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European Journal of Heart Failure 2008 10(10):1026-1032; doi:10.1016/j.ejheart.2008.07.018
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© 2008 European Society of Cardiology

Decision-analytic evaluation of the clinical effectiveness and cost-effectiveness of management programmes in chronic heart failure

Alexander Göhlera,b,c, Annette Conrads-Franka,c, Stewart S. Worrella, Benjamin P. Geislera,c, Elkan F. Halperna, Rainer Dietzb, Stefan D. Ankerb, G. Scott Gazellea,d and Uwe Sieberta,c,d,*

a Institute for Technology Assessment and Department of Radiology, Massachusetts General Hospital, Harvard Medical School Boston, USA
b Division of Cardiology, Charité Campus Virchow-Klinikum Berlin, Germany
c Department for Public Health, Medical Decision Making and Health Technology Assessment, University for Health Science, Medical Informatics and Technology, Hall i.T. Austria
d Department of Health Policy and Management, Harvard School of Public Health Boston, USA

* Corresponding author. Cardiovascular Research Program, MGH Institute for Technology Assessment, Harvard Medical School, 101 Merrimac Street, 10th Floor, Boston, MA 02114-4724, USA. Tel.: +1 617 724 4445; fax: +1 617 726 9414. E-mail address: usiebert{at}hsph.harvard.edu (U. Siebert).


   Abstract
Top
 Abstract
1. Introduction
 2. Methods
 3. Results
 4. Discussion
 Appendix A. Supplementary data
 Acknowledgments
 References
 
Background and aims: While management programmes (MPs) for chronic heart failure (CHF) are clinically effective, their cost-effectiveness remains uncertain. Thus, this study sought to determine the cost-effectiveness of MPs.

Methods and results: We developed a Markov model to estimate life expectancy, quality-adjusted life expectancy, lifetime costs, and the incremental cost-effectiveness of MPs as compared to standard care. Standard care was defined by the EuroHeart Failure Survey for Germany, MP efficacy was derived from our recent meta-analysis and cost estimates were based on the German healthcare system. For a population with a mean age 67 years (35% female) at onset of CHF, our model predicted an average quality-adjusted life expectancy of 2.64 years for standard care and 2.83 years for MP. MP yielded additional lifetime costs of {euro}1700 resulting in an incremental cost-utility ratio (ICUR) of {euro}8900 (95% CI: dominant to 177,100) per quality-adjusted life year (QALY) gained. Sensitivity analyses demonstrated that the ICUR was sensitive to age and sex.

Conclusion: MPs increase life expectancy in patients with CHF by an average of 84 days and increase lifetime cost of care by approximately {euro}1700. MPs improve outcomes in a cost-effective manner, although they are not cost-saving on a lifetime horizon.

Key Words: Hearth failure • Management programme • Cost-effectiveness analysis • Markov model

Received October 27, 2007; Revised April 30, 2008; Accepted July 24, 2008


   1. Introduction
Top
 Abstract
 1. Introduction
2. Methods
 3. Results
 4. Discussion
 Appendix A. Supplementary data
 Acknowledgments
 References
 
Despite therapeutic advances, chronic heart failure (CHF) remains a medical condition of increasing importance in public health. CHF causes or complicates approximately 20% of all hospitalisations in people in the developed world older than 60 years of age [1]. With a crude 1-year mortality rate of 33% to 61% in high risk patients [2], CHF has a prognosis similar to many cancers. Moreover, treatment costs for CHF in Europe and the United States account for 1-2% of total health care expenditures [3] with hospitalisation costs accounting for two-thirds of this amount [4].

In the hope of improving CHF outcomes, management programmes (MPs) have been developed to standardize and optimize CHF treatment. These programmes focus on disease education for patients and continuing support after hospital discharge. Recent meta-analyses have demonstrated that MPs for CHF are clinically effective; however, the cost-effectiveness of MPs remains uncertain [5].

The objective of this study was to assess the long-term clinical and economic consequences of MPs in the treatment of CHF patients in Germany.


   2. Methods
Top
 Abstract
 1. Introduction
 2. Methods
3. Results
 4. Discussion
 Appendix A. Supplementary data
 Acknowledgments
 References
 
2.1. Description of the Markov model
We developed a five-state Markov model that is identical in structure for both strategies. The model simulation begins at the time when a patient hospitalised for CHF is discharged alive and moves through subsequent rehospitalisation states (Fig. 1). The number of rehospitalisations is a valid and generally accepted proxy for disease progression in patients with CHF [6]. Since an individual's number of hospitalisations prior to trial enrolment is usually not known to the investigator, we defined the hospitalisation at which the patient was enrolled into the management programme as the index hospitalisation and counted the number of hospitalisations thereafter.


Figure 01
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  		Fig. 1 Schematic diagram of Markov model.

 
During each one month cycle, patients may remain in the current hospitalisation state, experience a rehospitalisation or die from cardiovascular or non-cardiovascular causes. The simulation was carried out until all patients who had not already died reached an age of 120 years.

2.2. Risks of hospitalisation and death
We utilized Weibull regression and logistic regression models [7] to derive transition probabilities that allowed us to simulate the natural history of chronic heart failure from original patient-level data obtained in the Beta-Blocker Evaluation of Survival Trial (BEST) [8]. Briefly, this multi-centre trial showed no overall survival benefit in 2708 patients with New York Heart Association (NYHA) class III or IV heart failure, who were randomly assigned to bucindolol or placebo.

The regression analyses allowed us to derive input parameters for the Markov model that are not available in the published literature, and thus to design the model in order to reflect the health care context of MP.

Probabilities of all-cause rehospitalisation and CV mortality (without hospitalisation within the previous 30 days) were estimated as a function of age, sex, number of previous hospitalisations, and time since last hospital discharge. Correlation among those parameters was accounted for using the Cholesky decomposition method [7].

Hospitalised subjects were assigned an increased 30 day-mortality risk as a function of their age, sex and number of previous hospitalisations. We thought a time period of increased mortality was superior to differentiating between in-hospital mortality and post-discharge mortality, since hospitals may use various admission and discharge criteria in CHF patients. The probability of dying from non-cardiovascular causes without prior hospitalisation was based on German life tables and only depended on age and sex [9]. Monthly transition probabilities for the base-case cohort are given in Table 1. For further details on the underlying statistical methods that were used to derive the input parameters, please refer to our accompanying online material (see Appendix A).


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  		Table 1 Transition probabilities and utilities by rehospitalisation stratum

 
2.3. Efficacy data
MP efficacy estimates were based on the meta-analysis that we performed using 36 randomised controlled studies from 13 countries with data from a total of 8341 patients. We performed an extensive literature search to identify studies that address ‘chronic heart failure’ and ‘disease management programme’ and reported all-cause mortality or all-cause rehospitalisation rates. Follow-up of the individual studies varied between 3 and 18 months with a median follow-up time across studies of 9 months. Detailed methods are described elsewhere [5].

Our meta-analysis yielded a pooled relative risk of 0.81 (95% CI 0.70 to 0.93) for all-cause mortality and a pooled relative risk of 0.84 (95% CI 0.77 to 0.92) for all-cause hospitalisation, both favouring MPs over standard care. The effect of age and sex on the overall effect of MPs was based on the results of our meta-regression analysis [5]. The beneficial effect of MPs decreased relatively by 1% for each 1 year increase in age (4% to 0%) in the case of mortality and by 3% (95% CI 8% to –1%) in the case of rehospitalisation. For men, MP was 92% (95% CI 297% to –92%) as effective with regard to mortality-reduction and 81% (95% CI 549% to –49%) as effective with regard to reduction in rehospitalisation when compared to women of the same age.

In the model, MP was assumed to influence the rate of the first rehospitalisation, all-cause mortality, and length of hospital stay.

2.4. Utilities
Given the difficulties in comparing interventions, cost-utility analyses were performed. Utilities by rehospitalisation stratum were derived from a subsample (1628 patients) of the Eplerenone Post-AMI CHF Efficacy and Survival Study (EPHESUS) and the utilities were then assessed using the EQ-5D questionnaire based on German preferences [10] (see Table 1).

2.5. Resource use and cost data
Costs of hospitalisations, physician visits, medications, and MP implementation were reported in 2007 Euros. The different cost components varied between the treatment groups but were not affected by the specific Markov state.

Since MP reported an effect on all-cause hospitalisation rather than CHF-specific hospitalisation, we estimated the average cost of a hospital day using information from the BEST trial and the German G-DRG grouper 2006 [11]. We calculated the average cost per hospital day for each disease category from the German G-DRG grouper 2006 using linear regression and translated the value into 2007 Euros. The mean cost for a hospital day was estimated by taking a weighted average of all disease categories in which the weight of each category was based on the disease category distribution for the first hospitalisation in the BEST trial. The mean cost per hospital day was {euro}560 (SE 41.6) and the average length of hospital stay was 7.8 (SE 1.1) days. During the simulation, costs for hospitalisations were event costs, i.e., they were only accounted for when a patient actually experienced a hospitalisation.

Outpatient health resource utilization frequencies were derived from a survey of cardiologists and general practitioners in Berlin. A detailed questionnaire regarding healthcare utilization by CHF patients was mailed to 220 physicians, most of whom were based in outpatient clinics. These physicians were asked to estimate the frequency of utilization for common outpatient procedures used in the care of CHF patients. Costs were then derived from the German healthcare reimbursement database ‘EBM’. In the standard care group, monthly cost parameters for general practitioners were {euro}69 (range: {euro}42 to {euro}96) and for cardiologists were {euro}114 (range: {euro}86 to {euro}142).

The prescription frequency for cardiovascular medication in Germany was obtained from the German section of the EuroHeart Failure Survey [12]. Drug reimbursement prices were derived from the German 2006 Red List prices [13] and the estimated average costs were {euro}40/month/patient.

Initiation costs for MP were derived from the published literature [14,15] and translated into 2007 Euros. Mean costs were found to be {euro}420 (range: {euro}210 to {euro}630). Finally, MP increased outpatient costs by 5% and decreased the hospital length of stay by 7%.

2.6. Cost-effectiveness analysis
Analyses were performed from a societal perspective using values based on the recommendations of the German Working Group on Methods in Health Economic Evaluations [16]. Costs and effects were both discounted at 5%/annum according to current German guidelines [16].

2.7. Base-case and sensitivity analyses
For the base case we used data as reported by Germany in the EuroHeart Failure Survey [1]. The cohort thus had a mean age of 67 years and comprised 35% women. We assumed that the benefit of MP remained for 9 months, the mean follow-up time in our meta-analysis [5], and then declined linearly to zero over the next 12 months. We also estimated incremental cost-utility ratios (ICUR) given reasonable boundaries for the duration of treatment effects. The first was that the effect of MP lasts for 9 months and then immediately drops to zero and the second was that effects observed in the meta-analysis continue for 21 months. The effect of MPs on costs remained according to the time period of the benefit.

Furthermore, we investigated the effect of management programmes on not only the first but also all consecutive hospitalisations. In addition, we also assessed the impact of age and sex by using the results from our meta-regression analysis [5]. To examine the robustness of the results, we performed 2 types of sensitivity analyses. In multiple one-way sensitivity analyses, the following input parameters were both halved and doubled: costs of hospitalisation, outpatient costs, MP implementation and maintenance costs, in- and out-of hospital mortality rate, risk of rehospitalisation and discount rate. In probabilistic sensitivity analysis, we accounted for the overall uncertainty in the estimated costs and effects of each different strategy by using a Monte Carlo simulation with 25,000 iterations [7].


   3. Results
Top
 Abstract
 1. Introduction
 2. Methods
 3. Results
4. Discussion
 Appendix A. Supplementary data
 Acknowledgments
 References
 
3.1. External validation
We externally validated our model using 1-year mortality data from the control arm of those MP studies included in our meta-analysis. The input parameters for the model were selected to reflect the age and sex observed in the studies.

Fig. 2 shows the all-cause mortality at 1 year as observed in the six studies separately and as predicted by our model. The model's prediction lies within the 95% confidence interval of the observed 1-year mortality for each of the six studies.


Figure 02
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  		Fig. 2 Validation plot of 1-year all-cause mortality as observed in the control arms of the studies included in our meta-analysis (boxes with error bars for 95% confidence interval) [5] vs. model prediction (circles).

 
3.2. Base-case analysis
The expected quality-adjusted life years, life years, and costs for patients who receive standard care and management programmes are shown in Table 2. Results are tabulated for the base case. Mean quality-adjusted life expectancy was estimated to be 2.64 for standard care and 2.83 for MP, resulting in a gain of 0.19 years for MP. Mean life expectancy was estimated to be 3.31 years for standard care and 3.54 years for MP, thus yielding a gain of 0.23 life years for MP. This gain in quality-adjusted life expectancy comes at the additional cost of {euro}1700, yielding a discounted incremental cost-utility ratio (ICUR) of {euro}8900 (95% Credibility interval [CI] dominant to 177,100) per quality-adjusted life year gained. The incremental cost-effectiveness ratio was {euro}7400 (95% CI dominant to 93,000) per life year gained. Fig. 3 shows the cost-effectiveness acceptability curves for the base-case scenario. The graphs represent the probability that management programmes are cost-effective when compared to standard care, given a particular willingness-to-pay.


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  		Table 2 Results base-case analysis (values are discounted at 5%/year)

 


Figure 03
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  		Fig. 3 Cost-effectiveness acceptability curve for base-case scenario.

 
When accounting for the effect of MPs on subsequent hospitalisations the ICUR dropped to {euro}4600/QALY (95% CI dominant to 59,400).

Deterministic sensitivity analyses of duration of treatment benefit, costs of hospitalisation, outpatient cost, MP costs, in- and out-of-hospital mortality rates, risk of hospitalisation, and discount rate indicated that the results were insensitive to changes in those parameters as the resulting values for the ICUR differed by less than 10%.

3.3. Effect of age and sex
Table 3 relates the effects of age and sex to the overall effect of MPs based on the results of 25,000 simulations. We report the values of the willingness to pay (WTP) at which MP was cost-effective when compared to standard care in 50% of the simulations as well as at 2.5% and 97.5% of the simulations and we then used these values to derive the appropriate credibility interval. Given the nature of MPs, we assumed that they could not increase mortality rates. However, it is possible that MPs could be only equally as effective as standard care but increase rehospitalisation rates, resulting in additional costs and thus be considered inferior to standard care. Since the results of the simulations did not fall into the third quadrant of the incremental cost-effectiveness plane (i.e., they were not less effective and less costly), the 50% threshold marks the point at which a MP is as likely to be cost-effective as not when compared to standard care, and thus the threshold at which society should implement MPs if their WTP is more than the threshold value.


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  		Table 3 Cost-utility analyses by age and sex

 
For men in the age groups 55, 65 and 75 years, the threshold was {euro}8100/QALY gained, {euro}13,100/QALY gained, and {euro}24,000/QALY gained, respectively. For women in the same age groups, the thresholds were {euro}3400/QALY gained, {euro}3700/QALY gained, and {euro}4000/QALY gained.


   4. Discussion
Top
 Abstract
 1. Introduction
 2. Methods
 3. Results
 4. Discussion
Appendix A. Supplementary data
 Acknowledgments
 References
 
Based on our decision analysis, management programmes for care of patients with chronic heart failure are likely to be cost-effective. The base-case incremental cost-utility ratio was {euro}8900/quality-adjusted life year gained. Sensitivity analyses demonstrated that the ICUR was sensitive to age and sex but insensitive to cost of care for CHF, the duration of the MP effect and our modelling assumption regarding disease progression.

Assumptions about the duration of a MP effect after the median follow-up time of 9 months had only little impact on the overall ICUR. This seems to be a reasonable estimate given that most first rehospitalisations occur within the first year of follow-up. The result differed substantially when we assumed that MPs not only affect the first rehospitalisation but also reduce subsequent rehospitalisations. Five of the 36 studies in our meta-analysis [5] reported the effect of a MP on subsequent rehospitalisations and demonstrated an even stronger effect on subsequent rehospitalisations in favour of MPs over standard care. For the base-case analysis, we felt that ignoring this benefit of management programmes on subsequent rehospitalisations would bias the analysis away from MPs and consequentially would be a conservative and preferable approach.

According to our sensitivity analyses, age and sex of the patient population strongly influenced the ICUR. The threshold (i.e., median WTP) at which the MP was as likely to be cost-effective as not when compared to standard care increased with age and was about twice to six times as high for men as for women. The correlation between an increase in the threshold and increasing age seems clinically plausible, since older patients may profit less from the long-term effects of MP. The more favourable result for women as compared to men is surprising as the target population has already developed CHF and thus the subsequent risks would be expected to be similar for both sexes. We believe that this observation may result from multiple factors. One possible explanation might be that women are more responsive to this type of intervention as observed for example in the German Interdisciplinary Network for Heart Failure (INH) study (personal communication Dr. Angermann on April 3, 2008) [17]. However, the effect might also be a consequence of a bias due to higher disease severity resulting from co-morbidities such as renal failure among women as compared to men. While the adjustment for NYHA class at baseline did not change the favourable efficacy estimate for women as observed in the meta-regression analysis [5], we did not have sufficient data to adjust for further co-morbidities. The fact that the credibility intervals for all age and sex subgroups overlap indicates substantial uncertainty around these estimates. Future studies should be designed in order to investigate the effect of age and sex on the efficacy of MP and thus reduce this uncertainty.

In contrast to specific interventions in cardiology, MPs assume a more holistic approach, affecting all-cause mortality and all-cause rehospitalisation rather than only addressing specific cardiovascular events. An important difference between previous models [18,19] and ours is that we linked all-cause rehospitalisations with disease progression instead of using CHF-related rehospitalisations. This allowed us to better assess the overall impact of MPs, especially given that CHF is the reason for hospital admission in only 20 to 30% of heart failure patients. If we had used CHF-specific rehospitalisations instead, we would have underestimated the effect of MPs by 70 to 80%.

We incorporated sex and age into our model and were able to increase the flexibility of our model in comparison to previous Markov models [18,19] because we directly estimated our model's transition probabilities using patient-level data from the BEST trial instead of using data from the published literature. This approach not only allowed us to adapt the model to the specific health care context of management programmes focused on all-cause hospitalisation but also allowed us to address the uncertainty around this point. This step was essential in performing valid probabilistic sensitivity analyses [7].

To our knowledge, this is the first cost-effectiveness analysis that assesses quality-adjusted life expectancy and lifetime costs for managed care programmes in chronic heart failure in Germany. Results of cost analyses among MP studies ranged from cost savings of US$720/MP patient [20] to additional costs of US$488/MP patient over a 3 month period [21]. Assessing only the financial impact of MPs over a 1-year period, Sidorov found MPs to be cost-saving [22] while Galbreath did not find an effect on resource utilisation [23]. In cost-effectiveness analysis along the trail, Smith found the ICER ranged from US$42,000 to US$147,000/QALY [24]. In a model-based analysis using the older SOLVD data, Chan found managed care programmes ranged from US$9700-US$15,000/life year for the US health care setting [25]. Our analysis differs from these other analyses in that we considered a lifetime horizon as opposed to an 18 month period and based our model input on data more recent than the SOLVD data.

The ICUR of {euro}8900/quality-adjusted life year gained indicates that MPs are cost-effective when compared to other well-accepted treatments in medicine. The ICUR was smaller than all 42 estimates of the willingness-to-pay per life year that were previously estimated [26]. In comparison, cost-effectiveness for other medical interventions was approximately US$25,000/life year gained [27] for treatment of moderate hypertension, and US$50,000/year of life year saved for renal dialysis [28].

This study had a number of limitations. To begin, the number of rehospitalisations does not reflect biological health states and therefore did not allow us to assign a starting distribution. However, this approach is widely accepted when modelling chronic heart failure [29]. Furthermore, heterogeneity in the population other than that due to age and sex was not considered. In addition, the analysis was based on a Markov model that combines different sources, extrapolates data beyond the end of studies and registries available and uses simplifying assumptions. Finally, it is important to note that a decision-analytic cost-effectiveness analysis is not a complete procedure for resource allocation in healthcare, because it cannot incorporate all the values relevant to such decisions. Nonetheless, it may inform clinical recommendations and national health policies [30].

In this study, we have developed, programmed, and validated a Markov model for the natural history of chronic heart failure using transition probabilities derived from patient-level data. We performed base-case analyses and sensitivity analyses to assess the cost-effectiveness of management programmes in the treatment of chronic heart failure.

Based on our decision analysis, MPs increase life expectancy in patients with CHF by 84 days on average and also increase lifetime costs by approximately {euro}1700. Even under the conservative assumption that a MP only has an effect on the first rehospitalisation, MPs improve outcomes in a cost-effective manner when compared to other well-accepted medical interventions, though they are most likely not cost-saving over a lifetime horizon.


   Appendix A. Supplementary data
Top
 Abstract
 1. Introduction
 2. Methods
 3. Results
 4. Discussion
 Appendix A. Supplementary data
Acknowledgments
 References
 
Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.ejheart.2008.07.018.


   Acknowledgments
Top
 Abstract
 1. Introduction
 2. Methods
 3. Results
 4. Discussion
 Appendix A. Supplementary data
 Acknowledgments
References
 
Dr. Göhler was supported by the "Boston-Scientific-Scholarship" of the German Cardiac Society. We thank Dr. Dieter Göhler for his help in conducting the survey to estimate outpatient costs. We thank all colleagues who participated in the survey. We also thank Jennifer Manne for her critical input on this manuscript as well as two anonymous reviewers for their very helpful comments on an earlier version of this manuscript.


   References
Top
 Abstract
 1. Introduction
 2. Methods
 3. Results
 4. Discussion
 Appendix A. Supplementary data
 Acknowledgments
 References
 

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