Single Technology Appraisal

Tixagevimab plus cilgavimab for preventing COVID-19 [ID6136]

Committee Papers

NATIONAL INSTITUTE FOR HEALTH AND CARE EXCELLENCE

SINGLE TECHNOLOGY APPRAISAL

Tixagevimab plus cilgavimab for preventing COVID-19 [ID6136]

Contents:

The following documents are made available to stakeholders:

Access the final scope and final stakeholder list on the NICE website.

1. Company submission from AstraZeneca

• a. Submission

• b. Additional evidence

2. Clarification questions and company responses

• a. Clarification response

• b. CE and BI results at list price

3. Patient group, professional group and NHS organisation submissions from:

• a. Action for Pulmonary Fibrosis

• b. Anthony Nolan

• c. Blood Cancer UK

• d. Chronic Lymphocytic Leukaemia Support

• e. Crohn’s & Colitis UK

• f. Clinically Vulnerable Families

• g. Evusheld for the UK

• h. Immunodeficiency UK

• i. Kidney Care UK

• j. Kidney Research UK

• k. Leukaemia Care

• l. Long COVID SOS

• m. LUPUS UK

• n. Lymphoma Action

• o. Multiple Sclerosis Trust

• p. Myeloma UK

• q. Scleroderma and Raynaud’s UK

• r. Vasculitis UK

• s. UK CLL Forum

• t. UK Renal Pharmacy Group

• u. NHS England

4. Expert personal perspectives from:

a. Antonio Pagliuca, Professor of Stem Cell Transplantation – clinical expert, nominated by AstraZeneca

• b. Jill Nicholson – patient expert, nominated by Blood Cancer UK

• c. Steve Jones – patient expert, nominated by Action for Pulmonary Fibrosis

• d. Sanjeev Patel, Consultant Rheumatologist - NHS England Clinical Advisor

• e. Mohammed Asghar, ICS Prescribing Governance Lead – commissioning expert, nominated by Frimley Health & Care ICS

Mandy Matthews – commissioning expert, nominated by NHS England (see document 3u.)

5. External Assessment Report prepared by the School of Health and Related Research, University of Sheffield

• a. EAG report

• b. EAG critique of the additional company evidence c. EAG addendum

6. External Assessment Report – factual accuracy check

• a. Factual accuracy check of the EAG report

• b. Factual accuracy check of the updated EAG report and the EAG critique of the additional company evidence

7. Correspondence between NICE and the MHRA regarding repeat dosing

8. Independent Advisory Group (IAG) report concerning the use of COVID-19 directed antibodies in the prophylaxis setting in the highest risk clinical subgroups

9. In Vitro Advisory Group (IVAG) report

10. NICE Managed Access feasibility assessment

• Any information supplied to NICE which has been marked as confidential, has been redacted. All personal information has also been redacted.

NATIONAL INSTITUTE FOR HEALTH AND CARE EXCELLENCE

Single technology appraisal Tixagevimab–cilgavimab for preventing COVID19 [ID6136]

Document B Company evidence submission

November 2022

Company evidence submission template for Tixagevimab–cilgavimab for preventing COVID19 [ID6136]

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Contents

Company evidence submission template for Tixagevimab–cilgavimab for preventing COVID19 [ID6136]

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Tables and figures

Tables

Table 1: The decision problem ............................................................................................ 12 Table 2: Description of the technology being appraised ...................................................... 16 Company evidence submission template for Tixagevimab–cilgavimab for preventing COVID19 [ID6136]

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Table 3: WHO clinical progression scale ............................................................................. 19 Table 4: Highest risk clinical subgroups .............................................................................. 21 Table 5: Selection of relevant studies from TLR (reasons for exclusion) ............................. 35 Table 6: Summary of PROVENT ......................................................................................... 36 Table 7: Summary of Young-Xu study ................................................................................. 38 Table 8: Summary of Kertes study ...................................................................................... 39 Table 9: Summary of TACKLE (safety of 600 mg dose) ...................................................... 40 Table 10: Comparative summary of study methodology ...................................................... 42 Table 11: Characteristics of participants in the studies across treatment groups ................. 47 Table 12: PROVENT inclusion and exclusion criteria .......................................................... 50 Table 13: Objectives and outcome endpoints ...................................................................... 52 Table 14: Demographics and baseline characteristics (80) ................................................. 53 Table 15: Comorbidities in PROVENT participants at baseline (primary analysis) (80) ....... 54 Table 16: Selected baseline characteristics (Young-Xu et al. 2022) .................................... 56 Table 17: Definition of conditions/treatments for Evusheld eligibility (Kertes et al. 2022) ..... 60 Table 18: Demographics and health characteristics of the study population by Evusheld administration status, MHS, Feb-May 2022 (Kertes et al. 2022) .......................................... 62 Table 19: Summary of statistical analyses .......................................................................... 64 Table 20: Simulated power by number of events ................................................................. 66 Table 21: Quality assessment results for RCTs (PROVENT) .............................................. 69 Table 22: Quality assessment results for non-RCTs (Young-Xu et al. 2022 and Kertes et al. 2022) .................................................................................................................................. 70 Table 23: Primary outcome of PROVENT* .......................................................................... 73 Table 24: Summary of qualifying symptoms for definition of primary efficacy endpoint, full preexposure analysis set, primary analysis data cut-off ........................................................... 74 Table 25: Incidence of participants who had a post-treatment response for SARS-CoV-2 nucleocapsid antibodies, full pre-exposure analysis set, primary analysis data cut-off ...... 76 Table 26: Relative effectiveness of Evusheld versus untreated controls using propensity-score matched analysis and difference-in-difference (Young-Xu et al. 2022) ................................ 80 Table 27: Factors associated with COVID-19 infection among selected Immunocompromised individuals (ICIs), logistic regression model, MHS, Feb-May 2022 (Kertes et al. 2022) ....... 83 Table 28: Overall summary of AEs in any category, safety analysis set, June 2021 data cutoff........................................................................................................................................ 87 Table 29: Most frequently reported (≥1%) AEs by preferred term, safety analysis set, June 2021 data cut-off ................................................................................................................. 88 Table 30: Deaths and AEs with an outcome of death by system organ class and preferred term, safety analysis set, June 2021 data cut-off ................................................................. 89 Table 31: Adverse events in the safety analysis set ............................................................ 90

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Table 32: Serious adverse events by system organ class and preferred term, safety analysis set ....................................................................................................................................... 91 Table 33: Summary list of published cost-effectiveness studies .......................................... 98 Table 34: Description of acute modelled health states ...................................................... 111 Table 35: Features of the economic analysis .................................................................... 114 Table 36. Patient Characteristics at Baseline .................................................................... 117 Table 37. Distribution of Hospitalised Patients .................................................................. 118 Table 38. Overall distribution of hospitalised and non-hospitalised patients ...................... 118 Table 39: Clinical effectiveness inputs for Evusheld .......................................................... 121 Table 40. Overall distribution of hospitalised and non-hospitalised patients (Evusheld) .... 122 Table 41: Proportion of patients with long COVID/ recovered (Evans et al. 2021(57)) ....... 123 Table 42. Distribution of patients at end of acute period – base case ................................ 123 Table 43. Distribution of patients at end of acute period – scenario analysis ..................... 123 Table 44. COVID-related mortality .................................................................................... 124 Table 45: Proportion of patients with long COVID – Evans et al. 2022 .............................. 126 Table 46. Hazard Ratios for mortality for recovered and long COVID patients .................. 128 Table 47: Summary of studies reporting utility values identified through SLR .................... 131 Table 48: Summary of studies reporting utility values identified through TLR .................... 132 Table 49. Prophylaxis related AE Incidence (Over 12 months) .......................................... 146 Table 50. SAE Disutility ..................................................................................................... 146 Table 51: Disutility associated with acute COVID-19 & hospitalisation .............................. 148 Table 52: Disutility hospitalisation from ScHARR Assessment Report in TA10936 – Scenario analysis ............................................................................................................................. 148 Table 53: EQ-5D-5L disutility values post discharge (5 months) – Evans et al. 2021 ........ 149 Table 54: EQ-5D-5L disutility values applied in the base case .......................................... 149 Table 55: Summary of utility values for cost-effectiveness analysis................................... 151 Table 56: Input Related to Treatment Acquisition Costs of Evusheld ................................ 153 Table 57: Evusheld administration costs ........................................................................... 153 Table 58: Monitoring costs ................................................................................................ 153 Table 59: Summary of unit costs associated with the technology in the economic model .. 154 Table 60: Calculation of MRU costs in acute phase .......................................................... 155 Table 61: Prophylaxis related AE incidence and Unit Costs .............................................. 155 Table 62: Summary of variables applied in the economic model ....................................... 156 Table 63: Assumptions underpinning the cost-effectiveness analysis ............................... 162 Table 64: Base case results .............................................................................................. 166 Table 65: PSA results ..................................................................................................... 168

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Table 66: DSA results ....................................................................................................... 173 Table 67: Scenario analysis results ................................................................................... 175 Figures Figure 1: Proportion of critically ill patients with confirmed COVID-19 who are immunocompromised, by vaccination status ....................................................................... 24 Figure 2: Proportion of clinically extremely vulnerable individuals taking additional precautions against COVID-19 ............................................................................................................... 25 Figure 3: Association between worsened health and clinical characteristics of participants 26 Figure 4: Proportion of COVID-19 survivors reporting issues 12.8 weeks after diagnosis ... 27 Figure 5: Proportion of partners and family members of COVID-19 survivors reporting issues 12.8 weeks after COVID-19 survivor diagnosis ................................................................... 28 Figure 6: Average range of length of COVID-19 hospitalisation with and without ICU admission in the UK ............................................................................................................................. 29 Figure 7: PRISMA diagram ................................................................................................. 32 Figure 8: PROVENT trial design ......................................................................................... 49 Figure 9: Trial design flow chart .......................................................................................... 49 Figure 10: Time to first COVID-19 RT-PCR-positive symptomatic illness occurring post dose of IMP KM curves by arm; Primary analysis (5 May 2021) .................................................. 75 Figure 11: Forest plot for efficacy for incidence of first SARS-CoV-2 RT-PCR-positive symptomatic illness by subgroup, full pre-exposure analysis set, primary analysis data cutoff(75) ................................................................................................................................. 78 Figure 12: Forest plot for efficacy for incidence of first SARS-CoV-2 RT-PCR-positive symptomatic illness by subgroup, full pre-exposure analysis set, primary analysis ............. 79 Figure 13: Cumulative risk of composite COVID-19 outcomes for Evusheld recipients compared to untreated controls (Young-Xu et al. 2022) ...................................................... 81 Figure 14: COVID-19 infection rates over time by AZD7442 administration status, KaplanMeier hazards ratios, MHS, Feb-May 2022 (Kertes et al. 2022) .......................................... 83 Figure 15: COVID-19 hospitalisation or death – Severe disease rates over time by Evusheld administration status, Kaplan-Meier hazards ratios, MHS, Feb-May 2022 (Kertes et al. 2022) ........................................................................................................................................... 85 Figure 16: Model structure ................................................................................................ 110 Figure 17: Recreated ScHARR curve ................................................................................ 125 Figure 18: Proportion of patients with long COVID, who remain in long COVID over time . 127 Figure 19: Proportion of all patients, who have long COVID over time .............................. 127 Figure 20: Incremental cost- effectiveness plane .............................................................. 169 Figure 21: Cost-effectiveness acceptability curve .............................................................. 170 Figure 22: Tornado diagram .............................................................................................. 172

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Table of Abbreviations

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Company evidence submission template for Tixagevimab–cilgavimab for preventing COVID19 [ID6136]

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Company evidence submission template for Tixagevimab–cilgavimab for preventing COVID19 [ID6136]

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B1 Decision problem, description of the technology and clinical care pathway

B1.1 Decision problem

The decision problem addressed in this submission is presented in Table 1.

The submission focuses on a specific target population within Evusheld’s marketing authorisation based on its expected use in UK clinical practice, as defined below. All other aspects of the decision problem align with the NICE scope.

1.1.1 Target population

Adults who are not currently infected with SARS-CoV-2 and who have not had a known recent exposure to a person infected with SARS-CoV-2 and:

• are at the highest risk of an adverse COVID-19 outcome, namely hospitalisation and death, or

• for whom COVID-19 vaccination is not recommended

The target population is clearly defined in clinical practice (as detailed in Section B1.3.5) and closely aligns with the ‘subgroups to be considered’ section of the NICE scope.

UK clinical experts advised that the target population represents people with the highest medical unmet need for prophylactic treatment and as such, is where Evusheld is anticipated to be used in UK clinical practice. See Section B1.3 for further details.

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Table 1: The decision problem

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marketing authorisation granted by the regulator. The impact of vaccination status or SARSCoV-2 seropositivity on the clinical evidence base of each intervention, generalisability to clinical practice and interaction with other risk factors will be considered in the context of the appraisal. The impact of different variants of concern of COVID-19 on the clinical evidence base of each intervention will be considered in the context of the appraisal.

Abbreviations: DHSC – Department of Health and Social Care; NA – Not applicable; NHS – National health service; NICE – National Institute for Health and Care Excellence; SARS-CoV-2 – severe acute respiratory syndrome coronavirus 2

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B1.2 Description of the technology being evaluated

Table 2: Description of the technology being appraised

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Abbreviations: ACE2 – Angiotensin-Converting Enzyme 2; CHMP – Committee for Medicinal Products for Human Use; COVID-19 – Coronavirus disease 2019; EMA – European medicines agency; Fc – Fragment crystallisable; IgG1κ – Immunoglobulin G, subclass 1, κ light chain; IM – intramuscular; KD – Dissociation constant; mg – Milligrams; MHRA – Medicines and Healthcare products Regulatory Agency; ml – millilitre; ng – Nanograms; nM – Nanomolar; pM – Picomolar; RBD – Receptor binding domain; PASLU - Patient Access Schemes Liaison Unit; PrEP – Pre-exposure prophylaxis; SARS-CoV-2 – Severe acute respiratory syndrome coronavirus 2; SmPC – Summary of product characteristics.

B1.3 Health condition and position of the technology in the

treatment pathway

B1.3.1 Disease overview

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a highly contagious novel coronavirus, caused a worldwide outbreak of symptomatic and potentially fatal respiratory disease, known as COVID-19.(3)

In December 2019, the first case of COVID-19 was recorded in China, and by March 2020, COVID-19 was declared by the World Health Organization (WHO) as a pandemic(3). This resulted in unprecedented life-limiting restrictions being put in place by governments across the world to prevent the spread of the virus.

Despite a highly effective global vaccine rollout, the development of effective treatments for COVID-19 (Section B1.3.6), and the lifting of UK restrictions in March 2022(4), COVID-19 remains a considerable public health issue in the UK:

• In 2022 the weekly rate of people in England estimated to be infected with COVID-19 has fluctuated, with a highest rate of 7.6% in March and a lowest rate of 1.29% in September(5). A total of 177,977 deaths within 28 days of a positive test have been recorded in the UK.(6,7)

• The Office of National Statistics (ONS) has estimated that 2.0 million people in the UK (2.9% of the UK population) have reported long COVID symptoms (see Section B1.3.7), which have a significant detrimental impact on quality of life.(8)

• The burden of COVID-19 on the UK healthcare system is still substantial. In 2020 and 2021, there were approximately 240,000 and 300,000 COVID-19 related admissions, respectively and since January 2022, there has been a similar number of COVID-19related admission to those seen over the whole of 2021 by September 2022.. These data demonstrate the ongoing burden that COVID-19 continues to place on health services in England.(9)

COVID-19 continues to pose an unprecedented challenge to the UK healthcare systems, e.g., through substantial increases in hospitalisations and intensive care admissions. This is further exacerbated by the continuous emergence of new variants, which can significantly impact how easily the virus spreads and the severity of disease.(10)

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B1.3.2 Clinical presentation and diagnosis

SARS-CoV-2 is transmitted via aerosolised airborne respiratory droplets, e.g., through sneezing and coughing.(11) The contagious nature of COVID-19 renders virus exposure periodically extremely difficult to avoid, an issue is exacerbated by the fact that many cases are asymptomatic. (11)

Clinical presentation of COVID-19 can vary, but symptoms often include a high temperature, a new continuous cough, and a loss or change to taste and/or smell. In addition, other flu-like symptoms can be present such as shortness of breath, fatigue, aches, headache, a sore throat, congestion, loss of appetite, diarrhoea, and feeling or being sick.(3,12)

SARS-CoV-2 viral presence is confirmed by Polymerase chain reaction (PCR). Previously, testing for SARS-CoV-2 infection was mandatory under UK COVID-19 guidelines. However, with UK restrictions uplifted, testing is no longer enforced(13) and as such, many people with SARS-CoV-2 infection remain unidentified and continue to spread the disease unknowingly.(14)

B1.3.3 Disease severity

COVID-19 has a spectrum of disease severity and has been well-defined by the WHO clinical progression scale on a score of 0-10 (Table 3).

The majority of people infected with COVID-19 present with mild ambulatory disease, which may be self-limiting and result in a requirement for additional assistance from family or carers. However, some people experience severe disease, which can result in hospitalisation and death.

Table 3: WHO clinical progression scale

Abbreviations: FiO2 – Fraction of inspired oxygen; pO2 – Partial pressure of oxygen; RNA – Ribonucleic acid; SpO2 – Peripheral capillary oxygen saturation; NIV – Non-invasive ventilation.

While most people who survive COVID-19 recover within 21 days,(15) many do not clear the virus during this time. A large proportion of patients harbour infection for longer than 28 days and prolonged infection can place individuals at higher risk of recurrent acute COVID-19 Company evidence submission template for Tixagevimab–cilgavimab for preventing COVID19 [ID6136]

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episodes, increasing their risk for adverse outcomes, including hospitalisation and death. In addition, this increases their risk for developing long-term clinical sequalae and new comorbidities; clinically defined as long COVID.(16)

Symptoms of long COVID have been reported across many studies as affecting the ability to carry out day-to-day activities compared to the time before COVID-19. (17–20)

These symptoms, include fatigue, shortness of breath, chest pain, heart palpitations, joint pain, depression, anxiety and changes to smell and taste and can be debilitating.(17–20)

B1.3.4 Epidemiology

The infection rate is currently low, with an estimated 1.29% of people in England believed to be infected in the week ending 5[th] of September 2022. Rates of infection fluctuate significantly and were as high as 7.6% in the week ending 30[th] of March 2022.(5) A total of 177,977 deaths within 28 days of a positive test have been recorded in the UK.(6,7)

Prolonged infection with SARS-CoV-2 virus risks the development of viral evolution and the emergence of new mutated viral variants that could be introduced into circulation – which may include variants of concern.(21) There are several variants currently circulating in the UK. Omicron (B.1.1.529) sub-lineage BA.5 is currently the predominant circulating variant of concern in the UK.(22) Other variants of concerns detected in the UK are Omicron (B.1.1.529) sub-lineages BA.1, BA.2 and BA.4.(22)

B1.3.5 High-risk populations

There are a number of risk factors for poor COVID-19 outcomes, including being elderly, certain underlying health conditions, and being immunocompromised.(11,23–27)

Immunocompromised individuals often suffer with an increased period of infection and are at an increased risk of severe outcomes from COVID-19 such as hospitalisation, intensive care unit (ICU) admission and death, due to their reduced rate of antibody development produced after infection and vaccination.(28)

In May 2022, the UK government recognised the need to identify highest risk individuals. The Department of Health and Social Care (DHSC) commissioned a report from an independent advisory group, chaired by Professor Iain McInnes and supported by the NHS England RAPIDC19 team, to identify “highest risk clinical subgroups upon community infection with SARSCoV-2”, defined as those whose immune system means they are at higher risk of serious illness from COVID-19.(1)

The purpose of the report was to determine who should be eligible to receive approved medications for COVID-19 treatment or prophylaxis, to complement the insufficient protection offered by currently available vaccines. Identified individuals were offered antivirals and neutralising monoclonal antibody (nMAB) treatment in the event of a positive test (see Section B1.3.6).(1)

The report identified a population of approximately 1.8 million people(29) in England, including certain patients from within the clinical subgroups listed in Table 4.

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Table 4: Highest risk clinical subgroups

Subgroup Down’s syndrome and other genetic disorders Solid cancer Haematological diseases and HSCT recipients Renal disease Liver diseases SOT recipients Immune-mediated inflammatory disorders Immune deficiencies HIV/AIDS - - Rare neurological and severe complex life limiting neuro disability conditions

Abbreviations: AIDS – Acquired immune deficiency syndrome; HIV – Human immunodeficiency virus; HSCT – Haematopoietic stem-cell transplantation; SOT – Solid organ transplant

B1.3.6 Current NHS care pathway for the management of COVID-19

There are a number of interventional strategies used in the current NHS care pathway for the management of COVID-19.

Strategies to prevent COVID-19 infection

Vaccination

Six COVID-19 vaccines are currently approved and available in the UK[1] ; 93.4% of the UK population (aged >12 years) have received a first dose, 87.6% are fully vaccinated (2 doses) and 69.8% have received a booster (as of 14[th] of July 2022) (30,31) The immune response, safety, and efficacy of the vaccines have been rigorously explored in clinical trials, and monitoring of vaccine effectiveness and population impact is ongoing.

The detrimental impact of COVID-19 for the general UK population health have been substantially reduced due to the successful rollout of the COVID-19 vaccination programme. Most people infected with COVID-19, who do not have an underlying health condition, will experience mild to moderate disease symptoms and make a full recovery, following a period of rest.

However, despite vaccine rollout, individuals who are clinically vulnerable (Table 4) for whom COVID-19 vaccination is less effective remain at the high risk of an adverse COVID-19 outcomes.

Furthermore, 0.00067% of the UK population are not able to be fully vaccinated with any available COVID-19 vaccines due to a documented history of severe adverse reactions to a COVID-19 vaccine or any of its components and as such are considered high-risk.(32) As such, these individuals remain unprotected compared to the rest of society.

1 Moderna (Spikevax), Oxford/AstraZeneca (Vaxzevria), Pfizer/BioNTech (Comirnaty), Janssen (Jcovden), Novavax (Nuvaxovid) and Valnea Company evidence submission template for Tixagevimab–cilgavimab for preventing COVID19 [ID6136]

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Lifestyle modifications

Despite the lifting of UK government enforced restrictions, some people continue to take precautions by making lifestyle modifications to prevent COVID-19 infection, including wearing face masks, reducing social interaction, and shielding whereby individuals do not leave their homes and minimise all face-to-face contact.(33)

In the absence of adequate protection from vaccination, a considerable proportion of high-risk individuals in the UK make fear-induced lifestyle modifications. Such life-restricting measures include staying at home more, avoiding social gatherings and limiting travel, in particular avoiding public transport.(20)

Strategies to treat COVID-19 infection

Oxygen therapy and ventilation

Hospitalised patients receive oxygen therapy, non-invasive or invasive ventilation aligned with the WHO clinical progression scale (Table 3). Length of stay in hospital depends on the severity of disease; Beigal et al. reported that an average 5, 7, 15 and 29 days for no oxygen, low-flow oxygen, high-flow oxygen, and invasive mechanical ventilation, respectively(34). Such interventions and length of stay in hospital substantially affects quality of life and more severe hospitalisations are known predictors of mortality(35).

Therapies

High-risk individuals identified by the DHSC (detailed in Section B1.3.5) may receive the following acute therapies to manage symptoms and reduce the risk of serious illness(36):

• Antivirals: Paxlovid (nirmatrelvir and ritonavir), Veklury (remdesivir), and Lagevrio (molnupiravir)

• Neutralising monoclonal antibody (nMAB): Xevudy (sotrovimab)

While their use in clinical practice acknowledges the extremely exposed position of high-risk populations, limitations remain as some individuals cannot take these treatments due to interaction with other medications or because of the fear and risks attributed to accessing medical care: (37)

• There is a short period in which patients are eligible for treatment following symptom development and confirmed infection, rendering access difficult. A recent study demonstrated that only 18% of eligible high-risk patients with COVID-19 were actually treated with an antiviral or nMAb in England between December 21 and April 22.(38)

• UK Clinical commissioning guidelines acknowledge several limitations with available treatments:

  • Paxlovid is contraindicated in severe liver disease, has multiple drug-to-drug interactions, and is not recommended for use in pregnancy (39,40)

  • Remdesivir is administrated intravenously with one infusion every day for three days, causing significant inconvenience and pain to patients; it is not

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recommended in individuals with severe liver disease (ALT>5 times upper limit of normal) and impaired renal function (39,40)

• Sotrovimab has limited efficacy against Omicron subvariants, is administered intravenously – with significant time and resource expenditure as well as associated discomfort to the patient(39,40)

Furthermore, these treatments are currently undergoing a NICE Multiple technology appraisal (MTA) TA10936 and are not currently routinely commissioned in England and Wales.

B1.3.7 Burden of COVID-19 in high-risk populations

The clinical, humanistic, and economic burden for high-risk, immunocompromised people is substantial and disproportionate compared to the general UK population.

Clinical burden

As discussed in Section B1.3.5, high-risk populations have an increased risk of suffering poor outcomes such as hospitalisation and death from COVID-19.

In particular, immunocompromised individuals make up a disproportionately high number of hospitalisations and deaths due to breakthrough COVID-19 (defined as infection despite vaccination). Despite only accounting for approximately 1-3%(29,42) of the general UK population, the immunocompromised represent:

• Over 40% of all UK vaccine breakthrough COVID-19 hospitalisations (43,44)

• 14.0 to 27.7% of all breakthrough UK ICU admissions(45)

• 13.1 to 17.7% % of breakthrough deaths in England (43,44)

According to a report published by the UK intensive care national audit and research centre (ICNARC), vaccine dose exposure seemed to correlate positively with hospital admissions among immunocompromised individuals – i.e., the more vaccine doses individuals received, the higher the proportion admitted to hospital (Figure 1). This could be an indication that multiple booster vaccinations may not sufficiently reduce hospitalisations in these populations compared to the general population and that further interventions may be warranted.(45)

These results are consistent with a recent analysis of Hospital Episode Statistics data looking at hospital admissions in England for a 12-month period ending May 30, 2022, which demonstrated that immunocompromised people that had received three or more COVID-19 vaccinations were disproportionally affected by COVID-19 compared to nonimmunocompromised people that had also received three or more COVID-19 vaccinations. (47)

This analysis also demonstrated that in-hospital mortality was approximately 50% higher in the highest risk population compared to the general population. Patients who survived and were discharged had a longer mean length of stay, especially those who received respiratory support.(47)

A recent retrospective cohort study conducted on behalf of NHS England analysed over 18 million adults in England and demonstrated that COVID-19 death rates generally decreased

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over the first three pandemic waves. However, groups more likely to experience impaired vaccine effectiveness did not see the same benefit in COVID-19 mortality reduction. Only small decreases in death rates were observed in patients with kidney disease, haematological malignancies or conditions associated with immunosuppression, all groups represented in the highest risk clinical subgroups.(48)

Figure 1: Proportion of critically ill patients with confirmed COVID-19 who are immunocompromised, by vaccination status

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Patients admitted from 1 May 2021 to 28 February 2022. Source: (45)

Humanistic burden

The protective measures discussed in Section B1.3.6 and anxiety of being under constant threat of severe illness have a profound negative impact on quality of life in high-risk populations and their close ones.

Lifestyle changes

A considerable proportion of high-risk individuals in the UK make fear-induced lifestyle modifications to protect themselves in the absence of adequate protection from vaccination.

A recent ONS survey on the impact of COVID-19 in clinically extremely vulnerable individuals found that the vast majority (82%) take extra precautions to protect themselves. As many as 13% resort to the extreme of completely shielding, and still do to this day despite no longer being recommended to do so (Figure 2).(20) These outputs are consistent with another recent ONS survey published in July 2022 which similarly reported that 82% of individuals who are at the highest-risk of COVID-19 adverse outcomes continue to take extra precautions and 13% still continue to shield entirely.(49)

When asked about additional measures to keep themselves safe from COVID-19(20):

• almost one third (31%) indicated that they are shielding or staying at home more

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• 37% avoid social gatherings

• 15% avoid the use of public transport

Such activity limits daily activities, often to an extreme extent, and reduces interaction with family and friends, resulting in social isolation.

Shielding has been shown to have detrimental impact on employment, mental health, healthrelated quality of life (HRQoL) and access to healthcare.(17,20,50–53) Within mental health alone, individuals have reported emotional distress, mood disorders, depressive symptoms, worsening or emergence of neuropsychiatric symptoms, acute stress disorder, insomnia, frustration, boredom, and loneliness.(50)

Figure 2: Proportion of clinically extremely vulnerable individuals taking additional precautions against COVID-19

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Source: (20)

Fear and anxiety

In addition to the life-limiting steps taken to feel safer, the burden of fear and anxiety itself weighs heavily on high-risk individuals.

Although most of the UK population is vaccinated and boosted, COVID-19 continues to be highly prevalent in society and remains an omnipresent threat to the less protected. Fear and uncertainty are further exacerbated by a number of asymptomatic transmissions(11) and the continuous emergence of new and highly transmissible variants.

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In a cross-sectional study conducted in the UK throughout the second wave of the pandemic, those shielding demonstrated significantly higher rates of health anxiety and fear of infection in comparison to other groups. Rates of anxiety were higher compared to at the start of the pandemic (March 2020).(54)

A longitudinal, web-based, survey completed by members of general population aged ≥18 years (of whom only 20% had previously been infected with COVID-19) across 13 countries between November and December 2020 quantified the impact of the COVID-19 pandemic on HRQoL as measured by the EQ-5D-5L instrument and its domains. A xxxxxxxxxxxxxxxxxx was reported (xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx), representing an xxxxxxxxxxx in HRQoL. Those with chronic diseases were more likely to report HRQoL decreases, and notably the psychosocial impact (anxiety and depression) was the domain most impacted for all respondents as shown in Figure 3.(55)

Figure 3: Association between worsened health and clinical characteristics of participants

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Source: (55). Abbreviations: PCHC – Paretian classification of health change.

COVID-19 infection

An acute infection with COVID-19 can substantially impact quality of life over a number of weeks, with EQ-5D disutility estimates ranging from -0.19 to -0.79 depending on severity on the WHO prognosis classification scale (Table 3).(56) Specifically, for hospitalised patients, EQ-5D scores range from 0.581 to 0.693 in those admitted to the ICU, compared to 0.613 to 0.724 in the general ward (see Appendix H).(57)

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Furthermore, for a proportion of people in the UK who are infected with COVID-19 and survive, the acute infection can result in long COVID; the ONS has estimated that 2.0 million people in the UK (2.9% of the UK population) have reported long COVID symptoms.

Long COVID considerably reduces well-being in the affected. A large (N=1,077), multi-centre, long-term follow-up study of adults discharged from UK hospitals with a clinical diagnosis of COVID-19(58) found the following:

• The vast majority of survivors (91.1%) experienced persistent symptoms

• As many as 20% suffered from new disability at an average follow-up of 5.9 months

• A 2021 global survey study measuring the HRQoL impact of COVID-19 in survivors, their partners and family members also found a major and persisting impact on HRQoL.(59) At 12.8 weeks after diagnosis, the majority of survivors reported pain and discomfort, problems with usual activities, anxiety and depression, and problems with mobility (Figure 4).

Figure 4: Proportion of COVID-19 survivors reporting issues 12.8 weeks after diagnosis

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Source: (59)

Caregiver burden

Lifestyle changes due to the fear of contracting COVID-19 has a considerable impact not only on the directly affected, but also on supporting individuals.

Many high-risk individuals rely heavily on support from carers, friends, or family.(60) Those who are close to vulnerable individuals must often take precautions themselves, in order to not put their loved one at risk. Additionally, they may need to support with everyday tasks, and care for vulnerable people.

• A Europe-wide caregiver study demonstrated an increased prevalence of informal caregiving outside the household during the first wave of the pandemic.(61)

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• Increased provision of informal care has been associated with an increased mental health burden for carers, including a significantly higher proportion of caregivers reporting sadness/depression and anxiousness/nervousness than non-caregivers.(54)

• Increased time spent caring for parents was also associated with a significant increase in feelings of sadness/depression and anxiousness/nervousness compared with those who maintained or reduced their time caring for parents.(54)

• More than half of participants who were shielding with others experienced high levels of empathetic health anxiety regarding the health of their loved one.(54)

• Furthermore, long COVID can have a long-term impact on partners and family members of the survivors, with almost everyone indicating being worried (Figure 5).

Figure 5: Proportion of partners and family members of COVID-19 survivors reporting issues 12.8 weeks after COVID-19 survivor diagnosis

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Source: (59)

Economic burden

While the majority of COVID-19 infections do not require healthcare intervention, more severe cases incur substantial direct and indirect costs.

Direct costs

Severe disease and resulting hospitalisations and ICU admissions are key drivers of the direct economic costs of COVID-19.

As discussed in Section B1.3.5, a disproportionate number of immunocompromised individuals require hospitalisation and ICU admission. As shown in Figure 6, the length of COVID-19 hospital stay in the UK varies considerably, ranging from a mean of 8.0 (SD: 8.4) to 9.1 (SD: 9.5) days for non-ICU admissions, and from 16.2 (SD: 12.0) to 29.7 (SD: 22.9) days for hospitalisations with ICU admission.(62)

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Figure 6: Average range of length of COVID-19 hospitalisation with and without ICU admission in the UK

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Abbreviations: ICU – Intensive care unit. Grey area indicates range, error bars represent standard deviation.

Non-urgent routine care has been widely paused to provide care for people with COVID-19, resulting in a growing backlog and reduced healthcare delivery capacity. It has been estimated that clearing this backlog over three years would require treating 1.5 million more patients a year beyond the long-term plan assumptions, at an additional cost of £1.9 billion per year (63). Reducing hospitalisations will be important in clearing this backlog.

Indirect costs

Indirect costs represent a considerable component of the economic burden of COVID-19, both to the affected individuals and society at large. Shielding among immunocompromised individuals negatively impacts their ability to engage in daily activities and return to work.(17,19,64) Among 623,000 vulnerable individuals who were in employment prior to shielding, approximately one in two reported that they were unemployed, furloughed or enrolled in the Self-Employment Income Support scheme following shielding.(64) In the followup analysis, 76% said they lost income, of which 62% stated that the loss was greater than expected prior to shielding.(64)

In a study by the ONS in those who have experienced long COVID, 40% reported that it was negatively affecting their work, with an even higher proportion in the age group 30-49 years (51%).(65) Economic inactivity (defined as neither working or looking for work) has increased by over 300,000 people in the age group 16 to 64 years during the pandemic. A main driver of this increase is long-term illness in people of working age, with long COVID considered one of the causes.(66) A report from the Resolution Foundation found that 600,000 adults in the UK reported working less as a consequence of either COVID-19 or fear of the virus.(67)

B1.3.8 Conclusions

There is a substantial medical unmet need for an effective prophylaxis in high-risk populations in which vaccines do not provide adequate protection, that can reduce the risk of COVID-19 infection and poor COVID-19 outcomes (hospitalisation or death).

However, there are currently no prophylaxis available in the UK that could adequately prevent COVID-19 infection and improve COVID-19 outcomes in the high-risk populations.

An ONS survey of people classed as “extremely clinically vulnerable” during the pandemic found that 68% expressed the desire for a prophylaxis. The survey found that almost half (46%) of respondents were “very” or “somewhat” worried about the effect the pandemic has had on their life. When asked about the cause of worry, 24% indicated concerns over whether

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vaccination gives adequate protection, and even in the group of respondents who had received four vaccine doses, 25% expressed concern about inadequate protection.(20)

According to UK clinical experts, awareness is increasing among high-risk individuals, and many are intimately familiar with the consequences of their condition in terms of immune response, antibody production and associated lack of protection from vaccination.(68)

UK clinical experts advised that the availability of a prophylaxis would not only reduce the risk of symptomatic infection and poor outcomes, but also improve patient HRQoL by reducing their fear and anxiety and allowing them to return to more normal levels of social functioning. Consequentially, patients, organisations, and the clinical community(68) identify a substantial unmet need for prophylactic options.

This need has been voiced in a recent consensus statement from the UK All-Party Parliamentary Group (APPG) on vulnerable groups co-signed by 18 charities and 125 physicians, calling for the use of treatments like Evusheld as a vaccine adjunct in immunocompromised populations.(28)

“The number of people being admitted to hospital with coronavirus remains high. As we learn to live with coronavirus, we must also learn to protect immunocompromised people. Protective antibody treatments like Evusheld could offer this solution and it is really important that the voice of patients and clinicians is heard.”

• Bob Blackman,

Member of Parliament and co-chair of the APPG on vulnerable groups

B1.4 Positioning of Evusheld in the management of COVID-19

Evusheld is the only COVID-19 PrEP authorised in the UK.

Aligned with its anticipated use in clinical practice, and for where the highest medical unmet need exists for patients, we propose that Evusheld should be positioned in a subgroup of its licenced indication:

Adults who are not currently infected with SARS-CoV-2 and who have not had a known recent exposure to a person infected with SARS-CoV-2 and:

• are at the highest risk of an adverse COVID-19 outcome, namely hospitalisation and death, or

• for whom COVID-19 vaccination is not recommended

The definition of individuals with the highest risk of an adverse COVID-19 outcomes aligns with the highest risk clinical subgroups identified by the UK DHSC (Table 4).

B1.5 Equality considerations

We do not expect assessment of this technology to raise any equality issues.

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B2 Clinical effectiveness

B2.1 Identification and selection of relevant studies

Full details of the process and methods used to identify and select the clinical evidence relevant to the technology being appraised are provided in Appendix D.

B2.1.1 Systematic literature review

A systematic literature review (SLR) was conducted to identify randomised clinical trial (RCT) and non-RCT evidence reporting on the efficacy and safety of Evusheld and other relevant prophylaxes for COVID-19.

Searches of Embase, Medline, and Cochrane databases using Ovid were conducted in October 2021. Supplementary hand searching of recent relevant congresses and health technology assessment (HTA) agency websites focussed on the time-period 2020 to October 2021.

Studies of interest included RCTs and non-RCTs investigating relevant PrEP treatments for COVID-19 which enrolled adult patients (≥18 years).

The aim of the SLR was to identify and synthesise evidence on the efficacy and safety of relevant preventative/prophylaxes for COVID-19 among:

• Healthy people who had not been exposed to coronavirus (PrEP; where healthy is defined as a negative PCR test and no symptoms of COVID-19)

• Healthy people who had been exposed but did not have a positive PCR test, and who had no symptoms (post-exposure prophylaxis [PEP])

• People with positive PCR test but without symptoms (pre-emptive treatment [PET])

A Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram detailing studies that were included and excluded at each stage of screening is provided in Figure 7. Full lists of included and excluded studies are provided in Appendix D.

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Figure 7: PRISMA diagram

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Abbreviations: n – Number; NMA – Network meta-analysis; WHO – World Health Organization

B2.1.1.1 Selection of relevant studies (SLR)

Of the four unique studies identified, only one included Evusheld as comparator: a Phase III randomised, triple-blinded, placebo-controlled, multi-centre PrEP study (PROVENT).(70,71)

B2.1.2 Targeted literature review

PROVENT included the relevant treatment and comparators as defined in the NICE scope. However, the trial was conducted in the early stages of the pandemic when:

• enrolled subjects were unvaccinated

• earlier COVID-19 variants (Alpha and Delta) were dominant

• individuals were treated with the 300 mg dose of Evusheld

To address these considerations, targeted updates to the SLR were conducted bi-monthly since October 2021 to identify additional sources of data on the clinical effectiveness of Evusheld. Additional identified studies may be considered more generalisable to the current environment, including populations who were:

• Predominantly vaccinated

• Infected during periods of COVID-19 when Omicron was dominant

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• Receiving prophylaxis with 600 mg of Evusheld

As of 30[th] May 2022, five comparative real-world evidence (RWE) studies of Evusheld were identified(2,72–75) These studies evaluated prophylaxis in immunocompromised populations who were predominantly vaccinated, during a period when Omicron sub-lineages were dominant.

B2.1.2.1 Selection of relevant studies (TLR)

The five additional studies are summarised below:

• Young-Xu et al. 2022 (2): a propensity-score-matched analysis in 8,087 immunocompromised veterans in the United States (US) during the Omicron wave, including the 300 mg and 600 mg dose of Evusheld; 87% of the investigated cohort were dosed at 600 mg.

  • Using electronic health records from US Department of Veterans Affairs, one of the largest integrated healthcare systems in the US, the study was able to demonstrate the clinical effectiveness of Evusheld in reducing the incidence of COVID-19 infections, COVID-19 hospitalisations, and all-cause mortality in the overall cohort - comprising of immunocompromised (92%) and patients at highrisk for COVID-19 (8%).

  • Among immunocompromised and severely immunocompromised cohorts, patients that received Evusheld had lower incidence of a composite of COVID19 outcomes compared to matched controls. In addition, the study also showed that Evusheld augmented the protection against COVID-19 infection in fully vaccinated individuals in the overall cohort akin to a fully vaccinated and boosted non-immunocompromised adult.

• Kertes et al. 2022 (72): an unmatched control study in 5,124 immunocompromised individuals evaluating the association between Evusheld 300 mg administration, SARS-CoV-2 infection and severe disease (COVID-19 hospitalisation and all-cause mortality), during a fifth Omicron-dominated wave of COVID-19 in Israel.

  • The study demonstrated that highly immunosuppressed individuals receiving Evusheld were half as likely to become infected with COVID-19 compared to the non-administered group. They were also 92% less likely to be hospitalised or die than the non-administered group.

  • Among the highly immunosuppressed individuals, those in the Evusheld group that received either anti-cluster of differentiate 20 (CD20) treatment in the last 6 months or those that were solid organ transplant recipients, reported lower rates of COVID-19 infection compared to those that did not receive Evusheld.

• Al-Jurdi et al 2022 (73): a retrospective matched control study comparing 222 solid organ transplant recipients (SOTRs) who had received Evusheld to 222 1:1 vaccine matched SOTRs who did not receive Evusheld (59% received 600 mg dose) in the US.

  • The primary outcome included breakthrough SARS-CoV-2 infection as defined by positive PCR or antigen test, whether performed for symptoms or for another indication. Secondary outcomes included hospitalisation or death from SARS-

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CoV-2 infection, changes in allograft function, and adverse events after receiving Evusheld.

• At a mean follow-up of 87±30 days after Evusheld administration, breakthrough infections occurred in 11 (5%) of Evusheld recipients vs 32 (14%) in the nonadministered group. Only one individual from the Evusheld group was hospitalised vs 6 from the non-administered group, while no individual from the Evusheld group died vs 3 from the non-administered group.

• Sustained protection was observed at an Evusheld 600 mg dose against BA.1, BA.2, and BA.2.12.1, over a prolonged period (up to 120 days).

• Bertrand et al 2022 (74): a retrospective case-control study among 860 kidney transplant recipients (KTRs) vaccinated with ≥3 SARS-CoV-2 vaccine doses, comparing primary outcomes of symptomatic COVID-19, hospitalisations, and deaths between KTRs who did not mount an immune response (non-responders) and received Evusheld 300 mg to those who received either casirivimab-imdevimab or no monoclonal antibody in France.

  • The study demonstrated that non-responders who received Evusheld (n=412) had a significantly lower incidence of symptomatic COVID-19, hospitalisations, and deaths than non-responders who received either casirivimab-imdevimab or no monoclonal antibody

• Kaminski et al 2022 (75): a retrospective case-control study investigating the association between adverse clinical outcomes (symptomatic SARS-CoV-2 infection, COVID-19 related hospitalisation, ICU admission and death) among 333 KTRs who mounted no/low serological response following 3 mRNA vaccinations against SARSCoV-2 in France

  • Among Evusheld recipients, significant reductions were reported across all primary outcomes (symptomatic SARS-CoV-2 infection, COVID-19 related hospitalisation, ICU admission and mortality) compared to those individuals that did not receive Evusheld

Of the five studies that included Evusheld as a comparator, Young-Xu et al and Kertes et al were considered to be the most appropriate for informing both (i) the clinical effectiveness of Evusheld in a real-world setting and (ii) have been deemed suitable for economic modelling. This is due to the large sample size, generalisability to the population in whom are likely to received treatment in UK clinical practice, and the reporting of clinical outcomes which can be used to inform the inputs of the economic evaluation of Evusheld. Further details on the rationale for inclusion and exclusion in the economic model is outlined in Table 5.

The TLR also identified a Phase III randomised, double-blind, placebo-controlled, multi-centre study for the treatment of COVID-19 in outpatient adults (TACKLE), which collected evidence for the higher Evusheld dose (600 mg). However, as the study considers treatment rather than PrEP for Evusheld, only safety evidence will be presented in this submission.

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Table 5: Selection of relevant studies from TLR (reasons for exclusion)

*Risk reduction of symptomatic infection and risk reduction of hospitalisation. Abbreviations: SLR – Systematic literature review; SOTR – Solid organ transplant recipients; TLR – Targeted literature review.

B2.1.3 Additional effectiveness evidence for variants of concern

Multiple independent in vitro studies have shown that Evusheld neutralises all current variants of concern. The neutralisation of antibodies is a known surrogate marker for clinical effectiveness(78), which supports the conclusion that Evusheld is effective versus all variants of concern, as observed in the clinical evidence base for this submission. A summary of the neutralisation effect of Evusheld and the link between in vitro neutralisation and clinical outcomes can be found in Appendix D.

B2.2 List of relevant clinical effectiveness evidence

B2.2.1 Summaries of relevant clinical effectiveness evidence

Based on the studies identified in Section B2.1, the relevant clinical effectiveness evidence for the submission were:

• PROVENT: The primary RCT to inform the clinical efficacy and safety data for Evusheld

• Young-Xu et al. 2022 (2) and Kertes et al. 2022 (72): Two key studies informing the clinical effectiveness of Evusheld in a real-world setting

• TACKLE: A key study informing the safety of the 600 mg dose of Evusheld

Summaries of the relevant clinical effectiveness studies and the safety study are found below (Table 6 – Table 9)

2 Levin et al. 2022 had not been published at the time of the original SLR, but PROVENT efficacy results were available to AstraZeneca and already included in the analysis. Company evidence submission template for Tixagevimab–cilgavimab for preventing COVID19 [ID6136]

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Table 6: Summary of PROVENT

3 The sub-study is still ongoing and therefore is not included as part of the clinical evidence in this submission. Company evidence submission template for Tixagevimab–cilgavimab for preventing COVID19 [ID6136]

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Abbreviations: AE – Adverse event; AESI – Adverse event of special interest; COVID-19 – Coronavirus disease 2019; IM – intramuscular; mAbs – Monoclonal antibodies; MAAEs – Medically attended adverse events; mg – milligrams; NA – Not applicable; RT-PCR – Reverse transcription polymerase chain reaction; SAEs – Serious adverse events; SARS-CoV-2 – Severe acute respiratory syndrome coronavirus 2; TEAEs – Treatment-emergent adverse events.

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Table 7: Summary of Young-Xu study

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Table 8: Summary of Kertes study

Abbreviations: COVID-19 – Coronavirus disease 2019; IM – intramuscular; PCR – polymerase chain reaction

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Table 9: Summary of TACKLE (safety of 600 mg dose)

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• Detection, levels, and change from baseline of virus (nasal swab) through day 29. • Duration of fever through day 29. • Incidence of ADA. • Pharmacokinetics: serum concentration, maximum serum concentration, time to maximum serum concentration, area under the plasma concentration-time curve (last measurable time point and extrapolated to infinity). *Supports marketing authorisation for Evusheld as acute treatment and is xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx Abbreviations: ADA – Anti-drug antibodies; AEs – Adverse events; AESIs – Adverse events of special interest; COVID-19 – Coronavirus disease 2019; IM – intramuscular; NA – Not applicable; PrEP – Pre-exposure prophylaxis; RT-PCR – Reverse transcription polymerase chain reaction; SAEs – Serious adverse events; USA – United States of America; WHO – World Health Organization

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B2.3 Summary of methodology of the relevant clinical effectiveness evidence

B2.3.1 Comparative summary of trial methodology

A comparative summary of the relevant studies is provided below in Table 10 and Table 11. Further details for each of the studies can be found below.

Table 10: Comparative summary of study methodology

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individual risk factors for COVID-19

aInvestigational products indicated for the treatment or prevention of SARS-CoV-2 or COVID-19, hydroxychloroquine, chloroquine, ivermectin, HIV protease inhibitors, COVID19 vaccine, contraceptive methods, blood/plasma donation, ova/sperm donation;[b] Follow-up period: date of Evusheld administration (intervention group) or date of pseudoadministration (control group) until 30[th] April 2022 or death (whichever occurred first);[c] Follow-up period: date of Evusheld administration (intervention group) or date of first contact (control group) until end of study period (26[th] of May 2022). Abbreviations: AE – adverse event; AESIs – Adverse events of special interest; COVID-19 – Coronavirus disease 2019; FDA – US Food and Drug Administration; IM – Intramuscular; MAAEs – Medically attended adverse events; mAb – Monoclonal antibody; mg – Milligram; MHS – Maccabi HealthCare Services; ml – Millilitre; NR – Not reported; RT-PCR – Reverse transcription polymerase chain reaction; ; SAEs – Serious adverse events; SARS-CoV-2 – Severe acute respiratory syndrome coronavirus 2; SoC – Standard of care; w/v – Weight per volume; VA – Veterans Affairs; USA – United States of America; WHO – World Health Organization

Table 11: Characteristics of participants in the studies across treatment groups

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aDefined as any high-risk, refer to Table 12; bBased on diagnosis or use of immunosuppressants, refer to section 0; cAs defined by the Israeli Ministry of Health, refer to section B2.5;[ d] After propensity-score matching. Abbreviations: SD – Standard deviation; SMD – Standardised mean difference

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B2.4 PROVENT study design

The efficacy and safety of Evusheld (150 mg each of tixagevimab and cilgavimab) compared to placebo for the PrEP of COVID-19 in high-risk adults with negative point of care SARSCoV-2 serology tests was assessed in the Phase III, randomised, multi-centre, triple-blind clinical trial PROVENT.(81)

Figure 8 provides an overview of PROVENT inclusion criteria, randomisation and primary endpoints.(71)

Figure 8: PROVENT trial design

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Source: Levin et al. 2021(70); Abbreviations: COVID-19 – Coronavirus disease 2019; IM – Intramuscular; mAb – Monoclonal antibody; MERS – Middle East respiratory syndrome; mo – Month; RT-PCR – Reverse transcription polymerase chain reaction; SARS – severe acute respiratory syndrome; SARS-CoV-2 – Severe acute respiratory syndrome coronavirus 2; TIXA/CILGA – Tixagevimab/cilgavimab.

Following up to seven days of screening, participants were randomised in a 2:1 ratio to receive either Evusheld or placebo and were subsequently followed up to 15 months (until day 457). Figure 9 shows the sequence of treatment periods. Given the extreme vulnerability of this population, it was important that PROVENT did not delay or obstruct vaccine access to those who were eligible to receive vaccination. Participants ongoing in the trial were advised that once they became eligible for vaccines, they should become unblinded. Those who had received Evusheld were asked to wait for six months prior to receiving their COVID-19 vaccine, those who received placebo were advised to get vaccinated as per their local health authority guidance.

Figure 9: Trial design flow chart

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----- Start of picture text -----
2: AZD7442
Stage Stage Primary Endpoint Final Analysis
1 2
1: Placebo
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Abbreviations: AZD7442 – Evusheld; DSMB – Data and safety monitoring board

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Enrolment occurred in two stages, to ensure the safety of participants:

Stage 1 enrolled approximately 300 patients (200 to Evusheld, 100 to placebo), which included a sentinel cohort of 15 patients.

Stage 2 was initiated after an independent Data Safety Monitoring Board (DSMB) had evaluated 7-day safety data from Stage 1 participants. In stage 2, all remaining patients were enrolled, ultimately reaching a total sample size of 5,197.

B2.4.1 Eligibility criteria

PROVENT was conducted in adults considered at risk of inadequate immune response to vaccination and/or severe COVID-19 due to living situation, occupation, or comorbidities. Participants were excluded if they had previously received a COVID-19 vaccine or had known prior or current SARS-CoV-2 infection. (71) Inclusion and exclusion criteria are summarised in (71) Inclusion and exclusion criteria are summarised in Table 12.

Table 12: PROVENT inclusion and exclusion criteria

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Source: Levin et al. 2021 (Supplementary appendix)(70);[ a] Clinically significant bleeding disorders (e.g., factor deficiency, coagulopathy, or platelet disorder), or history of significant bleeding or bruising following intramuscular injections or venipunctures. Abbreviations: COVID-19 – Coronavirus disease 2019; BMI – Body mass index; GFR – Glomerular filtration rate; HIV – Human immunodeficiency viruses; mAb – Monoclonal antibody; MERS – Middle east respiratory syndrome; SAE – Serious adverse event; SARS – severe acute respiratory syndrome; SARS-CoV2 – Severe acute respiratory syndrome coronavirus 2.

B2.4.2 Setting and location where data was collected

PROVENT was conducted in 87 sites in five countries (US, UK, Belgium, France and Spain).(71)

B2.4.3 Outcome measures

The primary efficacy endpoint was the incidence of symptomatic, virologically confirmed COVID-19 infection (positive nasopharyngeal RT-PCR test) within the period from injection to day 183. The primary safety endpoint was adverse events (AEs) occurring during the period from injection to day 457. PROVENT objectives and endpoints are summarised in Table 13. Further details can be found in the clinical study report.(81)

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Table 13: Objectives and outcome endpoints

Abbreviations: AE – Adverse event; AESI – Adverse event of special interest; COVID-19 – Coronavirus disease 2019; IM – Intramuscular; RT-PCR – Reverse transcription polymerase chain reaction; SAE – Serious adverse event; SARS-CoV-2 – Severe acute respiratory syndrome coronavirus-2.

B2.4.4 Patient characteristics

In general, baseline patient characteristics were balanced between the Evusheld and placebo arms (Table 14 and Table 15). Most patients in the Evusheld and placebo arms were white (73.6% and 71.9%, respectively), male (53.9% and 53.8%, respectively), and had a negative

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PCR (96.4% and 96.3%, respectively). Approximately 30% of participants had chosen to be unblinded at the time of the primary analysis (29.3% and 31.2%, respectively).(81)

Over double the number of participants in the placebo arm chose to be vaccinated compared to those in the Evusheld arm (31.0% vs 12.3%, respectively).(71) Demographics and baseline characteristics for the PROVENT population are presented in Table 14.

Table 14: Demographics and baseline characteristics (81)

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Source: Levin et al. 2021 (Supplementary appendix)(70). Lineage nomenclature from WHO. The Omicron variant (currently circulating VoC), Gamma variant (previously circulating VoC), and the Zeta, Eta, Theta, Kappa, Lambda, and Mu variants (previously circulating VoIs) were not identified in the PROVENT study population.[†] The Alpha and Beta variants were designated as currently circulating VoCs during the PROVENT study and were redesignated as previously circulating VoCs as of March 9, 2022.[‡] Includes subvariants B.1.617.2_1, _2, _3, and _4[§] The Delta variant was designated as a current circulating VoC on May 11, 2021.[║] Former VoI Epsilon; designated as previously circulating VOI as of July 6, 2021.[¶] Former VoI Iota; designated as previously circulating VOI as of September 20, 2021.[a] Includes all other participants (e.g., those who reported more than one race are reported under ‘Multiple’). Abbreviations: BMI – Body mass index; COVID-19 – Coronavirus disease 2019; RNA – Ribonucleic acid; RT-PCR – Reverse transcription polymerase chain reaction; SARS-CoV-2 – Severe acute respiratory syndrome coronavirus 2

The comorbidities of participants in PROVENT are shown in Table 15.

Table 15: Comorbidities in PROVENT participants at baseline (primary analysis) (81)

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Abbreviations: BMI - body mass index; COPD - chronic obstructive pulmonary disease; CV - cardiovascular. YoungXu et al. 2022 study design(82)

This retrospective observational study analysed Veterans Affairs (VA) healthcare electronic health records of veterans who were immunocompromised or otherwise at high-risk of COVID19. The study coincided with the Omicron BA.1 surge in the US, and the early BA.2 and BA.2.12.1 surge, assessing clinical effectiveness predominantly with the 600 mg dose.

Evusheld patients were identified based on prescriptions in the VA Pharmacy Benefits Management Emergency Use Authorisation prescription dashboard. The dashboard contains information on recipients, date, and dosage of medication administered in VA facilities. The first identified administration of Evusheld (150 mg each of tixagevimab and cilgavimab) was January 13, 2022.

After the US FDA’s revision of Evusheld’s Emergency Use Authorization (EUA) dose to 600 mg (tixagevimab 300 mg/ cilgavimab 300 mg) on February 24, 2022, patients who received the previously authorised lower dose were advised to receive an additional dose. The analysis included any patient receiving at least one dose of Evusheld. Overall, a total of 83% of study participants in the treatment arm received the 600 mg dose.

The trial population included two groups:

• Evusheld patients (defined as any patient who received at least one dose of Evusheld during the observation period)

• Propensity-score matched controls (immunocompromised or other high-risk patients who did not receive Evusheld)

Control group patients were assigned pseudo-administration dates matching the real Evusheld administration dates of the Evusheld group (index date). Baseline characteristics were assessed up to two years prior to index date, and follow-up was until 30 April 2022 or until the patient died (whichever occurred first).

B2.4.5 Eligibility criteria

The study enrolled veterans, aged ≥18 years and who were immunocompromised or otherwise at high-risk of COVID-19.

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Immunocompromised status was defined based on either receiving immunosuppressive medication during the 30 days before index date, or presence of ≥1 qualifying International Classification of Diseases 10[th] Revision (ICD-10) diagnosis within two years before the index date. Severe immunocompromise was defined as having had solid organ transplant or received anti-rejection medication for transplant or chemotherapy for cancer treatment in the month before the index date.(83)

B2.4.6 Outcome measures

The primary endpoint was a composite of any of the below occurring during the follow-up period:

• COVID-19 infection (confirmed by positive SARS-CoV-2 RT-PCR or antigen test)

• COVID-19 hospitalisation, defined as having both an admission and discharge diagnosis for COVID-19 from a hospital or within 30 days of positive SARS-CoV-2 RTPCR result or antigen test

• All-cause mortality

B2.4.7 Setting and location where data was collected

US Department of Veterans Affairs healthcare system provides care to almost 9 million veterans at 171 US medical centres and 1,112 outpatient clinics.(84,85) The VA data source used (VA Corporate Data Warehouse) contains information in all visits in VA medical facilities.

B2.4.8 Patient characteristics

Baseline characteristics measured included demographics, significant comorbidities, and healthcare utilisation. Comorbidities were based on diagnosis codes recorded in VA electronic data for healthcare visits two years before the index date. Significant comorbidities were defined according to an adaptation of Deyo-Charlson comorbidity index (DCCI).(86)

After propensity-score matching, the treatment group (n=1,733) and control group (n=6,354) were well balanced across baseline characteristics, as shown in Table 16.

Table 16: Selected baseline characteristics (Young-Xu et al. 2022)

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Abbreviations: DCCI – Deyo-Charlson Comorbidity Index; mRNA – Messenger ribonucleic acid; NR – Not reported; SD – Standard deviation; SMD – Standardised mean difference

B2.5 Kertes et al. 2022 study design

This retrospective observational study included immunocompromised individuals identified in the MHS (Maccabi HealthCare Services) database in Israel. The MHS is the second largest health management organisation in Israel, with six regional centres including 150 branches and clinics.

Immunocompromised individuals aged 12 and over identified in the MHS database were invited by SMS/email to receive Evusheld. Demographic information, comorbidities, coronavirus vaccination and prior SARS-CoV-2 infection and COVID-19 outcome data (infection, severe disease), were extracted from the database. The index date for the group that received Evusheld was the date of Evusheld administration, and they were followed until the end of the study period.

For the non-administered group, the index date was the date of the first SMS/email that they received notifying that they are eligible for Evusheld and were followed up until the end of the study period. Rates of infection and severe disease were compared between those administered Evusheld and those who did not respond to the invitation, over a three-month period.

The dominating variant during the study period was Omicron (predominantly BA1 between February and March 2022, and BA2 from April 2022).

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B2.5.1 Eligibility criteria

Evusheld 300 mg (150 mg each of tixagevimab and cilgavimab) was offered free of charge from February 2022 to MHS members who fulfilled all the following criteria:

• ≥12 years of age,

• weight ≥40 kg,

• no positive COVID-19 test result in the past month,

• no vaccination against COVID-19 in the last two weeks,

• evidence of a severe immunosuppression (Table 17).

Table 17: Definition of conditions/treatments for Evusheld eligibility (Kertes et al. 2022)

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Eligible MHS members were recorded in a daily updated database and contacted by SMS and Email with information on Evusheld eligibility, and a link to further information on Evusheld effectiveness, target population and contraindications. The message also encouraged the recipient to make an appointment.

Study population

The study population included all who had been contacted between February 23, 2022, and May 02, 2022. The population was grouped as follows:

• Contacted and had Evusheld administered

• Contacted but did not have Evusheld administered

Both groups were followed up between the date of first SMS/email and May 26, 2022.

B2.5.2 Outcome measures

• The primary endpoint was COVID-19 infection, defined as a recorded positive PCR or antigen test result in the follow-up period.

• The secondary study outcome was severe COVID-19 disease, defined as either COVID-19 related hospitalisation and/or all-cause mortality.

B2.5.3 Setting and location where data was collected

MHS is the second largest health maintenance organisation in Israel.

B2.5.4 Patient characteristics

Table 18 shows the baseline characteristics of the study population. The study population included severely immunocompromised patients, the majority had been fully vaccinated and one fourth had prior COVID-19 infection.

A total of 825 patients (16.1% of the total population) were administered Evusheld. No matching was performed and differences between the intervention and the control arm were observed, including:

• The Evusheld administered group were younger, with a larger proportion of males and from higher socioeconomic levels than those not administered Evusheld.

• The Evusheld patients were also more likely to have certain comorbidities, and more likely to have been fully vaccinated (at least three doses).

• Evusheld patients were less likely to have had prior COVID-19 infection compared to those not administered Evusheld.

Based on this, the authors speculate a potential underestimation of the effect of Evusheld, if unvaccinated individuals and those in lower socioeconomic groups were less inclined to test. In this case, more untested, positive COVID-19 cases would have been among the nonadministered group.

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Table 18: Demographics and health characteristics of the study population by Evusheld administration status, MHS, Feb-May 2022 (Kertes et al. 2022)

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Abbreviations: BMI – Body mass index; CAR-T – Chimeric antigen receptor T cells; CKD – Chronic kidney disease; CLL – Chronic lymphocytic leukaemia; HTN – Hypertension; Rx – Prescription

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B2.6 Statistical analysis and definition of study groups in the relevant clinical trials

Further details on each study, including a summary of the statistical analyses, are detailed in Table 19.

Table 19: Summary of statistical analyses

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Abbreviations: CI – Confidence Interval; COVID-19 – Coronavirus disease 2019; NR – Not reported; SoC – Standard of care

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B2.6.1 PROVENT statistical analysis

The primary efficacy outcome was calculated as a relative risk reduction (RRR), which is the incidence of symptomatic infection in the Evusheld group relative to the incidence of symptomatic infection in the placebo group, expressed as a percentage (i.e., 100% × relative risk).

Efficacy summaries were presented with a two-sided 95% Confidence interval (CI) and statistical significance was achieved if the two-sided p-value was <0.05.

B2.6.1.1 Sample size calculations (Intention to treat population)

Approximately 5,150 participants were planned to be randomised in a 2:1 ratio to receive a single IM dose of Evusheld (divided in two sequential injections, one for each mAb component) (the active group, n=approximately 3,433) or saline placebo (the control group, n=approximately 1,717) on day 1.

The sample size calculations were based on the primary efficacy endpoint and were derived following a modified Poisson regression approach.(87) The sample size necessary to achieve the power for the primary endpoint was calculated based on various assumed attack rates in the placebo group and an assumed true efficacy of 80% using Poisson regression model with robust variance, as shown in Table 20.

Table 20: Simulated power by number of events

Simulated power is based upon 10,000 simulations of trials assuming 80% efficacy (1 −[𝝀][𝑨𝒁𝑫𝟕𝟒𝟒𝟐] ⁄𝝀𝑷𝒍𝒂𝒄𝒆𝒃𝒐) , using Poisson regression model with robust variance, with no participants lost follow-up. Power is the proportion of trials with p-value < 0.05.

B2.6.1.2 Statistical considerations

During the trial, highly efficacious vaccines against COVID-19 were being deployed on a mass scale. Top priority target populations for vaccine administration in the UK were similar to the population being recruited for this trial, including the elderly, those with a chronic condition that increased their risk of developing severe COVID-19, as well as workers whose location or circumstances put them at increased risk of exposure to COVID-19. However, many people who are immunocompromised fail to adequately respond to vaccination and therefore remain at high-risk of adverse clinical outcomes due to COVID-19.

Given the extreme vulnerability of this population, it was important that PROVENT did not delay or obstruct vaccine access to those who were eligible to receive vaccination. Participants ongoing in the trial were advised that once they became eligible for vaccines, they should become unblinded. Those who had received Evusheld were asked to wait for six months prior Company evidence submission template for Tixagevimab–cilgavimab for preventing COVID19 [ID6136]

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to receiving their COVID-19 vaccine, those who received placebo were advised to get vaccinated as per their local health authority guidance.

A protocol amendment was used for the primary analysis to reduce the potential impact of unblinding and/or COVID-19 vaccination on the trial’s ability to robustly quantify placebocontrolled efficacy. The primary analysis was originally scheduled to occur after 183 days but was amended to take place either after 24 events or when the trial reached an unblinding rate of 30%. This resulted in a reduced follow-up time; median follow-up was 83 days for the primary analysis, and 196 days for the 6-month follow-up. All changes relevant to the primary analysis were made before unblinding the trial.

B2.6.2 Young-Xu et al. 2022 statistical analysis

B2.6.2.1 Propensity-score matching

The study used propensity-score models to account for observable baseline differences between intervention and controls. The propensity score covariates were measured before treatment initiation to avoid adjustment for potential mediators.

Missing or unknown values for the matching criteria were captured using indicator variables to retain patients in the study. The propensity-score matching used greedy nearest neighbour matching (calliper of 0.2 and ratio of 1:4 with replacement).(88)

The robustness of the matching was assessed by standardised mean difference (SMD). A match was deemed successful when at least 90% of covariates included in the propensityscore model had SMD ≤10.(89)

Control group patients were assigned pseudo-administration dates matching the actual Evusheld administration dates of the Evusheld group, to address immortal time bias. The generated pseudo-dates followed the same distribution as the actual administration dates for Evusheld recipients.(90,91)

Finally, Evusheld patients were matched to eligible controls based on date (or pseudo-date) and the facility where Evusheld was administered. To ensure focusing on new infections, any patient who had a positive RT-PCR or antigen result within 3 months of the date or pseudodate were excluded.

Cox proportional hazards regression was used to compare patients who received Evusheld and their matched controls.

B2.6.2.2 Difference-in-difference analysis

Difference-in-difference analysis was used to assess outcomes. A person-time denominator was calculated for Evusheld patients and controls by counting the number of days patients were enrolled during an extended study period (September 1, 2021 to April 30, 2022).

A per-period numerator was calculated as total number of outcomes (including multiple outcomes for a single patient). Outcome rates were then calculated during the baseline (September–December 2021) and observation periods.

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periods (before the intervention – e.g., administration date and pseudo-date – and after the intervention). The rates of each outcome were calculated for each cohort and compared before and after the intervention within the extended study period.

The incidence rate ratio (RR), defined as the rate of the outcome among Evusheld recipients divided by the rate of the outcome in the control arm, was calculated for each study outcome in the observation period (RRo) and the baseline period (RRb). The RR of the post-treatment period was divided by the RR of the pre-treatment period. The PERR was calculated per the following formula:

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The relative effectiveness of Evusheld to SPM (rE) is defined as:

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Falsification analysis was conducted using urinary tract infection (UTI) as outcome as healthcare encounters with UTI as primary diagnosis was unlikely to be associated with Evusheld. Propensity-score matched analysis showed similar effectiveness of Evusheld and control (hazard ratio [HR] 1.05; 95% CI: 0.68-1.62).

This lack of association between UTI and Evusheld supports that the protective effects associated with the treatment of Evusheld were unlikely to be due to bias or other major methodological flaws.

B2.6.3 Kertes et al. 2022 statistical analysis

Chi Square statistic or Fisher exact test were used to assess demographics and patient characteristics between the two study groups and compare the relationship between group and study outcomes.

The relationship between Evusheld administration (or non-administration) and outcome variables over time was analysed using Kaplan-Meier methodology. Variables associated with outcome variables were included in a logistic regression model.

Although no matching was undertaken between the individuals administered Evusheld and those that did not receive Evusheld, potential confounding variables (age, sex, socioeconomic status, comorbidities, prior COVID-19 infection) were adjusted for in the analysis of primary and secondary outcomes (COVID-19 infection & COVID-19 hospitalisation or all-cause mortality).

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B2.7 Quality assessment of the relevant clinical effectiveness

evidence

Table 21 and Table 22 show the quality assessment results for the relevant trials.

Table 21: Quality assessment results for RCTs (PROVENT)

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Adapted from systematic reviews: Centre for Reviews and Dissemination guidance for undertaking reviews in health care (University of York Centre for Reviews and Dissemination). Abbreviations: CSR – Clinical study report; IMP – Investigational medicinal product; ITT – Intention to treat; RCT – Randomised controlled trial

Table 22: Quality assessment results for non-RCTs (Young-Xu et al. 2022 and Kertes et al. 2022)

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Adapted from Critical Appraisal Skills Programme (CASP): Making sense of evidence 12 questions to help you make sense of a cohort study. Abbreviations: COVID-19 – Coronavirus disease 2019; PCR – Polymerase chain reaction; RCT – Randomised controlled trial

B2.8 Clinical effectiveness results of the relevant trials

B2.8.1 PROVENT

B2.8.1.1 Primary outcome (incidence of COVID-19 positive symptomatic

illness)

The primary endpoint (incidence of COVID-19 RT-PCR-positive symptomatic illness from administration to day 183) was met (Table 23). There was a statistically significant reduction in incidence of COVID-19 RT-PCR-positive symptomatic illness for participants who had received Evusheld compared to placebo (RRR 76.7%, 95% CI: 46.1-90.0%, p <0.001). The median (range) duration from dose of Evusheld to primary analysis data cut-off (5 May, 2021) was 83.0 (3–166) days.

At the median 6-month follow-up the magnitude of effect increased for participants who had received Evusheld compared to placebo (RRR 82.8%, 95% CI: 65.8-91.4% [11 (0.3%) compared to 31 (1.8%)]). The median duration from dose of Evusheld to 6-month follow-up was 196 days.

Additional pre-specified analyses were conducted to assess both the impact of unblinding and/or vaccination on the primary result as well as on all-cause mortality. The primary analysis was conducted after 30% of trial participants had become unblinded. All primary endpoint events (25 events) accrued up until the data cut-off (5 May, 2021) were included in the primary analysis. Participants who were unblinded to Evusheld assignment/took vaccine prior to experiencing a primary endpoint event were censored at the earlier time of unblinding/vaccine.

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In the full pre-exposure analysis set, 1,008 (29.3%) Evusheld participants were unblinded compared with 540 (31.2%) placebo participants. COVID-19 vaccinations were received by 424 (12.3%) Evusheld participants compared to 537 (31.0%) placebo participants.

Both analyses were consistent with the primary result indicating that neither the high unblinding and vaccination rates nor the non-COVID-19 related deaths affected the analysis of this endpoint. symptoms that qualified for the primary endpoint are summarised in Table 24. The events presented here are not censored at time of unblinding and/or COVID-19 vaccination so more participants with COVID-19 positive symptomatic illness are included in this table.

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Table 23: Primary outcome of PROVENT*

*The full pre-exposure analysis set consisted of all the participants who had undergone randomisation, received at least one injection of Evusheld or placebo, and did not have RT-PCR– confirmed SARS-CoV-2 infection at baseline. Estimates were based on a Poisson regression with robust variance. The model included trial group (Evusheld or placebo) and age at informed consent (≥60 years or <60 years), with the log of the follow-up time as an offset. Unadjusted RRRs (95% CI) for the primary end point were the same as the adjusted RRRs for both the primary analysis and the median 6-month follow-up. An estimated relative risk reduction greater than zero favoured Evusheld, with a p-value of less than 0.05 indicating statistical significance. †This analysis was not pre-specified in the trial protocol, so p-values were not calculated. Abbreviations: CI – Confidence intervals; COVID-19 – Coronavirus disease 2019; PCR – Polymerase chain reaction; SARS-CoV-2 – severe acute respiratory syndrome coronavirus 2

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Table 24: Summary of qualifying symptoms for definition of primary efficacy endpoint, full pre-exposure analysis set, primary analysis data cut-off

Events presented are not censored at time of unblinding and/or COVID-19 vaccination. Percentages are based on the total number of participants with SARS-CoV-2 RT-PCR-positive symptomatic illness. Presented event categories are mutually exclusive and participants are only counted once across the event categories. Abbreviations: COVID-19 – Coronavirus disease 2019; DCO – Data cut-off; IM – Intramuscular; N – Number of participants in the full pre-exposure analysis set; SARSCoV2 – Severe acute respiratory syndrome coronavirus 2; RT-PCR – Reverse transcription polymerase chain reaction

B2.8.1.2 Time to first COVID-19 RT-PCR-positive symptomatic illness

A Kaplan-Meier plot and Cox Proportional Hazards analysis of the time to first COVID-19 RTPCR-positive symptomatic illness is presented in Figure 10. Time to first COVID-19 RT-PCRpositive symptomatic illness was longer in the Evusheld arm compared to placebo: HR 0.23 (95% CI: 0.10-0.53); p-value <0.001.

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Figure 10: Time to first COVID-19 RT-PCR-positive symptomatic illness occurring post dose of IMP KM curves by arm; Primary analysis (5 May 2021)

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HR is from the PH model with Efron method. The 95% CI for the HR is obtained by taking 95% profile likelihood CI of the hazard ratio from the PH model with treatment group as a covariate, stratified by age at informed consent (≥ 60 years versus < 60 years). P-value is obtained from log-rank test, stratified by age at informed consent (≥60 years versus <60 years). Abbreviations: CI – Confidence interval; DCO – Data cut-off; HR – Hazard ratio; IMP – Investigational medicinal product; KM – Kaplan-Meier; PH – Proportional hazard; RT-PCR – Reverse transcription polymerase chain reaction; SARSCoV2– Severe acute respiratory syndrome coronavirus 2; + indicates a censored observation.

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B2.8.1.3 Sensitivity analysis of the primary endpoint

As a sensitivity analysis to handle missing follow-up time in the analysis of the primary efficacy endpoint, the primary analysis of the primary efficacy endpoint was repeated with multiple imputation for intercurrent events. The results were consistent with the primary analysis: RRR 77.29 (95% CI: 48.28-90.03); p < 0.001.

B2.8.1.4 Key secondary outcomes

B2.8.1.4.a Incidence of participants who had a post-treatment response for SARSCoV-2 Nucleocapsid antibodies

The incidence of a post-treatment response (negative at baseline to positive at any time post-baseline) for COVID-19 nucleocapsid antibodies (produced in response to a natural infection and therefore a measure of symptomatic and asymptomatic COVID-19 infection), was statistically significantly lower for participants who had received Evusheld compared to placebo, with an RRR of 51.1% (95% CI: 10.6-73.2%); p-value 0.020 (Table 25).

Table 25: Incidence of participants who had a post-treatment response for SARS-CoV-2 nucleocapsid antibodies, full pre-exposure analysis set, primary analysis data cut-off

Post-treatment response is defined as negative at baseline and positive at any time post-baseline. Estimates are based on a Poisson regression with robust variance. The model includes covariate for treatment (Evusheld versus placebo), and age at informed consent (≥60 years versus <60 years), with the log of the follow-up time as an offset. Estimated RRR greater than 0% provides evidence in favour of Evusheld with p-values less than 0.05 indicating statistical significance. Percentages are based on the number of participants in the analysis by arm (N). Abbreviations: CI – Confidence interval; IM – Intramuscular; N – Number of participants in the full pre-exposure analysis set; n – Number of participants with event; RRR – Relative risk ratio; SARSCoV2 – Severe acute respiratory syndrome coronavirus 2

Furthermore, the time from baseline to first positive nucleocapsid antibody test was significantly longer in the Evusheld arm compared to placebo: HR 0.48 (95% CI: 0.26-0.89), p = 0.018.

B2.8.1.4.b Incidence of SARS-CoV-2 RT-PCR-positive severe or critical symptomatic illness after dosing with Evusheld

There were no participants with COVID-19 RT-PCR-positive severe or critical symptomatic illness in the Evusheld arm, compared with one participant in the placebo arm. An additional two participants in the placebo group had COVID-19 RT-PCR-positive severe or critical symptomatic illness but these were censored due to unblinding.

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Too few events occurred to be able to calculate the time to first severe or critical COVID-19 RTPCR-positive severe or critical symptomatic illness after dosing with IMP.

B2.8.1.4.c Incidence of COVID-19 related emergency department visits occurring after dosing with Evusheld

Emergency department visits are distinct from hospitalisations and were captured on the emergency room visit electronic case report form where the primary reason for emergency room visit was selected as COVID-19 symptoms.

The participant was not required to have a positive PCR test and the COVID-19 symptoms were determined by the investigator and did not need to meet the qualifying symptoms or duration of symptoms that were applied to the primary endpoint.

The RRR of Evusheld compared with placebo for COVID-19-related emergency department visits could not be estimated due to low numbers: 6 participants in the Evusheld group compared with zero in the placebo arm.

Three of the six participants had PCR-positive symptomatic illness (and therefore also met the primary endpoint), however three participants tested negative by PCR for COVID-19 within 8 days of the emergency room visit. In addition, in the safety update, which was conducted at the June 2021 data cut-off, the proportion of participants who had COVID-19 emergency room visits was the same between the treatment groups (0.2%).

B2.8.1.5 Subgroup analyses

B2.8.1.5.a Primary endpoint

The PROVENT study included a subset population categorised as being immunocompromised (defined as at increased risk for inadequate response to active immunisation i.e., history of chronic kidney disease, immunosuppressed disease, immunosuppressive treatment, chronic liver disease, cancer, or solid organ transplant). In the full pre-exposure analysis set, xxxxxxxxxxxxxxxxx of participants were immunocompromised.

For the primary endpoint in PROVENT through a data cut-off of 5 May 2021, the efficacy results in the immunocompromised subgroup are in line with the results observed in the overall population xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx Note that the study was not designed to detect treatment differences within subgroups with high statistical power.

Subgroup analyses were conducted in pre-specified subgroups that included age, sex, race, ethnicity, COVID-19 co-morbidities at baseline, COVID-19 status at baseline, high-risk for severe COVID-19 at baseline, and various individual risk factors for COVID-19.

For the primary endpoint, the efficacy of Evusheld versus placebo was consistent across predefined subgroups (Figure 11 and Figure 12).

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Figure 11: Forest plot for efficacy for incidence of first SARS-CoV-2 RT-PCR-positive symptomatic illness by subgroup, full preexposure analysis set, primary analysis data cut-off(76)

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Abbreviations: AZD7442 – Evusheld; CI – Confidence interval; NE – Not estimable; RRR – Relative risk reduction; RT-PCR – Reverse transcription polymerase chain reaction; SARSCoV2 – Severe acute respiratory syndrome coronavirus 2

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Figure 12: Forest plot for efficacy for incidence of first SARS-CoV-2 RT-PCR-positive symptomatic illness by subgroup, full preexposure analysis set, primary analysis

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Estimates are based on Poisson regression with robust variance using full model or reduced model. The full model includes covariates for treatment group, age at informed consent (≥ 60 years versus < 60 years), subgroup and treatmentsubgroup interaction, and the log of the follow-up time as an offset. If it is not converged, a reduced model by excluding age at informed consent will be applied. P-values are for the treatmentsubgroup interaction. Within each level of a subgroup, same approach is utilised. Estimated RRR greater than zero provides evidence in favour of Evusheld. Percentages are based on the number of participants in the subgroup (if applicable) in the analysis set by arm. Abbreviations: CI – Confidence interval; COPD – Chronic obstructive pulmonary disorder; COVID-19 – Coronavirus disease 2019; CV – Cardiovascular; NE – Not estimable; RT-PCR – Reverse transcription polymerase chain reaction; RRR – Relative risk reduction; SARSCoV2 – Severe acute respiratory syndrome coronavirus 2; + indicates a censored observation

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B2.8.1.5.b Key secondary endpoint

Subgroup analyses for the key secondary endpoint were conducted in pre-specified subgroups that included age, sex, race, ethnicity, COVID-19 comorbidities at baseline, COVID-19 status at baseline, high-risk for severe COVID-19 at baseline, and various individual risk factors for COVID-19. For the key secondary endpoint, the efficacy of Evusheld compared with placebo was consistent across pre-defined subgroups (see Appendix E for further details).

B2.8.2 Young-Xu et al. 2022

B2.8.2.1 Primary outcomes

Results from the propensity-score matched analysis show that Evusheld recipients had a lower incidence of the composite of COVID-19 outcomes versus control patients in the overall cohort (HR 0.31; 95% CI: 0.18-0.53 [17/1733 [1.0%] vs 206/6354 [3.2%]]) (Table 26, Figure 13). Results were similar within the immunocompromised (HR 0.32; 95% CI: 0.18-0.62, severely immunocompromised (HR 0.44; 95% CI: 0.21-0.93), and age 65 or older (HR 0.33; 95% CI: 0.18-0.61).

Each of the disaggregated COVID-19 outcomes showed significant benefits in favour of Evusheld, including test-confirmed COVID-19 infection (HR 0.34; 95% CI: 0.13-0.87), COVID19 hospitalisation (HR 0.13; 95% CI: 0.02-0.99), and all-cause mortality (HR 0.36; 95% CI: 0.18-0.73).

Table 26: Relative effectiveness of Evusheld versus untreated controls using propensity-score matched analysis and difference-in-difference (Young-Xu et al. 2022)

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*DiD analysis was not performed on outcomes involving mortality data because matched cohorts were all alive at index dates; **Electronic data regarding immunocompromised conditions or immunosuppressant use were found; †Numbers not shown to protect patient information. Abbreviations: CI – confidence interval; COVID-19 – Coronavirus disease 2019

Figure 13: Cumulative risk of composite COVID-19 outcomes for Evusheld recipients compared to untreated controls (Young-Xu et al. 2022)

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Abbreviations: tixa/cilg – tixagevimab and cilgavimab (Evusheld)

B2.8.2.2 Sensitivity (difference-in-difference) analysis

The matched, PERR-adjusted effectiveness, as measured by incidence rate ratio was 0.32 (95% CI: 0.24-0.44%) against COVID-19 infection verified by a positive test, and 0.10 (95% CI: 0.05-0.22) against COVID-19-related hospitalisation, almost identical to the point estimates from propensity-score matched survival analysis (Table 26). This consistency across findings shows that the results are robust and the observed benefit of Evusheld is unlikely to be due to any confounding. Because both actual and pseudo-Evusheld use required the subjects to be alive, PERR analysis was not able to be performed on mortality, including the composite outcome.

This approach removes biases in post-intervention period comparisons between the treatment and control group possibly originating from permanent differences between the groups, as well as biases from comparisons over time in the treatment group that could be the result of trends due to other causes of the outcome.

This consistency across findings shows that the results are robust and the observed benefit of Evusheld is unlikely to be due to any confounding. Because both actual and pseudoEvusheld use required the subjects to be alive, PERR analysis was not able to be performed on mortality, including the composite outcome.

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B2.8.2.3 Falsification analysis

Healthcare encounters with UTI as the primary discharge diagnosis were unlikely to be associated with Evusheld; therefore, served as a falsification test. 163 UTI visits were observed during the follow-up period. Propensity scores matched analysis demonstrated a similar effectiveness of Evusheld versus control against UTI (HR 1.05; 95% CI: 0.68-1.62) (Table 26). This lack of association between UTI and the treatment is reassuring and provides evidence that the protective effects associated with the treatment of Evusheld were unlikely due to bias or other major methodological flaws.

B2.8.2.4 Summary

Using electronic health records from US Department of Veterans Affairs, one the largest integrated healthcare systems in US, the study was able to demonstrate the clinical effectiveness of Evusheld in reducing the incidence in COVID-19 infections, COVID-19 hospitalisations, and all-cause mortality in the overall cohort (comprising of immunocompromised (92%) and patients at high-risk for COVID-19 (8%)).

Among Immunocompromised and severely immunocompromised cohorts, patients that received Evusheld had lower incidence of a composite of COVID-19 outcomes compared to matched controls. In addition, the study also showed that Evusheld augmented the protection against COVID-19 infection in fully vaccinated individuals in the overall cohort akin to a fully vaccinated and boosted non-immunocompromised adult.

One of the key strengths of this study is its large sample size with 1,486 patient-years of observation. The study also utilises various approaches to adequately adjust for measured and unmeasured confounders. The study controls for confounding through propensity-score matching, while unmeasured residual confounders (time-varying factors) are adjusted using difference-in-difference analysis. More importantly, it is one of the few studies that includes patients that received the 600 mg dose of Evusheld (83% of the population).

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B2.8.3 Kertes et al. 2022

B2.8.3.1 Primary outcomes

A total of 29 patients (3.5%) in the Evusheld administered population, were infected with COVID-19 compared to 308 (7.2%) of the non-Evusheld administered population (p<0.001). This finding was consistent over time (Figure 14). The odds of infection for the Evusheld administered group compared to the non-administered Evusheld group was significantly reduced by almost 50% (OR: 0.51, 95% CI: 0.30-0.84) (Table 27).

Figure 14: COVID-19 infection rates over time by AZD7442 administration status, Kaplan-Meier hazards ratios, MHS, Feb-May 2022 (Kertes et al. 2022)

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Abbreviations: AZD7442 – Evusheld; COVID-19 – Coronavirus disease 2019; MHS – Maccabi HealthCare Services;

Univariate analysis identified that age, number of COVID-19 vaccines, prior COVID-19 illness, socioeconomic status, and CKD as presenting higher risk of COVID-19 infection (Table 27).

Table 27: Factors associated with COVID-19 infection among selected Immunocompromised individuals (ICIs), logistic regression model, MHS, Feb-May 2022 (Kertes et al. 2022)

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Abbreviations: CI – Confidence interval; COVID-19 – Coronavirus disease 2019; CKD – Chronic kidney disease; ICIs – Immunocompromised individuals; MHS – Maccabi HealthCare Services; OR – odds ratio

B2.8.3.2 Key secondary outcomes: Severe COVID-19 disease

Only 0.1% (n=1/825) in the Evusheld administered group was hospitalised for COVID-19 compared to 0.6% (n=27/4299) in the non-Evusheld administered group (p=0.07). No deaths occurred in the Evusheld administered group during the follow-up period, compared to 40 deaths (0.9%) in the non-Evusheld administered group (p-0.005). Only 0.1% of the Evusheld administered group had evidence of severe disease compared to 1.5% of the nonadministered group (p-0.001). This finding was consistent over time (Figure 15).

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Figure 15: COVID-19 hospitalisation or death – Severe disease rates over time by Evusheld administration status, Kaplan-Meier hazards ratios, MHS, Feb-May 2022 (Kertes et al. 2022)

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Abbreviations: COVID-19 – Coronavirus disease 2019; MHS – Maccabi HealthCare Services

Age and all comorbidities (except for obesity) were associated with severe disease outcomes in the univariate analysis. Vaccination status, socioeconomic status and prior COVID-19 illness were not associated with a severe disease outcome.

Due to the small number of patients with severe disease (n=64), a logistic regression was conducted, including age group and cardiovascular disease. After adjustment, the Evusheld group odds of having severe disease were 0.08 (95% CI: 0.01-0.54).

B2.8.3.3 Summary

This study demonstrated that highly immunosuppressed individuals receiving Evusheld were nearly 50% as likely to become infected with COVID-19 compared to the non-administered group (OR: 0.51, 95% CI: 0.30-0.84). They were also 92% less likely to be hospitalised or die than non-administered group (OR: 0.08, 95% CI: 0.01- 0.54).

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rates of COVID-19 infection compared to those that did not. While a similar trend was observed in other conditions (lymphoma, multiple myeloma and all other), due to the low sample size, the infections rates were not significantly different between those that received Evusheld and those that did not. The factors associated with higher rates of COVID-19 infection include age, socioeconomic status, number of prior doses of vaccine, prior COVID19 infection and CKD.

A key strength of the study is that it investigated a heterogeneous population of immunocompromised individuals (hypogammaglobulinemia, bone marrow transplant patients, patients who received anti-CD20 and CAR-T therapy, solid organ transplant recipients, patients with CLL, aggressive lymphoma and multiple myeloma).

Although no matching was undertaken between the individuals that were administered Evusheld and those that did not receive Evusheld, potential confounding variables (age, sex, socioeconomic status, comorbidities, prior COVID-19 infection) were adjusted for the analysis of primary and secondary outcomes (COVID-19 infection & COVID-19 hospitalisation or allcause mortality).

B2.9 Meta-analysis

The NICE scope of no prophylaxis is representative of the placebo arm in the PROVENT study. Since PROVENT is the only RCT identified evaluating Evusheld compared to no prophylaxis, no meta-analysis or indirect treatment comparison is required.

B2.10 Adverse reactions

B2.10.1.1 PROVENT

The co-primary endpoint of PROVENT was to assess the safety and tolerability of a single IM dose of Evusheld compared to placebo by assessing AEs, serious adverse events (SAEs), adverse events of special interest (AESIs), and medically attended adverse events (MAAEs).

Note that the occurrence of COVID-19 as an AE in this section does not align with the number of COVID-19 events in the efficacy analysis. AEs reported as COVID-19 without a corresponding positive lab result are not counted as primary endpoint events.

Efficacy was based on protocol pre-specified qualifying symptoms collected separately from AEs in the case report form with a corresponding RT-PCR-positive test. Furthermore, some COVID-19 symptoms may have been reported as separate AEs rather than as AEs related to COVID-19.

B2.10.1.2 Categories of AEs

The AE categories are presented for Evusheld versus placebo in Table 28. Approximately 45% of participants had at least one AE in the trial and there were no notable differences in the proportion of AEs in each category between the treatment groups.

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Table 28: Overall summary of AEs in any category, safety analysis set, June 2021 data cut-off

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*Participants may have had more than one event. †One participant was randomized to placebo and incorrectly received AZD7442; per study protocol this participant was assessed in the AZD7442 group for the SAS.[‡] Events were determined to be intervention-related by investigators based on their judgment. Page 41 of 49[§] Immune complex disease was removed as an AEs of special interest following adjudication.[║] All deaths were determined by the investigator to not be related to the study drug received.[¶] Participant died as a result of accidental exposure to two substances controlled under Schedule I of the 1961 United Nations Single Convention on Narcotic Drugs. 12 **Cases were adjudicated to be Covid-19 related by the independent and external Morbidity Adjudication Committee. AEs were coded using the Medical Dictionary for Regulatory Activities, version 24.0. Abbreviations: AE–adverse event; ARDS – acute respiratory distress syndrome; COVID-19 – coronavirus disease 2019; SAE – serious adverse event; SAS – safety analysis set.

The most frequently reported AEs (≥1% of participants) are presented for Evusheld versus placebo in Table 29. The most common adverse event of special interest was an injection site reaction, which occurred in 2.4% of the participants in the Evusheld group and in 2.1% of those in the placebo group. The incidence of serious adverse events was similar in the two groups.

Evusheld is administered as two sequential injections, therefore, a participant could potentially discontinue between injections. There were no participants with an AE leading to permanent discontinuation of Evusheld. Two participants in the Evusheld arm and one in the placebo arm discontinued treatment as part of the study. For missing data, participants who discontinued early from the study or were lost to follow-up before experiencing a primary endpoint event were censored in the Kaplan-Meier and Poisson regression analyses.

Table 29: Most frequently reported (≥1%) AEs by preferred term, safety analysis set, June 2021 data cut-off (76)

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AEs are defined as any AE that started or worsened in severity on or after the first dose of Evusheld through to the data cut-off. Percentages are based on the number of participants in the analysis set by treatment group. Preferred terms are sorted by decreasing order of total frequency. Participants with more than one event within a preferred term are counted only once. Percentages are based on the number of participants in the analysis set by treatment group. Abbreviations: AE – Adverse event; ARDS – Acute respiratory distress syndrome; COVID-19 – Coronavirus disease 2019; IM – Intramuscular; N – Number of participants in the safety analysis set

B2.10.1.3 Deaths

Deaths, and AEs with an outcome of death, are summarised by organ class and term in Table 30.The causes of death were adjudicated by an independent committee to determine whether deaths could be COVID-19 related. Overall, the number of deaths was low (n = 16 across both arms).

There were no cases of severe COVID-19- or COVID-19-related deaths in those treated with Evusheld. In the placebo arm, 2 (0.1%) participants died due to COVID-19 (preferred terms: COVID-19 pneumonia and acute respiratory distress syndrome).(76) Note that COVID-19 related deaths were excluded from the primary analysis as they occurred after unblinding. At the time of the June 2021 data cut-off, no participants receiving Evusheld had died.

Table 30: Deaths and AEs with an outcome of death by system organ class and - preferred term, safety analysis set, June 2021 data cut off (76)

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Reproductive system and breast 1 (<0.1) 0 1 (<0.1) disorders

Abbreviations: AE – Adverse event; COVID-19 – Coronavirus disease 19; IM – Intramuscular; N – Number of participants in the safety analysis set

B3.1.1 Young-Xu et al. 2022

No data was published on adverse reactions.

B3.1.2 Kertes et al. 2022

No data was published on adverse reactions.

B3.1.3 TACKLE

The overall TACKLE safety results are in line with those reported in PROVENT; current evidence indicates that the safety profile of the higher, 600 mg dose of Evusheld is in line with that of the 300 mg dose.

The primary safety endpoints in the TACKLE study were AEs, SAEs and AESI throughout the study. Final analysis will estimate the endpoints until day 457, published results available as of September 2022 have a median safety follow-up of 84.0 days in both arms (interquartile range: Evusheld 31.0–86.0, placebo 30.0–86.0). AEs were reported by 132 patients (29%) in the Evusheld arm, and 163 (36%) in the placebo arm. SAEs were reported by 33 (7%) in the Evusheld arm, and 54 (12%) in the placebo arm.

The incidence of AEs and AESIs (Table 31), as well as SAEs (Table 32) were similar over both arms, and the majority were of mild or moderate severity. The most common AE was COVID-19 pneumonia in both arms, experienced by 6% of Evusheld patients and 11% of placebo patients. While there were fewer COVID-19 reported deaths in the Evusheld group, all-cause mortality rates were similar (Table 31).(79)

Table 31: Adverse events in the safety analysis set

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Source: Montgomery et al. 2022(79) Abbreviations: AE – Adverse event; COVID-19 – Coronavirus disease 2019

Table 32: Serious adverse events by system organ class and preferred term, safety analysis set

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Source: Montgomery et al. 2022, supplementary material(79) Abbreviations: COVID-19 – Coronavirus disease 2019; SAE – Serious adverse event

B2.11 Innovation

As described in Section B1.3.8, despite a successful vaccine rollout, individuals with the highest risk of an adverse COVID-19 outcome remain clinically vulnerable, and there is a substantial medical unmet need for an effective prophylaxis in high-risk populations that can reduce the risk of COVID-19 infection and poor COVID-19 outcomes (hospitalisation or death).

Currently there are no prophylaxes routinely commissioned in the UK which could prevent COVID-19 infection and improve COVID-19 outcomes in high-risk populations. (28)

There is strong emerging evidence that prophylactic measures using monoclonal antibodies are an effective strategy for immunocompromised individuals, and further research and innovation has been highlighted as important to ensure that immunocompromised patients continue to be adequately safeguarded and protected during the coronavirus pandemic.(92)

Evusheld, a combination of two antibodies, is the first and only COVID-19 PrEP approved by the MHRA, with clinical evidence demonstrating clinically effectiveness and safety across RCT and real-world settings.

We therefore consider Evusheld to be innovative, as its introduction to the NHS would represent a step-change in the treatment pathway for individuals with the highest risk of an adverse COVID-19 outcome, namely hospitalisation and death, or in individuals for whom COVID-19 vaccination is not recommended.

B2.12 Interpretation of clinical effectiveness and safety evidence

B2.12.1.1 Summary of findings from the clinical evidence

Evusheld is the first and only COVID-19 PrEP approved by the MHRA, with clinical evidence demonstrating clinical effectiveness and safety across RCT and real-world settings. A large body of evidence shows that Evusheld PrEP significantly and substantially reduces symptomatic COVID-19 illness among higher risk patients and consequentially results in lower hospitalisation and (all cause) mortality

Clinical efficacy and safety in a randomised setting

The efficacy and safety of Evusheld has been demonstrated in a large (n=5,197), Phase III, randomised, triple-blind, placebo-controlled, multi-centre RCT (PROVENT), which met its primary and key secondary endpoints, demonstrating a consistent effect across all prespecified subgroups.

The primary efficacy analysis in PROVENT showed that Evusheld administered in a prophylactic setting significantly reduced the risk of developing symptomatic COVID-19

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compared with placebo: RRR 76.7% (95% CI: 46.05–89.96, p < 0.001). The magnitude of effect increased by 6-month follow-up: RRR 82.80% (95% CI: 65.79–91.35), and was consistent across all pre-specified subgroups.(76)

Secondary endpoint analyses demonstrated favourable outcomes for Evusheld compared to placebo:

• Time to first COVID-19 RT-PCR-positive symptomatic illness was longer in the Evusheld arm compared to placebo: HR 0.23 (95% CI: 0.10-0.53); p < 0.001.

• The incidence of a post-treatment response for COVID-19 nucleocapsid antibodies was statistically significantly lower for participants who had received Evusheld compared to placebo, with an RRR of 51.1% (95% CI: 10.6-73.2%); p = 0.020.

Safety analyses from the PROVENT study have demonstrated that the Evusheld 300 mg dose is well tolerated. For commonly reported AEs, there were no meaningful differences between the treatment groups except for COVID-19, which was reported by a smaller proportion of Evusheld participants (0%) compared with placebo participants (0.1%). Two participants in the Evusheld arm and one in the placebo arm discontinued treatment as part of the study, with no Evusheld discontinuations due to AEs. There were only 16 deaths (nine in the Evusheld arm and seven deaths in the placebo arm) recorded in the PROVENT study, and the investigator did not consider these to be related to Evusheld or placebo.(76)

Safety analyses from the TACKLE study have demonstrated that the Evusheld 600 mg dose is equally well tolerated. There were fewer AEs reported in the Evusheld arm (29%) compared to the placebo arm (36%), and fewer SAEs reported in the Evusheld arm (7%) compared to the placebo arm (12%).

Clinical effectiveness in a real-world setting

The significance and magnitude of the reduced risk observed in PROVENT has been confirmed in real-world settings, where consistent, significant efficacy was shown in immunocompromised populations, predominantly vaccinated, during surges dominated by Omicron variants.

Young-Xu et al. 2022(2): A large retrospective study (n=8,037) in US veterans, aged ≥18 years, receiving VA healthcare, compared individuals with at least one dose of intramuscular Evusheld with matched controls, selected from patients who were immunocompromised or otherwise at high-risk for COVID-19.

• The study aligned to the current UK environment, with 95% of patients having received COVID-19 vaccination and the analysis period was during high prevalence of Omicron BA.1 and the early BA.2 and BA.2.12.1 surge.

• Furthermore, 83% of patients received 600 mg dose of Evusheld.

• Results from the propensity-score matched analysis showed that Evusheld recipients had a lower incidence of the composite of COVID-19 outcomes versus control patients (HR 0.31; 95% CI: 0.18-0.53 [17/1733 [1.0%] vs 206/6354 [3.2%]]).

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• Each of the COVID-19 outcomes showed similar Evusheld benefits, including testconfirmed COVID-19 infection (HR 0.34; 95% CI: 0.13-0.87), COVID-19 hospitalisation (HR 0.13; 95% CI: 0.02-0.99), and all-cause mortality (HR 0.36; 95% CI: 0.18-0.73).

Kertes et al. 2022(72): A large retrospective study (n=5,124) in members of the Maccabi HealthCare Services (MHS) in Israel, aged 12 and over, and with evidence of a severe immunosuppression, compared individuals receiving Evusheld with unmatched controls.

• The study aligned to the current UK environment, with 98.8% of patients in the Evusheld group having received COVID-19 vaccinations. The analysis period took place when Omicron BA1 and BA2 were predominant.

• All patients received the 300 mg dose.

• Results from the analysis found that the odds of infection for the Evusheld administered group compared to the non-administered Evusheld group was significantly reduced by almost 50% (OR: 0.51, 95% CI: 0.30-0.84) (Table 27).

• Each of the COVID-19 outcomes showed similar Evusheld benefits, including risk of hospitalisation (0.1% in the Evusheld group compared to 0.6% in the non-Evusheld group [p = 0.07], risk of death (0% in the Evusheld group compared to 0.9% in the nonEvusheld group [p = 0.005], and severe disease (0.1% in the Evusheld group compared to 1.5% in the non-Evusheld group [p = 0.001]).

In addition, sustained protection against COVID-19 was demonstrated through a retrospective matched control study which investigated the efficacy of 600 mg of Evusheld in patients who had previously received an SOT.(73)

Finally, the inference of generalisability across COVID-19 variants is further demonstrated by studies which have shown that Evusheld maintains neutralisation against Omicron subvariants of concern(2) (see Appendix D).

B2.12.1.2 Strengths and limitations

The strengths of the clinical evidence base are:

• Evusheld has been evaluated in both large, randomised trials and real-world settings across 21,608 individuals; 7,167 of which were treated with Evusheld and 14,441 placebo/matched controls.

• Across different settings and populations, Evusheld demonstrates a consistent and significant benefit in preventing infection from COVID-19 and reducing poor outcomes in terms of hospitalisation and mortality.

• The magnitude of benefit across all study types and populations is large and clinically meaningful for patients.

• Evusheld has demonstrated a well tolerated safety profile across the 300 mg and 600 mg doses; in fact, safety data presented in this submission have shown more AEs associated with placebo compared to Evusheld.

The limitations of the clinical evidence base are:

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• Only xxxxx of participants in the PROVENT trial were immunocompromised, aligning with the target population for this submission.(70) However, treatment effectiveness was not shown to significantly differ between immunocompromised and immunocompetent participants (see Section B2.8.1.5). Furthermore, RWE studies have shown the clinical effectiveness of Evusheld in immunocompromised populations.

• Due to the evolving nature of COVID-19, PROVENT was not conducted in vaccinated patients or at a time when the Omicron variant was dominant. Nonetheless, RWE studies have demonstrated a consistent treatment effect in real-world settings during periods of Omicron; in different populations, across differing geographies and irrespective of vaccination status.

• The licenced dose for Evusheld is expected to become 600 mg, whilst PROVENT studied the 300 mg dose. However, the safety of a higher 600 mg dose has been confirmed in the TACKLE study (79), with tolerability observed to be consistent across the 300 mg and 600 mg doses. Furthermore, Young-Xu et al. 2022 (2), Al-Jurdi et al. (73) and Kertes et al. 2022(72) have demonstrated the clinical effectiveness of both 600 mg and 300 mg doses.

• It is noted that NICE DSU guidance TSD17(93) provides guidance on methods to identify and adjust for potential biases that may arise due to using non-RCT data for deriving treatment effectiveness. Young-Xu et al. 2022(2) did use a matching technique as advised in TSD17 to avoid the potential of known confounding variables, whilst Kertes et al. 2022(72) did not adjust for known confounding variables. In both studies, the impact of unknown confounding variables is not fully understood as access to the patient level data were not available at the time of submission. Nonetheless, NICE recently published their RWE framework which outlines their commitment to utilising RWE for decision making and a consistent treatment effect has been observed in RCT and RWE settings.(94)

In summary, the clinical evidence base demonstrates that PrEP treatment with Evusheld reduces the risk of developing symptomatic COVID-19 and reduces the risk of poor outcomes including hospitalisation and death. This offers much needed protection for high-risk populations and would represent a step-change in the treatment pathway for individuals with the highest risk of an adverse COVID-19 outcome, or in individuals for whom COVID-19 vaccination is not recommended.

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B3 Cost effectiveness

B3.1 Published cost-effectiveness studies

A SLR was undertaken on 6th of May 2022 to identify published cost-effectiveness studies relevant to the decision problem (see Section B1.1)

Please see Appendix G for the methods used to identify all relevant studies, in addition to a description and quality assessment of the cost-effectiveness studies identified.

In line with guidance from the Centre for Reviews and Dissemination(95), the population, interventions, comparators, outcomes and study type (PICOS) principal was used to define the following review question to identify relevant cost-effectiveness studies:

• What are the available economic evaluations on COVID-19 treatments, in any setting or indication (including prophylaxis)?

Overall, 21 publications were included in the SLR: one for PEP, two for outpatient treatments, 17 for inpatient treatments and one for outpatient and inpatient treatments (See Table 33). No economic evaluations evaluated PrEP.

B3.1.1.1 Post-exposure

Flaxman 2022 examined the health outcomes and net cost of implementing PEP with monoclonal antibodies (mAbs) against household exposure to COVID-19. The analysis was conducted from a US payer perspective using a decision tree structure, with a time horizon corresponding to one wave of SARS-CoV-2 transmission (roughly one month). No qualityadjusted life years (QALYs) or incremental cost-effectiveness ratio (ICER) information was published.

B3.1.1.2 Outpatient

Two studies conducted economic evaluations in outpatient treatments for COVID-19.

Jovanoski 2022 estimated the cost-effectiveness of casirivimab/imdevimab in ambulatory individuals with COVID-19. The analysis was conducted from a US payer perspective using a decision tree followed by a Markov model. A lifetime horizon with a cycle length of one year was considered, with both costs and outcomes discounted at 3%. Treatment with casirivimab/imdevimab was a cost-effective option for most outpatients with COVID-19 compared to usual care. In the base case, at a willingness to pay (WTP) threshold of $100,000, compared to usual care, treatment with casirivimab/imdevimab was found to be cost-effective in most patients, compared to usual care.

Marjiam 2022 evaluated the cost-effectiveness of sotrovimab versus SoC in a cohort of 1,000 outpatients with mild to moderate COVID-19 at high-risk of progression (the authors did not define high-risk). The analysis was conducted from a third-party US payer perspective using a Markov model structure over a lifetime horizon. Costs and outcomes were discounted at 3%. Sotrovimab was cost-effective compared to usual care, assuming a WTP threshold of $50,000 per QALY. Increased direct healthcare costs with sotrovimab for the 1,000-patient cohort were $1,355,765. The 1000-patient cohort receiving sotrovimab gained 122.19 QALY over their lifetime, resulting in an ICER of $11,096/QALY gained.

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B3.1.1.3 Inpatient

Seventeen studies conducted economic evaluations in inpatient treatments for COVID-19, three of which were UK studies and are summarised below (remaining studies are reported in Appendix G).

Kilcoyne 2022 assessed the clinical and economic benefits of lenzilumab plus SoC compared with SoC alone in the treatment of hospitalised patients with COVID-19 from the UK NHS England perspective. The analysis used a cost calculator to estimate the clinical benefits and costs of adding lenzilumab to SoC in newly hospitalised patients with COVID-19 over a period of 28 days. No discounting was applied due to this short time horizon. Overall, the findings supported the clinical and economic benefits of adding lenzilumab to SoC. In a base case population, adding lenzilumab to SoC was estimated to result in 2.40 bed days saved, 2.73 ICU days saved, and 3.33 mechanical ventilation (MV) days saved, and it led to inpatient cost savings of £1,162 per patient. In a weekly cohort of 4,754 newly hospitalised patients, adding lenzilumab to SoC resulted in 599 invasive mechanical ventilation (IMV) uses avoided, 352 additional lives saved, and more than £5,524,952 in cost savings.

Aguas 2021(96) evaluated the health and economic impact of dexamethasone in patients with COVID-19 from a UK healthcare provider perspective. The analysis used a decision tree structure over a 6-month time horizon. Dexamethasone was a cost-effective option, estimated to save an additional 12,000 lives and result in 102,000 life years (LY) gained compared with SoC, leading to a total incremental cost of £85,000,000.

Rafia 2022 (97) explored the cost-effectiveness of remdesivir in hospitalised patients with COVID-19 in England and Wales. The study was conducted from an NHS/Personal Social Services perspective using a decision analytic model considering an area under the curve approach. A lifetime horizon with a daily cycle length was used up to 70 days, followed by a weekly cycle for the remainder. Costs and health outcomes were discounted at 3.5%. The incremental QALY of 0.64 and costs of £3,332 translated into the cost-effectiveness ratio for remdesivir at £11,881/QALY gained compared with SoC.

B3.1.1.4 Outpatient / inpatient

Mulligan 2020 estimated the health-related benefits of two hypothetical treatments for COVID19: one that is effective in mild disease (outpatient setting), and one that benefits hospitalised patients with more severe disease (inpatient setting) in a US population. The study used decision tree over a lifetime horizon, and both cost and health outcomes were discounted at 3%. Compared to no treatment, the hypothetical treatment for COVID-19 resulted in health benefits and cost savings. Treatments for both mild and serious disease resulted in a significant cost savings, assuming 20% of the population was infected with COVID-19 by the end of 2021.

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Table 33: Summary list of published cost-effectiveness studies

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Total cost savings ($) =105.6B

Abbreviations: COVID-19 – Coronavirus disease 2019; CRP – C-reactive protein; GBP – Pound sterling; HFO – High-flow oxygen; HR – Hazard ratio; ICER – Incremental cost-effectiveness ratio; ICU – Intensive care unit; IMV – Invasive mechanical ventilation; LFO – Low-flow oxygen; LY – Life year; MOD – Multiorgan dysfunction; NHS – National Health Service; NNT – Number needed to treat; NR – Not reported; NV – Non-invasive ventilation; ONS – Office for National Statistics; OS – Overall survival; QALY – Quality-adjusted life years; SoC – Standard of care; SpO2 – Oxygen saturation; SWOV – Survival without ventilation; UK – United Kingdom

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B3.2 Economic analysis

Models identified through the SLR mainly considered decision tree and Markov model structures based on whether short or long-term time horizons were being considered. However, there were a couple exceptions including a decision analytic area under the curve approach and cost calculator.

A subsequent targeted literature search identified an Assessment Report for an economic evaluation of therapeutics for people with COVID-19 associated with TA10936, which was published by the School of Health and Related Research (ScHARR) on 30 June 2022 after the SLR searches were completed. The analysis adapted the structure by Rafia 2022 for hospitalised patients and considered a decision tree structure for non-hospitalised patients.

Unfortunately, while evidence synthesised as part of the SLR was informative for this decision problem, no model structures considered treatments for the PrEP of COVID-19, and therefore a de novo model structure was considered, using a decision tree to capture the costs and outcomes during an acute infection period phase followed by a Markov model to capture the longer-term costs and outcomes.

Short-term acute decision tree phases followed by long-term Markov models with lifetime horizons for outcomes have also been used in other published models for COVID-19, including economic modelling reports produced by the US Institute for Clinical and Economic Review (ICER)(56), Sheinson 2021(35) and Jovanoski 2022(99).

B3.2.1 Patient population

Aligned with the population with highest medical unmet and Evusheld’s anticipated use in clinical practice (see Section B1.3.8), the economic analysis models a subgroup of the licenced indication:

Adults who are not currently infected with SARS-CoV-2 and who have not had a known recent exposure to a person infected with SARS-CoV-2 and:

• are at the highest risk of an adverse COVID-19 outcome, namely hospitalisation and death, or

• for whom COVID-19 vaccination is not recommended

This aligns with the proposed positioning of Evusheld in the management of COVID-19 (see Section B1.4)

For the purposes of modelling, the vast majority of this patient population (>99%) are patients deemed to be at the highest risk of adverse COVID-19 outcomes due to underlying health conditions compromising their immunity (see Section B1.3.5). The remaining <1% of the population are patients for whom COVID-19 vaccination is not recommended (see Section B1.3.6).

B3.2.2 Intervention technology and comparators

The intervention (Evusheld) and comparator (no prophylaxis) are aligned with the NICE scope.

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Furthermore, as agreed with NICE and the Evidence Assessment Group (EAG) during the decision problem meeting on the 17[th] of August 2022, treatments under evaluation in TA10936 are not included as comparators nor as subsequent treatments in the model, since these treatments are not in routine commissioning.

The model considers a 1-year treatment duration for Evusheld (600 mg dose administered at time zero and at 6 months as per Section B1.2), based on the following rationale:

• Firstly, the environment for COVID-19 is constantly changing and it is unclear how long Evusheld will be prescribed as the risk of COVID-19 infection and associated adverse outcomes changes over time. As highlighted by APPG consensus statement, cosigned by 125 physicians treating people who are immunocompromised, prophylaxis treatments should only be delivered when the drug is effective and for those that still need protection.(28,103)

  • Mutations in the virus make it difficult to estimate what infection risk, attack rate, hospitalisation risk, and mortality risk may be within the timeframe of, for example, a typical influenza season, leading to even further uncertainty when extrapolating for longer time horizons.

  • How the management of COVID-19 will evolve over time is unknown due to the uncertainty in predicting COVID-19 variants and epidemiology parameters. As such, the long-term clinical effectiveness of any treatment strategy for COVID19 is uncertain.

• Secondly, while some clinicians may want to protect certain patients in the target population for more than 1-year, some clinical subgroups in the target population will only be treated until a certain date due to the management of an acute condition or future-dated procedure (e.g. patients with resected solid organ cancer, patients undergoing HSCT, patients with HIV/AIDs).

• It is therefore highly unlikely that all patients will require continuous prophylaxis over their lifetime (which equates to 50 or more years); in clinical practice treatment duration will likely vary from person to person.

• Therefore, in the base case we have assumed a 1-year treatment duration such that NICE can have some degree of certainty in interpreting the cost-effectiveness results. While we acknowledge that in clinical practice, some patients may be treated for longer, we are confident that the results are an accurate reflection of cost-effectiveness assuming this treatment duration.

• This challenging situation was acknowledged by NICE and the EAG during the decision problem meeting on the 17 August 2022. The EAG advised to explore alternative treatment durations, which would require structural changes to the model. We will look at possible routes to explore the long treatment duration scenarios during the appraisal process following submission.

Redosing was chosen at 6-months to align with the medial follow-up duration from the PROVENT study, where clinical efficacy and safety has been demonstrated.

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B3.2.3 Model structure

A de novo model structure considering a decision tree to capture the costs and outcomes during an acute infection period phase followed by a Markov model to capture the longer-term costs and outcomes was selected (Figure 16).

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Figure 16: Model structure

==> picture [698 x 367] intentionally omitted <==

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B3.2.3.1 Acute phase (decision tree):

The decision tree time horizon is 29 days in line with the longest modelled duration of stay taken from Beigel, et al. 2020.(34)

Prior to entering the acute phase, patients either receive Evusheld or no prophylaxis and are at risk of COVID-19 infection for 1-year, they then enter the acute phase, transitioning to the “infected & symptomatic” or “not infected/asymptomatic” health states.

Patients in the “not infected/asymptomatic” health state transition to either the “not infected” or “death” health states at the end of the decision tree, and subsequently enter the post-acute phase (Markov model).

Patients in the “infected & symptomatic” health state are assigned to one of six health states based on the severity of infection. These were aligned to WHO clinical progression scale, the efficacy endpoints used in clinical studies of Evusheld, and was validated with three UK clinical experts (Table 34). (104)

Table 34: Description of acute modelled health states

Abbreviations: FiO2 – Fraction of inspired oxygen; MV – Mechanical ventilation; NA – not applicable for the WHO clinical progression scale NIV – Non-invasive ventilation; pO2 – Partial pressure of oxygen; SpO2 – Peripheral capillary oxygen saturation; WHO – World Health Organization

B3.2.3.2 Post-acute phase (Markov model):

Following the acute phase (decision tree), patients transition to the post-acute phase (Markov model), whereby patients enter one of four health states: “not infected”, “recovered”, “long COVID”, or “death”:

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• Patients in the “not infected” health state either remain in this state or transition to the “death” state.

• Patients in the “recovered” health state either remain in this state or transition to the “death” state.

• Patients in the “long COVID" health state develop long-term sequelae that result in utility losses, reduced life expectancy, and costs associated with treating long COVID. These patients either remain in this state, transition to the “recovered” health state or transition to the “death” health state.

The possibility for COVID-19 infection risk after year 1 is considered by adjusting the total cost and QALY results derived from the structure described above. The adjustment considers a decrement to total QALYs and an increase in total costs for both arms based on the possibility for reinfection after year 1. This was included based on advice from the (EAG) during the decision problem meeting on the 17[th] of August 2022. For more details on methodology, see section B.3.3. (Reinfection after year 1).

The cycle length during the post-acute phase is 6 months, and a half cycle correction is applied.

B3.2.4 Time horizon

The model incorporates three distinct forms of time horizon for the analysis: one for the period over which COVID-19 infections are captured (1-year), short-term outcomes following this infection period captured by a decision tree (29 days) and long-term outcomes for patients with or without COVID-19 captured by a Markov model (lifetime).

The rationale for choosing an infection period of 1-year is based on the treatment duration of Evusheld (see Section B3.2.2). The rationale for then considering a 29-day decision tree is based on duration of stay in hospital taken from Beigel 2020.

The rationale for a lifetime horizon is to ensure the time horizon is sufficiently long to reflect all important differences in costs or outcomes between the technologies being compared are captured in line with the NICE reference case. Such benefits from treatment as improved survival and reductions in long COVID would be underestimated with any shorter time horizon.

The use of a 1-year time horizon for infections and lifetime horizon for outcomes is consistent with previously published COVID vaccine models (Kohli 2021). Short-term acute decision tree phases followed by long-term Markov models with lifetime horizons for outcomes have also been used in other published models for COVID, including economic modelling reports produced by ICER(56), Sheinson 2021(35) and Jovanoski 2022(99). This type of time horizon approach is also used in static seasonal influenza models (such as Chit 2015(105), RuizAragón 2022(106)).

B3.2.5 Perspective

An NHS and personal social services (PSS) perspective was chosen, in line with the NICE reference case.(107)

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B3.2.6 Discounting

Costs and utilities are discounted at 3.5% per annum, in line with the NICE reference case.(107)

B3.2.7 Features of the economic analysis

Table 35 describes the features of the economic analysis, compared to the ScHARR Assessment Report in TA10936.

It should be noted that the ScHARR Assessment Report in TA10936 did not consider prophylaxis and built separate models for hospitalised and non-hospitalised patients to evaluate a broad range of alternative COVID-19 treatments. Therefore, though the disease area of COVID-19 might be similar, the decision problem is considerably different between this submission and TA10936.

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Table 35: Features of the economic analysis

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Abbreviations: ACTT – Amlodipine Cardiovascular Community Trial; COVID-19 – Coronavirus disease 2019; ECMO – Extracorporeal membrane oxygenation; HFO – High-flow oxygen; HRQoL – Health-related quality of life; ICER – Institute for clinical and economic review; IMV – Intermittent mandatory ventilation; LFO – Low-flow oxygen; NIV – Noninvasive ventilation; OS – Ordinal scale; PIN – Primary Immunodeficiency Network; ScHARR – School of Health and Related Research; UK – United Kingdom

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B3.3 Clinical parameters and variables

B3.3.1 Population characteristics

Population characteristics for age, sex and weight are sourced from the PROVENT trial population (Table 36), since the selection criteria for PROVENT aligns closely with the population for Evusheld’s proposed use in terms of age, sex, and weight.

Table 36. Patient Characteristics at Baseline

Abbreviations: Kg – kilogram

No prophylaxis decision tree

B3.3.1.1 Risk of symptomatic infection

Symptomatic infection risk for no prophylaxis in the model over the initial 1-year period is calculated as 22.58% based on UK government data.(116)

It was calculated by averaging the 7- day attack rate (initial and subsequent attack rates) over the period August 2021 – August 2022 (accessed 11[th] August). The average 7-day attack rate was re-calculated to a 1-year attack rate using the following formulae:

1 𝑦𝑒𝑎𝑟 𝑖𝑛𝑓𝑒𝑐𝑡𝑖𝑜𝑛 𝑟𝑎𝑡𝑒= 1 −𝐸𝑋𝑃(−7𝑑𝑎𝑦𝑟𝑎𝑡𝑒∗52)

B3.3.1.2 Hospitalisation

The proportion of patients with symptomatic infection who were hospitalised for no prophylaxis was taken from a recent study by Shields et al. 2022.(46)

Shields et al. 2022 assessed the impact of vaccination on hospitalisation and mortality from COVID-19 in patients with primary and secondary immunodeficiency in the UK, which aligns closely with the target population for the submission.

The study included a cohort of 140 patients infected between January 2021 and March 2022. Study participants represent patients infected after the deployment of vaccination and the routine use of antiviral and monoclonaly antibody treatments in inpatient and outpatient settings. Furthermore, the majority of infections occurred later in the pandemic, after patients had received at least two vaccine doses, after the more transmissible B.1.1.529 (Omicron) SARS-CoV-2 variant became dominant, and after legal restrictions on social interactions had been lifted (16). Results from the study showed that 16.8% of patients with primary immunodeficiency and 18.2% of patients with secondary immunodeficiency required hospitalisation. A weighted average of 17.13% is used in the base case analysis.

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B3.3.1.3 Severity of hospitalisation

The severity of hospitalisation was defined based on the four hospitalisation health states: (no oxygen, low-flow oxygen, high-flow oxygen or non-invasive ventilation (NIV), or intermittent mandatory ventilation (IMV) or extracorporeal membrane oxygenation (ECMO)).

The proportions of patients in each of the health states were based on hospitalisation data from a South London hospital (Table 37); these data were not specific to immunocompromised patients and thus may underestimate the true severity of hospitalisation associated with COVID-19 infection in this high-risk cohort.

Table 37. Distribution of Hospitalised Patients

Abbreviations: ECMO – Extracorporeal membrane oxygenation; IMV – Invasive mechanical ventilation; NIV – Noninvasive ventilation

B3.3.1.4 Non-hospitalised patients

The proportion of symptomatic patients who were not hospitalised was calculated as 1 minus the percentage hospitalised. The split between the ‘not hospitalised – no assistance needed’ and ‘not hospitalised – assistance needed’ health states was assumed to be 50/50% in the base case. The model was built with the flexibility to incorporate health-state-specific data however, in the absence of such data, clinical, cost and QALY data between the two health states is assumed equal. As such, amendments to the percentage split between health states does not impact the ICER.

Table 38 summarises the distribution of patients between hospitalised and non-hospitalised states in the no prophylaxis arm.

Table 38. Overall distribution of hospitalised and non-hospitalised patients