title: Applying the payoff time framework to carotid artery disease management.
abstract: and
  
  
  
  Asymptomatic stenosis of the carotid arteries is associated with stroke. Carotid revascularization can reduce the future risk of stroke but can also trigger an immediate stroke. The objective was to model the generic relationship between immediate risk, long-term benefit, and life expectancy for any one-time prophylactic treatment and then apply the model to the use of revascularization in the management of asymptomatic carotid disease.
  
  
  
  In the "payoff time" framework, the possibility of losing quality-adjusted life-years (QALYs) because of revascularization failure is conceptualized as an "investment" that is eventually recouped over time, on average. Using this framework, we developed simple mathematical forms that define relationships between the following: perioperative probability of stroke (P); annual stroke rate without revascularization (r0); annual stroke rate after revascularization, conditional on not having suffered perioperative stroke (r1); utility levels assigned to the asymptomatic state (ua) and stroke state (us); and mortality rates (λ).
  
  
  
  In patients whose life expectancy is below a critical life expectancy (CLE = P/(1-P)r0-r1, the "investment" will never pay off, and revascularization will lead to loss of QALYs, on average. CLE is independent of utilities assigned to the health states if a rank ordering exists in which ua > us. For clinically relevant values (P = 3%, r0 = 1%, r1 = 0.5%), the CLE is approximately 6.4 years, which is longer than published guidelines regarding patient selection for revascularization.
  
  
  
  In managing asymptomatic carotid disease, the payoff time framework specifies a CLE beneath which patients, on average, will not benefit from revascularization. This formula is suitable for clinical use at the patient's bedside and can account for patient variability, the ability of clinicians who perform revascularization, and the particular revascularization technology that is chosen.
authors.affiliation: Division of Vascular Surgery, Pittsburgh, PA (THY).|RAND–University of Pittsburgh Health Institute, Pittsburgh, PA (THY)
authors.forename: Theodore H
authors.initials: TH
authors.lastname: Yuo
authors.affiliation: Center for Research on Health Care, Division of General Internal Medicine, Department of Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA (MSR, CHC, KLK)|Department of Health Policy and Management, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA (MSR)
authors.forename: Mark S
authors.initials: MS
authors.lastname: Roberts
authors.affiliation: Section on Value and Effectiveness, Division of General Internal Medicine, Department of Medicine, School of Medicine, New York University, New York, NY (RSB)
authors.forename: R Scott
authors.initials: RS
authors.lastname: Braithwaite
authors.affiliation: Center for Research on Health Care, Division of General Internal Medicine, Department of Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA (MSR, CHC, KLK)|Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA (CHC)
authors.forename: Chung-Chou H
authors.initials: CC
authors.lastname: Chang
authors.affiliation: Center for Research on Health Care, Division of General Internal Medicine, Department of Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA (MSR, CHC, KLK)
authors.forename: Kevin L
authors.initials: KL
authors.lastname: Kraemer
mesh_terms: D002340:Carotid Artery Diseases / Q000628:therapy*; D019468:Disease Management; D006801:Humans; D008962:Models, Theoretical; D019057:Quality-Adjusted Life Years
keywords: carotid revascularization; decision analysis; model; shared decision making