Lead team presentation - Dapagliflozin, in combination with insulin, for treating type 1 diabetes - NICE
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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
End of Part 1 53
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