Lead team presentation - Dapagliflozin, in combination with insulin, for treating type 1 diabetes - NICE

 
Public observer slides – Part 1 (contains no CIC data)

Dapagliflozin, in combination with insulin, for
treating type 1 diabetes
Lead team presentation
1st appraisal committee B meeting
Chair: Amanda Adler
Lead team: Nicholas Latimer, Nigel Westwood, Sarah Wild
ERG: Warwick Evidence
NICE technical team: Sharlene Ting, Ross Dent, Nicole Elliott
Company: AstraZeneca
26th March 2019
© NICE 2019. All rights reserved. Subject to notice of rights. The content in this publication is owned by multiple parties
and may not be re-used without the permission of the relevant copyright owner.
Key issues
• The company did not present any clinical data to demonstrate that
  dapagliflozin lengthens life in type 1 diabetes. Evidence for
  dapagliflozin shows a very small improvement in quality of life
  relative to placebo, but company model generates results that
  suggest dapagliflozin improves quality of life and extends life. Given
  the clinical evidence, do the model results have face validity?
• What is a clinically significant reduction in glycated haemoglobin
  (HbA1c)?
• Is dapagliflozin clinically effective?
  – At 52 weeks, dapagliflozin was associated with small reduction in
     HbA1c; weight loss; very small relative improvement in quality of
     life compared to placebo; increased risk of diabetic ketoacidosis
  – No data on mortality
• How should treatment waning be modelled?
• How should stopping treatment be modelled?                               2
History of appraisal
                     Company submission: no subgroup, licensed dose
9th October 2018
                     unknown (5mg or 10mg)
24th January to 21st Technical engagement: draft technical report including
February 2019        questions (based on company submission and ERG report)
                     CHMP positive opinion: “type 1 diabetes mellitus, when
                     insulin alone does not provide adequate control of blood
1st February 2019
                     glucose levels despite optimal insulin therapy. Patients …
                     should not have a body mass index below 27 kg/m2”
                     Stakeholder feedback to technical engagement
                     • Company: new evidence and analyses on indicated
21st February 2019
                       population
                     • 2 clinical experts nominated by company
                     Final technical report: updated based on stakeholder
                     feedback

                                                                                  3
Type 1 diabetes mellitus
•   Autoimmune, metabolic disease → destroys insulin-producing pancreatic cells
•   Haemoglobin A1c (HbA1c) measures ‘average’ blood glucose over time
•   Blood glucose and pressure, but not body weight drive risk of complications
•   Complications include:
    – Vascular disease: coronary, cerebrovascular, peripheral: ‘macrovascular’
    – Neuropathy: autonomic, sensory
    – Retinopathy, cataracts, maculopathy
    – Nephropathy
    – Other: diabetic ketoacidosis (DKA), skin, psychological, etc.
    – Treatment-related: low blood glucose (hypoglycaemia)
•   UK prevalence: about 0.5% (400,000 people)
•   Current treatment: insulin therapy
    – In England, structured education, for example, ‘DAFNE’, is the norm
•   In England, 70% of people with type 1 diabetes have HbA1c levels above 7.5%
    (recommended target
Patient and clinical perspectives
• Management of condition can be demanding
• Constant risks and insulin dose adjustment can have considerable
  psychosocial impact on wellbeing
• Affects quality of life
• No other therapy other than insulin, but difficult to achieve
  consistently in-range glucose levels
• Good control of diabetes remains an unmet need
• Better management of HbA1c, and more time in range can lead to
  fewer long-term complications
• Increased risk of diabetic ketoacidosis with dapagliflozin

                                                                   5
Relationship between high blood glucose
levels and complications – clinical evidence
• Hyperglycaemia associated with increased risk of complications
• 1 main randomised trial and follow-on epidemiological study from 1980s

                   Diabetes Control and Complications Trial (DCCT)
  In type 1 diabetes, does improving metabolic control lower incidence of
  diabetes-related complications over 5 to 10 years?
  P: No retinopathy or retinopathy (n=1,441, aged 13-39, USA/Canada)
  I: Insulin (multiple daily injections or pump) and target HbA1c ~6% (someone
  without diabetes)
  C: No more than 2 injections of insulin daily
  O: Complications
  Results: over mean follow-up of 6.5 years, reduced risk of microvascular
  complications by over half

        Epidemiology of Diabetes Interventions and Complications (EDIC)
 P: willing participants from DCCT (>90%)
 E: previously randomised to intensive glycaemic control
 C: previously randomised to less tight glycaemic control (conventional)
 O: complications                                                                6
Results of DCCT/EDIC: median HbA1c
     p
Dapagliflozin (ForxigaTM) in type 1 diabetes
• Inhibits sodium-glucose cotransporter-2 (SGLT-2) → prevents ~90% glucose
  reabsorbed in kidneys → increases urinary glucose excretion
• First oral medicine with an European licence (metformin has a French licence only)
• Marketing authorisation: “type 1 diabetes mellitus as an adjunct to insulin in
  patients with body mass index ≥ 27 kg/m2, when insulin alone does not provide
  adequate glycaemic control despite optimal insulin therapy”
   – BMI restriction reflects safety concerns about DKA and is a subgroup
   – Not recommended in patients with low insulin requirement
   – “During treatment, insulin therapy should be continuously optimised to prevent
     ketosis and diabetic ketoacidosis and insulin dose should only be reduced to
     avoid hypoglycaemia”
   – Patients should be able and committed to control ketone levels. They should be
     educated about risk factors for diabetic ketoacidosis and how to recognise its
     signs and symptoms
   – Administration: 5mg, once daily, any time, with or without food
 Does optimal insulin therapy include insulin pump, continuous glucose
  monitoring and flash glucose monitoring?
                                                                                      8
 How is ‘low insulin requirement’ defined?
9

Decision problem
Population is a subgroup of evidence presented to regulators
                          NICE scope                                Company
Population   Adults with type 1 diabetes on insulin Subgroup: patients with inadequate
             therapy that does not adequately       control of blood glucose and BMI
             control blood glucose levels           ≥ 27 kg/m2
Comparator Insulin with or without metformin         Metformin not associated with
                                                     improvement in glycaemic control
                                                     (recent REMOVAL trial; clinical experts
                                                     advise off-label use in UK is
DEPICT-1 and DEPICT-2 trials
Double-blind, randomised, placebo-controlled, international (UK sites)
Company defines ‘full analysis’ (FAS) and intention-to-treat’ (ITT) sets
8-week lead-in → 24-week double-blind treatment → 28-week extension (HbA1c unblinded)

               Adults with               Dapagliflozin (5mg or
 8 weeks                                                              Primary endpoint at
               inadequately               10mg) and insulin
     to                                                               24 weeks
               controlled type 1               therapy
 optimise                                                             • change from
               diabetes despite           (n=272) [N=1,059]
   diet /                                                                baseline in HbA1c
               optimised insulin
 exercise
               therapy (HbA1c                                          n, indicated population;
 / insulin                                Placebo and insulin
               7.5% to 10.5%)                                          N, overall trial population
                                               therapy
               “Indicated”                 (n=289) [N=532]
               population:
              BMI ≥27kg/m2         Functional unblinding: insulin dosages cut by ≤20% → blood
                                     glucose rise in placebo; on dapagliflozin → more urination
Key secondary endpoints:
• % of patients with fall in HbA1c by ≥0.5% without severe hypoglycaemia
• % change in bodyweight
• change in mean 24-hour blood glucose                                 No mortality data
• change in % of blood glucose readings outside range
• % change in total daily insulin                                                       10
Exploratory endpoints: EQ-5D-3L, Diabetes Treatment Satisfaction Questionnaire
Issue 1: Use of dapagliflozin in clinical practice
Background                   Stakeholder responses   Technical team consideration
• 8-week, lead-in period     • Optimised             • 8-week lead-in period in
  → too short to stabilise     management is part      DEPICT adequate
  HbA1c levels → may           of standard care;
                                                   • Any carry-over effects from
  affect results               individualised
                                                     optimised management
• Unclear if people with a • Glycaemia in 8          likely to affect dapagliflozin
  large reduction in HbA1c   weeks before HbA1c      and placebo arms similarly
  from optimised             measurement is
                                                   • Unclear if someone with
  management would           main driver of value
                                                     significant improvements in
  have a different
                           • Improvements in         HbA1c during optimisation
  response to
                             lead-in period would    period would respond
  dapagliflozin than
                             affect all trial arms   differently to dapagliflozin
  people who do not
                             equally                 than someone who did not

 What are the committee’s views?
                                                                                    11
‘Indicated’ population: baseline
characteristics
                                           DEPICT-1                  DEPICT-2
Mean (standard deviation), unless
                                   Dapagliflozin Placebo Dapagliflozin     Placebo
specified
                                     (n=145)      (n=154)     (n=127)       (n=135)
Age (years)                              46 (13)     45 (13)       43 (13)      45 (13)
Body mass index (kg/m2)                    32 (5)      32 (4)       32 (4)       32 (4)
Duration of diabetes (years)             21 (12)     23 (12)       20 (10)      21 (12)
HbA1c (%)                               8.5 (0.7)   8.4 (0.6)    8.4 (0.6)    8.4 (0.6)
Total insulin dose (rounded)
 - Dose (IU)                             72 (54)     73 (32)       72 (31)      69 (27)
 - Dose/weight (IU/kg)               0.79 (0.64) 0.77 (0.28)   0.77 (0.28)  0.75 (0.25)
Method of administering insulin, %
 - Injections                               57%         58%           54%          59%
 - Pump                                     43%         42%           46%          42%
Continuous glucose monitoring, %            29%         30%           36%          26%
HbA1c range at randomisation, %
 - ≥7.5% to
CONFIDENTIAL

Issue 3: Generalisability of DEPICT population
Background                       Stakeholder responses           Technical team
                                                                 consideration
• More people likely to take     • Patients in DEPICT likely      • Any guidance
  drugs that affect the renin-     reflect patients to be treated   recommending
  angiotensin-aldosterone          with dapagliflozin in NHS        dapagliflozin
  system (RAAS) in NHS than                                         for use in NHS
                                 • Greater use of RAAS
  in DEPICT (XXX)                                                   should exclude
                                   blocking agents in UK than
                                                                    people starting
• People on corticosteroids        in DEPICT would not affect
                                                                    on systemic
  excluded from DEPICT             HbA1c and weight lowering
                                                                    corticosteroid
                                   efficacy
                                                                    therapy
                                 • People on corticosteroids
                                   excluded from DEPICT so
                                   not to affect results
 What are the committee’s views?
 Would greater use of ACE inhibitors (and other factors) change absolute
  baseline risk and therefore absolute difference in events?
 Should dapagliflozin use exclude people starting on systemic corticosteroid
                                                                                  13
  therapy?
ERG comments on ‘indicated’ population
• Some results have not been provided by company so cannot
  comment about generalisability
• Randomisation in DEPICT did not account for BMI stratification →
  post-hoc subgroup breaks randomisation
• Compared to indicated population, UK patients have a:
    – lower BMI (25.4 – 27.2 kg/m2 vs 31.5 – 31.9 kg/m2 )
    – higher male population (56% – 60% vs 40% – 53%)
    – lower use of insulin pump therapy (15.3% in England and 5.8% in
       Wales vs 42% – 46%)
    – similar mean HbA1c levels (8.6% in England vs 8.4% – 8.5%)

                                                                        14
BMI, body mass index
‘Indicated’ population: pooled trial results
on HbA1c and weight
At 52 weeks, small changes in HbA1c and weight results differ by analysis set
Company base case used data at 52 weeks from full analysis set
                                 Adjusted mean change from baseline (standard error)
                                          24 weeks                     52 weeks
                                Dapagliflozin     Placebo      Dapagliflozin   Placebo
Full analysis set (FAS): all randomly assigned patients who received at least one dose
of study drug during 24-week treatment period
HbA1c change                       -0.44 (0.05)   -0.01 (0.05)   -0.26 (0.06) 0.08 (0.06)
Weight change (%)               -3.11 (0.29)   -0.01 (0.30)    -3.42 (0.29) 0.49 (0.30)
Weight change (kg)             -2.86 (0.27)    -0.01 (0.27)    -3.15 (0.26) 0.45 (0.28)
Regardless of stopping randomised treatment (equivalent to Intention-to-treat; ITT)
HbA1c change                   -0.43 (0.05) -0.01 (0.05)       -0.24 (0.06) 0.09 (0.06)
Weight change (%)                 -3.12 (0.29) -0.02 (0.30)      -3.35 (0.27) 0.25 (0.28)
Weight change (kg)                -2.87 (0.26) -0.02 (0.27)      -3.08 (0.25) 0.23 (0.26)

 Which analysis set is preferred?                                                     15
Issue 2: Minimum clinically significant reduction in HbA1c
Background                                          Stakeholder comments
• Minimum clinically                                • Benefits of HbA1c reductions on complications are
  significant change in                                continuous and not discrete
  HbA1c levels →                                    • Changes in glucose variability, hypoglycaemia and
  consider measurement                                 weight are important outcomes
  error, natural variability                        • Absolute reduction of 0.3% is meaningful
  in readings over time                             • A 10% reduction in risk is clinically meaningful
  and baseline HbA1c                                • Dependent on baseline: achieving a 0.5%
  levels                                               reduction is more difficult starting from 7.5% than
                                                       9.5%
                                                       – Suggest 0.4% for a baseline HbA1c of
‘Indicated’ population: insulin dose at 24
weeks using full analysis set
                       Adjusted mean change from
                                                             Difference from placebo
                            baseline (95% CI)
                                                            (95% CI; nominal p value)
                      Dapagliflozin      Placebo
                                -10.1                 1.5                            -11.5
DEPICT-1
                         (-13.1, -7.0)        (-2.1, 5.2)         (-15.2, -7.5; p
DEPICT: results on quality of life
                            Adjusted mean change from baseline (95% CI),
                                      dapagliflozin vs placebo
Full analysis set
                                24 weeks                    52 weeks
                          DEPICT-1     DEPICT-2       DEPICT-1    DEPICT-2
‘Indicated’ population
Overall treatment                   1.7              1.7             1.0           1.7
satisfaction (DTSQ)        (0.67, 2.82)     (0.56, 2.92)   (-0.27, 2.20) (0.48, 2.85)
Perceived frequency                -0.6             -0.2            -0.3          -0.1
of hyperglycaemia        (-0.88, -0.27)    (-0.55, 0.10)   (-0.58, 0.08) (-0.50, 0.25)
Perceived frequency                -0.1             -0.1            -0.1          -0.2
of hypoglycaemia          (-0.41, 0.23)    (-0.45, 0.25)   (-0.40, 0.29) (-0.52, 0.19)
Health status                       1.4              7.8             2.6           6.6
(EQ VAS)                  (-1.96, 4.66)   (2.25, 13.36)    (-0.33, 5.51) (0.70, 12.59)
Overall trial population
Health status                       1.5             2.0              1.6             1.6
(EQ VAS)                  (-0.80, 3.89)   (-1.88, 5.77)    (-0.50, 3.76)   (-2.30, 5.49)
Health status                      0.01           -0.02             0.01           -0.02
 (EQ-5D-3L)               (-0.02, 0.03)   (-0.04, 0.01)    (-0.01, 0.04)   (-0.04, 0.01)
 Is dapagliflozin effective at improving quality of life?                            18
CI, confidence interval
Adjudicating DKA in DEPICT

                             19
‘Indicated’ population: 52-week safety
Dapagliflozin is associated with ‘ketone-related’ adverse events in ‘indicated’ population
Company confirmed that there were no cases of Fournier’s gangrene in DEPICT
                                                   DEPICT-1                     DEPICT-2
Full analysis set                         Dapagliflozin Placebo Dapagliflozin Placebo
                                             (n=159)       (n=154)         (n=127)       (n=135)
≥1 AE related to drug                                37%        16%                32%       17%
AE leading to stopping                              3.8%       3.9%               8.7%      5.2%
≥1 SAE related to drug                              2.5%       0.6%               4.7%      2.2%
SAE leading to stopping                             1.9%       0.6%               4.7%      1.5%
Death                                                  0           0                  0        0
AE of special interest
  - Genital infection                                18%       3.9%                12%      4.4%
  - Urinary tract infection                          10%       6.5%                13%      7.4%
  - Renal function events                           2.5%       0.6%               1.6%      0.7%
  - Fractures                                       1.9%       3.9%               2.4%      0.7%
  - Volume depletion events                            0       1.9%               3.9%      2.2%
  - Hypersensitivity                                6.3%       3.2%               7.9%      8.1%
  - Cardiovascular event                            0.6%       0.6%               0.8%      0.7%
≥1 ketone-related SAE*                              1.3%       0.6%               3.9%      0.7%
Ketone SAE* leading to stopping                        0           0              3.1%         0
Definite diabetic ketoacidosis event                   1.3%          1.3%               2.4%   0.7%
*Includes diabetic ketoacidosis, ketoacidosis and ketosis; (S)AE, (serious) adverse event
                                                                                                 20
 Are patients/clinicians likely to accept the increased risk of DKA?
‘Indicated’ population: hypoglycaemia over
52 weeks (FAS)
Dapagliflozin is associated with an increased risk of hypoglycaemia
                                                 DEPICT-1              DEPICT-2
Full analysis set                          Dapagliflozin Placebo Dapagliflozin Placebo
                                             (n=159)     (n=154)   (n=127)     (n=135)
Events, n                                           4038    4158         3868     3730
Patients with ≥1 event, %                           83%      79%          90%      85%
Severe – requires 3rd party help
  - Events, n                                        27      17            16      45
  - Patients with ≥1 event, %                      13%       8%          10%      10%
 - Exposure-adjusted incidence rate per            17.8     12.3         13.6     38.3
  100 patient years
Documented symptomatic glucose 70
  mg/dL (10.5 mmol/l)
  - Events, n                                      3295    3453         3203     2967
  - Patients with ≥1 event, %                      79%      73%          87%      81%
  - Exposure-adjusted incidence rate per         2177.7   2495.1       2719.2   2522.6
   100 patient years
 Are patients/clinicians likely to accept the increased risk of
                                                                                    21
  hypoglycaemia?
Summary of clinical evidence
• At 52 weeks, data from pooled DEPICT trials showed that
  dapagliflozin was associated with:
  – small reduction in HbA1c (0.26%) compared with baseline
  – weight loss (3.15kg) compared with baseline
  – very small relative improvement in quality of life compared to
    placebo
  – increased risk of diabetic ketoacidosis compared to placebo

• No data on mortality

 Is dapagliflozin clinically effective?                             22
Cost effectiveness

                     23
Where do QALY gains come from in
company’s model?
                                      Treating
                                   type 1 diabetes

                       Company assumes        Company assumes
                        QALY gains here        QALY gains here

                 Length of life                      Quality of life

           Increase in QALYs comes from improving quality of life
                  and increasing length of life as a result of:
          • reduction in HbA1c that lowers the risk of diabetes-
            related complications                                 24

          • weight loss that is associated with an increase in utility
QALY, quality-adjusted life year
Company’s Cardiff type 1 diabetes model
  • Patient-level, fixed-time-increment, Monte Carlo microsimulation → simulates
    disease progression using risk equations over life-time horizon
  • 6 month cycle; no half-cycle correction
  • Risk equations to fit data from DCCT/EDIC for microvascular complications
    and Swedish National Diabetes Registry for macrovascular complications
  • Cohorts of 1,000 individual patients in each ‘run’ of model
  • Company models each patient with same starting conditions: identical set of event
    probabilities, unit costs and utility values are applied to their simulated progression.
    Model captures random variability in outcomes between identical patients in each
    cohort
  • Patient cohort enters model with baseline characteristics and modifiable risk
    factors. Variables’ values may change as simulation progresses, affecting risk of
    complications
  • Company assumed no progressive increase in risk factors (for example, HbA1c
    and weight) based on clinical advice
    In the company’s publication of model (McEwan et al. 2016), a progressive
     increase in HbA1c of 0.045% was included compared to 0% in this appraisal.
     Which is an appropriate assumption?
                                                                                                                 25
DCCT, Diabetes Control and Complications Trial; EDIC, Epidemiology of Diabetes Interventions and Complications
Studies in type 1 diabetes
Study                      Description                                               Modelled?
DCCT/EDIC                 See slides 6 and 7. Assessed incidence and predictors      Yes
                          of macrovascular and microvascular events
Wisconsin Epidemiologic Population-based study of 955 patients with T1DM in          No
Study of Diabetic         South Wisconsin, USA. Examined cumulative incidence
Retinopathy (WESDR)       of macular oedema and relation to risk factors
Pittsburgh Epidemiology Prospective cohort of 1,124 patients with T1DM in or         No
of Diabetes Complications near Pittsburgh, USA. Investigated risk of microvascular
Study (EDC)               complications over time
Finnish Diabetic          Prospective cohort of 29,906 patients with T1DM aged       No
Nephropathy
Validation of model: company feedback (1)
  • Model similar to established T1DM models: modelling approach and use of
    DCCT/EDIC data to model disease progression (Company submission,
    page 117)
  • Cardiff model: internal and external validation, 2 peer-reviewed articles,
    Mount Hood Diabetes Challenge (Company submission, page 177)
  • Mount Hood Challenge: involve simulation of outcomes for hypothetical
    patient cohorts and validation of model predictions against real-world data.
    Ability of models to predict outcomes of clinical trials and observational
    studies is assessed and compared
       – No differences in prediction of events between model used in Mount
          Hood and in this submission
       – Company is unable to provide a comparison of Cardiff model results
          against other modelling groups for T1DM analysis (Company’s
          clarification response #1, B5)

                                                                                 27
T1DM, type 1 diabetes mellitus
Validation of model: company feedback (2)
•     Internal and external model validation (McEwan et al. 2016)
•     Internal validation of: CT1DM Model’s equations to source data, and results of model’s
      clinical endpoint predictions
•     Available external clinical validation studies suitable for assessing model’s predictive
      performance are limited in T1DM → DCCT/EDIC is basis of model’s progression rates
•     External consistency of model’s predictions: compared with 5 other T1DM models (Sheffield
      model; CRC; McQueen et al.; CORE model; Wolowacz et al.)
      – CT1DM model started with baseline cohort, cost and health utility profiles consistent with
        other models, and outputs compared over relevant time horizons
      – Validation coefficient of determination for: clinical endpoints, R2 = 0.863 (internal R2 =
        0.999; external R2 = 0.823); total costs R2 = 0.979; total QALYs R2 = 0.951
•     External consistency of model’s predictions: compared with outputs from 3 economic
      evaluations that used CORE model in NG17 (long-acting insulin and insulin regimens,
      HbA1c thresholds, glucose monitoring strategies)
      – CT1DM model started with baseline characteristics, costs and treatment profiles
        consistent with NG17, and predicted outputs compared over relevant time horizons
      – High degree of linear correlation between predicted endpoints in CT1DM model and
        NG17; overall validation coefficient of determination R2 = 0.988
                                                                                                     28
     In what ways has the model been validated? In what ways has it not?
Validation of model: DECLARE-TIMI 58 trial
Background                           Stakeholder comments
Company:                             • It is not appropriate to model outcomes for a
• No long-term data on                  population with type 2 diabetes using
  dapagliflozin use in type 1           epidemiological evidence from type 1
  diabetes                              diabetes → Cardiff T1DM model would not
• Data from type 2 diabetes             be expected to accurately predict outcomes
  supports continuation of treatment    observed in DECLARE-TIMI 58 trial
  differences between dapagliflozin
  and placebo over 4 years           • Benefits detected in DECLARE-TIMI study
  (DECLARE-TIMI 58)                     are likely to apply to people with type 1
                                        diabetes at same dose of drug. But patients
                                        with type 1 diabetes may not be at such risk
                                        of cardiovascular events because they will
                                        be younger, less insulin resistant, and less
                                        obese.

 If DECLARE-TIMI, the large cardiovascular placebo-controlled safety
  trial, is appropriate for cardiovascular safety, then how well does the
  Cardiff T1DM model predict the cardiovascular results?
                                                                                 29
Areas of uncertainty
                         Issue                          Why issue is important                Impact on ICER
   Company used data from DCCT/EDIC       Unclear if lower                                If effectiveness of
   to develop risk equations → predict    magnitude of HbA1c                              dapagliflozin on
   relationship between changes in HbA1c changes seen in DEPICT                           reducing risk of some
   levels (among other risk factors) and  than in DCCT would                              long-term
   some long-term complications           translate to reduced risk                       complications are over-
   • Over 10 years, DCCT: intensive vs    of long-term                                    estimated in model →
      conventional → 10 mmol/mol (2%)     complications observed in                       likely worsen cost-
      reduction in HbA1c (Slide 7)        DCCT/EDIC                                       effectiveness estimates
   • DCCT relative changes are larger
      than in DEPICT (0.26% at 1 year)
   ERG not able to validate all parameter These are important                             Unknown
   inputs for 3 of the 4 sub-models:      components of the
   • Diabetic retinopathy and macular     simulation model
       oedema progression
   •   Diabetic nephropathy
   •   Diabetic neuropathy

    Do small reductions in HbA1c over a much shorter time period have a
     proportional effect?
                                                                                                                 30
DCCT, Diabetes Control and Complications Trial; EDIC, Epidemiology of Diabetes Interventions and Complications
Cardiff type 1 diabetes model: model run
                        • Retinopathy and macular oedema:
                          background diabetic retinopathy,
                          peripheral diabetic retinopathy,
                          severe vision loss, macular oedema
                        • Nephropathy: micro-albuminuria,
                          macro-albuminuria, end-stage renal
                          disease, dialysis, renal transplant
                        • Neuropathy: diabetic peripheral
                          neuropathy, ulcer and amputation
                          events (uncomplicated ulcer, deep
                          foot infection, foot ulcer and critical
                          ischaemia, minor amputation, major
                          amputation and fatal amputation)
                        • Cardiovascular disease: fatal and
                          non-fatal events
                        • Hypoglycaemia: symptomatic,
                          nocturnal and severe hypoglycaemia
                        • Depression not captured
                                                              31
Modelled baseline patient characteristics
based on DEPICT
Baseline characteristic           Mean (rounded)          Standard error
Current age (years)                                  45               0.56
Proportion female                                  0.54               0.02
Proportion smokers                                 0.06               0.01
Duration of diabetes (years)                       21.6               0.50
HbA1c                              8.4% [10.8 mmol/mol]        0.03 [0.03]
Total cholesterol (mmol/l)*                         4.8               0.04
HDL cholesterol (mmol/l)*                           1.6               0.02
Systolic blood pressure (mmHg)                      126               0.62
Diastolic blood pressure (mmHg)                      77               0.40
Weight (kg)                                        92.1               0.69
eGFR (mL/min/1.73m2) ‡                               87               0.73
*Converted from mg/dL

                                                                        32
Modelled clinical history at baseline
                                                                  Proportion of cohort
                                                                             Standard
                                                                  Mean
                                                                               error
Cardiovascular disease                                               0.23            0.02
Background retinopathy                                               0.33            0.02
Microalbuminuria                                                     0.11            0.01
Neuropathy                                                           0.26            0.02
Peripheral vascular disease                                          0.03            0.01
Lower extremity amputation minor (assumed)                           0.01               0
Hyperlipidaemia                                                      0.55            0.02
Hypertension                                                         0.46            0.02
Renin-angiotensin-aldosterone system inhibitor therapy               0.49            0.03
Proliferative retinopathy, severe vision loss, macular oedema,
macroalbuminuria, macroalbuminuria with impaired
glomerular filtration rate, dialysis, transplant, uncomplicated          0               0
foot ulcer, deep foot infection, foot ulcer and critical
ischaemia, major amputation

 Does the modelled population represent the type of patient who cannot
  otherwise achieve good glycaemic control in England?
 What is the appropriate population to model – DEPICT population or people in
  England with type 1 diabetes?                                                          33
Issue 4: Data in economic model
Background              Stakeholder responses                    Technical team
                                                                 consideration
Model should              • Model cannot accommodate both 24     Not possible to
incorporate all available   and 52 week data at same time        include both 24-
trial data at 24 weeks    • In company base case, 52-week        week and 52-
and 52 weeks                treatment effects applied            week data in
                          • Sensitivity analysis using 24-week   current model
                            treatment effects: improved ICER
                            estimate (£7,106 versus £9,175 in
                            base case)

                        ERG comments: disagree with
                        company’s response around inability to
                        implement suggested changes

 Is the model fit for purpose if it cannot accommodate all trial data?             34
Modelled treatment effects
Based on pooled DEPICT data            Mean (standard error)      Company base
at 52 weeks only                                                     case
                                    Dapagliflozin Standard of Life years Utility
                                                     care      gained    gained
HbA1c change (%)                     -0.26 (0.06)   0.08 (0.06)      0.17   0.23
Total cholesterol change (mmol/l)     No change     No change           -      -
HDL change (mmol/l)                   No change     No change           -      -
Systolic blood pressure change        No change     No change           -      -
(mmHg)
Diastolic blood pressure change       No change     No change           -      -
(mmHg)
Weight change (kg)                   -3.15 (0.26)   0.45 (0.28)        0    0.15

   In which risk equation does lower HbA1c increase length of life?
   How does the company model HbA1c over the long term?
   What complications does weight change affect, if any?
   Do the changes on life-years and utility have face validity?               35
Issue 5: Extrapolating treatment effects after DEPICT (1)
Background                              Stakeholder comments
If treatment effect is not maintained   • DEPICT showed no evidence of waning of
over time → health gains related to       treatment effect on weight loss at 52 weeks
dapagliflozin would be lower →            → continued and undiminished efficacy
worsen cost-effectiveness estimate
                                        • For HbA1c, clinical experts suggest a
                                          gradual increase after initial decrease, but
ERG comments                              HbA1c would not return to baseline
• Treatment effects on HbA1c wane • Sensible to account for any potential
  from 24 to 52 weeks                  waning effect of treatment; unlikely all
• Benefit at 52 weeks is small and     benefits will return to baseline for all
  may add little benefit in preventing patients. Biological efficacy of drug does not
  long-term complications except in    seem to change with time, suggesting any
  patients with a high risk of         treatment waning may reflect clinical issues
   cardiovascular disease

 How are treatment effects of dapagliflozin expected to change over
  time while on treatment?
                                                                                     36
Issue 5: Extrapolating treatment effects after DEPICT (2)
Company base case:
• 52-week pooled DEPICT effects on HbA1c and weight applied to 1st cycle →
  effects maintained while patients remain on treatment
• Stopping treatment: annual probability because of adverse events only in year 1
• Following stopping, risk factor levels return to baseline

            Effect
Scenario                         Loss of effect                     Treatment stopping
             data
 Base                                                         1-year probability because of
           52-week   Maintained while on treatment
 case                                                         adverse events only
   I                                                          1-year probability because of
           24-week   Maintained while on treatment
                                                              adverse events only
    II     52-week   HbA1c and weight effects lost over
   III     24-week   second year of dapagliflozin treatment   1-year probability because of
   IV      52-week   HbA1c effect lost over second year of    adverse events + all remaining
    V                dapagliflozin treatment; weight effect   patients stop at 2 years
           24-week
                     maintained

 Which scenario is most clinically plausible?                                                 37
Changes in HbA1c and weight

 Description of scenarios IV and V suggests that weight effect of dapagliflozin is
  maintained, different to graphs. What happens to weight effect?
                                                                                      38
 How do HbA1c and weight change in standard of care arm for each scenario?
Issue 6: Stopping treatment
Background
• Some people may stop treatment for any reason in year 1 and beyond
• Treatment should stop if there is no improvement in glycaemic control, based on a
  combination of change in HbA1c and hypoglycaemic events

Stakeholder comments
• Reasons to stop treatment: adverse events (diabetic ketoacidosis), renal decline,
   not effective (HbA1c, weight, hypoglycaemia, glycaemic variability), risk factors for
   adverse events (not compliant with ketone testing)
• No explicit stopping rule; decision left to physician and individual patient
• Stopping rules:
   – lack of response (HbA1c
Incidence of stopping treatment, adverse
events, DKA and hypoglycaemia – 52 weeks
                                         Mean (Standard error)
‘Indicated’ population
                                    Dapagliflozin   Standard of care
Annual probability
 - Stopping due to adverse events        0.06 (0.01)        0.05 (0.01)
 - Urinary tract infection               0.11 (0.02)        0.07 (0.02)
 - Genital tract infection               0.15 (0.02)        0.04 (0.01)
 - Diabetic ketoacidosis                 0.02 (0.01)        0.01 (0.01)
Annual number of events
 - Non-severe, symptomatic
                                        24.15 (0.30)       25.08 (0.31)
hypoglycaemia
 - Severe hypoglycaemia                  0.16 (0.02)        0.24 (0.03)

Overall trial population            Dapagliflozin        Placebo
                                    5mg (n=271)          (n=272)
% stopping for any reason                     14.4%                18%

                                                                       40
Stopping treatment in standard of care arm
Company base case:
• Stopping treatment due to adverse events in 1st year. After stopping,
  simulated risk factors (HbA1c and weight) revert to baseline levels

Rationale: to model both arms in a consistent manner based on
DEPICT data

 Given that all patients are having background insulin therapy, is it
  appropriate to model treatment stopping in patients in standard of care
  arm?
                                                                            41
Issue 7: Modelling adverse events
Background                     Stakeholder responses        Technical team
                                                            consideration
• DKA and severe               • Rarity of Fournier’s       Diabetic ketoacidosis
  hypoglycaemia carry an         gangrene precludes         and severe
  important risk of death and    including it in model in   hypoglycaemia carry
  this should be accounted for   any meaningful way         an important risk of
  in the model                                              death and should be
                               • Literature suggests that   accounted for in model
• Emerging serious adverse       associated deaths
  events should be included      related to severe
  in model                       hypoglycaemia and
                                 diabetic ketoacidosis
                                 are rare in UK

 The company did not include the possibility of death from DKA,
  hypoglycaemia or Fournier’s gangrene in its base case. Should these
  be included?                                                                   42
Issue 8: Utility approach
Background            Stakeholder responses                      Technical team
                                                                 consideration
Utility drives model  • DEPICT not powered to detect             While it may be
as most of difference   differences in quality of life, and      appropriate to use
in quality-adjusted     longer-term trials required to capture   utility values
life years (QALYs)      beneficial effect of HbA1c and           sourced from
are from differences    reducing weight on complications         literature for
in quality of life                                               modelling, technical
rather than length of • Substantial evidence base linking        team would still
life                    diabetes-related complications with      have preferred to
                        quality of life is more robust than      see scenario
                        using short-term trial data              analysis including
                                                                 trial EQ-5D data
                      ERG comments: satisfied with the
                      company’s overall approach

                                                                                    43
Issue 9: Disutilities
Background                    Stakeholder responses                           Technical team
                                                                              consideration
•  Same source                • Some utility decrements were not              • DKA impacts
   (Peasgood et al. 2016)       sourced from Peasgood et al. because            quality of life
   should be used for as        they lacked face validity                       and should be
   many of utility changes    • Sensitivity analysis demonstrates that          recognised in
   as possible                  use of non-significant event disutilities       modelling
• Company used Lee et           and/or disutility related with BMI increase   • Preferred if
   al. 2005 for utility         from Peasgood et al. do not alter cost-         company
   change per change in         effectiveness conclusions of base case          provided
   BMI → higher than            analysis                                        scenario
   Peasgood et al.            • Most appropriate application of utility         analysis using
• DKA have an important         decrements is additive                          approach in
  impact on quality of life                                                     NG17
  and should be in model      ERG comments:                                   • Unclear if
• Whether an additive or      • agrees with company’s approach to               ‘additive’
  multiplicative approach       estimating baseline utility                     approach is
  should be used for          • DKA disutility should be in base case           most
  disutilities depends on     • prefers to see 3rd approach to modelling        appropriate
  source of data                utilities from NG17 (minimum utility)

 What are the committee’s views?                                                              44
Source of utilities
Parameter (Disutilities assumed equal in all       Source
years)
Baseline utility T1DM and disutilities for         Peasgood et al. 2016 (UK study reporting
background diabetic retinopathy, uncomplicated     utility and disutility of T1DM complications
foot ulcer, minor and major amputation             from DAFNE)
Cardiovascular disease, proliferative diabetic
retinopathy, severe visual loss, macular oedema,
                                                   NG17; Beaudet et al. 2014 (type 2 diabetes)
dialysis, transplant, neuropathy, deep foot
infection, foot ulcer and critical ischaemia
Microalbuminuria                                   NG17
                                                   Thokala et al. 2014 (Sheffield Type 1
Macroalbuminuria
                                                   Diabetes Policy Model)
                                                   Lee et al. 2005 (UK study with mean BMI
Body mass index, per unit change
                                                   similar to DEPICT-1 [27.3 vs 28.5 kg/m2])
                                                   Currie et al. 2006 (multivariate model →
                                                   severity/frequency of hypoglycaemia related
Hypoglycaemia                                      to fear of hypoglycaemia and changes in
                                                   utility (EQ-5D) using UK population of 1,305
                                                   patients with T1DM and T2DM)
Diabetic ketoacidosis                              NG17
Urinary or genital tract infection                 Barry et al. 1998
                                                                                                  45
Utilities used in company model
                                                    Company base case: cumulative
               Parameter                   Mean                   events
                                                    Dapagliflozin     Standard of care
Baseline utility T1DM                       0.878                   -                  -
Cardiovascular disease (fatal/non-fatal)    0.075                723                723
Background diabetic retinopathy             0.027                443                466
Proliferative diabetic retinopathy          0.070                229                275
Severe vision loss                          0.074                 57                 64
Macular oedema                              0.040                362                410
Microalbuminuria                            0.000                319                359
Macroalbuminuria                            0.017                211                241
Dialysis                                    0.169                   -                  -
Transplant                                  0.023                  11                12
Neuropathy                                  0.084                441                480
Uncomplicated foot ulcer                    0.125                973               1022
Deep foot infection                         0.170                496                524
Foot ulcer and critical ischaemia           0.170                204                214
Minor amputation                            0.117                197                207
Major amputation                            0.117                 97                103
Body mass index, per unit change           ±0.008                   -                  -
Urinary or genital tract infection          0.003                   -                  -
                                                                                      46
Issue 10: Costs
Background                   Stakeholder responses                      Technical team
                                                                        consideration
• Average cost of insulin    • Average cost of insulin included         Effect of additional
  should include human         human insulin and insulin analogues      ketone monitoring
  insulin and not only       • To align with SmPC, reduction in         should be explored in
  insulin analogues            insulin dose related to dapagliflozin    model. Company’s
• Reduction in baseline        from DEPICT has not been included        scenario analysis in
  insulin dose at both 24      in model. Differences in total insulin   which ketone
  weeks and 52 weeks           dose are modelled between arms as        monitoring for people
  should be used to be         a result of different weight profiles    having dapagliflozin is
  consistent with efficacy   • Suggest daily ketone monitoring for      3 times more than
  data                         1st week and then ≥ weekly for 1st 3     that of people having
• Effect of additional         months, then ‘sick day’ rules            standard of care most
  ketone monitoring                                                     closely reflects
  should be explored in      ERG comments: agrees with                  clinical experts’
  model                      company’s approach to calculate insulin    comments
                             treatment costs

 What are the committee’s views?                                                             47
Costs of ketone monitoring (1)
Company base case:
• Patients monitor ketones during periods at risk → independent of treatment choice
  → cost of ketone monitoring balanced across treatment arms → no additional cost
  of ketone monitoring

Company modelled 3 scenarios:
• On starting dapagliflozin, 4 weeks of daily ketone monitoring (period corresponds to
  drop in total daily insulin dose observed in DEPICT trials) → additional one-off cost
  of £49.11 in dapagliflozin arm (3 packs)
• 1 pack of ketone testing strips in standard of care arm vs 2 packs for dapagliflozin
  arm
• 1 pack of ketone testing strips in standard of care arm vs 3 packs for dapagliflozin
  arm (based on proportion experiencing ketosis in dapagliflozin (3/286=1.0%) vs
  placebo (1/289=0.3%) arms in DEPICT trials)

                                                                                         48
Costs of ketone monitoring (2)
• Ketone monitoring checked during acute illness, stress or when glucose is
  elevated. DAFNE ‘sick day’ rules:
   – minor illness: no ketones (
Summary of company base case for
indicated population
• Company models effect of 52-week changes to HbA1c and weight
• Modelled treatment effects applied to risk factors in 1st cycle; after, risk
  factors assumed to remain constant while patients on treatment.
• Stopping dapagliflozin: due to adverse events in 1st year. After stopping,
  HbA1c and weight revert to baseline levels and company assumes
  hypoglycaemia, diabetic ketoacidosis and adverse events rates same as
  placebo
• Stopping standard of care: due to adverse events in 1st year. After
  stopping, HbA1c and weight revert to baseline levels
• Include treatment-related adverse events (urinary and genital tract
  infections), DKA and hypoglycaemia
• Baseline utility: estimated from Peasgood et al. and value reflected
  baseline characteristics of indicated population (0.865)
• Insulin cost: daily insulin cost per kg (£0.019/kg)
                                                                                 50
Company cost-effectiveness results
                                                                   ∆ cost (£) ∆ QALY    ICER
Company base case                                                     £3,575     0.39    £9,175
A. 24-week effects                                                    £3,002     0.42    £7,106
B. 52-week effects wane and treatment stops at 2 years                  £480     0.04   £11,011
C. Apply annual stopping rate for any reason to year 1 onwards        £1,348     0.18    £7,604
D. Apply disutility of 0.0091 to DKA                                  £3,575     0.39    £9,198
E. 4% DKA fatal                                                       £3,406     0.35    £9,618
F. 4.45% severe hypoglycaemia fatal                                   £5,709     0.71    £8,037
G. Utility estimates from Peasgood et al. (inc. BMI)                  £3,575     0.28   £12,620
H. Ketone monitoring I (one-off 3 packs dapagliflozin)                £3,625     0.39    £9,301
I. Ketone monitoring II (1 pack placebo vs 2 packs dapagliflozin)     £3,824     0.39    £9,813
J. Ketone monitoring III (1 pack placebo vs 3 packs dapagliflozin)    £4,070     0.39   £10,444
K. Use ITT results                                                    £3,627     0.37    £9,850
L. Annual probability of stopping in placebo arm = 0                  £3,564     0.40    £8,964
M. Multiplicative utility decrements                                  £3,575     0.27   £13,038
Cumulative application of multiple alterations listed above:
    - C, D, E, F, G, J, K & L                                         £2,026     0.31    £6,618
    - C, D, E, F, G, J, K, L & A                                      £2,171     0.29    £7,487
    - C, D, E, F, G, J, K, L & B                                        £836     0.09    £9,465
    - C, D, E, F, G, J, K, L & M                                      £2,026     0.29    £7,018
                                                                                           51
Other issues
Innovation
• Dapagliflozin is first adjunct to insulin licensed in UK, using a
  different mechanism of action to insulin
• It may not represent a step-change in management of type 1
  diabetes
Equalities
• No equalities issues identified in submissions or academic report

                                                                      52
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