidemiological associations with urinary source, gastrointestinal source, and febrile neutropenia-related BSIs were analysed using univariable and multivariable binary logistic regression models. FINDINGS: E. coli BSIs increased 9% from 4.0 to 4.4 per 10,000 admissions comparing the 2014/15 and 2016/17 financial years. Eighty-nine cases (36%) had a urinary source; 30 (34%) of these were classified as urinary catheter-associated urinary tract infections (UTIs). Forty-five (18%) were related to febrile neutropenia, and 38 (15%) had a gastrointestinal source. Cases were rarely associated with surgical procedures (11, 4%) or indwelling vascular devices (seven, 3%). Female gender (odds ratio: 2.3; 95% confidence interval: 1.2-4.6) and older age (1.02; 1.00-1.05) were significantly associated with a urinary source. No significant associations were identified for gastrointestinal source or febrile neutropenia-related BSIs. Forty-seven percent of the isolates were resistant to ciprofloxacin, 37% to third-generation cephalosporins, and 22% to gentamicin. CONCLUSION: The gastrointestinal tract and febrile neutropenia together accounted for one-third of E. coli BSI locally but were rare associations nationally. These sources need to be targeted locally to reduce an increasing trend of E. coli BSIs.

Supervised machine learning for the prediction of infection on admission to hospital: a prospective observational cohort study.

BACKGROUND: Infection diagnosis can be challenging, relying on clinical judgement and non-specific markers of infection. We evaluated a supervised machine learning (SML) algorithm for diagnosing bacterial infection using routinely available blood parameters on presentation to hospital. METHODS: An SML algorithm was developed to classify cases into infection versus no infection using microbiology records and six available blood parameters (C-reactive protein, white cell count, bilirubin, creatinine, ALT and alkaline phosphatase) from 160203 individuals. A cohort of patients admitted to hospital over a 6 month period had their admission blood parameters prospectively inputted into the SML algorithm. They were prospectively followed up from admission to classify those who fulfilled clinical case criteria for a community-acquired bacterial infection within 72 h of admission using a pre-determined definition. Predictive ability was assessed using receiver operating characteristics (ROC) with cut-off values for optimal sensitivity and specificity explored. RESULTS: One hundred and four individuals were included prospectively. The median (range) cohort age was 65 (21-98) years. The majority were female (56/104; 54%). Thirty-six (35%) were diagnosed with infection in the first 72 h of admission. Overall, 44/104 (42%) individuals had microbiological investigations performed. Treatment was prescribed for 33/36 (92%) of infected individuals and 4/68 (6%) of those with no identifiable bacterial infection. Mean (SD) likelihood estimates for those with and without infection were significantly different. The infection group had a likelihood of 0.80 (0.09) and the non-infection group 0.50 (0.29) (P < 0.01; 95% CI: 0.20-0.40). ROC AUC was 0.84 (95% CI: 0.76-0.91). CONCLUSIONS: An SML algorithm was able to diagnose infection in individuals presenting to hospital using routinely available blood parameters.

Comparison of national strategies to reduce meticillin-resistant Staphylococcus aureus infections in Japan and England.

BACKGROUND: National responses to healthcare-associated infections vary between high-income countries, but, when analysed for contextual comparability, interventions can be assessed for transferability. AIM: To identify learning from country-level approaches to addressing meticillin-resistant Staphylococcus aureus (MRSA) in Japan and England. METHODS: A longitudinal analysis (2000-2017), comparing epidemiological trends and policy interventions. Data from 441 textual sources concerning infection prevention and control (IPC), surveillance, and antimicrobial stewardship interventions were systematically coded for: (a) type: mandatory requirements, recommendations, or national campaigns; (b) method: restrictive, persuasive, structural in nature; (c) level of implementation: macro (national), meso (organizational), micro (individual) levels. Healthcare organizational structures and role of media were also assessed. FINDINGS: In England significant reduction has been achieved in number of reported MRSA bloodstream infections. In Japan, in spite of reductions, MRSA remains a predominant infection. Both countries face new threats in the emergence of drug-resistant Escherichia coli. England has focused on national mandatory and structural interventions, supported by a combination of outcomes-based incentives and punitive mechanisms, and multi-disciplinary IPC hospital teams. Japan has focused on (non-mandatory) recommendations and primarily persuasive interventions, supported by process-based incentives, with voluntary surveillance. Areas for development in Japan include resourcing of dedicated data management support and implementation of national campaigns for healthcare professionals and the public. CONCLUSION: Policy interventions need to be relevant to local epidemiological trends, while acceptable within the health system, culture, and public expectations. Cross-national learning can help inform the right mix of interventions to create sustainable and resilient systems for future infection and economic challenges.

Delivering precision antimicrobial therapy through closed-loop control systems.

Sub-optimal exposure to antimicrobial therapy is associated with poor patient outcomes and the development of antimicrobial resistance. Mechanisms for optimizing the concentration of a drug within the individual patient are under development. However, several barriers remain in realizing true individualization of therapy. These include problems with plasma drug sampling, availability of appropriate assays, and current mechanisms for dose adjustment. Biosensor technology offers a means of providing real-time monitoring of antimicrobials in a minimally invasive fashion. We report the potential for using microneedle biosensor technology as part of closed-loop control systems for the optimization of antimicrobial therapy in individual patients.

A systematic review of clinical decision support systems for antimicrobial management: are we failing to investigate these interventions appropriately?

OBJECTIVES: Clinical decision support systems (CDSS) for antimicrobial management can support clinicians to optimize antimicrobial therapy. We reviewed all original literature (qualitative and quantitative) to understand the current scope of CDSS for antimicrobial management and analyse existing methods used to evaluate and report such systems. METHOD: PRISMA guidelines were followed. Medline, EMBASE, HMIC Health and Management and Global Health databases were searched from 1 January 1980 to 31 October 2015. All primary research studies describing CDSS for antimicrobial management in adults in primary or secondary care were included. For qualitative studies, thematic synthesis was performed. Quality was assessed using Integrated quality Criteria for the Review Of Multiple Study designs (ICROMS) criteria. CDSS reporting was assessed against a reporting framework for behaviour change intervention implementation. RESULTS: Fifty-eight original articles were included describing 38 independent CDSS. The majority of systems target antimicrobial prescribing (29/38;76%), are platforms integrated with electronic medical records (28/38;74%), and have a rules-based infrastructure providing decision support (29/38;76%). On evaluation against the intervention reporting framework, CDSS studies fail to report consideration of the non-expert, end-user workflow. They have narrow focus, such as antimicrobial selection, and use proxy outcome measures. Engagement with CDSS by clinicians was poor. CONCLUSION: Greater consideration of the factors that drive non-expert decision making must be considered when designing CDSS interventions. Future work must aim to expand CDSS beyond simply selecting appropriate antimicrobials with clear and systematic reporting frameworks for CDSS interventions developed to address current gaps identified in the reporting of evidence.

Understanding antibiotic decision making in surgery-a qualitative analysis.

OBJECTIVE: To investigate the characteristics and culture of antibiotic decision making in the surgical specialty. METHODS: A qualitative study including ethnographic observation and face-to-face interviews with participants from six surgical teams at a teaching hospital in London was conducted. Over a 3-month period: (a) 30 ward rounds (WRs) (100 h) were observed, (b) face-to-face follow-up interviews took place with 13 key informants, (c) multidisciplinary meetings on the management of surgical patients and daily practice on wards were observed. Applying these methods provided rich data for characterizing the antibiotic decision making in surgery and enabled cross-validation and triangulation of the findings. Data from the interview transcripts and the observational notes were coded and analysed iteratively until saturation was reached. RESULTS: The surgical team is in a state of constant flux with individuals having to adjust to the context in which they work. The demands placed on the team to be in the operating room, and to address the surgical needs of the patient mean that the responsibility for antibiotic decision making is uncoordinated and diffuse. Antibiotic decision making is considered by surgeons as a secondary task, commonly delegated to junior members of their team and occurs in the context of disjointed communication. CONCLUSION: There is lack of clarity around medical decision making for treating infections in surgical patients. The result is sub-optimal and uncoordinated antimicrobial management. Developing the role of a perioperative clinician may help to improve patient-level outcomes and optimize decision making.

Effect of adding a mobile health intervention to a multimodal antimicrobial stewardship programme across three teaching hospitals: an interrupted time series study.

Objectives: To evaluate the impact of adding a mobile health (mHealth) decision support system for antibiotic prescribing to an established antimicrobial stewardship programme (ASP). Methods: In August 2011, the antimicrobial prescribing policy was converted into a mobile application (app). A segmented regression analysis of interrupted time series was used to assess the impact of the app on prescribing indicators, using data (2008-14) from a biannual point prevalence survey of medical and surgical wards. There were six data points pre-implementation and six data points post-implementation. Results: There was an increase in compliance with policy (e.g. compliance with empirical therapy or expert advice) in the two specialties of medicine (6.48%, 95% CI = -1.25 to 14.20) and surgery (6.63%, 95% CI = 0.15-13.10) in the implementation period, with a significant sudden change in level in surgery ( P < 0.05). There was an increase, though not significant, in medicine (15.20%, 95% CI = -17.81 to 48.22) and surgery (35.97%, 95% CI = -3.72 to 75.66) in the percentage of prescriptions that had a stop/review date documented. The documentation of indication decreased in both medicine (-16.25%, 95% CI = -42.52 to 10.01) and surgery (-14.62%, 95% CI = -42.88 to 13.63). Conclusions: Introducing the app into an existing ASP had a significant impact on the compliance with policy in surgery, and a positive, but not significant, effect on documentation of stop/review date in both specialties. The negative effect on the third indicator may reflect a high level of compliance pre-intervention, due to existing ASP efforts. The broader value of providing an antimicrobial policy on a digital platform, e.g. the reach and access to the policy, should be measured using indicators more sensitive to mHealth interventions.

Behaviour change interventions to influence antimicrobial prescribing: a cross-sectional analysis of reports from UK state-of-the-art scientific conferences.

BACKGROUND: To improve the quality of antimicrobial stewardship (AMS) interventions the application of behavioural sciences supported by multidisciplinary collaboration has been recommended. We analysed major UK scientific research conferences to investigate AMS behaviour change intervention reporting. METHODS: Leading UK 2015 scientific conference abstracts for 30 clinical specialties were identified and interrogated. All AMS and/or antimicrobial resistance(AMR) abstracts were identified using validated search criteria. Abstracts were independently reviewed by four researchers with reported behavioural interventions classified using a behaviour change taxonomy. RESULTS: Conferences ran for 110 days with >57,000 delegates. 311/12,313(2.5%) AMS-AMR abstracts (oral and poster) were identified. 118/311(40%) were presented at the UK's infectious diseases/microbiology conference. 56/311(18%) AMS-AMR abstracts described behaviour change interventions. These were identified across 12/30(40%) conferences. The commonest abstract reporting behaviour change interventions were quality improvement projects [44/56 (79%)]. In total 71 unique behaviour change functions were identified. Policy categories; "guidelines" (16/71) and "service provision" (11/71) were the most frequently reported. Intervention functions; "education" (6/71), "persuasion" (7/71), and "enablement" (9/71) were also common. Only infection and primary care conferences reported studies that contained multiple behaviour change interventions. The remaining 10 specialties tended to report a narrow range of interventions focusing on "guidelines" and "enablement". CONCLUSION: Despite the benefits of behaviour change interventions on antimicrobial prescribing, very few AMS-AMR studies reported implementing them in 2015. AMS interventions must focus on promoting behaviour change towards antimicrobial prescribing. Greater focus must be placed on non-infection specialties to engage with the issue of behaviour change towards antimicrobial use.

Counting the cost of an outbreak of carbapenemase-producing Enterobacteriaceae: an economic evaluation from a hospital perspective.

OBJECTIVE: To perform an economic evaluation on the cost associated with an outbreak of carbapenemase-producing Enterobacteriaceae (CPE). METHODS: We performed an observational economic evaluation of an outbreak of CPE (NDM-producing Klebsiella pneumoniae) affecting 40 patients in a group of five hospitals across three sites in West London. Costs were split into actual expenditure (including anti-infective costs, enhanced CPE screening, contact precautions, temporary ward-based monitors of hand and environmental practice, and environmental decontamination), and 'opportunity cost' (staff time, bed closures and elective surgical missed revenue). Costs are estimated from the hospital perspective over the 10-month duration of the outbreak. RESULTS: The outbreak cost €1.1m over 10 months (range €0.9-1.4m), comprising €312 000 actual expenditure, and €822 000 (range €631 000-€1.1m) in opportunity cost. An additional €153 000 was spent on Estates renovations prompted by the outbreak. Actual expenditure comprised: €54 000 on anti-infectives for 18 patients treated, €94 000 on laboratory costs for screening, €73 000 on contact precautions for 1831 contact precautions patient-days, €42 000 for hydrogen peroxide vapour decontamination of 24 single rooms, €43 000 on 2592 hours of ward-based monitors, and €6000 of expenditure related to ward and bay closures. Opportunity costs comprised: €244 000 related to 1206 lost bed-days (range 366-2562 bed-days, €77 000-€512 000), €349 000 in missed revenue from 72 elective surgical procedures, and €228 000 in staff time (range €205 000-€251 000). Reduced capacity to perform elective surgical procedures related to bed closures (€349 000) represented the greatest cost. CONCLUSIONS: The cost estimates that we present suggest that CPE outbreaks are highly costly.

Isolation demand from carbapenemase-producing Enterobacteriaceae screening strategies based on a West London hospital network.

OBJECTIVE: To estimate the isolation demands arising from high-risk specialty-based screening for carbapenemase-producing Enterobacteriaceae (CPE), and the potential fraction of CPE burden detected. METHODS: Clinical specialty groups from three London hospitals were ranked by incidence of carbapenem resistance among Escherichia coli and Klebsiella spp. Contact precaution bed-days were estimated for three screening strategies: Strategy 1, 'circulation science and renal medicine'; Strategy 2, Strategy 1 plus 'specialist services'; and Strategy 3, Strategy 2 plus 'private patients'. Isolation bed occupancy rates and potential CPE detection rates were estimated. RESULTS: Of 99,105 admissions to the three hospitals in Financial Year 2014/15, Strategies 1, 2 and 3 would have screened 4371 (4.4%), 7482 (7.6%), and 13,542 (13.7%) patients, respectively. The specialties' isolation bed occupancy rates varied between 3% and 696% depending on strategy, number of consecutive tests, and whether or not pre-emptive isolation had been applied. Expected detection rates of the potential CPE burden in the hospital network would have varied between 17.1% and 47.5%. CONCLUSIONS: High-risk specialty-based screening has the potential to detect nearly half of the potential CPE burden, and would be more pragmatic than patient-level risk-factor-based screening. Pre-emptive isolation increases isolation requirements substantially. CPE screening strategies need to balance risk and resources.