Download Cost/utility analysis (CUA)

S. Iannazzo, L. Pradelli, O. Zaniolo
AdRes Health Economics & Outcomes Research, Turin, Italy
Cost/utility analysis (CUA)
of valsartan for the treatment
of congestive heart failure (CHF) in Italy
Objective
Aim of the study is to assess the cost/utility of the use of valsartan when added to standard treatment in congestive heart failure (CHF) management in Italy.
Methods
A probabilistic Markov model has been developed to extrapolate the findings of the Valsartan Heart Failure Trial (Val-HeFT) and apply them
to the Italian population. The Markov model has 4 states, corresponding to NYHA classes II, III, and IV, and to death (Fig. 1). The total time of
the simulation is 10 years, divided in 1-month cycles.
Two groups have been evaluated: valsartan added to conventional treatment (valsartan group) and conventional treatment alone (placebo
group). Treatment with valsartan is prolonged for the first 23 months (as 23 months is the average follow-up time in Val-HeFT). We made the
(conservative) assumption that after the end of the treatment its efficacy drops to zero. This implies that all model parameters become identical
in valsartan and placebo groups from the cycle 24 to the end of simulation.
In general, probabilistic simulation models can account for three levels of uncertainty:
• uncertainty on patient characteristics, due to inter-individual heterogeneity;
• uncertainty on model parameters, due to cognitive uncertainty about experimental values or assumptions made;
• intrinsic uncertainty, due to the natural randomness of events.
We considered the first two levels of uncertainty by first performing a patient-level probabilistic simulation and then calculating the Probabilistic
Sensitivity Analysis (PSA) on model parameters. Each patient is characterized by his/her:
• gender;
• initial age;
• use of ACE-inhibitors (ACEi).
The model has been developed in WinBUGS (version 1.4.3).
NYHA III
NYHA II
NYHA IV
Death
Figure 1. Markov chain
Transition probabilities
Transition probabilities in our Markov model represent individual mortality and probability of improvement or worsening of the condition. Mortality is obtained from the Italian general population mortality tables (accessed by sex and age), multiplied by a relative risk factor specific of
the NYHA state. The transition probabilities among NYHA states derive from results in the Val-HeFT.
CHF hospitalizations
In both the valsartan and placebo group the number of hospitalizations caused by CHF is calculated based on the results of the Val-HeFT published by Carson and colleagues. They showed an average 0.3 and 0.36 hospitalizations per patient in the valsartan and the placebo groups,
respectively. The data presented a great variability when analyzed by subgroups defined by age, gender, NYHA class and use of ACEi.
The model is informed with NYHA state-specific baseline hospitalization probabilities, obtained from the frequencies recorded in the trial. We
then applied different relative risk factors in function of the age, gender and use of ACEi of the specific patient. Before using it in the model,
we validated this scheme by simulating the number of the hospitalizations in a cohort with the characteristics of the population enrolled in the
Val-HeFT. Doing so, we obtained only a slight discrepancy (-0.5% to 1.2%) between the number simulated and the actual observed hospitalizations.
Quality of life
State-specific utilities derive from a sub-study on the quality of life (SF-36 questionnaire-based) of the ECHOES study, a large prevalence study
of CHF patients. To correlate the average SF-36 score with the utility we used the model proposed by Nichol. The utilities obtained vary in function of the age of the patient.
Subgroups
Valsartan
Placebo
All patients
0.30
0.36
Males
relative risk
0.30
1.000
0.36
1.000
Females
relative risk
0.29
0.967
0.37
1.028
Age ≥ 65
relative risk
0.35
1.167
0.42
1.167
Age < 65
relative risk
0.26
0.867
0.31
0.861
ACEi +
relative risk
0.31
1.033
0.35
0.972
ACEi relative risk
0.18
0.600
0.51
1.417
Table 1. Number of hospitalization/patient/year for acute heart failure, and
relative risk, recorded in Val-HeFT study subgroups
ACEi +: patients assuming ACE-inhibitors. ACEi -: patients not assuming ACE-inhibitors
Costs
Costs are calculated in the perspective of the SSN – Servizio Sanitario Nazionale (Italian National Health System) and account for drugs purchasing and CHF hospitalizations based on current prices, tariffs and published cost studies.
Costs and health outcomes have been discounted at a 3.5% annual rate, as recommended by NICE and accepted by the international health
Utilities: correlation by age
Example (age=74 years)
economists community.
Population simulated
The study population has been defined in order to represent the cohort of Italian patients with CHF, LVEF<40% and more than 45 years (inclusion criteria of Val-HeFT), based on the current Italian population and available prevalence data. This scheme yield to an estimation of 339,489
subjects (49.9% males) with mild to severe CHF (NYHA class II-IV) and LFEV<40% with a mean age of 74.8 and 79.1 years, respectively for
males and females. The estimated rate of ACEi use is 76.6%.
NYHA II
0.81355 - 0.0006*age
0.769
NYHA III
0.74984 - 0.0006*age
0.705
NYHA IV
0.67958 - 0.0006*age
0.635
Table 2. Utility weights used in the Markov model, in function of the age. We
exemplified the correlation estimate for a 74-years old patient
Results
Results have been calculated through 30,000 iterations of the probabilistic, patient-level simulation.
Cost-utility analysis
In the 10-years time frame of the simulation each patient lives an avValsartan (CI 95%)
Placebo (CI 95%)
Incremental (CI 95%)
erage of about 4.4 years, which, weighted for the quality of life, yield
an average of 3.2 QALYs per patient. The increment in the valsartan
Survival [LY/pt]
4.444 (0.887 – 7.807)
4.426 (0.881 – 7.800)
0.018 (0.004 – 0.025)
group is slight, since treatment does not directly affect survival. The efQuality of Life [QALY/pt]
3.206 (0.636 – 5.669)
3.184 (0.632 – 5.650)
0.022 (0.005 – 0.035)
ficacy of the treatment is clearer if we look at the hospitalizations for
CHF, which, on the average, are reduced by about 10%. This reduction
CHF Hospitalizations [N/pt]
3.078 (0.425 – 6.024)
3.421 (0.535 – 7.390)
0.344 (0.021 – 1.448)
also causes the decrease in total costs: valsartan costs about 890 Euro
Costs [Euro/pt]
12,490 (2,227 – 22,760)
13,000 (2,235 – 27,440)
-508 (-4,924 – 684)
per patient, but induces a saving in hospitalization costs of about 1,398
Cost of Drug
890 (448 – 1,049)
890 (448 – 1,049)
Euro per patient. The treatment with valsartan added to standard care
11,600 (1,777 – 21,740)
13,000 (2,235 – 27,440)
-1,398 (-5,938 – -85)
Cost of Hospitalizations
results thus to be dominant with respect to standard care alone, since
Table 3. Cost-utility analysis on the population of Italian patients with CHF and LVEF < 40%
the former is simultaneously more effective and money-saving.
We performed the cost-utility analysis separating the whole cohort in LY: life years. QALY: quality adjusted life years. Pt: patient
subgroups according to age, sex, use of ACEi and initial NYHA class.
The treatment with valsartan resulted to be dominant in every subgroup except in patients already using ACEi and in those with only mild CHF (NYHA class II). Anyway also in these two subgroups (numerically very
relevant) the incremental cost-utility ratio is below the commonly accepted threshold (about 30,000 Euro/QALY) under which a health program is considered cost-effective.
Sensitivity analysis
The PSA 18, 19 has been performed by applying a simultaneous variation
of each parameter of the model in its original source 95% CI. Where
the 95% CI was unavailable from the original data source, a ±10%
range has been assumed. The resulting scatterplot of the 30,000 iterations in the cost-utility plane confirms that the model is substantially
stable and the results are reliable.
Incremental Survival
[LY/pt]
Incremental Quality
of Life [QALY/pt]
Incremental Costs
[Euro/pt]
CE
[Euro/LY]
CU
[Euro/QALY]
Total Population
0.018 (0.004 – 0.025)
0.022 (0.005 – 0.035)
-508 (-4,924 – 684)
Dominant
Dominant
Males
0.019 (0.005 – 0.025)
0.022 (0.005 – 0.035)
-333 (-4,439 – 685)
Dominant
Dominant
Females
0.018 (0.004 – 0.025)
0.022 (0.005 – 0.035)
-693 (-5,125 – 413)
Dominant
Dominant
Age < 65 years
0.009 (0.003 – 0.019)
0.021 (0.016 – 0.030)
-457 (-3,833 – 639)
Dominant
Dominant
Age ≥ 65 years
0.020 (0.008 – 0.025)
0.022 (0.008 – 0.035)
-480 (-4,683 – 685)
Dominant
Dominant
ACEi +
0.018 (0.004 – 0.025)
0.020 (0.005 – 0.026)
460 (131 – 684)
30,170 (9,680 – 84,230)
27,200 (6,017 – 73,880)
ACEi -
0.018 (0.004 – 0.025)
0.028 (0.008 – 0.036)
-3,702 (-5,233 – -1,381)
Dominant
Dominant
NYHA II
0.017 (0.004 – 0.024)
0.019 (0.006 – 0.026)
266 (-934 – 661)
19,420 (-75,670 – 100,400)
17,330 (-43,920 – 61,830)
NYHA III
0.027 (0.006 – 0.038)
0.031 (0.006 – 0.043)
-324 (-2,559 – 361)
Dominant
Dominant
NYHA IV
0.014 (0.002 – 0.020)
0.021 (0.002 – 0.048)
-1,963 (-13,430 – 1,050)
Dominant
Dominant
Table 4. Cost-utility analysis by subgroups. Values (CI 95%)
Figure 3. Scatterplot on the cost-utility plane of the 30,000 iterations for the PSA
LY: Life Years. QALY: Quality Adjusted Life Years. Pt: patient. CE: incremental cost-efficacy ratio. CU: incremental cost-utility ratio. ACEi +: patients using ACE-inhibitors.
ACEi -: patients not using ACE-inhibitors
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