Ceftazidime with avibactam for treating severe aerobic Gram-negative bacterial infections: technology evaluation to inform a novel subscription-style payment model

Microbiology-directed treatment

In the microbiology-directed usage scenario, feedback from stakeholders and clinical experts indicated that cUTIs have high prevalence and a slower clinical course than, for example, HAP and VAP. They are also responsible for a high proportion of bloodstream infections (BSI), the reduction of which is a key priority for NHS England (NHSE). cUTI infections were therefore selected as the infection site for the microbiology-directed HVCS, with additional analysis also provided for HAP/VAP in the MDS.

Clinical and stakeholder advice also indicated that CAZ-AVI would be reserved for infections with limited treatment options, where susceptibility is demonstrated. This suggests CAZ-AVI should be reserved to treat infections caused by carbapenemase-producing pathogens. As discussed in High-value clinical scenarios, CAZ-AVI is not active against MBL mechanisms, or against A. baumannii pathogens, and serine carbapenemase mechanisms are not often found in Pseudomonas aeruginosa. The patient group for the HVCS will, therefore, be limited to patients with infections caused by serine Enterobacterales. For this patient group, OXA-48 and KPC resistance mechanisms are most predominant. Patients with pathogens with KPC generally have more treatment options than those with OXA-48 and we therefore focus on OXA-48 in the HVCS.

Ceftazidime-avibactam can be used as a monotherapy but may also be used in combination with other treatments, as indicated by microbiology and gene testing. In clinical practice, alternative treatment options (comparators) would be defined by the results of susceptibility and gene testing.

Risk-based empiric treatment

In the risk-based empiric usage scenario, feedback from stakeholders and clinicians indicated that the most frequent clinically urgent infections are HAP/VAP and BSI. cUTI infections were not considered relevant in this setting since they have a slower clinical course, giving time for AST to be performed. Given the time and resources available for this project, the focus was on the HAP/VAP sites as this was considered the most common indication for empirical antibiotics in high-risk patients such as those in the intensive care units/high-dependency units (ICU/HDU) (whereas patients with BSI are more likely to have had microbiology). Patients will be those who have a high risk of an OXA-48 Enterobacterales. Focusing on this high-risk group was highlighted by the clinical advisors to this project as preferable to considering a broader group of patients with suspected carbapenem resistance, even if deteriorating rapidly on current therapy, as the latter group would be difficult to define and may lead to high levels of prescribing with associated risks of resistance emergence. Three patient characteristics were considered as relevant by our clinical advisors in identifying patients at high risk of an OXA-48 infection: a high rate of OXA-48 Enterobacterales in a healthcare setting where the patient was previously admitted, an outbreak of OXA-48 Enterobacterales in the ward where the patient is currently admitted, or previous cultures (taken during the current or previous hospital stays) showing the patient was colonised by an OXA-48 Enterobacterales. CAZ-AVI may be used as monotherapy in this usage scenario, or may be used in combination with other treatments to provide a broader spectrum of coverage. A range of comparators are relevant in this setting. Once microbiology has confirmed the susceptibility profile and mechanisms of resistance of the pathogen, treatment may be continued or stopped, dosage may be altered or different AMs may be initiated.

Susceptibility studies, PK/PD studies and breakpoints

Susceptibility studies are in vitro studies that report the results of AST. AST is a laboratory method where isolates taken from patients (from infections or during screening) are grown in vitro (cultured), and tested for their susceptibility to various AM treatments. The AM being tested is applied at increasing concentrations to separate cultures of the sampled isolate, and the degree to which microbial growth is inhibited at each concentration is assessed. The lowest concentration at which microbial growth is inhibited is known as the minimum inhibitory concentration, or MIC.

Clinical breakpoints distinguish between isolates where there is a likelihood of treatment success and those where treatment is more likely to fail. 18 If the MIC of a given isolate is at or below the breakpoint, the isolate is judged to be ‘susceptible’. If it is above the breakpoint, the isolate is judged to be ‘resistant’. For some AMs, there is also an intermediate category, which more recently has become ‘susceptible – increased exposure’, indicating that a higher dose of the drug should be used to elicit a response. They may also report the concentration at which 50% of isolates were inhibited (MIC 50), and the concentration at which 90% were inhibited (MIC 90).

The methods for setting breakpoints are not standardised. Currently, they are generally set by considering:19

• The PK data: how the body affects the drug with respect to absorption, distribution, metabolism and excretion, usually obtained from studies in healthy volunteers.

• The PD data: how the drug affects the body (efficacy and toxicity) at its site(s) of action, usually obtained from in vitro studies, hollow-fibre studies, animal studies and human studies. These data are used to set PD targets, for example for time above MIC.

• Mathematical models (e.g. Monte Carlo simulation) to assess the likelihood of achieving the targets suggested by the PD data.

• Any available clinical data linking treatment to clinical outcomes (e.g. from RCTs or observational studies).

Pharmacokinetic/pharmacodynamic studies are conducted to estimate how much drug will be available at the site of interest, and for what period of time at a given dose. One of its primary uses is by manufacturers and regulatory bodies to decide on the appropriate dose and dose frequency of the drug, such that it is likely to be available at concentrations that are likely to have an effect at the sites of interest.

There are two main organisations that set breakpoints, the Clinical Laboratory Standards Institute (CLSI) in the USA, and the European Committee on Antimicrobial Susceptibility Testing (EUCAST) in Europe. These two organisations use different methodologies to set breakpoints, leading to differences in the breakpoints set both in absolute and in relative terms, between treatments. They also describe different laboratory methods to assess MICs. In addition, many labs may use commercial assays, conducted according to manufacturer’s instructions. Clinical advisors to EEPRU indicated that it was unclear to what extent CLSI, EUCAST and commercial methods would produce the same absolute values, and, in the event that values were different, whether relative values between treatments would also be different (i.e. the difference in absolute values was not consistent across treatments). In the UK, the British Society for Antimicrobial Chemotherapy (BSAC) now recommends use of EUCAST methods and breakpoints.

Susceptibility studies tend to report the proportion S, I and R, or list the number of isolates at each MIC. An example is given in Table 3. Here, for cefepime, the breakpoint is 1 mg/l, and since all isolates had MICs higher than the breakpoint, none were susceptible. For CAZ-AVI, with a breakpoint of 8 mg/l, 90.9% were susceptible, since only one isolate had a MIC above this point.

Keyword mapping

Citations that met the inclusion criteria listed in Table 5 were tagged in Endnote by one reviewer, according to key study characteristics: treatment (CAZ-AVI); study design (RCT, observational, susceptibility, PK/PD); mechanism (OXA-48, KPC, other); pathogen (Enterobacterales, other) and site (cUTI, HAP/VAP, BSI, other). All potential sources of evidence, including RCTs, observational studies, in vitro studies and national, local or international datasets identified in the grey literature, were included in this stage of mapping.

Key characteristics mapping

A subset of studies that met the inclusion criteria listed in Table 5 were selected for key characteristics tabulation by one reviewer. The full texts of RCT and observational studies identified as being potentially relevant based on their title and abstract were consulted in the first instance, and studies were tabulated and assessed for relevance against the key characteristics mapping criteria, and for relevance to the model. Since an assessment of this map concluded that insufficient relevant in vivo evidence was identified (see Reviews 1 and 2), the next level of evidence (susceptibility studies) was also tabulated.

Key study characteristics tailored to the study designs of interest (e.g. sample size, population, pathogen, mechanism, site, outcomes reported, susceptibility methodology; see Report Supplementary Material 1) were tabulated by one reviewer. Data relating to numeric outcomes were not extracted and quality assessment was not performed at this stage.

Study selection

At the final stage of study selection, only susceptibility studies were considered since other sources did not meet the requirements of the project. The reasons for this decision are detailed in Study selection results (reviews 1–3). The inclusion criteria are listed in Table 5.

Advice was sought from clinical advisors to aid the assessment of the relevance of susceptibility studies to the HVCSs, and to inform the final selection of evidence. Factors including location, date of recruitment, OXA-48 versus OXA-48-like mechanisms, sampling strategy, screening and outbreak populations, and susceptibility testing methodologies were considered, and decisions made (Table 6). At this point, a decision was made not to review the PK/PD data, since these data are reviewed when setting breakpoints, and since clinical advisors to EEPRU stated that since the treatment and comparators penetrate to the sites of interest it was therefore reasonable to link directly between susceptibility and clinical outcomes (Table 5).

Due to time restrictions on the project, only studies reporting susceptibility to both CAZ-AVI and also to any one of the comparators listed in Table 2 were included. This is a pragmatic approach to evidence retrieval, since ideally all susceptibility data relating to all comparators would have been included in the evidence synthesis, but searches to identify this evidence would have been large. No studies reported combinations of AMs, the process for estimating efficacy for combination treatments using the results of the evidence synthesis is described in Clinical parameters – susceptibility evidence. Consequently, studies reporting susceptibility to both CAZ-AVI and also to any one of the comparators listed in Table 2 were included.

Data extraction

Data sources selected for inclusion in the review were data extracted by one reviewer and extractions were checked by a second. The initial key characteristics tabulation was expanded to include numerical outcome data for the susceptibility studies, and data were checked by a second reviewer. Data sources not selected for use in the model or clinical review were tabulated and reasons for their exclusion provided but they were not assessed further.

Quality assessment

Since there is no published quality assessment tool for susceptibility studies, a bespoke set of questions was developed and applied, relating to internal bias and relevance. This tool was developed by consulting two tools developed for the assessment of prevalence studies25,26 (since studies report the prevalence of susceptibility), the risk of bias in non-randomised studies (ROBINS)-1 checklist27 for non-randomised studies (since the studies are comparative, but non-randomised), Cochrane’s risk of bias 2 (RoB2)28 tool [since the network meta-analysis (NMA) will assume the study arms are equivalent to randomised arms of a RCT], and the Newcastle–Ottawa Scale29 (since these are observational studies). Questions from all tools were considered for inclusion, and adapted to the specifics of this review. The tool was reviewed by other members of the reviewing team, but no further validation work was undertaken. The final tool is reported in Appendix 2, Table 36. Risk of bias was assessed using this tool by one reviewer.

Studies reporting the susceptibility of OXA-48 Enterobacterales isolates to CAZ-AVI and at least one comparator

Fifty-eight studies that met or potentially met the inclusion criteria on the basis of their abstract were selected from the mapping review. A further two datasets were obtained, one through a data request to PHE (see Report Supplementary Material 1 for details),31 and one through a data request to Pfizer,33 meaning a total of 60 sources were appraised for relevance. After consideration of their full text, 2831–46,53,54,59–68 met the inclusion criteria for the review and 32 studies were excluded (see Report Supplementary Material 1).

Two surveillance databases were also considered for inclusion in the review (ATLAS and SENTRY). 23,24 Both were excluded to avoid double counting, and to avoid underestimation of between-study heterogeneity since published, studies (providing more isolates or more information) drawing from these databases were already included in the review. Full details are provided in Report Supplementary Material 1.

Policy Research Unit in Economic Methods of Evaluation in Health and Social Care Interventions first considered whether any one of the studies met all the requirements of the assessment (ideally consecutive English data from a multisite study reporting outcomes for all relevant comparators, using BSAC/EUCAST breakpoints and laboratory methods), and could fulfil the evidence needs of the project without need of a meta-analysis. The data requested from PHE31 were the most relevant source of evidence since they are derived from English isolates. However, they also had several limitations: isolates have not historically been routinely submitted by testing centres, which may limit how representative these data are of the true distribution of OXA-48 susceptibilities in England. In addition, there is inconsistency in the testing methodologies used by local laboratories (albeit the majority use EUCAST). 69 This presents problems as outlined in Table 6. Finally, not all isolates were tested for each comparator, and a compromise had to be made in conducting the analysis whereby to preserve internal validity only isolates tested among all comparators were included (see Report Supplementary Material 1), which may have introduced selection bias; the PHE data did not report susceptibility for levofloxacin or ceftriaxone. Four other studies32,64,65,67 reported data from the PHE antimicrobial resistance and healthcare-associated infections (AMRHAI) programme, and all were subject to similar limitations. Due to these limitations, EEPRU considered it prudent to review and synthesise in a NMA other available evidence on the susceptibility of OXA-48 Enterobacterales to CAZ-AVI, to supplement the PHE data and to fill evidence gaps for fosfomycin, levofloxacin and ceftriaxone (the PHE data included evidence for fosfomycin, but the numbers were deemed too small to reliably use, with only eight isolates included).

Characteristics of studies entering the network meta-analysis

Of the 2831–46,53,54,59–68 data sources identified, 1253,54,59–68 were excluded from the meta-analysis. The reasons for these exclusions, and a table detailing the characteristics of these studies, can be found in Report Supplementary Material 1. Seven34,59,62–65,67 were excluded to avoid double counting of isolates, three53,54,66 were excluded as they related to outbreaks, and two60,68 were excluded as they reported MIC 50 and MIC 90, but not per cent susceptible. Consequently, between the 28 data sets and the two surveillance databases identified, 1631–46 were eligible for inclusion in the statistical synthesis (see Table 7).

Across the 15 studies that were not academic in confidence,31,32,34–46 sample size ranged from n = 30535 to n = 11 isolates46 [studies with < 10 isolates were excluded from the analysis due to time constraints (see Report Supplementary Material 1 and one study in Appendix 2, Table 38)], with three studies33–35 reporting > 300 isolates. Two31,32 included only isolates submitted to the PHE AMRHAI laboratory because of suspected unusual carbapenem resistance, and were of high relevance to the HVCS. However, one32 of these studies included isolates that were collected at least as long ago as 2008, which is before CAZ-AVI began being investigated, and this diminishes the relevance of this study since resistance in response to use of CAZ-AVI was unlikely to have emerged at this point. Consequently, a modelling scenario was planned using just the more recent PHE data to inform comparator susceptibility, rather than a synthesis of the two English studies, but the older study was retained in the full evidence synthesis analysis (see next paragraph). The remaining 14 studies collected isolates internationally (n = 5),33,34,37,40,41 from multisite locations in a single country (two from Spain,35,36 and one study from each of China,39 Greece44 and India46) from a single site in a single country (one study from each of France42 and Spain43), or were unclear on the number of sites included (one study from each of Turkey38 and Australia45). Expert advice indicated that resistant infections tend to arrive in the UK from around the world, and consequently isolates collected from any location were of relevance to the assessment. All studies were therefore retained in the analysis.

Isolates were generally collected since 2012, with two exceptions: the UK study already noted above32 and an international study which used isolates collected between 2002 and 2017. Expert advice indicated that isolates collected since 2012 were of highest (but not exclusive) relevance, and since the numbers of isolates collected before this period were likely to be small (69 isolates collected between 2002 and 2017,40 and 19 isolates collected at unknown time points32), all studies were retained in the analysis.

Quality assessment of studies entering the meta-analysis

The 16 studies31–46 included in the meta-analysis were assessed for internal and external risk of bias using a bespoke tool developed for this project (see Keyword mapping, study selection, data extraction and quality assessment and Appendix 2). A summary of scores in provided in Table 8. Across these studies, no one study scored well for all risk-of-bias items. There was a high proportion of categories scored ‘unclear’ across the assessment (35/64, 55%), which may reflect the current lack of standardisation around bias assessment and reporting for susceptibility studies. The study that had the lowest overall risk of bias was Vazquez-Ucha 2021,35 which scored low risk for three of the summary items (target population, outcome measurement and missing data) and unclear risk for one (sampling strategy) because it was not clear if the sample was consecutive or representative, and therefore whether it was an unbiased representation of the distribution of susceptibility. The majority of studies scored low risk for two categories overall, but there was no consistency as to which two categories scored well. Outcome measurement scored high risk most often, with 5 out of 16 studies scoring high risk, largely due to studies using laboratory methods from a different guideline than the breakpoints used. Missing data scored low risk most often, with 13 out of 16 studies scoring low risk, since usually all isolates were tested for all treatments. Target population was unclear in 12 out of 16 studies, due to studies not being clear if they included isolates with co-carriage of MBLs, or how they selected isolates for mechanism testing. The strategy for sampling isolates from the population was unclear in 11 studies. Reasons for the scores are provided in Appendix 2, Table 37.

Network meta-analysis including all studies

The analysis including the full data from all 16 studies indicated an extremely high amount of heterogeneity, with the between-study standard deviation (SD) estimated to be 1.99 (95% CrI 1.64 to 2.44). Results from the UME model suggested possible inconsistency in the network. Although the DIC of the UME model was substantially higher than the NMA model, suggesting a poorer model fit, the between-study SD was smaller. Inconsistency was therefore explored further by inspecting the deviance contribution plot (see Appendix 4.4, Figure 30). Two data points were highlighted as having substantially lower deviance contributions under the UME model: the tigecycline arm of Han 202039 and the colistin arm of Johnston. 40 The NMA model was re-fitted on a reduced data set with these two inconsistent observations removed. No further inconsistency was detected following the removal of these data points (see Appendix 4.3, Figure 31).

Sixteen studies contributed to the NMA using reduced data, considering a total of 14 comparators, and the full network diagram is shown in Appendix 4.2, Figure 17. Nine of the studies32,36,43–45,78–81 contained zero susceptibility counts for one or more of the included comparators and therefore had a continuity correction applied prior to synthesis.

The relative susceptibility for each comparator relative to colistin is shown in Figure 2. The model fitted the data well, with a total residual deviance of 109.9, which was close to the number of data points included in the analysis of 109. The between-study SD was 1.56 (95% CrI 1.28 to 1.93), which, while still indicating extremely high heterogeneity, offers a noticeable reduction compared to the full analysis including the inconsistent data points. CAZ-AVI was associated with a statistically significant higher susceptibility relative to colistin (OR 7.24, 95% CrI 2.58 to 20.94) with probability 100% of being the most effective treatment; median rank 1. The remainder of the treatments were associated with lower susceptibility than colistin (OR < 1) although this was not statistically significant for tigecycline or amikacin based on the 95% CrI. For all comparators the extremely high between-study SD results in wide 95% PrI.

FIGURE 2.

Forest plot of OR vs. colistin for reduced data set, NMA model.

Selection of the reduced data set (with inconsistent observations removed) was based on the statistical identification of inconsistency in the network. Inclusion of these observations resulted in extremely high heterogeneity and unfeasibly large uncertainty estimates for the relative effects. No data extraction errors or reasons to exclude the entire studies (rather than individual arms) based on the inclusion/exclusion criteria were identified. The planned meta-regression and subgroup analyses were therefore conducted using both the full dataset as planned (in an attempt to explain the identified inconsistency shown in Appendix 4.3) and the reduced dataset. The conclusions of the analyses were consistent between using the full dataset and reduced dataset. The reduced data set was selected for use in the decision-analytic model scenario analyses.

Network meta-analysis using EUCAST subgroup

Six studies33,34,39–41,45 used CLSI breakpoints and methodologies, two35,38 used EUCAST or BSAC breakpoints and methodologies, three32,37,44 used CLSI methods, but EUCAST breakpoints, one46 used CLSI methods and EEPRU applied EUCAST breakpoints and three36,42,43 did not report one or both elements. As already noted, it was unclear what methods were used by the submitting laboratories in the PHE data;31 it was assumed that the majority would comply with BSAC recommendations and use EUCAST methods and breakpoints.

A subgroup analysis containing only the two EUCAST and one PHE study was performed. The network diagram for this subgroup of studies is shown in Appendix 4, Figure 18. The susceptibility to each comparator relative to colistin is shown in Appendix 4.2, Figure 3. The model fitted the data well, with a total residual deviance of 28.21, which was close to the number of data points included in the analysis of 28. The between-study SD was 0.98 (95% CrI 0.62 to 1.63), indicating high, but noticeably reduced, heterogeneity compared to the analysis including all 16 studies.

FIGURE 3.

Forest plot of OR vs. colistin for EUCAST studies subgroup.

Ceftazidime-avibactam was associated with a higher susceptibility relative to colistin (OR 2.15, 95% CrI 0.60 to 8.01); however, the magnitude of the point estimate is lower than that of the full analysis and the result is not statistically significant. The remainder of the treatments were associated with lower susceptibility than colistin (OR < 1) although this was not statistically significant for amikacin or meropenem based on the 95% CrI.

Methods

A comprehensive review of the safety of comparators was not possible within the timeframe of this evaluation. AEs included in the model for the intervention and comparators are described in Modelling direct patient net health effects in HVCS. Clinical advisors to EEPRU indicated that CAZ-AVI is predominantly a safe treatment, but that colistin and aminoglycosides have significant AEs relating to acute kidney injury (AKI). Another key AE related to antibiotic use is the emergence of C. difficile in a patient’s digestive tract, which can lead to diarrhoea and serious damage to the colon. EEPRU conducted a review of the RCT trial evidence for CAZ-AVI to establish whether it supported the clinical view that CAZ-AVI is a safe treatment. EEPRU were especially interested in establishing safety comparative to toxic alternatives (colistin and aminoglycosides) and the other ‘safer’ treatments used in the HVCSs.

Rates of serious treatment-related AEs, nephrotoxicity AEs and C. difficile infections were extracted from the included RCT publications and/or their ClinicalTrials.gov NCT record. Only RCTs relating to the sites of interest were reviewed, due to time and resource constraints. Only RCTs were considered as these give comparative data. In the absence of nephrotoxicity events, other kidney and renal AEs were extracted. The data were then synthesised narratively for any important safety signals.

Results

The extracted AE data in the RCTs for CAZ-AVI are presented in Table 11.

As described in Review 1, there were three RCTs in cUTI47,49,50 and one in VAP. 48 Statistical comparisons were not reported for AE rates in any of the trials.

The proportion of patients with serious adverse events (SAEs) was numerically similar or slightly higher when compared to best available therapy (4.61% and 3.27%),47 doripenem (4.1% and 2.4%)50 and meropenem (19% and 13%),48 and appeared numerically higher when compared to imipenem-cilastatin (8.82% and 2.99%). 49 However, doripenem and imipenem-cilastatin are not comparators within the HVCSs.

Among renal AEs, rates were very low, and were in some studies a little higher in CAZ-AVI arms (see RECAPTURE 1 and 2 and REPROVE in Table 11), whereas in one trial they were lower (see REPRISE in Table 11). 47 This may be due to the small proportion of patients in the best available therapy arm who received colistin or aminoglycosides (3.6%).

Only one study reported C. difficile rates. Again, the event rates were extremely low, with one event (0.4%) in the CAZ-AVI arm, and zero in the doripenem arm. 50

Event rates were generally low for other AEs in both arms.

Overall, no strong signal for additional AEs to be included in the modelling were identified. The data appeared to bear out the clinical view that CAZ-AVI is a largely safe treatment with no or extremely little effect on renal AEs or on the emergence of C. difficile when used to treat infections at the sites of interest.

Population

The patient populations modelled align with the decision problem outlined in Chapter 4. These are summarised in Table 13.

Intervention

Ceftazidime-avibactam is considered as monotherapy only due to a lack of in vivo or in vitro evidence about how it performs in combination with other agents. The clinical advisors confirmed that monotherapy was more likely to be used in practice.

Comparators

A wide range of drugs is considered relevant in the HVCS, and different drugs were considered relevant depending on the site, pathogen, mechanism and setting. The full list of comparators is provided in PICOS for high-value clinical scenarios. Due to the paucity of data available to inform the comparative effectiveness assessment (see Chapter 5), and our reliance on in vitro susceptibility data to inform comparative effectiveness, it was possible to take a simplified approach to modelling these comparators rather than conducting a fully incremental analysis of all available AM options as is typically recommended in economic evaluation. The approach taken is documented in the following section.

Model structure for microbiology-directed setting

In the MDS, each patient’s susceptibility to available treatment options is known, and treatment can be tailored accordingly. Based on feedback from our clinical advisors, the two main reasons for initiating treatment with CAZ-AVI (provided patients are susceptible to it) within the MDS HVCS would be that patients are either: (1) not susceptible to any other available treatment options (i.e. patients are completely MDR to relevant existing treatment options) or (2) the only other treatments to which they are susceptible carry an elevated risk of nephrotoxicity. We include colistin and aminoglycosides within the category of nephrotoxic drugs as our clinical advisors indicated that they are likely to be associated with elevated levels of nephrotoxicity. To reflect these considerations, patients within the MDS are divided into three categories based on their susceptibility to existing therapies and, within each category, further subdivided according to their susceptibility to CAZ-AVI.

Table 14 shows these subgroups, how they determine treatment choice under existing care, and how that would change if CAZ-AVI was to become available to this patient group. The groups for which a switch to CAZ-AVI is expected are highlighted in bold.

In the group of patients who are susceptible only to colistin or an aminoglycoside, and susceptible to CAZ-AVI, CAZ-AVI offers a safety advantage. In the group of patients who are not susceptible to any available treatment options and, in the absence of the new treatments under evaluation, would receive multidrug salvage therapy, CAZ-AVI offers a safety and efficacy advantage. This is because, for many patients, multidrug salvage therapy would be expected to include a colistin or aminoglycoside component. Throughout the modelling, isolates classed as intermediate-resistant are grouped with those which are resistant as patients infected with intermediate-resistant and resistant pathogens are expected to experience similar outcomes in the HVCS based on feedback from EEPRU’s clinical advisors, and much of the data relating mortality and hospitalisation to susceptibility follow this grouping. Our clinical advisors noted that it may be possible to overcome intermediate resistance via higher dosing, but also considered that it would be difficult to evidence this within the model. Given the diverse range of data sources informing susceptibility and the link between susceptibility and outcomes, and the level of reporting within these studies, it was not feasible to explore the implications of differential outcomes between intermediate-resistant and resistant patients.

In the MDS the model is, therefore, driven by the proportion of individuals within each category of ‘susceptibility to existing drugs’ and the proportion of individuals susceptible to CAZ-AVI. This is based on susceptibility data as documented in Clinical parameters – susceptibility evidence. The comparison made within the model is between the overall MDS cohort who receive tailored therapy with the new drug available (column four of Table 14) and the overall MDS cohort who receive tailored therapy under existing treatment options only (column 3 of Table 14).

Importantly, the fact that the susceptibility profile is known prior to initiation of treatment in the MDS, alongside the assumption that susceptibility is the sole predictor of treatment effectiveness, means that we do not need to model each individual treatment option within the MDS. For example, it is not relevant (to clinical outcomes) whether a patient is susceptible to fosfomycin or aztreonam, as susceptibility to these treatments would be assumed to result in the same outcomes. Although there are differences in the costs of therapies, these are modest in relation to other costs such as that of hospitalisations which may include periods in the ICU/HDU. In practice, patients may receive a combination of agents, but this is not modelled explicitly due to a lack of evidence. The clinical advisors considered it reasonable to assume that, in the MDS, patients susceptible to a single AM within a multi-agent regimen perform as well as those susceptible to all components of that regimen (i.e. it does not matter if you are susceptible to drug A, drug B or drug A and B as long as you are susceptible to one of the agents received).

Following receipt of treatment in the MDS, patients are modelled to experience one of four alternative 30-day outcomes which determine their long-term outcomes (Figure 5). A decision tree is used to determine the distribution of patients across these categories at 30 days. This is as shown in (Figure 6). Probabilities highlighted in bold differ by treatment in this figure. In the MDS we only model one line of treatment explicitly, though hospitalisation and mortality evidence will reflect the fact that some patients entering the MDS receive multiple lines of therapy.

FIGURE 5.

Thirty-day outcomes in the MDS.

FIGURE 6.

Decision tree used to calculate impact of AKIs on 30-day outcomes in MDS.

All patients face a risk of death due to their infection and comorbidities (p_bgrdD30d_MDS). The risk differs according to whether patients have received a treatment to which they are susceptible or not. In the MDS, given that treatments are tailored according to patients’ known susceptibility profiles, only patients infected with a fully MDR infection (who receive multidrug salvage treatment) are expected to face the elevated risk of death of those non-susceptible to treatment. The efficacy advantage of CAZ-AVI is, therefore, driven by the proportion of people who switch from ‘multidrug salvage’ to CAZ-AVI in Table 14 as these patients switch from experiencing the mortality of non-susceptible patients to experiencing the mortality of susceptible patients.

In addition, patients face differing drug-related risks of experiencing an AKI. Patients who experience an AKI face an elevated risk of death compared to those who do not. When modelling the effect of AKI on mortality, we account for the fact that the available mortality data already reflect both the underlying risk of AKI associated with currently available non-colistin/aminoglycoside AMs and the background risk of AKI associated with patients underlying comorbidities and infection (Figure 6). Patients who experience an AKI and survive until 30 days face a risk of adverse long-term outcomes according to whether they have (1) recovered their renal function or (2) suffered irreversible renal failure, that is, developed chronic kidney disease (CKD).

At 30 days, patients who are discharged alive without renal dysfunction are assigned a comorbidity-adjusted QALY outcome estimated using an alive-dead area-under-the-curve approach. This is independent of the assigned treatment, as patients alive at 30 days without a history of AKI are assumed to experience similar outcomes regardless of the treatment they received for their infection.

Patients discharged with recovered renal function face the same HRQoL outcomes, but they face an additional risk of progressing to CKD and elevated mortality. Patients discharged with CKD or who develop CKD face further elevated mortality, reduced HRQoL and additional healthcare costs. The experience of the two groups of patients with a history of AKI is modelled as a semi-Markov process (with transition probabilities dependent on time in model) for all transitions, as shown in Figure 7.

FIGURE 7.

Markov model used to calculate post-30-day outcomes in patients with recovered renal function and irreversible renal failure.

Model structure for the risk-based empiric setting

The approach taken in the ES is similar to that taken in the MDS in terms of the possible 30-day outcomes patients can experience and the long-term implications of these outcomes. However, the decision tree describing differences across comparators in the first 30 days is more complex for two reasons. Firstly, there is a need to model outcomes both in those correctly identified as having the pathogen–mechanism combination suspected, as well as in those who were labelled as high-risk but in fact have a different causative pathogen or mechanism. Secondly, there is a need to model both the ES phase of treatment and progression of some patients to the MDS for further treatment.

Unlike in the MDS, in the ES the susceptibility of patients to treatments provided is unknown at the time of initiating empiric treatment. It is, therefore, necessary to model the probability of susceptibility to individual treatment combinations as this determines clinical outcomes and, in particular, the need for further treatment. Since, as documented in PICOS for high-value clinical scenarios, there are a number of feasible treatment combinations for these patients, to simplify the modelling we compare empiric use of CAZ-AVI in the ES to two alternative treatment options:

1. the non-colistin or aminoglycoside-based treatment combination with the current highest estimated susceptibility in the UK population

2. the colistin or aminoglycoside-based treatment combination with the current highest estimated susceptibility in the UK population.

When considering possible treatment pathways in the ES, the following possible pathways are relevant (shorthand labels E1, E2nca etc. are used in the results section):

1. E1: empiric use of CAZ-AVI followed by existing treatments in the MDS.

2. E2nca: empiric treatment using non-colistin or aminoglycoside-based existing therapies followed by existing therapies in the MDS.

3. E2ca: empiric treatment using colistin or aminoglycoside-based existing therapies followed by existing therapies in the MDS.

4. E3nca: empiric treatment using non-colistin or aminoglycoside-based existing therapies followed by CAZ-AVI use if indicated in the MDS.

5. E3ca: empiric treatment using colistin or aminoglycoside-based existing therapies followed by CAZ-AVI use if indicated in the MDS.

Repeated usage of CAZ-AVI in the MDS for patients who fail CAZ-AVI in the ES was not modelled as this was not considered to represent a priority use for CAZ-AVI.

Thirty-day outcomes in the ES are determined by a decision tree which comprises three subcomponents:

1. the risk of carrying the pathogen–mechanism of concern

2. outcomes at the point at which patients are assessed for MDS treatment, that is, at around 3–5 days when susceptibility results report

3. 30-day outcomes following MDS assessment.

Each of these is considered in more detail below.

1. Risks of carrying the pathogen–mechanism of concern

Patients may or may not have the suspected pathogen–mechanism of concern. We assume that patients who do not have the pathogen–mechanism of interest experience the same effectiveness outcomes regardless of the choice of empiric treatment (though safety differs), as our clinical advisors confirmed that these patients represented a broadly susceptible population (rather than a population enriched with pathogens carrying other resistance mechanisms) and that for this reason effectiveness is likely to be similar across all empiric treatment options considered. For simplicity we assume that patients who have a different pathogen–mechanism experience the susceptibility associated with colistin/aminoglycoside-based therapy in people with the pathogen–mechanism of interest regardless of the choice of treatment. Colistin/aminoglycoside-based therapy was chosen as more representative of outcomes across susceptible patients as this treatment class showed robust and high susceptibility across subgroups and scenarios. The structure of this element of the ES model is presented in Figure 8.

FIGURE 8.

First component of 30-day outcomes model for ES: risk of carrying pathogen–mechanism of concern.

1. Outcomes at the point at which patients are assessed for MD treatment

At initiation of empiric treatment patients are classified by the model as susceptible or non-susceptible to their empiric therapy (Figure 9). As in the MDS, susceptibility is the driver of differences in effectiveness across treatments. Note that we are able to model differences in susceptibility across treatments used in the ES dependent on whether a patient is susceptible or non-susceptible, even though clinicians will not observe this information until patients enter the MDS.

FIGURE 9.

Second component of 30-day outcomes model for ES: outcomes at the point at which patients are assessed for MD treatment. Note that mortality (p_bgrd_Dst_S and p_bgrdDst_nonS) is also adjusted to reflected differences in mortality due to AKI, in the same way as shown in Figure 10, but this is not shown for parsimony.

At the point at which patients’ microbiology results become available, patients may have died, may require initiation of a new AM treatment (e.g. due to lack of efficacy) or may complete their course of empiric treatment (Figure 9). The probability of these three outcomes depends on whether patients were susceptible to their empiric treatment or not, but not directly on the choice of specific treatment. Patients who have received empirically a treatment to which they are later found not to be susceptible are all assumed to require further treatment in the MDS, provided they survive until microbiology results are available. This assumption is based on evidence presented in Tumbarello et al. 2013, which found that all patients who received inappropriate empiric treatment and survived until their microbiology results were received switched to appropriate therapy (further details on Tumbarello are provided in Clinical parameters – AKI risk and subsequent outcomes). 109

1. Thirty-day outcomes following assessment for MD treatment

People who survive until the time point of assessment for MD treatment enter the third part of the decision tree which is shown in Figure 10. Those requiring no further treatment face a risk of dying between this point and 30 days, which depends on whether they experienced an AKI. Those surviving to 30 days face the possibility of entering the (1) alive, (2) alive with recovered renal function or (3) alive with irreversible renal failure health states described in Figure 10. While patients may experience an AKI following empiric treatment, clinicians confirmed that in this patient group, where treatment options are limited, the AKI alone would not typically trigger a treatment switch, provided the treatment was effective.

FIGURE 10.

Third component of 30-day outcomes model for ES: 30-day outcomes following assessment for MDS treatment.

Patients who require further treatment enter the MDS component of the model. Their outcomes depend on whether they experienced an AKI following first-line treatment (i.e. this is ‘remembered’ within the model) as this determines both their outcomes (patients who experience an AKI experience elevated mortality and the implications of reversible or irreversible kidney damage) and their choice of treatment in the MDS. Our clinical advisors informed us that patients requiring further treatment in the MDS who experienced an AKI following treatment in the ES are unlikely to receive colistin- or aminoglycoside-based treatment in the MDS. Patients who fit this profile, and are only susceptible to colistin or aminoglycoside-based treatment, are, therefore, assumed to receive multidrug salvage therapy in the MDS, or the new drug if available. For these patients, multidrug salvage therapy is assumed not to include colistin or an aminoglycoside. Instead, they are assumed to receive the outcomes of multidrug salvage therapy without elevated risk of AKI.

In the absence of evidence to support more detailed modelling, we assume that a patient’s susceptibility to treatment is the same in the ES and MDS. In reality, patients entering the MDS who were already assessed as having a high risk of carrying a highly resistant pathogen in the ES are likely to receive aggressive treatments in the ES which may change their resistance profile in the MDS. The nature of the effects on individual resistance are hard to predict as they are influenced by the treatment received in the ES, the effectiveness of this treatment and the development of acquired resistance. These are not, therefore, considered within the model.

Identification of evidence

Susceptibility evidence and evidence linking susceptibility to mortality and hospitalisation were obtained via the systematic reviews and structured expert elicitation described in Chapters 5 and 6. Other key clinical parameters were obtained from existing systematic reviews where possible, otherwise clinical parameters were obtained from existing UK cost-effectiveness models. HRQOL weights (utilities) were obtained from a systematic review (described below) and cost parameters via targeted searches.

Clinical parameters – susceptibility evidence

The susceptibility data used in the model base case analysis are summarised in Table 15. These represent the mean values of the samples used in the probabilistic sensitivity analysis, along with 95% percentile-based confidence intervals. Five key susceptibility parameters inform the model. One parameter describes susceptibility to CAZ-AVI in the MDS and ES. Two parameters describe susceptibility to colistin/aminoglycoside-based therapy and to non-colistin/aminoglycoside-based therapy in the ES. Two parameters describe the number of individuals in each category of susceptibility in the MDS as shown in Table 14 (namely susceptible to a non-colistin/aminoglycoside AM, susceptible only to a colistin/aminoglycoside AM).

Susceptibility to existing drugs is obtained from both the analysis of PHE data and the NMA, as described in Statistical synthesis. These analyses can be combined to provide evidence on absolute rates of susceptibility to AMs for the HVCSs. This evidence required further adjustment before it could be used in the economic model, with different adjustments for the ES and MDS. The methods employed to obtain estimates for these two settings are discussed in turn, with further details provided in Appendix 8 and Appendix 8, Table 53. Of note, while evidence on susceptibility to meropenem was available, this was not used in the economic modelling. This is because clinical advice was that, for meropenem, susceptibility among carbapenem-producing pathogens was not a good surrogate predictor of clinical outcomes. This reflects advice in the literature. 67,110 Hence, while meropenem is included as a comparator in the PICOS, it is assumed to have zero efficacy in the economic modelling (and so not actively modelled).

Susceptibility is estimated to be specific to the pathogen–mechanism subgroup of interest but is assumed to generalise across sites and settings. This was considered a reasonable assumption by our clinical advisors and preferable to further subgrouping the susceptibility data given the small sample sizes available to inform these parameter estimates for the HVCSs. Due to this assumption, there was one deviation from the PICOS, with tigecycline used for both sites (cUTI and HAP/VAP). In the PICOS, tigecycline was only included for HAP/VAP.

Clinical parameters – linking susceptibility to 30-day outcomes in the MDS

The remaining clinical evidence predicting 30-day outcomes in the MDS is presented in Table 17. Thirty-day mortality differs across comparators via two mechanisms in the MDS. Thirty-day mortality does not differ in the MDS setting if infections are susceptible to existing treatments because patients will be treated with the correct AM, though it does differ if patients have infections resistant to existing options but susceptible to CAZ-AVI, as their recovery will be more likely if they take CAZ-AVI. In addition, patients’ mortality risk varies according to the AKI-rate associated with the AM used.

As documented in Review questions 4 and 5, several studies have explored the link between whether patients have been administered a treatment to which they are susceptible and their 30-day mortality outcomes in the infection sites of interest for the HVCSs. However, these studies have focused on the empiric setting and none was available relating specifically to the MDS where outcomes are expected to differ substantively. Patients in the MDS who receive an inappropriate drug are much more likely to be MDR than patients receiving inappropriate treatment in the ES and are more likely to be in critical state that reduces the possibility of further treatment.

This data gap is perhaps unsurprising as multidrug resistance (including to colistin) remains rare and it may, therefore, be difficult to recruit or include sufficient patients in this setting. Given the absence of data to inform this important parameter, a structured expert elicitation exercise was conducted. The methods and results of the expert elicitation are described in Chapter 6. These estimates were elicited separately for cUTI, HAP and VAP as these infection sites are expected to have quite different mortality rates. Separate estimates were not produced by causative pathogen–mechanism. This is because, among those receiving a treatment to which they are susceptible, outcomes are expected to be similar across the pathogen–mechanism combinations relevant to the CAZ-AVI HVCSs. Similarly, among patients receiving multidrug salvage therapy due to multidrug resistance, outcomes are expected to be similar across the pathogen–mechanism combinations relevant to the CAZ-AVI HVCSs.

Clinical parameters – AKI risk and subsequent outcomes

Rates of nephrotoxic-drug-induced AKI and their implications are assumed to generalise across sites, pathogens and mechanisms in the absence of subgroup-specific data. The evidence from the CAZ-AVI RCTs did not provide any meaningful evidence on the safety implications of aminoglycoside/colistin use (see Results) and these data were not considered by our advisors to be generalisable to the highly comorbid patients who tend to acquire carbapenem-resistant infections. These parameters were therefore obtained from existing systematic reviews where possible or, if not available, from UK-specific sources.

Several recent systematic reviews and meta-analyses estimated the pooled cumulative incidence of AKI in patients treated with colistin or polymyxins B111,112,114,115 and two of these reported differences in the rates of AKI between colistin- or polymyxin B-based therapy and other agents. 112,114 The absolute risk of an AKI and the likelihood that an AKI resulted in irrecoverable kidney damage was derived from Sisay et al. 2021111 as this study had the most recent searches, included a broad range of study designs and was restricted to studies using the Risk Injury Failure Loss end-stage renal disease (ESRD) (RIFLE) criteria. The difference between colistin- (or polymyxin B, a similar drug from the same class) based therapy and other agents was obtained from Chien et al. 2020112 as this review made some attempt to control for confounding. Chien et al. 2020112 included both RCTs and comparative cohort studies but excluded studies considered poor quality as assessed by the Newcastle-Ottawa scale (in particular the authors state that only cohort studies of parallel design with patients of comparable clinical characteristics were included). Alternative sources for these parameters are explored as scenario analyses.

The excess death from AKI was derived by comparing in-hospital mortality rates in the UK for individuals who experienced an AKI as defined by the Acute Kidney Injury Network (AKIN) criteria and individuals without AKI using the East Kent Hospitals University NHS Foundation Trust (EKHUFT) dataset from Kerr et al. 2014. 113 The latter gathers admission records from three inpatient hospitals in the South of England. The analysis of the EKHUFT dataset was deemed more appropriate than that obtained using the HES dataset as EKHUFT includes older and more comorbid patients who are, therefore, more similar to our patient population, and is more likely to include all AKIs than the HES dataset.

The impact of AKI on mortality was estimated by the authors adjusting for a range of covariates including history of hospital admission, comorbidities and primary diagnosis. We assumed the relative increase in mortality associated with AKI observed in the Kerr et al. analysis113 applied to the baseline risk of mortality in our HVCS despite the patients within our HVCS exhibiting a much higher baseline mortality risk. AKI is more prevalent in patients with poor prognosis; although Kerr et al. attempted to adjust for these factors the elevated mortality estimated was considered high by expert advisors. 113 A scenario analysis was, therefore, run whereby the excess mortality associated with AKI was halved from the reported value.

Clinical evidence – linking susceptibility to 30-day outcomes in the ES

The evidence informing the decision tree predicting 30-day outcomes in the ES is presented in Table 18. The mechanisms database described in Appendix 1 was searched to identify papers providing quantitative estimates of the risk of carrying the pathogen–mechanism of interest among patients with specific characteristics. This was supplemented by papers known to the study team. Two searches were conducted. The first was to identify UK-specific studies. This had two concepts; the first was to identify ‘risk’ studies (any field containing any of ‘risk’, ‘prevalence’, ‘incidence’, ‘character*’, or ‘outbreak’), returning 1696 studies. The second concept was for UK studies [abstract contains any of (‘United Kingdom’, ‘Great Britain’, ‘England’, ‘UK’, ‘NHS’, ‘Trust’, ‘London’, ‘*Shire’)], returning 119 studies. Combining both concepts provided 61 studies for the first search. The second search was expanded to identify non-UK risk models and returned 51 studies based on their title containing ‘Risk’. No risk models were identified from either search. Indeed, even in the wider population of patients at risk of a carbapenem-resistant infection, there is a paucity of UK data available to estimate the risk of having a carbapenem-resistant infection among patients with relevant risk factors. 117

The probability that a patient entering the ES who actually has the suspected pathogen–mechanism was obtained from the Second Generation Surveillance System (SGSS) data supplied by PHE as shown in Table 18 (for further discussion, see Quantitative extrapolation to expected usage). These data provide the number of tests for a given mechanism of resistance and the proportion of those tests that returned a positive result. These data are unlikely solely to reflect the ES HVCS of focus in the current analysis. For example, testing may be conducted due to a suspicion in the lab rather than at the level of the treating clinician (e.g. a lab finding of carbapenem non-susceptibility might trigger a test), some tests may be run following treatment failure or may be run in the ES but at a lower level of suspicion than considered in our HVCS.

Given these uncertainties in the available data, we also conducted a survey of the mailing list of the BSAC. This survey asked microbiologists and infectious disease specialists how many times they saw patients who would fall into our ES HVCS of interest, and the proportion of those who had the pathogen–mechanism of interest. A survey was used in preference to the structured expert elicitation as this parameter was expected to vary according to local epidemiology and history of outbreaks of resistant infections and it was not considered realistic that the expert elicitation exercise could include enough experts to adequately reflect this geographical heterogeneity. Unfortunately, the response to the survey was low, with only nine experts providing usable responses. On average these experts reported that, of the OXA-48 Enterobacterales HAP/VAP seen where there was a high suspicion of the mechanism of interest, 57% of patients would be confirmed as having the mechanism. These values are used in a sensitivity analysis. Given the high level of uncertainty around this parameter, sensitivity analysis results are shown for a wide range of alternative values.

Mortality at the time of assessment for entry to the MDS conditional upon susceptibility status, and 30-day mortality among patients not requiring further treatment, was obtained from Tumbarello et al. 2013. 109 This study was conducted in 110 ICU patients with confirmed Pseudomonas aeruginosa pneumonia in a hospital in Italy and compared 30-day mortality in patients who were susceptible to initial empiric treatment and those who were not. Surviving patients who were not susceptible to empiric treatment were switched to definitive therapy, on average ~62 hours after symptom onset. Tumbarello 2013109 was chosen as it reported a relatively high incidence of MDR strains in infecting organisms (42/110) compared to the other studies identified in the review and was the only study reporting Kaplan–Meier curves (see Review question 6 for details). No UK studies were identified.

The probability of requiring further treatment for susceptible patients was taken from the CAZ-AVI arm of the APEKS-NP study. 116 Within the studies included in the CAZ-AVI mappings, this was identified as the only study representing a predominantly empirically treated susceptible population of HAP/VAP patients that also reported subsequent treatment rates.

Clinical evidence – long-term mortality

All patients surviving to 30 days face an ongoing mortality risk based on the CARBAR57 and Merrick118 studies. Both studies included UK patients with infections caused by carbapenem-resistant organisms and were, therefore, considered relevant in terms of capturing the highly comorbid nature of patients who acquire these infections. Searches were conducted as described in Appendix 1, but did not identify any further evidence of relevance. A targeted search indicated a lack of data on long-term outcomes in both HAP/VAP and cUTI. It also seemed unlikely that outcomes in all-comer HAP/VAP and cUTI patients would reflect those of MDR patients who tend to have developed MDR infections as a result of multiple contacts with the health systems, reflecting a wide range of comorbidities. We therefore chose to focus our review of long-term mortality on patients with resistant infections.

CARBAR57 was used to inform mortality in the base case as it included more geographically diverse patients, had a longer follow-up (2 years compared to 1 year in Merrick) and provided continuous survival estimates over time (i.e. Kaplan–Meier curves). Merrick118 reported all-cause mortality at 1 year of 31%, which is similar to the 1-year mortality in CARBAR of 34%.

Kaplan–Meier curves from CARBAR were digitised and we used a published algorithm to recover pseudo-individual patient data from the Kaplan–Meier curve for analysis. We fitted parametric survival models to these data to facilitate extrapolation beyond the observed data. Data from 30 days onwards were used as these were of most relevance to the model. We followed guidance from the NICE Technical Support119 document and fitted a range of parametric survival models: exponential, Weibull, Gompertz, log-logistic, log-normal and generalised gamma. Model fit was assessed according to Akaike’s Information Criteria (AIC), log-cumulative hazard plots, hazard plots and visual assessment of the concordance between model predictions and Kaplan–Meier plots. No specific external data were identified to support validation of long-term predictions, so probabilities of death predicted by each model were compared to general population mortality over 20 years to assess plausibility. A summary of these assessments is provided in Appendix 8, Table 54. Overall, the Weibull, log-logistic and log-normal models were all considered plausible candidates and in the absence of further evidence log-normal was selected to offer a middle ground with the Weibull and log-logistic trialled in scenario analyses. Mortality is restricted so that it too remains above that in the general population within the model.

In addition, patients alive with recovered renal function face an elevated risk of death and a risk of developing irreversible renal failure (CKD). 120 Patients alive with irreversible renal failure face the elevated risk of death of CKD patients.

A recent body of evidence, with which our group of experts agreed, suggests that AKI and CKD are closely linked and interconnected, whereby CKD is a risk factor for experiencing subsequent AKI and AKI is a promoter or instigator of CKD. It was, therefore, considered important to capture the fact that AKI is not a ‘self-limited process’ and that patients with recovered renal function post-AKI are at risk of adverse renal outcomes and of developing CKD.

In our literature searches to identify evidence of the impact of AKI on the development of CKD and on long-term survival, we looked for studies that would control for the confounding impact of comorbidities as stringently as possible as we aimed to estimate the causal effect of AKI on subsequent outcomes. The US study by Bucaloiu 2012120 was selected as it compared outcomes of patients with hospital-associated AKI (with recovered renal function) against a non-AKI patient population matched for a wide range of relevant clinical and demographic characteristics. A total of 1610 patients with AKI and 3652 without were followed up from 90 days post discharge to approximately 6 years. A limitation of this study is that the propensity score matching process excluded the most comorbid patients due to a lack of sufficiently closely matching controls, and the study excluded patients with impaired kidney function prior to hospitalisation. This evidence was used to inform the increased risk of death and the increased risk of developing CKD in patients with recovered renal function after an AKI. Relevant parameters are shown in Table 19.

There are several limitations to the approach taken to reflect the long-term implications of AKI within the model:

1. The CARBAR data will have included patients who experienced AKI and, therefore, including additional mortality risk associated with AKI, and CKD development is likely to exaggerate mortality risk in the model.

2. The risk of CKD development is likely to be higher than estimated from Bucaloiu120 in the highly comorbid patient group considered within the HVCS.

3. The hazard ratios on mortality are applied multiplicatively despite the much higher baseline risk of death in the patient population considered within the HVCS.

Scenarios are explored to address each of these assumptions in turn:

1. CARBAR mortality rate reduced by 10% reflecting an assumed AKI rate of 20% and an assumed excess mortality associated with AKI of 1.48 (95% CI 1.19 to 1.82) based on Bucaloiu 2012. 120

2. Patients in HVCS face double risk of CKD compared to patients in Bucaloiu 2012.

3. Patients in HVCS face absolute increase in mortality risk observed in Bucaloiu 2012.

4. All of the above applied simultaneously.

We did not account for life-years accrued within the first 30 days in the model as these were expected to have a marginal effect on the model results.

Health-related quality of life

The HRQoL implications of the infection are not modelled as these are expected to be short-lived and, therefore, are not expected to impact substantively on the model results. However, to quality adjust the life expectancy estimates accurately, we did consider it important to reflect the underlying comorbidities of the patients within the HVCS. We did not identify any relevant utility data from existing models, most of which assumed that post-infection patients would return to the HRQoL of the general population. Therefore, we conducted a review of utility studies that provide evidence according to the Charlson Comorbidity Index (CCI). The CCI is a summary score of comorbidity based on 17 included comorbidities. The comorbidities considered in the CCI have been selected and then weighted based on their ability to predict 1-year mortality among hospitalised patients. Importantly, the CCI is reported within the CARBAR study for patients with infections caused by carbapenem-resistant organisms, allowing utility values presented by CCI score to be re-weighted to reflect the CCI scores in a population similar to that included in our HVCSs.

The methods for this review are described in Report Supplementary Material 3. This identified two studies reporting utilities by CCI in the general population. Both studies were based on large national surveys in France and Germany and estimated the SF-6D based on the SF-36 and SF-12, respectively. 121,122 The French study was chosen in preference to the German study as the latter controlled for several variables likely to be associated with CCI (pain level, socio-demographic variables and health behaviours). Utility values by CCI score are reported in Appendix 8, Table 55. These are weighted by the distribution of CCI scores observed in CARBAR, also shown in Appendix 8, Table 55; these reflect UK general population valuations. This produced an overall weighted utility score of 0.66 for the CARBAR population based on their comorbidities; this is intended to reflect their long-term HRQoL rather than the immediate impact of infection. This was used to compute a multiplicative reduction in HRQoL associated with comorbidities by comparing the CARBAR population to the general population (assumed to have a CCI score of 0). General population HRQoL was derived from a regression model estimated by Ara and Brazier 2010123 using Health Survey for England (HSE) survey data for the years 2003 and 2006 (n = 26,679). This produced a baseline utility value of 0.73 for all patients. This resulted in a utility-multiplier of 0.66/0.73 = 0.90. This was then applied to the age- and gender-specific EQ-5D HRQoL weights of the general UK population based on Ara and Brazier. 123

Patients who have recovered their renal function post AKI are not expected to experience further reductions in HRQoL (disutility) unless they develop CKD. The HRQoL decrement applied to the CKD patients is computed using pooled estimates from a systematic review and meta-analysis by Wyld et al. (2011). 124 The authors reported HRQoL decrements of 0.02 (−0.04, 0.09) for those in CKD pre-treatment and of 0.11 (0.08, 0.15) for those with CKD in dialysis, where the latter were estimated to represent 2% of the diagnosed CKD population based on UK data. 125 These were applied to the baseline utility value of 0.73 such that the utility of those with CKD pre-treatment was 0.71 and the utility of those with CKD in dialysis was 0.62.

Resource use and costs

The model includes costs relating to hospital stay, infection control during hospitalisation, AKI-related costs during hospitalisation, long-term costs associated with CKD and costs relating to use of existing AMs. The purchase price of CAZ-AVI is not included in the costings as the objective of the value assessment is to inform the payment for CAZ-AVI. Costs relating to testing (for pathogen, resistance mechanism or AM susceptibility) were not included as, in the HVCS populations, these tests were expected to be conducted to the same degree regardless of the introduction of CAZ-AVI.

An important cost driver in the model is time spent in hospital. Data on time in hospital for patients according to their treatment pathway and outcomes are presented in Table 20. As for 30-day mortality, we did not identify any studies in the MDS linking treatment susceptibility to duration or type of hospitalisation. This was, therefore, elicited as part of the structured expert elicitation exercise. LoS and the proportion of time spent in ICU or HDU were estimated conditional upon susceptibility for patients with cUTI and HAP/VAP separately. In the base case, all patients in the ES were assumed to spend 5 days in hospital prior to receipt of their microbiology results, the median wait reported in CARBAR. 57

The LoS for patients successfully treated in the ES was estimated from the LoS in patients who are susceptible to treatment in the MDS based on structured expert elicitation, the time to receiving MDS from CARBAR57 and the relative reduction in the LoS associated with receiving appropriate empiric treatment from Muscedere 2012. 126 The proportion of time spent in the ICU for patients who received a treatment to which they are susceptible and who did not require further treatment was derived from Muscedere 2012. 126 The study was conducted in 350 adult ICU patients with VAP (any pathogen and resistance profile) in Canada who received empiric treatment with meropenem or meropenem + ciprofloxacin. The study reported hospital and ICU LoS in patients who were susceptible to their empiric treatment and those who were not. Muscedere126 was chosen as it was the only study identified in the review in Review question 6 that reported LoS conditional upon AM susceptibility in patients with HAP/VAP. The LoS reported by Muscedere 2012 was skewed. The mean LoS was derived by fitting a log-normal distribution to the reported median and interquartile range. The derived mean LoS in patients who received appropriate and inappropriate treatment (43.1 days and 85.7 days, respectively) were used to derive the relative reduction in the LoS associated with receiving appropriate treatment. The derived mean LoS and stay in ICU were used to derive the proportion of hospital stay that was spent in ICU.

The additional hospitalisation costs associated with in-hospital AKI are informed by estimates derived from Kolhe et al. 2014. 127 This study used the NHS costing system’s relative value units that capture cost information associated with several cost items including LoS on wards, drugs, physiotherapy, radiology and medical staff costs. Unit costs were obtained from standard sources and in consultation with those suggested in the manufacturer submissions. Where necessary, costs were adjusted to 2019/20 prices using standard sources. 129 The daily cost of cUTIs treated on general medical wards was derived from the weighted average cost of non-elective short-stay for kidney or urinary tract infections with/without interventions (LA04H to LA04S). The daily cost of HAP/VAP treated on general medical wards was derived from the cost of non-elective short-stay bronchopneumonia with or without interventions (DZ23H to DZ23N). The daily cost of ICU was assumed to be the weighted average cost of non-specific, general adult critical care (CCU01) with zero to six organs supported (XC01Z to XC07Z), assuming that ventilation cost is reflected in the organ support costs. The daily cost of HDU was assumed to be the weighted average cost of medical adult patients in critical care (CCU03) with zero to six organs supported (XC01Z to XC07Z). Weighting was based on the overall volume of each type of organ support reported for the NHS. The daily cost of isolation was derived from Knight et al. 2018128 and included the cost of gloves, aprons and infectious waste stream. We assumed that all patients would be subject to isolation measures as they are either highly suspected of having or confirmed to have a MDR infection. One-off costs of stock disposal are not included as these are assumed to apply equally to all patients.

Following discharge, patients’ long-term costs are determined by their health state. Patients alive without a history of AKI, or with recovered renal function, experience no further costs. Patients with irreversible renal failure (i.e. CKD) face a weighted average cost that reflects the CKD-severity distribution in England and requirement for dialysis. Kerr et al. 2012125 estimated the annual per-patient NHS expenditure on CKD direct care, dialysis and transplants. The mean annual cost of direct CKD care per patient not on dialysis (that is antihypertensive drugs, primary care tests and consultations, nephrology consultations and cost due to excess incidence of cardiovascular events) was estimated at £278, while the annual cost of CKD-related care for a patient on dialysis was estimated at £31,933. As for HRQoL, 2% of the diagnosed CKD population were estimated to be receiving dialysis based on UK data. 125 The clinical advisors to EEPRU for this project indicated that this may be an overestimate but use of a lower value is unlikely to substantively change the results of the modelling. Our clinical advisors expected that, in the highly comorbid group considered within the HVCSs, transplantation would be rare, so the costs of transplantation were not included in the CKD cost estimates. This results in a weighted average cost of CKD of £911 per annum in 2019/20 prices.

We did not include differential rates of discharge to long-term care facilities in the base-case analysis as no evidence was found comparing UK usage of care among those with and without AKI that adjusted for differences between patients with and without AKI. US data suggest that AKI is associated with an elevated risk of discharge to long-term care even with adjustment for other predictive factors. Liangos et al. [Liangos O, Wald R, O’Bell JW, Price L, Pereira BJ, Jaber BL. Epidemiology and outcomes of acute renal failure in hospitalized patients: a national survey. Clin J Am Soc Nephrol 2006;1(1):43–51] found that 8.9% of patients without AKI will be discharged to long-term care and this is elevated to 17.8% in those with an AKI [reflecting an adjusted OR of 2.2 (95% CI 2.1, 2.2)]. We combine this with information on the costs of long-term care and model a scenario based on this. We use a weekly cost of £1049 (average of private-sector nursing home and local-authority own-provision residential care for older people129) and apply this for the lifetime of the patient. This is likely to be an overestimate as some patients may be discharged from long-term care and the full cost of this care may not fall on the NHS/PSS budgets.

We did not include the cost of end-of-life or palliative care as this was considered unlikely to substantively influence model outcomes.

Drug acquisition costs were based on the cost of the daily dose derived from published sources,92,130 the daily doses reported by WHO Collaboration Centre for Drug Statistics Methodology12 and the treatment duration derived from published literature. 94,95,105,130 When more than one formulation or pack size was available, we based costs on the largest pack size and IV formulations. When the treatment duration was provided as a range, we used the longest duration, to reflect the high severity of infections. When more than one AM was available for treatment in a particular setting (e.g. colistin or aminoglycosides for the treatment of HAP/VAP in MDS), the most expensive treatment was chosen to reflect that often combination or higher doses of therapy may be used. In the ES, patients who require a treatment switch following availability of their susceptibility results are assumed to receive 5 days of treatment, whereas those who do not require a treatment switch receive the full course.

The unit cost of all comparators is shown in Appendix 8, Table 56. The drug acquisition costs used in the model are summarised in Table 21.

Drug administration costs were assumed to be included in the cost of hospital stay, where patients are assumed to be treated.

Predicting the future sizes of the HVCS

The objective of this analysis was to statistically model changes in the number of patients within the HVCS over time, to inform a quantitative forecast of the number of patients presenting in the HVCS over the next 20 years.

Data on the number of infections over time for the mechanisms of interest were provided as a time series by PHE. One population was included: CPE with an OXA-48 mechanism.

Data were supplied for invasive infections which are predominantly infections where the specimen sample relates to a BSI or cerebrospinal fluid infection. It was assumed that, for each pathogen–mechanism of interest, the trends in population size for invasive infections generalise to the HVCS. This was considered reasonable by the clinical advisors to the project. The small number of invasive infections made it challenging to reliably identify if there was a trend in the growth of the HVCS. As such, this analysis is supplemented by a secondary analysis which looks at trends in the number of screening isolates. These isolates are from screening specimen sites. Screening samples were broadly categorised as samples from swabs, wounds and the lower gastrointestinal tract. This includes potential infections as well as isolates from people who don’t have infections but may be colonised by a MDR pathogen. These screening isolates were only used to confirm or refute the potential presence of a trend rather than inform the growth estimates as they may be influenced by screening policy changes over time which may not feed through to changes in identified infections. Data on both invasive infections and screening isolates were obtained from the AMRHAI national reference laboratory. These data were provided by PHE as monthly counts and are available from 2004 to April 2021. During 2018, guidance on which samples should be sent to AMRHAI changed, and charges were introduced. This led to a gradual ‘artificial’ decrease in referrals. Further detail on the nature of this dataset is provided in Report Supplementary Material 1.

Due to small numbers, data on invasive infections were aggregated to quarterly for analyses and restricted to October 2012 onwards. The last observations used were for March 2018 (inclusive), as after this point the observed numbers decreased. For screening isolates numbers were larger so monthly data were used. For these, the first observation was set to be the first time point for which there were no future months with zero counts (June 2013).

Time-series (state-space exponential smoothing) models were used to forecast the isolate data. For the invasive isolates the use of other time-series models was also considered. Further details on the models considered and the justification and implementation of the state-space models are provided in Appendix 8.6. Three state-space models were considered. These varied with regard to the assumptions made about any long-term trends in the growth of the HCVS:

• No growth (no trend).

• Growth in the short term that in the long term changes to no growth (a ‘damped trend’ model; the degree of dampening is estimated from the data and influences how quickly the growth tends to zero).

• Persistent growth (trend that is not damped).

Within-sample goodness of fit statistics for the three models and the two datasets are provided in Appendix 8.5, Table 57. Estimates of population growth are provided in Figure 11. Figure 11 shows the change in population size over time for both the dataset of invasive infections and the screening isolates.

FIGURE 11.

Change in population size over time (top panel = invasive isolates, bottom = screening isolates). OXA, number of isolates with Enterobacterales OXA-48.

Table 57 (for which comparisons should only be made within columns) shows that for both isolate types, the model with a persistent trend provides the best within-sample fit. However, the difference between values is relatively small, while the differences in extrapolations are very large. The largest population growth was estimated for the screening isolates with a non-damped trend and the smallest (non-constant) population growth was for the invasive isolates with a damped trend model. For the screening isolates it is unclear if long-term increases in the mechanism reflect genuine increases or the results of increased testing. It is also unclear if any genuine increases would persist into the future. As long-term estimates were very sensitive to the choice of model, and there were few statistical grounds to choose between the two models, both the damped trend and trend models for invasive isolates were considered within the decision-analytic modelling.

Details on how the estimates of future change in the HVCS were used in the economic model are provided in Appendix 8.6.

Predicting future rates of resistance for current practice

The objective of this analysis was to characterise historical changes in resistance to existing AMs among patients with OXA-48 Enterobacterales to inform a quantitative forecast of how resistance might change in the future.

This analysis used time-series data provided by PHE, obtained from the same evidence sources as described in the previous subsection (i.e. the AMRHAI national reference unit). Analyses were restricted to comparators used in the economic model. Resulting data were available for:

• aminoglycosides (gentamicin, amikacin, tobramycin)

• aztreonam

• cephalosporins (cefotaxime, ceftazidime, cefepime)

• ciprofloxacin

• colistin

• tigecycline.

For AM classes with evidence from multiple AMs, the most resistant result was retained in the supplied data. It is not expected that retaining the least resistant result would have a noticeable impact on estimates of resistance over time. As already described, isolates that were reported as ‘intermediate’ resistant were assumed to represent resistant isolates for the purpose of this analysis. Hence any tested isolate was either categorised as ‘susceptible’ or ‘resistant’ for this analysis. Combining ‘intermediate’ and ‘resistant’ categories was based on advice from clinical advisors. It is, however, noted that current EUCAST guidance is to combine ‘intermediate’ and ‘susceptible’ when only two categories are used. 139 The methods used to generate forecasts are broadly the same as those considered in the previous section, and are discussed in more detail in Appendix 8.6, Table 58. Due to the sparsity of the available evidence trends in the resistance (or susceptibility) to comparator AMs were not incorporated into the decision-analytic model.

Predicting future resistance trajectories for CAZ-AVI

The time-series data on susceptibility provided by PHE (as detailed in the previous subsection) include CAZ-AVI. Hence, one approach is to use the forecasts from the previous subsection as inputs to the economic model. However, the economic modelling explores different scenarios of CAZ-AVI use, such as in the ES or MDS. As CAZ-AVI has high rates of susceptibility in the HVCSs (and a good safety profile), then without penalising drug use the scenarios with the highest levels of CAZ-AVI would provide the greatest health benefits. A disadvantage of increased AM use is the potential for increased levels of resistance, which would decrease the future effectiveness of CAZ-AVI. There is a large body of evidence demonstrating a relationship between AM use (and prescribing) and the development of AM resistance. 140–142 However, this relationship has been shown to vary by both type of AM and geographical setting and, in some situations, there is no apparent relationship. 141,143,144 Hence there is uncertainty about the relationship between CAZ-AVI use (in the economic model) and subsequent changes in rates of resistance.

Evidence on trends in CAZ-AVI resistance for the UK in the HVCSs is available from PHE but there is no corresponding evidence on CAZ-AVI use for this population. These data also did not suggest that there was any trend in resistance to CAZ-AVI over time. Data sources are available for other AMs and other patient populations (henceforth referred to as ‘external AM use-resistance data’) which include both use and resistance, but their relevance to CAZ-AVI in this HVCS is unclear.

Two approaches were used to identify external AM use-resistance data that may inform the use-resistance association. First, the entire database of studies that were used during the reviewing process (for both CAZ-AVI and cefiderocol) was searched. Studies were filtered to include those which included ‘use’, ‘usage’, ‘volume’ or ‘consumption’ and these were searched for any relevant evidence. In addition, to identify any English studies (which may use evidence from PHE, or the online portal ‘fingertips’) a Web of Science search was conducted with the terms ‘(antimicrobial* OR antibiotic* OR resistant*) AND (fingertip OR “Public health England”)’. These searches were complemented by any studies that were identified via other reviewing activities or already known to the study team. As a result, three studies were identified that, while not using data in the public domain, provided information on a use-resistance relationship. 145–147 Details of these studies are provided in Appendix 8.6, Table 59. These existing studies informed the de novo analyses reported here by suggesting that ARIMA models would be suitable time-series models for capturing use-resistance associations, with a lag of 1 year between use and resistance when using annual data.

In addition, several studies used publicly available surveillance data. 143,148–152 These data were re-analysed for this project to identify potentially useful associations. For this project there were two types of data that were of interest:

• English data on AM use and AM resistance, from the ‘AMR local indicators profile’;153

• European data on AM use and AM resistance from the European Antimicrobial Resistance Surveillance Network (EARS-Net) and European Surveillance of Antimicrobial Consumption Network (ESAC-Net), respectively. 154,155 These are available as annual data.

Further details on these evidence sources are provided in Appendix 8.6, Table 60 and Report Supplementary Material 3. For CAZ-AVI, increases in resistance will be from a low starting point. Observed trajectories for external evidence which also showed an increase from a low starting point were only identified for the European data, so these were used in subsequent analyses.

Thirty countries from the European Union contribute data to EARS-Net on AM resistance for up to eight pathogens. 156 These data were further filtered based on the following criteria:

• Pathogen is included in the HVCS (Escherichia coli as a CPE).

• Data were available for both AM use and AM resistance (cephalosporins of all types, and carbapenems).

• Countries with at least 5000 isolates were tested, baseline resistance (average over the first 3 years of available data) was less than 3% (CPE), with at least 10 years of observations for carbapenems and 15 years of observations for cephalosporins (these did not have to be consecutive).

This resulted in the following 16 pathogen–drug–country combinations:

• Escherichia coli, carbapenems: France, Greece, Netherlands, Norway.

• Escherichia coli, cephalosporins: Bulgaria, Croatia, Estonia, Finland, France, Greece, Ireland, Luxembourg, Malta, Norway, Slovenia, Sweden.

For these countries, ARIMA models were used to estimate the impact of increasing AM use [defined daily doses (DDDs) per 1000 inhabitants per day] in a given year on resistance to that AM in the following year. Of the 16 combinations considered:

• Half provided a significant association (8/16; Escherichia coli = 2/4 for carbapenems and 6/12 for cephalosporins).

• Of the eight significant associations, four were positive associations (increasing use led to an increase in resistance), while four were negative (decreasing use led to an increase in resistance). All of the negative associations were for Escherichia coli cephalosporins.

Hence this analysis resulted in up to four significant positive associations that could be used to link increases in AM use to AM resistance in the economic model. Increases in AM use are driven by increases in the eligible population over time.

Projections of expected usage for CAZ-AVI from Chapter 8 were linked to these estimates of the relationship between usage and resistance to predict emergence of resistance to CAZ-AVI over time. Even under more extreme usage predictions and the strongest associations between usage and resistance emergence this predicted modest absolute increases in resistance of ˂ 1% over 20 years (see Report Supplementary Material 3 for more details). EEPRU considered that this may represent an underestimate of the potential for resistance emergence for two reasons. Firstly, the spread of MDR infections is influenced by international travel and the ‘importation’ of MDR pathogens. Resistance emergence may, therefore, be influenced by CAZ-AVI usage outside the UK which is not accounted for in these projections.

Secondly, the relationships between usage and resistance characterised in the available data reflect all tested isolates in the community and hospital settings. Resistance emergence may be much higher within the HVCSs where usage will be concentrated. For this reason, EEPRU has conducted a range of scenario analyses to characterise the potential emergence of resistance to CAZ-AVI. These were informed by considering the absolute increases in resistance for the drug–pathogen combinations and countries discussed above, where there was a statistically significant increase (see Report Supplementary Material 3 for more details). The highest absolute increase in resistance (an annual absolute increase of 1.65%, leading to a projected 20-year increase of 33%) was used to bound these analyses. The second largest increase was 0.95% per year (19% over 20 years). Based on these considerations, EEPRU ran analyses with resistance emergence reaching 1%, 5%, 10% and 30% at 20 years. It is noted that the upper scenario may be very extreme.

Of note, this analysis was focused on datasets which demonstrated an increase in resistance over time. Hence any significant associations between AM use and decreasing resistance were not explored. As an alternative to an ARIMA model, a dynamic differential equations model was also developed. This was designed to incorporate AM use and resistance, as well as the spontaneous loss or gain of resistance over time as well as the impact of deaths. Details of this model are provided in Report Supplementary Material 3; when evaluated in a simulation study it was shown to provide biased parameter estimates. This was potentially due to the non-identifiability of the model (due to the number of potential AM drivers considered), so this model was not considered further.

As a face-validity check of the estimates of AM use employed in the model, these were compared to hospital inpatient drug use as reported in the 2019/20 ESPAUR report. 5 This provided an estimate of 2.4 DDD/1000 inhabitants for all AMs used in an inpatient setting. Drug use during the first year of the economic model for both CAZ-AVI and cefiderocol (combining results from both evaluations) for the sites cUTI, IAI, HAP/VAP and BSI (all four in CPE, Pseudomonas and Stenotrophomonas) was estimated to be 0.00018 DDD/1000 inhabitants, hence representing 0.01% of all hospital inpatient AM use. This estimate, as an upper bound on the potential use of both cefiderocol and CAZ-AVI, was felt by the modelling team to have face validity.

Predicting the impact of reduced drug use on resistance

Introducing CAZ-AVI (compared with the situation when it is not available) may lead to a reduced use of comparator AMs. As the economic model includes an association between increased use of CAZ-AVI and increased resistance (as described in the previous subsection), then intuitively a decrease in AM use would be expected to lead to a decrease in resistance. However, AM use in the population of interest is only one of a multitude of potential drivers for increases in AM resistance. Other potential drivers include the number of invasive procedures, AM use in other countries, environmental factors, and AM use in animals. 157,158 The existing evidence on the effect of reduced AM use on AM resistance is mixed,159 with findings including no decrease, a decrease and even an increase in AM resistance. 160–162 Hence, while the introduction of a new AM is expected to lead to an increase in resistance over time, reducing AM use has less predictable effects on resistance. Due to the heterogeneity in the existing literature and the lack of evidence for the population and AMs of interest, it was assumed that reductions in use of existing AMs did not lead to reductions in resistance over time.

Areas of expected usage

Quantitative extrapolation to expected usage

Enablement value

Improved treatment of pre- and post-operative MDR infections is included within our HVCS and expected usage projections. There is some uncertainty as to whether the full benefits of treatment of pre-operative infections are reflected within our analysis. We assume that all patients who are alive at 30 days experience the same survival. If, however, the speed of resolution of an infection influences whether a procedure or treatment can go ahead, then it is possible that 30-day survival is longer for patients whose infection resolves more quickly as they may be more likely to receive procedures. The magnitude of this effect is uncertain due to uncertainties about the number of patients who experience infections pre-operatively, the impact of infection duration on the likelihood that operations will go ahead, and the implications of operations not going ahead (which will depend on the type of procedure, and whether the procedure is not conducted at all or delayed).

Enablement value may be also realised if the risk of MDR infection and clinicians’ ability to treat a MDR infection influences a decision about whether to bring a patient in for a procedure. An example of this scenario was provided by our clinical advisory group, whereby if a MDR infection is known to be circulating in a haematology unit, certain patients may not receive planned procedures or treatments. This is particularly likely to apply for patients in whom existing antibiotics for MDR infections are not an option. Here, the specific example of myeloma patients was highlighted as myeloma patients are predisposed to renal impairment, which rules out key effective treatments for MDR infections such as colistin. There is uncertainty with respect to the number of patients who would be affected as this would depend on both the number of patients whose treatment would be impacted by an outbreak and the frequency of outbreaks in key units such as haematology. There is also uncertainty about the consequences for patients not receiving planned therapy, as this will depend on the nature of the procedure/treatment and whether therapy is not received at all or delayed. These effects are not captured within the EEPRU modelling or any quantitative assessments submitted by the manufacturer.

A related way in which enablement value may be realised is if the availability of effective treatments for MDR infections allows wards to be kept open in the face of outbreaks. EEPRU considers it unlikely that CAZ-AVI would have this effect as most patients with drug-resistant infections do have alternative (albeit more toxic) treatment options – namely colistin. These effects are not captured within the EEPRU modelling or any quantitative assessments submitted by the manufacturer.

A final way in which enablement value may be realised is by use of CAZ-AVI freeing up healthcare resources. For example, use of CAZ-AVI may reduce time in hospital (alleviating pressure on beds), including time in the ICU/HDU. This may be particularly important where patients with MDR infections consume additional resources and staff time due to the need for additional infection-control procedures, including isolation measures. Any freed-up resources can then provide care for other patients within the hospital. To the extent possible, the impact of CAZ-AVI on resource use has been captured in the EEPRU modelling. When calculating the INHEs of CAZ-AVI we have translated cost savings to health benefits using standard measures of health opportunity cost (which allow monetary savings in healthcare resources to be translated to health gains across the NHS).

Diversity value

Our clinical advisors indicated that, within the HVCS, diverse prescribing strategies (e.g. randomly allocating patients with similar clinical indications to different treatments) were unlikely to be appropriate given the lack of safe and effective alternative treatments. They were not supportive of the use of CAZ-AVI in broader populations as part of a diverse prescribing strategy due to the desire to reduce emergence of resistance to CAZ-AVI and concerns that the evidence for diverse prescribing was uncertain. This is in contrast to the views of the manufacturer, who emphasised the potential for CAZ-AVI to be used alongside other therapeutic options in patients at high risk of a resistant infection. Diverse prescribing strategies were not, therefore, included in our quantitative assessments of population INHEs. Diverse prescribing strategies were included in the quantitative modelling presented by the manufacturer. However, as presented in Review of manufacturer’s model for CAZ-AVI, EEPRU has concerns about the extent to which that modelling appropriately reflected likely uses of CAZ-AVI within the NHS, the conceptualisation of the model and the lack of clarity about what was driving the model results. No information was presented by the manufacturer on the extent to which diversity value was driving the model results.

There is uncertainty about how reduced use of existing agents (e.g. colistin) due to availability of the CAZ-AVI will contribute to the emergence of resistance to these drugs. Due to these uncertainties this was not reflected in the EEPRU modelling. If reduced use of existing agents reduces resistance to existing drugs within areas of expected usage for CAZ-AVI this will reduce the INHEs associated with CAZ-AVI; however, if resistance reduces outside areas of expected usage for CAZ-AVI this will increase the INHEs associated with CAZ-AVI. Given the potential for these countervailing effects, and the wide range of factors driving resistance to existing drugs, this is not expected to have a large impact on INHEs. Again, this was captured within the manufacturer’s quantitative modelling but the same caveats apply.

Insurance value

The scenarios modelled can be considered to reflect this form of insurance value as they involve heavily restricting usage to preserve long-term effectiveness.

It is generally agreed that the value of CAZ-AVI will depend on the trajectory of emergence of MDR infections over time. Within the HVCS we have used statistical forecasting methods and explored uncertainty around these to understand the possibility that CAZ-AVI results in the avoidance of significant/catastrophic health losses. This is presented as distributions of population INHEs to inform the committee’s deliberations about whether avoidance of these extreme events should be considered differentially to other forms of health losses.

There is uncertainty around whether these distributions adequately reflect the uncertainty around high-consequence/low-probability outcomes. There are several reasons why this may not fully quantify longer-term benefits:

1. We may see higher levels of resistance to existing drugs within the areas of expected usage over time.

2. We may see MDR pathogens, against which the new drugs are effective, emerge that are currently rare or even unknown.

3. We may see pathogens that are currently treatable with existing therapies (and are not, therefore, included in the areas of expected usage) become resistant.

If one or more of these factors emerge, they would be expected to occur in the long term and quite possibly beyond 20 years.

Conducting quantitative modelling of these effects is unavoidably highly speculative. However, on request from the NICE committee, EEPRU developed an additional scenario to explore the magnitude of these effects. The scenario aimed to assess the effect on INHEs of CAZ-AVI in the case of emergence of multiresistant pathogens against which CAZ-AVI l is the only effective treatment and, in that product’s absence, clinicians would be forced to use multidrug salvage therapy. A simple Excel tool was developed to explore the potential magnitude of these effects informed by parameters obtained from the NICE Committee. This showed an additional benefit of 0.8–58.4 QALYs, which is low relative to other expected benefits of CAZ-AVI as estimated by the main analysis (896–3559 QALYs). This reflects the low likelihood of the emergence of a highly resistant strain and the low number of individuals affected by the strain in the medium term. Further details of the methods and results of this additional analysis are presented in Appendix 9.5, Tables 66–68.

Transmission value

Our clinical advisors indicated that the direction of effect of introduction of CAZ-AVI on transmission was uncertain, but that overall, the magnitude of effect was expected to be small. This reflects the fact that introducing a new effective drug for the treatment of MDR infections has a number of countervailing effects. If the drugs reduce time in hospital this is expected to reduce transmission. However, among MDR patients with poor prognosis, more effective treatments may, feasibly, increase time spent in hospital by reducing mortality. In addition, where use of the new drugs reduces mortality this will increase the number of people returning colonised to the community as CAZ-AVI was considered unlikely to eradicate colonisation by the clinical advisors to this project. This may contribute to increased transmission in the community or via further hospitalisations in this highly comorbid population.

The key drivers of transmission of OXA-48 Enterobacterales, are broad and driven by transmission in populations beyond the HVCS (e.g. colonised individuals in the community and in the hospital, and importation of drug resistance from abroad), making this a challenging area to model. Given the views of our clinical advisors that this would not be a key driver of population INHE and these modelling challenges we did not attempt to quantify transmission value using transmission modelling.

To support the committee in its decision-making, we do, however, provide a summary of the impact of each drug on time in hospital and time alive post discharge. Briefly, CAZ-AVI led to a short reduction in the hospital LoS of 0–1.1 days, and increased the length of life by up to 33 days (0.05–0.09 years). We note that time post discharge is likely to include further periods spent in hospital given the patient population though we did not quantify these.

A number of advisors discussed the substantial impact of outbreaks of MDR infections in terms of disrupting healthcare provision and incurring large costs due to the need for more extensive infection-control measures. However, no evidence was provided that CAZ-AVI would substantially impact on the likelihood of an outbreak or its spread. The possibility of outbreaks leading to large numbers of cases and the additional potential value of CAZ-AVI as a treatment in this scenario are discussed under insurance value.

Transmission effects were included in the quantitative modelling presented by the manufacturer. However, as presented in Review of manufacturer’s model for CAZ-AVI, EEPRU has concerns about the extent to which that modelling appropriately reflected likely uses of CAZ-AVI within the NHS, the conceptualisation of the model, and the lack of clarity about what was driving the model results. No information was presented by the manufacturer on the extent to which transmission value was driving the model results.

Spectrum value

Our clinical advisors and other stakeholders did not consider spectrum value to be a significant consideration for CAZ-AVI, which has a broad spectrum of activity. Therefore, this was not considered in our quantitative assessments of population INHE.