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In Search of Efficiency, Consistency, Fairness, and Impact in HTA: Modelling screening and treatment pathways for diabetic retinopathy Graham Scotland

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Page 1: Cadth 2015 a1 (gs090315)

In Search of Efficiency, Consistency, Fairness, and

Impact in HTA:Modelling screening and treatment pathways

for diabetic retinopathy

Graham Scotland

Page 2: Cadth 2015 a1 (gs090315)

Acknowledgments

• Dr Helen Looker (University of Dundee)• Professor Helen Colhoun (University of Dundee)• Professor Paul McKeigue (University of Edinburgh)• Professor Graham Leese (NHS Tayside)• Dr John Olson (NHS Grampian)• Dr Sam Philip (NHS Grampain)

The work being presented was funded by the Chief Scientist Office of the Scottish Government Health and Social Care Directorates. The author accepts full responsibility for this presentation.

I am not aware of any actual or potential conflicts of interest in relation to this presentation

Page 3: Cadth 2015 a1 (gs090315)

Background

• Diabetic retinopathy / maculopathy – a leading cause of visual loss and blindness – proliferative retinopathy– macular oedema

• Early signs can be identified on retinal photographs• Early identification and treatment can reduce the risk

of visual loss• Scottish National Screening Programme established

in 2006

Page 4: Cadth 2015 a1 (gs090315)

The Scottish diabetic retinopathy screening programme

• Established in 2006, based on annual / 6-monthly screening using digital retinal photography

• Eligible screening population: 247,017• Number screened 199,268 (8% increase on previous

year)• Prevalence of diabetes growing by 4% annually • ~4% of patients referable in one annual round of

screening

Page 5: Cadth 2015 a1 (gs090315)

Diabetic retiopathy treatment pathways

• Severe background retinopathy (R3)– Monitor for progression to R4, and treat DMO if present

• Proliferative retinopathy (R4)– Pan-retinal photocoagulation – Vitrectomy (for complications of PDR)

• Maculopathy (M2) / diabetic macular oedema (DMO)– Monitor– Focal laser treatment (prevent moderate visual loss)– Intravitreal Anti-VEGF injections (for symptomatic disease)

Page 6: Cadth 2015 a1 (gs090315)

Cost-effectiveness of risk stratified screening intervals

• Risk of progression to referable disease associated with: – type of diabetes, duration of diabetes, sex, current

observed grade, prior observed grade (Looker et al., 2013)*

• Large proportion of the current annual screening cohort have progression risk < 1%

• Scope exists to improve efficiency of screening by adopting risk stratified screening intervals

*Looker HC et al. Predicted impact of extending the screening interval for diabetic retinopathy: the Scottish Diabetic Retinopathy Screening programme. Diabetologia. 2013; 56(8):1716-25

Page 7: Cadth 2015 a1 (gs090315)

Progression risks by sex, current/prior grade and duration of Type 2 diabetes

Current / prior gradeDuration of

diabetes (years)

Men - probability of any referable disease (%)

Women - probability of any referable disease

(%)

1 year 2 years 1 year 2 years

Current grade no DR /

0 0.14 0.39 0.14 0.375 0.20 0.64 0.23 0.69

10 0.26 0.90 0.31 1.0415 0.31 1.13 0.38 1.38

Current grade no DR / Prior grade no DR

0 0.07 0.25 0.08 0.255 0.11 0.43 0.13 0.48

10 0.14 0.61 0.18 0.7415 0.18 0.78 0.24 1.00

Current grade no DR / Prior grade mild DR

0 0.42 0.96 0.32 0.685 0.82 2.09 0.76 1.80

10 1.24 3.42 1.31 3.4015 1.61 4.63 1.84 5.04

*Looker HC et al. Predicted impact of extending the screening interval for diabetic retinopathy: the Scottish Diabetic Retinopathy Screening programme. Diabetologia. 2013; 56(8):1716-25

Page 8: Cadth 2015 a1 (gs090315)

Aim

Model the clinical and cost-effectiveness of adopting extended intervals for groups of patients defined by selected clinical and demographic variables routinely available to screening programmes.

Page 9: Cadth 2015 a1 (gs090315)

Conceptual screening model

Starting cohort Non-referable

screening participants (age,

sex, type of diabetes,

duration of diabetes,

current / previous grade)

In screening programme Referred

M2

R3/R4

R3/R4 +DMO

DMO

No DMO

R3

R4

Monitor

Monitor/ Treat

Monitor

Treat/ Monitor

Reduced risk of visual loss

Non-referable

Referable

M2

R3/R4

R3/R4 +DMO

Treat/ Monitor

Visual loss

Page 10: Cadth 2015 a1 (gs090315)

Some preliminary results

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Risk distribution in the annual screening cohort

Risk DecileRange of estimated one year risks of

progressionMin Max

1 0.000731 0.0009582 0.001005 0.0011543 0.001168 0.00134 0.00137 0.001445 0.001503 0.0017336 0.001746 0.0022547 0.002255 0.0081968 0.008634 0.0262259 0.026246 0.051352

10 0.051378 0.360943

Total 0.000731 0.360943

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Cost-effectiveness of biennial versus annual screening by risk decile

Progression risk 0.8-2.6%

Current practice: annual for all

Biennial for all

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Annual versus biennial screening by current / prior screened grade

Biennial for all with no DR / annual for everyone else

Biennial if no DR and no history of DR / annual for everyone else

Current practice: annual for everyone

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Summary of preliminary findings

• Annual screening appears unlikely to be cost-effective against accepted cost per QALY thresholds, if the 1-year forward risk of progression < ~1%

• Individuals with no retinopathy and no history of retinopathy (55% of the annual screening cohort) have an estimated risk <1%

• Adoption of biennial intervals in this group could lead to a substantial resource savings for very small QALY losses

Page 15: Cadth 2015 a1 (gs090315)

Further research plans

• Sub group analysis (by type of diabetes)

• Further characterisation of uncertainty

• Interaction between screening approach and variation in downstream treatment pathways

• Identify more efficient screening/treatment pathway configurations

• Improve and update the model over time

Page 16: Cadth 2015 a1 (gs090315)

Discussion

Benefits• Pathway modelling provides a flexible framework to inform changes in clinical pathways

over time• Individual level simulation can deal with complexity in the pathway and heterogeneity in the

cohort – Requires individual patient data

Challenges• Time consuming to build/debug/analyse• Limited availability of causal evidence can lead to many uncertainties

– Difficult to pin down precise estimates of cost-effectiveness– Extensive sensitivity analysis required– Difficult to validate

• Not a substitute for collecting primary randomised data to inform decision problems at different points in the care pathway