A cost-effectiveness analysis of hypertrophic cardiomyopathy sudden cardiac death risk algorithms for implantable cardioverter defibrillator decision-making
Nathan GreenYang ChenConstantinos O’MahonyPerry ElliottRoberto Barriales‐VillaLorenzo MonserratAristides AnastasakisElena BiaginiJuan R. GimenoGiuseppe LimongelliMenelaos PavlouRumana Z. Omar
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Abstract Aims To conduct a contemporary cost-effectiveness analysis examining the use of implantable cardioverter defibrillators (ICDs) for primary prevention in patients with hypertrophic cardiomyopathy (HCM). Methods A discrete-time Markov model was used to determine the cost-effectiveness of different ICD decision-making rules for implantation. Several scenarios were investigated, including the reference scenario of implantation rates according to observed real-world practice. A 12-year time horizon with an annual cycle length was used. Transition probabilities used in the model were obtained using Bayesian analysis. The study has been reported according to the Consolidated Health Economic Evaluation Reporting Standards checklist. Results Using a 5-year SCD risk threshold of 6% was cheaper than current practice and has marginally better total quality adjusted life years (QALYs). This is the most cost-effective of the options considered, with an incremental cost-effectiveness ratio of £834 per QALY. Sensitivity analyses highlighted that this decision is largely driven by what health-related quality of life (HRQL) is attributed to ICD patients and time horizon. Conclusion We present a timely new perspective on HCM-ICD cost-effectiveness, using methods reflecting real-world practice. While we have shown that a 6% 5-year SCD risk cut-off provides the best cohort stratification to aid ICD decision-making, this will also be influenced by the particular values of costs and HRQL for subgroups or at a local level. The process of explicitly demonstrating the main factors, which drive conclusions from such an analysis will help to inform shared decision-making in this complex area for all stakeholders concerned.Economic evaluation in the form of reports of cost-effectiveness of the treatment and prevention of disease has only recently found widespread application in the visual sciences. While economic evaluation takes a number of forms: cost-minimization analysis, cost-benefit analysis, and cost-effectiveness analysis--it is the latter that is seen most often in the evaluation of vision-related health programs. Cost-effectiveness analysis is in particular seen most commonly in its very particular form of cost-utility analysis. Decision analysis is the analytic method most commonly used to perform cost-effectiveness analysis. In decision analysis, the expected cost and effectiveness of a health program are estimated in a rigorous fashion. In this report, we take the reader through the process of decision analysis including building the tree; populating the model with parameters for risk, cost and benefit; estimating expected cost and benefit; and deterministic and probabilistic sensitivity analysis. Examples employed include prominent studies of the cost-effectiveness of photodynamic therapy for treatment of neovascular macular degeneration and the treatment ocular hypertension to prevent glaucoma.
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1. Introduction 2. Overview of the Methods 3. Planning the Study 4. Information Retrieval 5. Data Collection 6. Advanced Issues in Meta-Analysis 7. Statistical Methods in Meta-Analysis 8. Other Statistical Issues in Meta-Analysis 9. Complex Decision Problems 10. Estimating Probabilities 11. Utility Analysis 12. Advanced Cost Effectiveness Analysis 13. Utility and Cost-Utility Analysis 14. Exploring Heterogeneity 15. Sensitivity Analysis 16. Reporting Results 17. Limitations
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AbstractCost-utility analysis is a form of cost-effectiveness analysis in which outcomes are adjusted for quality and quantity of life. This type of analysis is used widely in Europe and is being used increasingly in the United States. This article provides an overview of cost utility analysis and quality adjusted life years, a commonly used effectiveness measure in CUA when comparing two or more treatments or interventions.Key Words: Cost-utilityanalysiscost-effectivenessquality adjusted life yearsQALYCUA Additional informationNotes on contributorsVijay N. JoishCherokee Layson-Wolf, PharmD, is Assistant Professor at the University of Maryland School of Pharmacy. At the time that this review was conducted, she was a Community Care Pharmacy Practice Resident at Virginia Commonwealth University.Perry G. Fine, MD, is Professor of Anesthesiology, School of Medicine and Associate Medical Director, Pain Management Center at the University of Utah Health Sciences Center, Salt Lake City; and National Medical Director, VistaCare, based in Scottsdale, AZ. This commentary is based on an article in Dr. Fine's VistaCare Palliative Medicine Monitor.Jonathan R. Gavrin, MD, is the Internet editor for the Journal. He is Associate Professor of Anesthesiology and Adjunct Associate Professor of Medicine at the University of Washington School of Medicine; Associate Member, Fred Hutchinson Cancer Research Center; and Associate Director for Clinical Anesthesia Services, Harborview Medical Center.Philip J. Wiffen, is the Regional Pharmaceutical and Prescribing Adviser, Anglia & Oxford Region of the National Health Service Executive, a member of the Oxford Regional Pain Relief Unit and Coordinating Editor of the Cochrane Collaboration Pain Palliative and Supportive Care Collaborative Review Group.Philip J. Wiffen, BPharm, MRPharmS, MFPHM (Hon) is Regional Pharmaceutical and Prescribing Adviser, Anglia & Oxford Region of the National Health Service Executive, a member of the Pain Relief Unit, Churchill Hospital, and Coordinating Editor, Cochrane Collaboration Pain Palliative and Supportive Care Collaborative Review Group.Howard A. Heit, practices pain medicine and addiction medicine in Fairfax, Virginia, and is Assistant Clinical Professor of Medicine at Georgetown University, Washington, DC. Dr. Heit was a member of the Liaison Committee on Pain and Addiction.Last Acts is a Robert Wood Johnson Foundation funded campaign to improve end-of-life care by a coalition of professional and consumer organizations. This coalition works to improve palliative care, focused on managing pain and making life better for individuals and families facing death. Last Acts envisions a world in which dying people and their loved ones receive excellent care and are honored and supported by their community.S. R. Ghooi, MBBS, is a Medical Consultant in New Delhi.Gustavo G. De Simone is Medical Oncologist (with Diploma in Palliative Medicine) and Pallium Latinoamérica Association Medical Director and Chief, Section on Palliative Care, Hospital Bonorino Udaondo, Bonpland 2287 (1425) Ciudad de Buenos Aires, Argentina ( pallium@elsitio.net.The Reverend John S. Lunn, RN, MDiv, is Palliative Care and Hospice Consultant, Global Ministries for the Disciples of Christ and United Church of Christ, and former President, Board of Directors, Kauai Hospice, Hawaii.Jan Stjernswärd, MD, PhD, FRCP (Edin), is Former Chief, Cancer and Palliative Care, World Health Organization, Geneva, Switzerland, and International Director of the Oxford University International Centre for Palliative Care and World Health Organization Collaborating Centre for Palliative Care, Churchill Hospital, Oxford, UK. He also serves on the Steering Committee of the Diana Palliative Care Initiative, Diana Princess of Wales Memorial Fund in the UK, as a Consultant to the Open Society Institute in New York, and continues to serve as a World Health Organization advisor.Barbara L. Kass-Bartelmes, MPH, CHES, and Ronda Hughes, PhD, wrote this report for the Agency for Health Care Research and Quality (AHRQ) of the U.S. Public Health Service.Robert J. Adams, PharmD, was at the time of this study Primary Care Resident, Pharmacy Service, Carl T. Hayden Veterans Affairs Medical Center, Phoenix, AZ.Stephen P. Lordon, MD, was at the time of this study, Attending Physician in the Pain Management Center, University Hospitals and Clinics, and Clinical Assistant Professor of Anesthesiology, School of Medicine;Arthur G. Lipman, PharmD, is Professor of Pharmacotherapy, College of Pharmacy, Adjunct Professor of Anesthesiology, School of Medicine and Director of Clinical Pharmacology, Pain Management Center, University Hospitals and Clinics; University of Utah Health Sciences Center.Christopher Stock, PharmD, is Clinical Pharmacist, Substance Abuse Treatment Programs Pharmacist at the George E. Wahlen Veterans Affairs Salt Lake City Health Care System, and Clinical Associate Professor, College of Pharmacy, University of UtahPerry G. Fine, MD, is Professor of Anesthesiology, School of Medicine, and Attending Physician at the Pain Management Center, University of Utah Health Sciences Center, Salt Lake City, UT; and Senior Medical Advisor, VistaCare, based in Scottsdale, AZ.Phillip J. Wiffen, BPharm, MRPharmS, MFPHM(Hon), is Director of Training for the U.K. Cochrane Center, a member of the Oxford Regional Pain Relief Unit at Churchill Hospital, and Coordinating Editor of the Cochrane Collaboration Pain Palliative and Supportive Care Collaborative Review Group.William J. Rusho, MS, RPh, is Professor (Clinical) of Pharmacotherapy, University of Utah College of Pharmacy and a former member of the FDA Advisory Committee on Pharmacy Compounding. He was clinical coordinator of sterile product compounding at the University of Utah Hospitals and Clinics for 32 years.Jonathan R. Garvin, MD, is Director of Symptom Management and Palliative Care, Hospital of the University of Pennsylvania; Clinical Associate Professor of Anesthesia and Adjunct Associate Professor of Medicine, School of Medicine, University of Pennsylvania, Philadelphia.Philip J. Wiffen, BPharm, MSc, MRPharmS, MFPHM (Hon), is a member of the Oxford Regional Pain Relief Unit, Churchill Hospital, Coordinating Editor, Cochrane Collaboration Pain Palliative and Supportive Care Collaborative Review Group, and Director of Training, U.K. Cochrane Center.Dr. Fishman has developed this feature based on columns he has developed in collaboration with the American Pain Foundation Pain Monitor and Discovery Health. Dr. Fishman retains copyright for this material and has granted permission to the Journal of Pain & Palliative Care Pharmacotherapy to publish it.
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Abstract In a resource‐limited environment, it is important to determine whether a proposed health program represents a good use of scarce resources (money, labor, land, capital, etc.). Economic evaluation provides a framework to approach and inform difficult decisions about the best use of limited resources, through an assessment of both the costs and outcomes associated with a proposed program. This article first examines the foundations of economic evaluation in welfare economic theory and cost‐benefit analysis. The article then moves quickly on to a presentation of cost‐effectiveness analysis incorporating a discussion of incremental cost‐effectiveness decision rules, methods for handling uncertainty, and the use of value of information for decision making. The article focuses on the requirements of a health care policy maker attempting to determine whether to reimburse a health care technology given the information and uncertainty surrounding the decision. In addition, the article examines how the policy maker might approach the choice about whether to request further research to inform the decision. A stylized case study is used to illustrate the concepts and approach. The article links with those on decision analysis, uncertainty analysis, and value of information analysis.
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Clinical decisions must be made, often under circumstances of uncertainty and limited resources. Decision analysis and cost-effectiveness analysis are methodologic tools that allow for quantitative analysis and the optimization of decision-making. These methods can be useful for decisions regarding individual patient evaluation and treatment options or in formulating healthcare policy. We overview the methodology of expected value decision analysis and of cost-effectiveness analysis, including cost-identification, cost-effectiveness, cost-benefit, and cost-utility analyses. Examples are provided of these methods and a user's guide to cost-effectiveness analysis is outlined.
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In this primer, the reader is introduced to the concepts governing decision analysis and cost-effectiveness analysis. The construction of decision trees and Markov models is presented to provide the necessary background to critique research articles in published literature. Specific sub-topics related to cost-effectiveness analysis are discussed including quality adjustment and utilities (patient preferences for health states), discounting, and sensitivity analysis including Monte Carlo simulation. Evidence based methods to critique decision and cost-effectiveness analysis are provided, and limitations to these analytic methods are examined. In summary, the major functions of decision analysis and cost-effectiveness analysis are to provide: (1) a quantitative summary of existing data, and (2) hypothesis generation for further research.
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While a population-wide strategy involving lifestyle changes and a high-risk strategy involving pharmacological interventions have been described, the recently proposed personalized medicine approach combining both strategies for the prevention of hypertension has increasingly gained attention. However, a cost-effectiveness analysis has been hardly addressed. This study was set out to build a Markov analytical decision model with a variety of prevention strategies in order to conduct an economic analysis for tailored preventative methods.The Markov decision model was used to perform an economic analysis of four preventative strategies: usual care, a population-based universal approach, a population-based high-risk approach, and a personalized strategy. In all decisions, the cohort in each prevention method was tracked throughout time to clarify the four-state model-based natural history of hypertension. Utilizing the Monte Carlo simulation, a probabilistic cost-effectiveness analysis was carried out. The incremental cost-effectiveness ratio was calculated to estimate the additional cost to save an additional life year.The incremental cost-effectiveness ratios (ICER) for the personalized preventive strategy versus those for standard care were -USD 3317 per QALY gained, whereas they were, respectively, USD 120,781 and USD 53,223 per Quality-Adjusted Life Year (QALY) gained for the population-wide universal approach and the population-based high-risk approach. When the ceiling ratio of willingness to pay was USD 300,000, the probability of being cost-effective reached 74% for the universal approach and was almost certain for the personalized preventive strategy. The equivalent analysis for the personalized strategy against a general plan showed that the former was still cost-effective.To support a health economic decision model for the financial evaluation of hypertension preventative measures, a personalized four-state natural history of hypertension model was created. The personalized preventive treatment appeared more cost-effective than population-based conventional care. These findings are extremely valuable for making hypertension-based health decisions based on precise preventive medication.
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