Background
Carbapenem antibiotics are clinically effective and well tolerated for the treatment of antibiotic-resistant Gram-negative bacteria and hence extremely important for tackling life-threatening infections in UK hospitals [
1]. The most common cause of blood stream infections in England is Gram-negative bacteria, specifically Enterobacteriaceae [
2]. The number of infections with carbapenem-resistant Enterobacteriaceae (CRE) is on the rise [
3]. These infections lead to increased morbidity, mortality and cost [
4].
Carbapenemase-producing carbapenem-resistant Enterobacteriaceae (CP-CRE) are effectively a sub-population of CRE which represent a further threat, because the genes encoding the mechanisms of resistance (a carbapenemase) can be transferred between bacterial species and confer elevated levels of resistance compared with other mechanisms of carbapenem resistance [
5,
6]. CP-CRE have shown a notable rise in the number of cases over the last decade in England [
7]. They were close to absent in 2006 but have since increased to more than 2500 isolates being referred to the national reference laboratory in 2016 [
8]. Outbreaks have also been detected in some centres [
9], and others report endemic CP-CRE [
10]. As a result, increasing numbers of patients have extremely limited therapy options; thus preventive infection control practices such as active screening and single room isolation play an even more important part in our clinical settings [
11]. CP-CRE colonisation has been shown to correlate positively with the incidence of infection attributed to CP-CRE organisms, particularly in an intensive care unit (ICU) setting [
12]. There are currently no accepted decolonisation protocols for CP-CRE organisms; hence early detection of asymptomatic carriage and isolation are key tools to prevent transmission. However, there is insufficient data on the effectiveness or cost-effectiveness of various CP-CRE screening algorithms.
US (Centers for Disease Control and Prevention, CDC), European (European Centre for Disease Prevention and Control, ECDC) and UK (Public Health England, PHE) organisations have published guidance highlighting the importance of patient screening for CP-CRE in order to identify the carriers and prevent subsequent infection and spread [
13,
14]. The European Society of Clinical Microbiology and Infectious Diseases (ESCMID) recommended patient screening on admission in both endemic and epidemic settings as well as pre-emptive isolation in a single room in an epidemic setting [
15]. However, there are a number of challenges associated with this, such as the high cost of patient screening on admission, which may not always be optimal due to the wide range of prevalence on admission in different areas [
16,
17]. Moreover, some CP-CRE screening methods take up to 48 h to give a result. Hence, pre-emptive isolation may not be an option, as it could result in a high number of patients being isolated unnecessarily for a prolonged period of time [
18], nor may it be feasible given the limited availability of isolation facilities.
The most common existing CP-CRE screening methods include conventional culture-based approaches, which have good sensitivity and specificity but take several days to return a result [
13], and molecular polymerase chain reaction (PCR)-based methods, which are much faster, at least as sensitive, but substantially more expensive [
19]. Importantly, CRE is defined phenotypically, whereas CP-CRE is a genotypic phenomenon most commonly determined by means of a molecular-based test. Whilst PCR-based methods can only detect known carbapenemase-encoding genes that they are designed to detect [
20], culture-based methods do not detect non-expressed genetic mechanisms [
21]. This suggests that a combination of culture- and PCR-based tests should be used in order to detect all phenotypic resistance and also to confirm the underlying genetic mechanisms.
Mathematical models, often used in the field of infectious diseases, provide the ideal platform from which to simulate a range of laboratory screening options to detect CP-CRE. Their use in the field of healthcare-associated infections is well documented, with previous models of antibiotic-resistant Gram-negatives mostly focused on the ICU [
22‐
24] to evaluate interventions and screening algorithms [
25].
Our aim was to compare the impact of different currently used screening algorithms for CP-CRE using data from Imperial College Healthcare NHS Trust (ICHNT) using a newly designed mathematical model. By comparing different scenarios, parameterised by data from a group of London teaching hospitals, we were able to explore the predicted clinical impact and the comparative cost of different molecular- and culture-based screening tools. This will help to inform both ICHNT and other hospital trusts as to which screening methods to use in clinical settings to help combat this increasing threat.
Discussion
We estimated the number of CP-CRE days at risk to be lowest when using our (A) Direct PCR algorithm, owing to the rapidity of result confirmation by PCR. This applied to all scenarios, but was most pronounced in the scenarios with 100% screening coverage levels. However, the false positive rate of the (A) Direct PCR algorithm (a direct false positive rate of 1% vs. an effective false positive of 0.01% for (B) Culture + PCR) resulted in a higher number of inappropriate isolation days and hence substantially higher costs per CP-CRE carrier risk day averted and total costs for the (A) Direct PCR algorithm. This meant that, in terms of cost per CP-CRE carrier risk day averted, the (B) Culture + PCR algorithm performed best.
The differences between algorithms were reduced under scenarios with high prevalence, when the cost per CP-CRE carrier risk day averted was similar for all three algorithms, independently of screening prevalence. This highlights the importance of local epidemiology on determining the impact of screening algorithms. At ICHNT, the screening coverage has risen considerably since the study was performed (now at 96% for the ICU) due to quality improvement work in the trust. This suggests that for the ICHNT ICU setting, where CP-CRE prevalence is low but near 100% coverage (similar to our baseline scenario), the (A) Direct PCR algorithm would give the smallest number of days at risk. However, the (B) Culture + PCR algorithm would be substantially better in terms of cost per CP-CRE carrier risk day averted. The (C) PHE algorithm, which is basically three repeats of the (B) Culture + PCR algorithm with an additional high-performance PCR, performs slightly better in terms of risk days averted than (B) Culture + PCR; however, the cumulative costs of these repeats result in a higher cost per CP-CRE carrier risk day averted. This is reflected in substantial average incremental costs, suggesting that the ICHNT should continue to use (B) Culture + PCR.
The same pattern of algorithm performance is seen in the three other ICHNT specialities considered, which have CP-CRE prevalence on admission ranging from 0.4 to 1.9%, but different bed numbers and lengths of stay. Data on CP-CRE admission prevalence across England (0.1% from another London hospital in 2015 [
21]) and Europe (1.1% in a Spanish hospital in 2006–2010 [
31]) are scarce, but the level is likely to lie within the range considered here (< 8%) [
29]. Thus, the cost per CP-CRE carrier risk day averted is likely to be lowest for (B) Culture + PCR in other English settings. Only as prevalence increases will the false positive rate of Direct PCR algorithms be counterbalanced, and the use of multiple screens or a direct-from-swab PCR have decreasing cost per CP-CRE carrier risk day averted. This is similar to cost-effectiveness results from the USA, where culture-based algorithms were found to be cost-effective, whilst PCR gave much higher costs [
32].
The impact of these screening algorithms on the demand for isolation bed days rapidly increases with both screening coverage and CP-CRE prevalence. Isolation beds (side rooms) are in demand for other uses than CP-CRE, and thus even before the > 100% demand under all the high CP-CRE (20%) prevalence scenarios is seen, such requirements may not be available. However, increasing screening coverage is very important in reducing the number of days at risk (and hence the potential for onward transmission). In our case, increasing screening coverage from 63 to 100% reduced the number of days at risk by more than 50%. This, however, was only true when we ignored the limits of the existing isolation bed day capacity. When we included this, the number of days at risk was similar, as the limit on the days at risk was not screening coverage but isolation bed availability. Thus, pairing increased screening coverage with concurrent isolation bed day availability should be a focus of hospitals [
25], especially as CP-CRE prevalence on admission rises. Alternatives such as nurse cohorting or increased contact precautions for certain patients, instead of speciality isolation beds, could also be employed. Laboratory capacity should also be considered under increased prevalence demands, as well as screening outside of only high-risk areas, neither of which we included here.
The rapidity of direct PCR tests or equivalent “point-of-care” rapid diagnostic tests makes them an attractive option for hospitals. However, their cost and the impact on isolation bed day capacity cannot be ignored. As CP-CRE prevalence increases, this cost would be reduced due to the lower proportion of false positives and the rapidity of detection. Our costings include the extra cost generated from a patient isolated following a false positive test. We considered only existing technology; however, future diagnostic tests for CP-CRE including “lab on a chip” mechanisms would be rapid and highly specific. As shown here, if these tests can improve on the false positive detection rate of PCR, potentially by combining phenotypic and genotypic output, then at a low cost, these could be greatly improved algorithms, reducing dramatically the number of days at risk.
The clinical impact of our results is to provide evidence for hospitals to decide between screening strategies for detection and isolation of CP-CRE carriers to prevent ongoing transmission. One aspect of the screening algorithms modelled in this paper that is not captured in the outcomes that we evaluated is that the Direct PCR algorithm would result in more rapid identification of the specific carbapenemase involved: this has value for the rapid identification of potential clusters and understanding short-term local epidemiological trends. In addition, these screening results, in the identification of CP-CRE carriage, can aid in the design of antimicrobial treatment if subsequent infection occurs (or is present already at admission). Whilst the greatest clinical impact would be achieved by the most rapid test, clinical settings operate with strict budgets, and thus a comparison such as those presented here must be made for on-the-ground decision making.
This study’s main strength is its direct linkage to a “real” hospital setting. This leading London teaching hospital group, with 15,000 admissions a month and 1300 beds, provided screening coverage data, CP-CRE prevalence by speciality and LoS data, making the modelling outputs based on “real” data, rather than hypothesised parameters. This makes the applicability of the model better and supports the reliability of the results.
The main weakness of this study was that transmission was not explicitly included in the model due to a lack of reliable estimates for CP-CRE transmission rates. Therefore, the effects of isolation on CPE prevalence could not be explored, and we do not capture the indirect impact of these screening algorithms. However, the number of CP-CRE days at risk is a proxy for heightened levels of transmission, and our comparison of screening tests would be similar with the addition of indirect transmission effects, although the likely impact may change, potentially non-linearly, with increasing resistance prevalence. There is likely to be considerable uncertainty in the transmission rate from individual patients (those isolated and not) and between settings, making the addition of this complexity unlikely to clarify or improve on our results. Such “colonisation” burden proxies of transmission risk have been proposed before [
33], with analysis showing that they link directly to acquisition rates of other resistant pathogens [
34,
35]. Other weaknesses, in terms of modelling assumptions, come from our assumption that those with CP-CRE and NCP-CRE could be grouped as having the same LoS distribution, i.e. that those with CRE have the same LoS. Apart from CRE status, other bacterial and all host heterogeneities were missing, such as risk factors for carriage. We also did not undertake a cost-effectiveness analysis, instead looking at the overall effect of different variables on the impact of screening algorithms. In addition, although we explored implementation through exploring the impact of limited numbers of isolation bed days, we did not include further financial or technical constraints.
There are also limitations to our cost calculations, in particular, the specific nature of our parameterisation, which makes the costing results specific to our setting and does not include the variation that may be seen. This limits the generalisability to broad conclusions about the comparative nature of the algorithms. We also decided to use “cost per CP-CRE carrier risk day averted” as the main cost comparison method. As we did not include transmission, we could not include the cost of CP-CRE outbreaks, and instead only the proxy of “risk days”. In terms of clinical impact, time to detection of CP-CRE may override this cumulative “risk day” calculation (if we believe that transmission from a patient could saturate), but we believe that this is unlikely due to rapid patient movement and therefore the same ongoing transmission risk from every “risk day”. Thus rapidity of test result may not be optimal when making difficult value-based decisions for infection control.
The key next step for this work depends on an improved understanding of the transmission routes and pathways that lead to patients being carriers of CP-CRE, as well as the length of time patients are carriers. Once this is available, adding in transmission, and the effect of isolation on transmission, would allow for estimation of the impact on the additional indirect effects of screening. Similarly, with information on the quantitative impact of different risk factors on CP-CRE carriage (e.g. by what relative increase are those who travel abroad more likely to carry CP-CRE) and the prevalence of these risk factors, more heterogeneity in the patient host population could be included.