Clinical and Economic Implications of the Multicenter Automatic Defibrillator Implantation Trial-II

Context The Multicenter Automatic Defibrillator Implantation Trial (MADIT)-II has shown that implantable cardioverter defibrillators (ICDs), compared with conventional therapy, appreciably improve survival in patients who have had a myocardial infarction and have an ejection fraction of 0.3 or less. However, the cost of following these recommendations has not been adequately assessed. Contribution Implantable cardioverter defibrillators are projected to improve survival by 1.80 discounted years, with an incremental cost-effectiveness ratio of $50500 per life-year gained. Sensitivity analysis shows that the cost of replacing ICD batteries and leads exerts greater effect on cost-effectiveness ratios than other factors. Implications The large number of patients eligible for ICDs under MADIT-II criteria may strain societal ability to perform and pay for these procedures. The Editors Sudden cardiac death is a major public health problem in the United States and claims the lives of approximately 300000 people annually (1). Therapy with implantable cardioverter defibrillators (ICDs) has been proven to reduce the risk for sudden cardiac death in certain patient populations, including survivors of cardiac arrest caused by ventricular tachycardia or fibrillation and patients with a history of myocardial infarction (MI), an ejection fraction of 0.4 or less, nonsustained ventricular tachycardia, and inducible sustained ventricular tachycardia on electrophysiologic testing (2-4). In the Multicenter Automatic Defibrillator Implantation Trial (MADIT)-I and Multicenter UnSustained Tachycardia Trial (MUSTT), such patients had appreciably better survival with an ICD compared with antiarrhythmic medications or no antiarrhythmic therapy (3, 4). The Multicenter Automatic Defibrillator Implantation Trial-II demonstrated that patients with a history of MI and an ejection fraction of 0.3 or less have a 31% relative risk reduction in mortality when treated with an ICD compared with conventional medical therapy (5). While these studies have demonstrated the efficacy of ICD therapy, implantation and maintenance of ICDs are costly. To date, the cost-effectiveness of implanting ICDs in patients meeting MADIT-II criteria (that is, those with a history of MI and ejection fraction 0.3) is unknown. We assessed the long-term clinical and economic implications of implanting ICDs in all patients who meet the eligibility criteria for MADIT-II. Methods Study Sample Our study sample consisted of patients at least 21 years of age who had a history of MI and an ejection fraction of 0.3 or less and underwent cardiac catheterization at Duke University Medical Center between 1 January 1986 and 31 December 2001. Patients who had had an MI within 30 days of catheterization were included only if they had more than 1 month of follow-up data available. Patients who underwent a revascularization procedure within 3 months of catheterization were included only if they had more than 3 months of follow-up data available. The study start date for these patients corresponds to either 1 month after MI or 3 months after the revascularization procedure, respectively. The start date for all other patients is the date of catheterization. Patients were excluded from this analysis if they had New York Heart Association class IV symptoms, advanced cerebrovascular disease, any condition other than cardiac disease associated with a high likelihood of death within 1 year, or no ejection fraction data. Patients who had a previously implanted ICD or who received an ICD after catheterization were also excluded. Criteria for implantation of ICDs at our institution over the years have been in accordance with guidelines on implantation of antiarrhythmia devices from the American College of Cardiology, the American Heart Association, and the Heart Rhythm Society. As a result of our exclusion criteria, none of the patients in our study received an ICD for a MADIT-II indication. We obtained the approval of our institutional review board before the inception of the study. Data Collection Data for the study were collected by using the Duke Cardiovascular Database. This database systematically compiles the clinical experience of all cardiology patients who had cardiac catheterization at Duke University Medical Center (6, 7). Patient information available through the system includes symptoms at time of cardiac procedures, diagnoses, electrocardiographic findings, medications, severity of coronary artery disease, and measures of left ventricular function. The database incorporates post-hospital follow-up at 6 months, 1 year, and annually thereafter; follow-up is complete in 95% of patients (8-10). The National Death Index is periodically searched to monitor the survival of patients lost to follow-up. Statistical Analysis Baseline Characteristics Baseline characteristics for the Duke cohort are presented as means (SD) for continuous variables and as percentages for categorical variables. Statistical tests comparing the baseline characteristics of the Duke cohort with MADIT-II patients were based on the sufficient statistics (mean, frequency, and standard deviation) from the MADIT-II published data and the Duke cohort. Chi-square tests were applied for discrete variables, and 2-sample t-tests were used for continuous variables. Statistical significance was determined at the 2-sided 0.05 level. Survival Comparisons We compared survival distributions within 3 years to assess whether the short-term survival for the Duke cohort was similar to that of the MADIT-II population. To produce the MADIT-II survival curves, we scanned and plotted the published survival curves using digitization software (UnGraph 4.0, Biosoft, Ferguson, Missouri). Estimates of the area under the survival curves and the 3-year survival rate were used to quantify differences in survival between the MADIT-II and Duke populations. We constructed a Cox regression model with adjustments for the severity of coronary artery disease, age, sex, and indicator variables based on a patient's study start date (11). To mirror ischemic heart disease and heart failure management in the MADIT-II era, the survival model was averaged over all patients, with the study start date adjusted to the most recent era (between 1998 and 2001). Lifetime Survival Models Patients in the Duke cohort had a maximum of 15 years of follow-up. To extrapolate from these data for a lifetime cost-effectiveness analysis, we constructed treatment-specific survival curves. The right-hand tail of the survival curves was created by estimating a log-hazard ratio comparable to the survival of an age- and sex-matched cohort from the U.S. population (12, 13). In this analysis, we assumed that this hazard ratio remained constant after 15 years. The lifetime survival model for the hypothetical Duke ICD group was constructed by assuming a constant hazard ratio of 0.69, as observed in the ICD arm of MADIT-II. To test the importance of this assumption, we performed a sensitivity analysis that assumed that the benefit of an ICD remained at a hazard ratio of 0.69 for the 3 years following the study start date and increased to a hazard ratio of 1.00 thereafter. Clinical Events The Duke Information System for Clinical Computing (DISCC) database was used to obtain data on the following clinical events: MI, percutaneous coronary inter-vention, coronary artery bypass graft surgery, rehospitalization, and death. To adjust for censored data due to staggered entry, we calculated estimates for mean number of clinical events using a nonparametric partitioned estimator (14). We selected 5 evenly spaced time partitions: 0 to 3 years, 3 to 6 years, 6 to 9 years, 9 to 12 years, and 12 to 15 years. Within each time partition, we calculated the average number of events per year. Medical Costs Total in-hospital costs were estimated by using a series of regression models derived from the Global Use of Strategies To Open Occluded Coronary Arteries (GUSTO)-IIb Economic and Quality of Life Substudy (14, 15). The perspective of the analyses was societal, although some societal costs (nonmedical costs, outpatient care, and productivity costs) were omitted. All costs were converted to 2002 U.S. dollars. Lifetime Medical Costs To extrapolate medical costs beyond 15 years, we multiplied the average in-hospital medical cost per year alive by the remaining life expectancy. To calculate the lifetime cost for the Duke medical therapy group, we modeled the observed clinical events data for the initial 15 years and then extrapolated the clinical events for an entire lifetime. The clinical events data were then converted to costs by using a series of regression models from previously conducted clinical trials (15, 16). Lifetime ICD Costs Lifetime costs for the Duke ICD arm were separated into 2 categories: 1) in-hospital costs not directly related to ICD therapy and 2) costs directly related to ICD therapy. Medical costs not related to ICD therapy were estimated by multiplying the average cost per year alive for the medical therapy arm (as described earlier) by the projected survival for the ICD arm. This assumption reflects our understanding that ICDs reduce the risk for sudden cardiac death but not the other risks associated with having coronary artery disease and low ejection fraction. To determine ICD-related costs, we developed a template for ICD placement, follow-up visits, and battery replacement based on practice standards at Duke University Medical Center. In our primary or base-case analysis, follow-up visits were scheduled at 3-month intervals and batteries were replaced every 5 years following the implantation. Rates of complications were based on a publication by Kennergren (17) and the Medtronic product performance report for the first quarter in 2003 (18). Professional fees for ICD placement were estimated by using North Carolina Medicare reimbursement rates. Hospital costs for ICD placement and