AHSN Business Case User Guide: Improving AF Identification and Optimising Management to Prevent AF-Related Stroke Version: 8 March 2017

Introduction This business case template has been created so that each organisation can assess the potential for improvements in the identification and management of Atrial Fibrillation (AF) in four key areas: Diagnosis of AF: Detection Gap Identification of those at high risk of stroke (CHA2DS2-VASc): Protection Gap 1 (no risk assessment) Initiation of anticoagulant therapy in line with national guidelines: Protection Gap 2 (eligible and untreated) Maintenance of adequate anticoagulation: Perfection Gap User modifications: The AHSN or CCG can be selected on Sheet 2: Detection Gap

The business model cost analysis Using either the default numbers provided by the model or the numbers provided by the user, the business model evaluates the identified management gaps and calculates: The clinical impact (number of preventable strokes / risk of bleed) potentially achieved by addressing each management gap The total potential cost investment to address each gap The overall estimated cost impact for addressing each opportunity over a one, two and three year period This generates an automatic business case report that provides a summary of the cost impact and cost savings for each management gap User modifications: The user can modify the planned interventions and their expected impact on the number of people with AF treated with an anticoagulant on Sheets 7 and 8: Planned Interventions

Data sources: Clinical and cost inputs The business case model has been created using publically available primary practice data, as well as published data inputs: Latest Quality Outcomes Framework (QOF) AF indicators (2015/2016 datasets) using on CHA2DS2-VASc score indicators are the principal AF management comparators (see later slide for list of QOF AF indicators) The predicted AF population is calculated using the age-sex specific data from the National Cardiovascular Intelligence Network (NCVIN) predictive model, using aggregated July 2016 GP practice demographics (previous calculation were based on 2013 data) Current AF-related stroke rates are as reported by Sentinel Stroke National Audit Programme (SSNAP) (April 2015-March 2016 datasets) Data used in the TA256 and TA249 technology appraisals and NICE CG180 costing template have been used as the default clinical (risk of stroke and risk of bleed) and cost inputs in the cost impact analysis. More recent meta-analysis (such as Ruff et al, 2014) or local data can be substituted in the model Anticoagulant treatment data was taken from from the NHS Business Services Authority Medicines Optimisation CCG Dashboard (April 2016-June 2016)

Data sources: Clinical inputs User modifications: The clinical assumptions can be modified for the local situation on Sheet 9: Clinical inputs Clinical input variables: Annual risk of stroke: 1) untreated, 2) treated with warfarin, 3) treated with NOAC Annual risk of major bleed: 1) untreated, 2) treated with warfarin, 3) treated with NOAC The assumptions included in the model are based on the AF data used in the TA256 and TA249 technology appraisals and NICE CG180 costing template.

Data sources: Cost inputs User modifications: The cost assumptions can be modified for the local situation on Sheet 11: Cost inputs Cost input variable: Year 1 per patient cost of stroke care Yr 2+ per patient cost of stroke care (Default values taken from Youman et al (2003) used in NICE CG180 /TA256) Cost of a major bleed (Default values as used in NICE CG180 and TA256) Cost of screening for AF (per patient screened) (Default values are from Hobbs et al (2005) using the mid-point value and applying the HCHS inflator) Cost of treatment (Warfarin and NOAC) Warfarin (drug cost + screening) based on NICE CG180 and TA256 NOAC price based on 2016 NHS prices, weighted by use

Data sources: Planned interventions User modifications: At the end of the Excel work sheet the user can input free text to describe their planned current interventions for each management gap. This information will then be used to help populate the business case template. (They can also add their current activities in a separate sheet if they would like to include this in the report for comparison) The anticipated impact and timelines for the planned interventions can be changed on Sheets 7 and 8: Planned Interventions This enables the user to modify the potential level of ‘correction’ that could be anticipated for each gap: A default correction level of 50% is set for the Detection Gap A default correction level of 90% is set for Protection Gap 1 A default correction level of 80% is set for the Protection Gap 2 and the Perfection Gap

Data sources: Anticoagulation treatment strategy User modifications: The default cost analysis uses the current NOAC and warfarin prescribing patterns in the calculations. This ratio can be modified on Sheet 12: Treatment strategy. The ratio can be modified for years 1, 2 and 3 Projected % NOAC use can be changed for both patients inadequately anti-coagulated and new patients NOTE: This will not model the impact of switching therapy for existing stable patients – for this purpose you should use the NICE costing model accompanying CG180. If you would like to do a comparison between different treatment approaches we recommend running the model each time and creating a separate report for each alternative approach. The two (or more) reports can then be combined and reported in Word format, as required.

Detection Gap Summary Total population in 2014-15 and 2015-16 have been obtained by aggregating individual GP practice list sizes for each organisation. QOF AF001 register 2015/2016 datasets are used for patients on the AF register in each GP practice and compared with 2014/2015 datasets The predicted AF population is calculated using the age-sex specific data from the NCVIN predictive model, using July 2016 GP practice demographics Data on AF-related stroke are as reported by the April 2015-March 2016 Sentinel Stroke National Audit Programme (SSNAP) reporting

Detection Gap Summary: User modifications No data modification are permitted on the Detection Gap calculations but it is recommended that the predicted AF population data are reviewed in comparison with the ethnic mix and underlying disease profile when applied to specific regional populations populations compared with the NCVIN benchmark population. The GP practice lists show that the proportion of the population >65 years varies from <10% in inner cities up to >25% in some rural and coastal areas. These numbers should therefore be seen as indicative rather than absolute. To adjust the Detection Gap the potential impact (% increase in number of patients likely treated) can be altered on Sheet 7: Planned Interventions

Protection Gap 1 (no risk assessment) Summary QOF AF006 2015/2016 denominator and exceptions are combined to provide the number of patients requiring a risk assessment This is compared with the QOF AF006 2015/2016 target achieved data set to calculate the number of missing risk assessments The number of potential patients eligible for treatment is then calculated using the assumption from NICE CG180 that 84.2% of patients have a CHA2DS2-VASc score >2 User modifications: No user modifications permitted

Protection Gap 2 (eligible and untreated) Summary QOF AF007 2015/2016 denominator and exceptions are combined to provide the number of patients currently considered eligible for anticoagulation This number is compared with the QOF AF007 2015/2016 target achieved data set to calculate number of patients eligible but untreated with anticoagulant User modifications: No user modifications permitted

Perfection Gap Summary QOF AF007 2015/2016 target achieved data set provides the number of patients currently treated An analysis of practice level data prescribing data provides the anticipated treatment levels with NOAC or warfarin (assumption: 90% NOACs are for AF; remaining patients are assumed to receive warfarin)* Assumption data are inputted for the current effectiveness of anticoagulation (Perfection gap) *See the following slide for further detail of NOAC and warfarin treatment calculations User modifications: The user can modify the assumptions around the current effectiveness for NOAC and warfarin treatment based on regional knowledge in order to modify the anticipated Perfection gap.

Anticoagulant calculations In relation to anticoagulant therapy, the proportion of anticoagulated patients on warfarin vs NOACs were identified from the NHS Business Services Authority Medicines Optimisation CCG Dashboard, which is based on an analysis of prescribing for the period April 2016-June 2016. The proportion of patients in each CCG on NOAC therapy identified from this analysis were applied to the number of anticoagulated patient, derived from QOF AF007, to arrive at the number of patients receiving either NOAC or warfarin.

QOF AF indicators QOF target AF001: Each GP practice maintains a register of patients with AF QOF target AF006: The percentage of patients with AF in whom stroke risk has been assessed using the CHA2DS2-VASc score risk stratification scoring system in the preceding 12 months (excluding those patients with a previous CHADS2 or CHA2DS2-VASc score of 2 or more) QOF target AF007: In those patients with AF with a record of a CHA2DS2-VASc score of 2 or more, the percentage of patients who are currently treated with anti-coagulation drug therapy

Data references QOF data: NHS Digital. Quality and Outcomes Framework (QOF) 2015-16. Prevalence, achievements and exceptions, cardiovascular group at GP practice level. Available at: http://www.content.digital.nhs.uk/catalogue/PUB22266/qof-1516-prev-ach-exc-cv-prac-v2.xlsx Accessed 16/12/16 GP practice population and demographic data: NHS Digital. Numbers of patients registered at a GP practice. 1/7/2016 GP registered patients. Available at: http://www.hscic.gov.uk/catalogue/PUB21150/gp-reg-patients-prac-quin-age.csv Accessed 16/12/16 Prescribing data: NHS Digital. GP Practice prescribing presentation-level data (July 2015-June 2016). Available at: https://data.gov.uk/dataset/prescribing-by-gp-practice-presentation-level (1 file per month). Accessed 16/12/16 NCVIN prevalence rates: Public Health England: National Cardiovascular Intelligence Network. Atrial fibrillation prevalence estimates. Available at: http://www.yhpho.org.uk//resource/view.aspx?RID=207905 Accessed 16/12/16 Public Health England: National Cardiovascular Intelligence Network. Atrial fibrillation prevalence estimates. Technical document for sub-national English atrial fibrillation prevalence estimates: application of age-sex rates in a Swedish region to the English population. Available at: http://www.yhpho.org.uk//resource/view.aspx?RID=207904 Accessed 16/12/16

Key References National Institute for Health and Care Excellence. Atrial fibrillation: management - Clinical guideline (CG180) - costing template. June 2014. Available at: https://www.nice.org.uk/guidance/cg180/resources/costing-template-243732205 (Accessed December 2016) National Institute for Health and Care Excellence. Rivaroxaban for the prevention of stroke and systemic embolism in people with atrial fibrillation (TA256) - costing template. March 2012. Available at: https://www.nice.org.uk/guidance/ta256/resources/costing-template-425082781 (Accessed December 2016) National Institute for Health and Care Excellence. Dabigatran etexilate for the prevention of stroke and systemic embolism in atrial fibrillation (TA249) - costing template. March 2012. Available from: https://www.nice.org.uk/guidance/ta249/resources/costing-template-424946701 (Accessed November 2016) Norberg J, Backstrom S, Jansson J-H, Johansson L. Estimating the prevalence of atrial fibrillation in a general population using validated electronic health data. Clin Epidemiol. 2013: 5: 475-481 Ruff CT, Giugliano RP, Braunwald E et al. Comparison of the efficacy and safety of new oral anticoagulants with warfarin inpatients with atrial fibrillation: a meta-analysis of randomised trials. Lancet 2014;383:955-62 Statistics Sweden. Foreign born persons by region. Available at: http://www.statistikdatabasen.scb.se/pxweb/en/ssd/START__BE__BE0101__BE0101E/UtrikesFoddaTotNK/?rxid=b2913b8e-bdfa-4451-ba93-2d6f9b77d03c Accessed 16/12/16 Youman P, Wilson K, Harraf F, Kalra L. The economic burden of stroke in the United Kingdom. Pharmacoeconomics 2003;21 Suppl1:43-50