Fractional flow reserve (FFR) versus angiography in guiding management to optimise outcomes in non-ST segment elevation myocardial infarction (FAMOUS-NSTEMI) developmental trial: cost-effectiveness using a mixed trial- and model-based methods

Julian Nam, Andrew Briggs, Jamie Layland, Keith G Oldroyd, Nick Curzen, Arvind Sood, Kanarath Balachandran, Raj Das, Shahid Junejo, Hany Eteiba, Mark C Petrie, Mitchell Lindsay, Stuart Watkins, Simon Corbett, Brian O'Rourke, Anna O'Donnell, Andrew Stewart, Andrew Hannah, Alex McConnachie, Robert Henderson, Colin Berry, Julian Nam, Andrew Briggs, Jamie Layland, Keith G Oldroyd, Nick Curzen, Arvind Sood, Kanarath Balachandran, Raj Das, Shahid Junejo, Hany Eteiba, Mark C Petrie, Mitchell Lindsay, Stuart Watkins, Simon Corbett, Brian O'Rourke, Anna O'Donnell, Andrew Stewart, Andrew Hannah, Alex McConnachie, Robert Henderson, Colin Berry

Abstract

Background: In the Fractional flow reserve (FFR) versus angiography in guiding management to optimise outcomes in non-ST elevation myocardial infarction (FAMOUS) clinical trial, FFR was shown to significantly reduce coronary revascularisation, compared to visual interpretation of standard coronary angiography without FFR. We estimated the cost-effectiveness from a UK National Health Service perspective, based on the results of FAMOUS.

Methods: A mixed trial- and model-based approach using decision and statistical modelling was used. Within-trial (1-year) costs and QALYs were assembled at the individual level and then modelled on subsequent management strategy [coronary artery bypass graft (CABG), percutaneous coronary intervention (PCI) or medical therapy (MT)] and major adverse coronary events (death, MI, stroke and revascularisation). One-year resource uses included: material, hospitalisation, medical, health professional service use and events. Utilities were derived from individual EQ5D responses. Unit costs were derived from the literature. Outcomes were extended to a lifetime on the basis of MACE during the 1st year. Costs and QALYs were modelled using generalized linear models whilst MACE was modelled using logistic regression. The analysis adopted a payer perspective. Costs and outcomes were discounted at 3.5 %.

Results: Costs were related to the subsequent management strategy and MACE whilst QALYs were not. FFR led to a modest cost increase, albeit an imprecise increase, over both the trial [£112 (-£129 to £357)] and lifetime horizons [£133 (-£199 to £499)]. FFR led to a small, albeit imprecise, increase in QALYs over both the trial [0.02 (-0.03 to 0.06)] and lifetime horizons [0.03 (-0.21 to 0.28)]. The mean ICER was £7516/QALY and £4290/QALY over the trial and lifetime horizons, respectively. Decision remained high; FFR had 64 and 59 % probability of cost-effectiveness over trial and lifetime horizons, respectively.

Conclusions: FFR was cost-effective at the mean, albeit with considerable decision uncertainty. Uncertainty can be reduced with more information on long-term health events.

Figures

Fig. 1
Fig. 1
Model structure showing subsequent management strategy and MACE. Model structure is the same for angiography
Fig. 2
Fig. 2
Cost-effectiveness plane displaying incremental costs vs. incremental QALYs
Fig. 3
Fig. 3
Cost-effectiveness acceptability curves for the trial and lifetime time horizons

References

    1. Hamm CW, Bassand J-P, Agewall S, Bax J, Boersma E, Bueno H, et al. ESC Guidelines for the management of acute coronary syndromes in patients presenting without persistent ST-segment elevation. The task force for the management of acute coronary syndromes (ACS) in patients presenting without persistent ST-segment elevation of the European Society of Cardiology (ESC) Eur Heart J. 2011;32(23):2999–3054. doi: 10.1093/eurheartj/ehr236.
    1. National Institute for Health and Care Excellence . Unstable angina and NSTEMI: the early management of unstable angina and non-ST-segment-elevation myocardial infarction [CG94] [Online] London: National Institute of Health and Care Excellence; 2010.
    1. Wijns W, Kolh P, Danchin N, Di Mario C, Falk V, Folliguet T, et al. Guidelines on myocardial revascularization The task force on myocardial revascularization of the European Society of Cardiology (ESC) and the European Association for Cardio-Thoracic Surgery (EACTS) Eur Heart J. 2010;31(20):2501–2555. doi: 10.1093/eurheartj/ehq277.
    1. Botman KJ, Pijls NH, Bech JW, Aarnoudse W, Peels K, van Straten B, et al. Percutaneous coronary intervention or bypass surgery in multivessel disease? A tailored approach based on coronary pressure measurement. Catheter Cardiovasc Interv. 2004;63(2):184–191. doi: 10.1002/ccd.20175.
    1. Selby JV, Fireman BH, Lundstrom RJ, Swain BE, Truman AF, Wong CC, et al. Variation among hospitals in coronary-angiography practices and outcomes after myocardial infarction in a large health maintenance organization. N Engl J Med. 1996;335(25):1888–1896. doi: 10.1056/NEJM199612193352506.
    1. White CW, Wright CB, Doty DB, Hiratza LF, Eastham CL, Harrison DG, et al. Does visual interpretation of the coronary arteriogram predict the physiologic importance of a coronary stenosis? N Engl J Med. 1984;310(13):819–824. doi: 10.1056/NEJM198403293101304.
    1. Pijls NH, de Bruyne B, Peels K, van der Voort PH, Bonnier HJ, Bartunek J, et al. Measurement of fractional flow reserve to assess the functional severity of coronary-artery stenoses. N Engl J Med. 1996;334(26):1703–1708. doi: 10.1056/NEJM199606273342604.
    1. Pijls NH, van Schaardenburgh P, Manoharan G, Boersma E, Bech JW, van’t Veer M, et al. Percutaneous coronary intervention of functionally nonsignificant stenosis5-year follow-up of the DEFER study. J Am Coll Cardiol. 2007;49(21):2105–2111. doi: 10.1016/j.jacc.2007.01.087.
    1. De Bruyne B, Pijls NH, Bartunek J, Kulecki K, Bech J-W, De Winter H, et al. Fractional flow reserve in patients with prior myocardial infarction. Circulation. 2001;104(2):157–162. doi: 10.1161/01.CIR.104.2.157.
    1. Curzen N, Rana O, Golledge P, Zaman A, Oldroyd K, Hanratty C, et al. Does routine pressure wire assessment influence management strategy at coronary angiography for diagnosis of chest pain? The RIPCORD Study. Circ Cardiovasc Interv. 2014;7(2):248–255. doi: 10.1161/CIRCINTERVENTIONS.113.000978.
    1. Tonino PA, De Bruyne B, Pijls NH, Siebert U, Ikeno F, vant Veer M, et al. Fractional flow reserve versus angiography for guiding percutaneous coronary intervention. N Engl J Med. 2009;360(3):213–224. doi: 10.1056/NEJMoa0807611.
    1. Leesar MA, Abdul-Baki T, Akkus NI, Sharma A, Kannan T, Bolli R. Use of fractional flow reserve versus stress perfusion scintigraphy after unstable angina: effect on duration of hospitalization, cost, procedural characteristics, and clinical outcome. J Am Coll Cardiol. 2003;41(7):1115–1121. doi: 10.1016/S0735-1097(03)00057-3.
    1. Sels JWE, Tonino PA, Siebert U, Fearon WF, van’t Veer M, de Bruyne B, et al. Fractional flow reserve in unstable angina and non–ST-segment elevation myocardial infarction. experience from the FAME (Fractional flow reserve versus Angiography for Multivessel Evaluation) study. JACC Cardiovasc Interv. 2011;4(11):1183–1189. doi: 10.1016/j.jcin.2011.08.008.
    1. Layland J, Oldroyd KG, Curzen N, Sood A, Balachandran K, Das R, et al. Fractional flow reserve vs. angiography in guiding management to optimize outcomes in non-ST-segment elevation myocardial infarction: the British Heart Foundation FAMOUS–NSTEMI randomized trial. Eur Heart J. 2015;36(2):100–111. doi: 10.1093/eurheartj/ehu338.
    1. Bossuyt PM, Irwig L, Craig J, Glasziou P. Comparative accuracy: assessing new tests against existing diagnostic pathways. BMJ. 2006;332(7549):1089–1092. doi: 10.1136/bmj.332.7549.1089.
    1. di Ruffano LF, Hyde CJ, McCaffery KJ, Bossuyt PM, Deeks JJ. Assessing the value of diagnostic tests: a framework for designing and evaluating trials. Bmj. 2012;344:e686. doi: 10.1136/bmj.e686.
    1. Schünemann HJ, Oxman AD, Brozek J, Glasziou P, Jaeschke R, Vist GE, et al. Grading quality of evidence and strength of recommendations for diagnostic tests and strategies. BMJ. 2008;336(7653):1106–1110. doi: 10.1136/.
    1. Buxton MJ, Drummond MF, Van Hout BA, Prince RL, Sheldon TA, Szucs T, et al. Modelling in ecomomic evaluation: an unavoidable fact of life. Health Econ. 1997;6(3):217–227. doi: 10.1002/(SICI)1099-1050(199705)6:3<217::AID-HEC267>;2-W.
    1. Roberts M, Russell LB, Paltiel AD, Chambers M, McEwan P, Krahn M. Conceptualizing a model a report of the ISPOR-SMDM modeling good research practices task force–2. Med Decis Mak. 2012;32(5):678–689. doi: 10.1177/0272989X12454941.
    1. Glick HA, Doshi JA, Sonnad SS, Polsky D. Economic evaluation in clinical trials. Oxford: Oxford University Press; 2014.
    1. Kent S, Briggs A, Eckermann S, Berry C. Are value of information methods ready for prime time? An application to alternative treatment strategies for NSTEMI patients. Int J Technol Assess Health Care. 2013;29(04):435–442. doi: 10.1017/S0266462313000433.
    1. Information Services Division Scotland . Theatre—direct cost per hour, by specialty [R142X] [Online] Scotland: ISD Scotland; 2012.
    1. Dolan P. Modeling valuations for EuroQol health states. Med Care. 1997;35(11):1095–1108. doi: 10.1097/00005650-199711000-00002.
    1. Palmer S, Sculpher M, Philips Z, Robinson M, Ginnelly L, Bakhai A, et al. Management of non-ST-elevation acute coronary syndromes: how cost-effective are glycoprotein IIb/IIIa antagonists in the UK National Health Service? Int J Cardiol. 2005;100(2):229–240. doi: 10.1016/j.ijcard.2004.08.042.
    1. Manning WG, Mullahy J. Estimating log models: to transform or not to transform? J Health Econ. 2001;20(4):461–494. doi: 10.1016/S0167-6296(01)00086-8.
    1. Manning WG, Basu A, Mullahy J. Generalized modeling approaches to risk adjustment of skewed outcomes data. J Health Econ. 2005;24(3):465–488. doi: 10.1016/j.jhealeco.2004.09.011.
    1. Barber J, Thompson S. Multiple regression of cost data: use of generalised linear models. J Health Serv Res Policy. 2004;9(4):197–204. doi: 10.1258/1355819042250249.
    1. Van Buuren S, Groothuis-Oudshoorn K. MICE: Multivariate imputation by chained equations in R. J Stat Softw. 2011;45(3):1–67. doi: 10.18637/jss.v045.i03.
    1. R Core Team . R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2015.

Source: PubMed

Sponsorer og samarbeidspartnere

Medisinsk tilstand

Legemiddelintervensjoner

3
Abonnere