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Modeling payback from research into the efficacy of left-ventricular assist devices as destination therapy

Published online by Cambridge University Press:  01 April 2007

Alan J. Girling
Affiliation:
University of Birmingham
Guy Freeman
Affiliation:
University of Warwick
Jason P. Gordon
Affiliation:
University of Birmingham
Philip Poole-Wilson
Affiliation:
Imperial College London
David A. Scott
Affiliation:
University of Southampton and Oxford Outcomes Ltd.
Richard J. Lilford
Affiliation:
University of Birmingham

Abstract

Objectives: Ongoing developments in design have improved the outlook for left-ventricular assist device (LVAD) implantation as a therapy in end-stage heart failure. Nevertheless, early cost-effectiveness assessments, based on first-generation devices, have not been encouraging. Against this background, we set out (i) to examine the survival benefit that LVADs would need to generate before they could be deemed cost-effective; (ii) to provide insight into the likelihood that this benefit will be achieved; and (iii) from the perspective of a healthcare provider, to assess the value of discovering the actual size of this benefit by means of a Bayesian value of information analysis.

Methods: Cost-effectiveness assessments are made from the perspective of the healthcare provider, using current UK norms for the value of a quality-adjusted life-year (QALY). The treatment model is grounded in published analyses of the Randomized Evaluation of Mechanical Assistance for the Treatment of Congestive Heart Failure (REMATCH) trial of first-generation LVADs, translated into a UK cost setting. The prospects for patient survival with second-generation devices is assessed using Bayesian prior distributions, elicited from a group of leading clinicians in the field.

Results: Using established thresholds, cost-effectiveness probabilities under these priors are found to be low (∼.2 percent) for devices costing as much as £60,000. Sensitivity of the conclusions to both device cost and QALY valuation is examined.

Conclusions: In the event that the price of the device in use would reduce to £40,000, the value of the survival information can readily justify investment in further trials.

Type
GENERAL ESSAYS
Copyright
Copyright © Cambridge University Press 2007

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References

REFERENCES

1.Ades, AE, Lu, G, Claxton, K. Expected value of sample information calculations in medical decision modeling. Med Decis Making. 2004; 24: 207227.CrossRefGoogle ScholarPubMed
2.American Heart Association. Heart disease and stroke statistics – 2006 Update. Dallas, TX: American Heart Association; 2006.Google Scholar
3.Anon. Ventricular assist system: Summary of safety and effectiveness. Food and Drug Administration. 2002. Available at: http://www.fda.gov/ohrms/dockets/ac/02/briefing/3843b1_01_SSE_draft.pdf.Google Scholar
4.Anon. Special Report: Cost-effectiveness of left-ventricular assist devices as destination therapy for end-stage heart failure. Technol Eval Cent Asses Program Exec Summ. 2004; 19: 1.Google Scholar
5.Brown, A, Young, T, Meenan, B. Medical device prices follow the experimental curve. J Med Marketing. In press.Google Scholar
6.Claxton, K, Lacey, LF, Walker, SG. Selecting treatments: A decision theoretic approach. J R Stat Soc Ser A Stat Soc. 2000; 163: 211225.Google Scholar
7.Claxton, K. The irrelevance of inference: A decision-making approach to the stochastic evaluation of health care technologies. J Health Econ. 1999; 18: 341364.CrossRefGoogle Scholar
8.Claxton, K, Ginnelly, L, Sculpher, M et al. , A pilot study on the use of decision theory and value of information analysis as part of the NHS health technology assessment programme. Health Technol Assess. 2004; 8: 1103, iii.CrossRefGoogle ScholarPubMed
9.Claxton, K, Posnett, J. An economic approach to clinical trial design and research priority-setting. Health Econ. 1996; 5: 513524.Google Scholar
10.Claxton, K, Sculpher, M, Drummond, M. A rational framework for decision making by the National Institute For Clinical Excellence (NICE). Lancet. 2002; 360: 711715.CrossRefGoogle ScholarPubMed
11.Clegg, AJ, Scott, DA, Loveman, E et al. , The clinical and cost-effectiveness of left ventricular assist devices for end-stage heart failure: A systematic review and economic evaluation. Health Technol Assess. 2005; 9: 1148.CrossRefGoogle ScholarPubMed
12.Cleland, JG. Heart failure: A medical hydra. Lancet. 1998; 352 (Suppl 1): SI1SI2.CrossRefGoogle ScholarPubMed
13.Day, GS, Montgomery, DB. Diagnosing the experience curve. J Mark. 1983; 47: 4458.Google Scholar
14.Derose, J, Jarvik, R. Axial flow pumps. In: Goldstein, DJOz, MC, eds. Cardiac assist devices. New York: Futura Publishing; 359374. 2000:Google Scholar
15.Dominguez, LJ, Parrinello, G, Amato, P et al. , Trends of congestive heart failure epidemiology: Contrast with clinical trial results. Cardiologia. 1999; 44: 801808.Google ScholarPubMed
16.Evans, RW. Cardiac replacement: Estimation of need, demand and supply. In: Rose, EAStevenson, LW, eds. Management of end-stage heart disease. Philadelphia PA: Lippincott-Raven; 1998: 1324.Google Scholar
17.Federal Drug Administration. FDA approves heart assist pump for permanent use. FDA. 2002. Available at: http://www.fda. gov/bbs/topics/NEWS/2002/NEW00851.htm.Google Scholar
18.Felli, JC, Hazen, GB. Sensitivity analysis and the expected value of perfect information. Med Decis Making. 1998; 18: 95109.Google Scholar
19.Fenwick, E, Claxton, K, Sculpher, M et al. , Improving the efficiency and relevance of health technology assessment: The role of iterative decision analytic modelling. Report No. Discussion Paper 179. York: Centre for Health Economics; 2000.Google Scholar
20.Fisher, DC, Lake, KD, Reutzel, TJ et al. , Changes in health-related quality of life and depression in heart transplant recipients. J Heart Lung Transplant. 1995; 14: 373381.Google ScholarPubMed
21.Garthwaite, PH, Kadane, JB, O'Hagan, A. Statistical methods for eliciting probability distributions. J Am Stat Assoc. 2005; 100: 680700.CrossRefGoogle Scholar
22.Henderson, BD. The application and misapplication of the experience curve. J Bus Strategy. 1984; 4: 39.CrossRefGoogle Scholar
23.HM, Treasury. The green book: Appraisal and evaluation in central government. London: HMT; 2003.Google Scholar
24.Hoshi, H, Shinshi, T, Takatani, S. Third-generation blood pumps with mechanical noncontact magnetic bearings. Artif Organs. 2006; 30: 324338.Google Scholar
25.Hussey, JC, Bond, ZC, Collett, D et al. , 2004. on behalf of UK Transplant Cardiothoracic Advisory Group. Long-term patient survival for heart transplant recipients in the UK.Google Scholar
26.John, R. Donor management and selection for heart transplantation. Semin Thorac Cardiovasc Surg. 2004; 16: 364369.Google Scholar
27.Kirklin, JK, Holman, WL. Mechanical circulatory support therapy as a bridge to transplant or recovery (new advances). Curr Opin Cardiol. 2006; 21: 120126.CrossRefGoogle ScholarPubMed
28.Lietz, K, Miller, LW. Will left-ventricular assist device therapy replace heart transplantation in the foreseeable future? Curr Opin Cardiol. 2005; 20: 132137.Google Scholar
29.Lilford, RJ, Braunholtz, D. The statistical basis of public policy: A paradigm shift is overdue. BMJ. 1996; 313: 603607.CrossRefGoogle ScholarPubMed
30.Long, JW, Kfoury, AG, Slaughter, MS et al. , Long-term destination therapy with the HeartMate XVE left ventricular assist device. Improved outcomes since the REMATCH Study. Congest Heart Fail. 2005; 11: 133138.CrossRefGoogle ScholarPubMed
31.Miller, LW, Nelson, KE, Bostic, RR et al. , Hospital costs for left ventricular assist devices for destination therapy: Lower costs for implantation in the post-REMATCH era. J Heart Lung Transplant. 2006; 25: 778784.CrossRefGoogle ScholarPubMed
32.Morris, PA. Decision analysis expert use. Manage Sci. 1974; 20: 12331241.CrossRefGoogle Scholar
33.Moskowitz, AJ, Weinberg, AD, Oz, MC et al. , Quality of life with an implanted left ventricular assist device. Ann Thorac Surg. 1997; 64: 17641769.CrossRefGoogle ScholarPubMed
34.Noon, GP, Morley, D, Irwin, S et al. , The DeBakey ventricular assist device. In: Goldstein, DJ, Oz, MC eds. Cardiac assist devices. New York: Futura Publishing; 2000:375386.Google Scholar
35.Oz, MC, Gelijns, AC, Miller, L et al. , Left ventricular assist devices as permanent heart failure therapy: The price of progress. Ann Surg. 2003; 238: 577583.CrossRefGoogle Scholar
36.Park, SJ, Tector, A, Piccioni, W et al. , Left ventricular assist devices as destination therapy: A new look at survival. J Thorac Cardiovasc Surg. 2005; 129: 917.Google Scholar
37.Philbin, EF. Comprehensive multidisciplinary programs for the management of patients with congestive heart failure. J Gen Intern Med. 1999; 14: 130135.Google Scholar
38.Raiffa, H, Schlaifer, R. Applied statistical decision theory. Boston: Harvard University, Graduate School of Business Administration; 1961.Google Scholar
39.Rawlins, MD, Culyer, AJ. National Institute for Clinical Excellence and its value judgments. BMJ. 2004; 329: 224227.Google Scholar
40.Richards, PS, Nelson, KA, Frazier, OH et al. , Why referred potential heart donors aren't used. Tex Heart Inst J. 1993; 20: 218222.Google ScholarPubMed
41.Rose, EA, Moskowitz, AJ, Packer, M et al. , The REMATCH Trial: Rationale, design, and end points. randomized evaluation of mechanical assistance for the treatment of congestive heart failure. Ann Thorac Surg. 1999; 67: 723730.CrossRefGoogle ScholarPubMed
42.Rose, EA, Gelijns, AC, Moskowitz, AJ; and the Randomized Evaluation of Mechanical Assistance for the Treatment of Congestive Heart Failure (REMATCH) Study Group. Long-term use of a left ventricular assist device for end-stage heart failure. N Engl J Med. 2001; 345: 14351443.Google Scholar
43.Savage, LJ. The foundations of statistics. New York: John Wiley; 1954.Google Scholar
44.Siegenthaler, MP, Westaby, S, Frazier, OH et al. , Advanced heart failure: Feasibility study of long-term continuous axial flow pump support. Eur Heart J. 2005; 26: 10311038.Google Scholar
45.Spiegelhalter, DJ, Abrams, KR, Myles, JP. Bayesian approaches to clinical trials and health-care evaluation. New York: Wiley; 2004.Google Scholar
46.Stevenson, LW, Shekar, P. Ventricular assist devices for durable support. Circulation. 2005; 112: e111e115.CrossRefGoogle ScholarPubMed
47.Takatani, S. Progress of rotary blood pumps. Artif Organs. 2006; 30: 317321.CrossRefGoogle ScholarPubMed
48.Tsukui, H, Winowich, S, Stanford, E et al. , Does a rotary pump provide full cardiac decompression and circulatory support?— from clinical experiences of HeartMate II with severe congestive heart failure patients. ASAIO. 2005; 51: 2.CrossRefGoogle Scholar
49.Willan, AR, Pinto, EM. The value of information and optimal clinical trial design. Stat Med. 2005; 24: 17911806.CrossRefGoogle ScholarPubMed
50.Zaman, SN. Managing elderly patients with end-stage heart failure. CME J Geriatr Med. 2001; 3: 105109.Google Scholar