Cost-Effectiveness of Statins for Primary Cardiovascular Prevention in Chronic Kidney
Disease
(Technical Appendix)
Table of Contents:
Text:







Mortality in End-Stage Renal Disease
Modeling CKD Progression
Determining Baseline Cardiovascular Risk
Calibrating Rates of Non-Cardiovascular Death
Selected Model Assumptions
Deterministic Sensitivity Analysis Results and Exploratory Analyses
Probabilistic Sensitivity Analysis
Tables:











Table S1: Studies on Risk Reduction from Statins in Mild-to-Moderate (Stage 3) CKD
Table S2: Modeled Versus Reported Life Expectancy in Men under Different Scenarios
(prior to use of Statins)
Table S3: Modeled Versus Reported Life Expectancy in Women under Different
Scenarios (prior to use of Statins)
Table S4: Statins at Average Retail Prices – Health Benefits, Costs, and Incremental
Cost-Effectiveness Ratio from Statin Therapy for Patients with Different Age, Sex, and
Cardiovascular Risk Profiles
Table S5: Detailed Results from the Base Case
Table S6: Annual Mortality Rate with ESRD by Incident Age
Table S7: Studies on Annual Rate of CKD Progression
Table S8: Framingham-Based Probability of Stroke in One Year by Age in Patients with
Moderate Hypertension
Table S9: Framingham-Based Probability of Myocardial Infarction in 1 Year by Age in
Patients with Moderate Hypertension
Table S10: Probabilistic Sensitivity Analysis Inputs
Table S11: Costs and Distributions for ESRD
Figures:

Figure S1a: Model Schematic – Cardiovascular Disease Model



Figure S1b: Model Schematic – Chronic Kidney Disease Model
Figure S1c: Model Schematic – Statin Toxicities Model
Figure S2: Cost-effectiveness of Statins under Different Assumptions about CKD
Progression
Figure S3: Statins at Average Retail Prices – Differing Cardiovascular Risk Groups in
Women




























Figure S4: Statins at Average Retail Prices – Differing Cardiovascular Risk Groups in
Men
Figure S5 Sensitivity Analysis – Severity of CKD upon Statin Initiation
Figure S6: Sensitivity Analysis – Relative Risk Reduction in CKD Progression from
Statins in the Base Case
Figure S7: Sensitivity Analysis Assuming Statins Cause Diabetes
Figures S8: Sensitivity Analysis Assuming Irreversible Memory Loss from Statins
Figure S9: Probabilistic Sensitivity Analysis in 65 Year Old Men
Figure S10: Probabilistic Sensitivity Analysis in 50 Year Old Men
Figure S11: Probabilistic Sensitivity Analysis in 65 Year Old Women
Figure S12: Probabilistic Sensitivity Analysis in 50 Year Old Women
Figure S13: Survival after Developing ESRD by Age of Onset
Figure S14: Proportion of Simulated Cohort in Each CKD Stage Assuming Constant
GFR Decline for Each Individual
Figure S15: Proportion of Simulated Cohort in Each CKD Stage Assuming New Rate of
Decline for Each Individual per Year
Figure S16: Modeled CKD Progression in 50 Year-olds under Different Assumptions
about Within-person Variability in Rate of Progression
Figure S17: Modeled CKD Progression in 65 Year-olds under Different Assumptions
about Within-person Variability in Rate of Progression
Figure S18: Modeled CKD Progression in 80 Year-olds with Different Assumptions
About Within-person Variability in Rate of Progression
Figure S19: 10-year Probability of MI in Men with Baseline Characteristics by Age
Figure S20: 10-year Probability of MI in Women with Baseline Characteristics by Age
Figure S21: Relative Increase in Mortality in Men with Stage 3a CKD vs. No CKD by Age
Figure S22: Relative Increase in Mortality in Men with Stage 3b CKD vs. No CKD by Age
Figure S23: Relative Increase in Mortality in Men with Stage 4 CKD vs. No CKD by Age
Figure S24: Estimated Cost of Healthy Year
Figure S25: Quality of Life in Healthy Individuals with Age
Figure S26: Sensitivity Analysis of Rate of CKD Progression
Figure S27: Sensitivity Analysis – Relative Risk Reduction in CV Events from Statins in
Stage 3 CKD
Figure S28: Sensitivity to Rate of Rhabdomyolysis for 50 Year-old Men Experiencing
Different Relative Risk Reductions from Statins.
Figure S29: Deterministic Sensitivity Analyses in Women
Figure S30: Deterministic Sensitivity Analyses in Men
Additional Results when Statins are obtained at Average Retail Prices:


Table S12: Statins at Average Retail Prices – Detailed Results from the Base Case
Figure S31: Statins at Average Retail Prices – Cost-effectiveness of Statins under
Different Assumptions about CKD Progression
Mortality in End-Stage Renal Disease (ESRD)
Upon progression to end-stage renal disease, we no longer accounted for different
causes of death. Consequently, major health events following progression to ESRD (stage 5
CKD) such as CV events, kidney transplantation, timing of dialysis therapy, and dialysis
modality, were not explicitly modeled. Instead, mortality each year was a function of 1) Age of
development of ESRD; 2) Years since development of ESRD. Mortality rates were calculated
from the United States Renal Data System (USRDS) report of all-cause mortality in the ESRD
population, and include all ESRD patients in the United States. Consequently, the average
mortality rate includes mortality from the mix of ESRD patient modalities (such as peritoneal
dialysis or kidney transplant) at each age range in the United States. For each interval reported
by USRDS, we assumed a constant hazard of death. For example, USRDS publishes ESRD
mortality in the 1st 3 years of ESRD, then mortality in the 1st 5 years of ESRD. To determine
mortality rates in years 3 – 5, we divided survival to 5 years by survival to 3 years and
calculated the associated annual rate of death. Annual rates were calculated using the most
recent survival data available. (See table S6 and figure S13 for detail on ESRD mortality
estimates).
To account for a range of uncertainty due to potential differences between the
composition of ESRD patients of a given age group nationally and within modeled cohorts, we
applied a range of +- 20% for mortality in ESRD in deterministic sensitivity analysis. Estimates
for probabilistic sensitivity analysis were based from varying the proportion of transplant
recipients and mix of patient co-morbidities in the ESRD population.
Modeling CKD Progression
In the base case, we assumed that patients had mild-to-moderate stage 3a CKD with
hypertension and a glomerular filtration rate (GFR) of 50 ml/min/1.73m2. Probability of
progression from stages 3a to 3b, 3b to 4, and 4 to 5 (ESRD) were obtained from a separate
microsimulation model implemented in SAS software. Published observations from several
comparable cohorts of CKD patients were used to estimate a mean annual rate of GFR decline
and population standard deviation (1-4). In the base case, patients were randomly assigned an
eGFR decline from a population distribution with a mean annual eGFR decline of 1.36
ml/min/1.73m2 and standard deviation of 3.75. Because these patient cohorts included many
patients with hypertension, the effect of hypertension on CKD progression was not modeled
separately. Additionally, because data suggest statins do not modify CKD progression (5-7)
despite modifying incidence of CVD, we assumed that patients who develop CVD do not
experience differential rates of CKD progression. (Table S7)
The cohort analyses reporting average rates of GFR decline are generally of limited
duration. For example, the mean duration of follow-up in the 4 studies used for estimating rate
of GFR decline was 3.12 years. There are few reports in the literature about within person
variation in rate of progression over a longer period of time. For example, if a person’s GFR
declines by 2 ml/min/1.73m2 in one decade it is unknown whether they can be expected to
progress faster, slower, or at the same rate over the next decade. The degree of correlation in
rate of decline within individuals has a significant impact on timing and the proportion of a cohort
ultimately progressing to more advanced stages of CKD and ESRD. Figures S14 and S15
illustrate differences in the proportion of simulated individuals progressing through each CKD
stage over time depending on assumptions about within individual correlation (prior to including
death as a competing risk). When each patient is assumed to receive a constant rate of GFR
decline (Figure S14), some patients progress rapidly to ESRD, while others remain in stage 3a
indefinitely. In contrast, if there is no correlation within a patient’s rate of progression each year,
eventually the entire population progresses to stages 4 then 5 CKD, although progression to
more advanced CKD stages takes longer (Figure S15).
To determine which of these two assumptions produces CKD progression rates more
reflective of what occurs when actual population cohorts are observed over long periods of time,
we simulated our CVD and CKD model without statins under varying assumptions about withinpatient correlation in CKD progression in the base case. Because death is a competing risk
with ESRD, the proportion of a cohort progressing to ESRD over time is determined by the
relative rates of death and rate of CKD progression since patients must survive to progress to
ESRD. Several cohort studies over 10-15 year periods have reported the proportion of patients
with stages 3 and 4 CKD who progress to ESRD (4,8-10). In a European cohort of 3047
patients with stage 3 CKD the 10-year incidence of ESRD was 4% (95% CI 3%-6%) (4), while
the HUNT II study of patients in Norway found a 10-year rate of ESRD of 2.2% in those with a
GFR between 45-60 ml/min/1.73m2 (8). A comparison of the population from HUNT II to the
U.S. population using NHANES concluded progression to ESRD is 2.5 times more likely in the
U.S., which would translate to approximately 5.3% progression to ESRD in the U.S. (8). In a
U.S. cohort from the Cardiovascular Health Study of older patients (mean age 75), 3% of
patients with eGFR between 45 and 60 ml/min/1.73m2 progressed to ESRD in 9.7 years of
follow-up (9) while the MRFIT study of patients at high CV risk in 1973 found a risk of ESRD
between 1.5 and 2% at 10 years, 2.5% at 15 years, and 5% at 25 years (10). The results from
MRFIT are likely to be an underestimate of current day progression rates due to higher mortality
rates in patients prior to progression to ESRD in the 1970s and 1980s. These studies suggest
that progression to ESRD in the U.S. occurs in approximately 2 to 5% of patients at 10 years
with perhaps twice that amount over a longer period such as 25 years.
Figures S16-18 illustrate the proportion of 50, 65 and 80 year-olds who progress to CKD
stages 4 and 5 under 1) Assumption of constant rate of CKD progression within patients; 2)
Assumption of new rate of progression assigned at random from the population distribution
every year. As the figures illustrate, if CKD progression occurs at a constant rate for each
individual, in 10 years approximately 25% of 50 year-olds, 21% of 65 year-olds and 9% of 80
year-olds will have progressed to ESRD. In contrast, if patients receive new progression rates
every cycle, 5% of 50 year-olds, 4% of 65 year-olds, and 1% of 80 year-olds progress to ESRD
in 10 years. Because the rates of ESRD progression resulting from the assumption that an
individual’s rate of CKD progression can vary every year are more consistent with those
observed in long-term cohorts, we used this assumption in the simulation model.
Determining Baseline Cardiovascular (CV) Risk
Baseline risk for myocardial infarction (MI) and stroke were derived from Framingham
Risk scores (11,12). 1-year risk of MI and stroke were estimated for men and women with no
hypertension, mild hypertension (SBP 120-130 on treatment) and moderate hypertension (SBP
130-140 on treatment) separately. Baseline CV risk was estimated for a hypothetical patient
being treated for moderate hypertension, with a total cholesterol between 160mg/dl and
200mg/dl and no other “traditional” cardiovascular risk factors aside from age.
The annual probabilities of stroke for each age in men and women with and without
hypertension were calculated using a Framingham prediction model described by Wolf et al
(12). (Table S8)
Framingham risk scores were calculated for men and women at each age between 50
and 80 for each CV risk (in the base case a patient with moderate hypertension with a total
cholesterol between 160mg/dl and 200mg/dl and no other cardiovascular risk factors aside from
age) (11). Since the Markov Model operated in cycles less than 1 year in duration, and extra
step was taken to transform 10-year probabilities of MI predicted from Framingham risk scores
into 1-year probabilities for inclusion in the Markov model. To do this, the annual rate of MI at
each age was expressed as a function of age and two constants in the following form:
(Rate of MI)Age = α*e^β*(Age)
A solver function was used to determine α and β such that the sum of the absolute values of the
difference between the 10-year probabilities of MI resulting from the estimated rate function and
the 10-year probabilities of MI predicted from the Framingham risk scores were minimized. For
men with the baseline characteristics, the Framingham-based 10-year probability of MI reached
a maximum of 20% at age 75 while for women it reached a maximum of 14% at age 75. As a
result, the solver function minimized differences in 10-year probabilities between ages 50 to 75
in men and women. However, to allow for a smooth transition to the maximum annual rate (the
constant annual rate associated with 20% 10-year probability of MI in men and 14% probability
of MI in women) the rates produced from the rate function were used for several years after
ages 75 in men and 70 in women until the annual rate reached the maximum rate, at which time
the maximum rate was used for all subsequent ages. (Table S9; Figure S19; Figure S20)
Because the model tracks patient events at 3 month intervals, 1-year probabilities of MI were
converted to 3-month rates using an exponential transformation:
𝑃3−𝑚𝑜𝑛𝑡ℎ = 1 − 𝑒
1−𝑙𝑛(1−𝑃12−𝑚𝑜𝑛𝑡ℎ𝑠 )
)
4
−(
Calibrating Rates of Non-Cardiovascular Death
Prior to Inclusion of Chronic Kidney Disease (CKD):
Acute complications of MI and stroke and an increased risk of death following these events
comprise a sizable portion of mortality in the general population represented in U.S. Life Tables
and in patients with chronic kidney disease. Consequently, by including MI and stroke – and an
increased hazard of death following these events – as a competing risk of death our model
would overestimate mortality in the population by “double counting” death associated with these
events. To correct for this, an age and gender-based adjustment factor was applied to the rate
of death obtained from U.S. Life Tables in every cycle of the model to estimate the rate of nonCV mortality. The adjustment factor was first determined prior to including additional risks
associated with CKD in the model. The following steps were performed to make this
adjustment:
1) The prevalence of hypertension in the U.S. was obtained from the National Health and
Nutrition Examination Survey for men and women at each decade of age (13).
Probabilities of MI and stroke as a function of age for non-hypertensive men and women
were generated from Framingham scores using the method described above.
2) The cardiovascular model was run for individual cohorts of men and women ages 50, 60,
70, and 80 prior to inclusion of assumptions related to CKD.
For each age and gender
cohort the model was run twice. First, it was run for individuals without hypertension
(using Framingham-based cardiovascular risks derived for that population). Second, it
was run for individuals with moderate hypertension. For each age cohort, the model was
run for 10 years and annual mortality rates were calculated over that period.
3) For each simulated year, a weighted average of the mortality rate from the hypertensive
and non-hypertensive cohorts was obtained based on prevalence of hypertension in the
U.S. population for a given age group and gender.
4) Average life expectancy over 10 years (based on the weighted average of the mortality
rates in the hypertensive and non-hypertensive cohorts) was compared with 10-year life
expectancy from U.S. life tables. An age and gender based adjustment was made to
rate of death in each 10-year age and sex group such that 10 year mortality was within
0.1 percent of that from the U.S. life tables.
After Inclusion of Stages 3 and 4 CKD:
Although there are few reports in the literature of the increased hazard of noncardiovascular death in patients with CKD compared to those without CKD, several large
epidemiologic studies have described the magnitude of the increased risk of cardiovascular
events and death from all causes experienced by patients with stages 3 and 4 CKD compared
with the non-CKD population (14-16). In our decision analytic model, in patients not given
statins the presence of CKD (stages 3a, 3b, and 4) modify the likelihood of mortality through the
following mechanisms: 1) Increasing the risk of MI and stroke; 2) Increasing the risk of death
from acute MI and stroke; 3) Increasing the risk of death following MI and stroke; 4) Increasing
the risk of death from non-cardiovascular causes. Since the increased risk of CV events (14,15)
and increased risk of death (16) due to CV events in the CKD vs. non-CKD population is well
described, and the increased risk in death from all causes (14-16) is well described , we
imputed the hazard of non-cardiovascular death associated with CKD stages 3a, 3b and 4 such
that the model produced accurate ratios in the rate of death from any cause in the CKD versus
non-CKD populations. This hazard of non-CV death was applied prior to acute cardiovascular
events. This was done in the following steps:
1) 4 separate versions of the model were created. One version consisted of cohorts with
hypertension but no CKD (i.e. without the increased probabilities of death and CV events
associated with CKD). A second version consisted of cohorts with stage 3a CKD who
did not progress to 3b, a third consisted of cohorts with stage 3b who did not progress to
stage 4, and a fourth version consisted of cohorts with stage 4 CKD who did not
progress to ESRD.
2) The rates of mortality from all causes in the stage 3a, 3b and 4 CKD versions of the
model were compared with mortality rates in the non-CKD versions to obtain risk ratios
associated with each stage of CKD.
3) For each comparison – 1) stage 3a CKD versus no CKD; 2) stage 3b versus no CKD; 2)
stage 4 CKD versus no CKD – eight separate cohorts were created including men and
women starting the Markov model at ages 50, 60, 70 and 80. Each cohort was followed
for 10 years from the start age, and the annual rate of mortality in each year was
calculated.
4) Mortality rates for identical age-and-gender-matched cohorts were compared (e.g.
mortality for 60 year old women with stage 4 CKD was compared with mortality for a 60year-old woman with hypertension and no CKD). Among matching cohorts, a risk ratio
was calculated each year by dividing the mortality rate in the CKD group by the mortality
rate in the non-CKD group.
5) For each comparison, the average of the ratios for all 8 paired cohorts was calculated.
This number provided an estimate of the relative difference in all-cause mortality in
patients with CKD stages 3a, 3b, and 4 compared with no CKD
6) The imputed values – non-cardiovascular hazard of death associated with CKD stages
3a, 3b and 4 – were incrementally increased until the average all-cause mortality in CKD
versus no-CKD produced from the modeled cohorts was equal to that reported in the
literature (1.2 for stage 3a CKD, 1.8 for stage 3b CKD and 3.2 for stage 4 CKD). (14,16)
(See figures S21-23)
Tables S2 and S3 illustrate how life expectancy by age for men and women changes with the
addition of each disease state. Life expectancies from U.S. Life Tables are included for
comparison. Each addition of a new disease state (hypertension, non-progressive CKD,
progressive CKD) reduces life expectancy as expected.
Selected Model Assumptions
Probability and Hazard Rate Assumptions:
The probabilities of death due to an acute MI or stroke were derived from large
observational studies from the general population (16-23). The added probability of death due to
CKD was derived from comparisons of mortality rates following acute MI and stroke in patients
with and without CKD (16). The increased hazards of death following an acute MI and stroke
were derived from observational studies estimating the increased hazard of death following CV
events after controlling for other patient characteristics (20-24).
Cost, quality of life, and incidence of Myopathy:
The probability of myopathy while on statins was obtained from a report in the literature.
(25) Patients with myopathy experienced a one-time cost of checking a creatinine kinase (CK)
level, obtained from Medicare Laboratory Fees (26), while the quality of life loss due to myalgias
was derived from the quality of life reported for mild osteoarthritis of the hip that does not impair
function (27).
Probability of Rhabdomyolysis:
Literature on adverse events such as myalgias and rhabdomyolysis is not conclusive.
Many of the clinical trials involving statins found no increased risk of rhabdomyolysis, although
patient selection, study size, and closer monitoring in the trial setting likely contributed to the
absent risk. Studies of adverse events when statins have been given in the community provide
important insight about the magnitude of risk in the general population (28-30). For example,
one review found a rate of 0.44 cases of rhabdomyolysis per 10,000 person-years of patients
taking statins (29). Excluding patients taking cerivastatin (which has been removed from the
market due to muscle toxicity) a study of adverse events reported to the U.S. Food and Drug
Administration (FDA) found rates consistent with 0.61 cases per 10,000 patient-years assuming
patients fill 2 prescriptions per year (31), while a study in Sweden found rates consistent with
1.72 cases per 10,000 person years (30).
It is unclear to what degree CKD elevates this risk. One epidemiologic survey of patients
with kidney disease found a rate of 7.62 cases per 10,000 person years, which was comparable
to the rate found among healthy people taking high-dose statins (28). The same analysis
compared risk of rhabdomyolysis in CKD using a multivariate model and found an increase in
the odds of rhabdomyolysis of 3.75 in people with kidney disease compared with those without
(28).
In our base case, we used an average of the upper and modified lower values that have
been reported. To develop a modified lower value, we multiplied the rate of 0.44 cases per
10,000 person-years reported by Graham et al. for the general population by the increased odds
of rhabdomyolysis associated with CKD (3.75). The upper value was the 7.62 cases per 10,000
patient-years in patients with CKD reported by McClure et al. (28). The average of these
estimates (used in our base case) was 4.64 cases per 10,000 person years. Due to large
uncertainty about the risk of muscle toxicity in CKD we explored a wide range on sensitivity
analysis, varying the estimated risk of rhabdomyolysis by factors of 10 in each direction, which
is similar in magnitude to the differential rates of rhabdomyolysis reported by McClure et al.
between patients in the general population taking high dose and low dose statins (7.7 vs. 0.86
cases per 10,000 individuals) (28).
Our assumptions about the probability of death among patients who develop
rhabdomyolysis were derived from literature reporting mortality rates from rhabdomyolysis in the
general population (25,28-30,32,33). The cost associated with rhabdomyolysis was obtained
from case reports of patients hospitalized with rhabdomyolysis (34). We assumed patient
experienced zero QALYs during a 2-week admission for rhabdomyolysis.
Risk Reduction from Statins:
A number of secondary analyses of large clinical trials on statins have estimated the
treatment effect from statins for both primary and secondary prevention in patients with early
CKD (35-38). The recently reported SHARP trial describes reduction in cardiovascular events
in patients with more advanced CKD (39). For patients with stage 3 CKD, we used the relative
risk reduction in cardiovascular mortality published from a Cochrane Review of statins in CKD
(5). This analysis was primarily reflective of results from the Pravastatin Pooling Project, (37)
and reports risk reductions similar in magnitude to two recently published meta-analyses (6,7).
(Table S1) The ranges on sensitivity analysis came from a combination of the meta-analysis
and Pravastatin Pooling Project. For patients with stage 4 CKD, we used risk reduction from the
SHARP trial. When testing cost-effectiveness of brand name rosuvastatin we used results from
a secondary analysis of patients with CKD from the JUPITER trial (38).
Cost of healthy year:
Medical costs associated with the healthy state (i.e. no history of cardiovascular disease,
prior to addition of costs associated with CKD stage) were obtained from published literature
(40). To avoid bias associated with the categories used for cost reporting, we interpolated a
smoothed cost curve. This was done with the following assumptions:
1) Costs increase linear with age between reported categories
2) Costs reported for each age group represent the cost incurred at the median age of
that cohort.
Figure S24 illustrates a comparison of the reported and interpolated health care costs.
Additional Cost of Stage III CKD, Stage IV CKD, and ESRD:
The incremental increase in medical costs due to stages III and IV CKD were obtained
from Smith et al. (41). They represent the incremental cost associated with CKD observed in a
managed care population (Kaiser Permanente Northwest), after adjusting for demographic and
comorbid conditions in a multivariate analysis. These costs were derived from departmental
expenditures, administrative costs, indirect costs and joint costs multiplied by utilization volume.
The cost of ESRD was obtained from the average paid per patient by Medicare in 2008
reported by the USRDS Atlas of ESRD. We used Medicare costs because it is the primary
payer for ESRD. Because the USRDS Atlas also includes outpatient prescription drug costs for
patients with and without prior kidney transplant, these were included in the cost estimates.
While we did not explicitly include the cost of statins for patients who develop ESRD in the
model, the ESRD total costs used in the model do reflect the degree of continued statin use that
occur in patients with ESRD in the United States from whom these costs were estimated.
Cost of Myocardial Infarction and Stroke:
The cost of an acute myocardial infarction was obtained by Kauf et al. (42) and was
based upon the average Medicare hospital and physician reimbursement paid in 2002. The
costs in the first year and in subsequent years after an MI were obtained from cost data
collected from a clinical trial (43). Costs in the first year after an MI were obtained from an
average of the cost following an MI in the treatment and control arms of the clinical trial, while
the cost for subsequent years was obtained from reported costs in the treatment arm since
control arm costs appeared unrepresentatively low.
The cost of an acute stroke, the cost of the 1st year following a stroke, and the cost in
subsequent years following a stroke were derived from three studies (44-46). The cumulative
first year costs (cost of acute stroke and remaining first year costs) were comparable in the
three studies (In 2010 dollars: $33,158 in Samsa et al. (44), $26,287 in Leibson et al. (45) and
$23,243 to $31,665 depending on age by Taylor et al. (46)). We used the estimates from
Leibson et al. in the base case since their estimates are in the range Taylor et al. reports and
comparable to the cost reported by Samsa et al. which comes from Medicare claims. The
estimates from Leibson et al. are based on inpatient and outpatient charges in a healthcare
system in Rochester, Minnesota in 1987 – 1989.
The cost of subsequent years following a stroke were obtained from Samsa et al. and is
based from Medicare claims in patients over 65 in the 4 years following a diagnosis of cerebral
infarction in 1991 (44). The estimate ($6,060 per year in 2010 dollars) is comparable to the
lower age estimate from Taylor et al. (46), which was acceptable since Taylor et al. reports both
direct and indirect costs of a stroke.
Cost of Statins:
In the base case, the annual cost of generic statins available from discount retailers and
integrated health systems was derived from a Wal-Mart online price list (47) and Zen Rx
Research. (48) Average retail prices were obtained from Consumer Reports “Best Buy Drugs”
(47). Cost of laboratory testing and physician monitoring came from the Medicare clinical
laboratory and physician fee schedules (26,49).
Quality of life in a healthy year:
Quality of life in the healthy population varied with age in accordance with the addition of comorbidities that occur with population aging. These estimates were obtained from the Beaver
Dam study (50) and were used as a baseline from which to add any additional quality of life
decrements associated with chronic kidney disease, cardiovascular disease, cerebrovascular
disease, or myopathy. Age-based QALYs are reported in Beaver Dam study for specific age
groups. To allow for a continuous decrement of QALYs with aging, rather than step-wise, we
made the following assumptions:
1) The QALY associated with a given age range was equal to the QALY for individuals at
the median age within that range.
2) QALY decreases linearly between any two age ranges.
From these assumptions, we interpolated QALYs for each age and sex. Figure S25 illustrates
the interpolated vs. reported values.
Quality of life based on CKD stage:
Quality of life has been shown to decline due to burden of CKD. While the decline in quality
of life is small in early-moderate stage 3 CKD, it becomes more significant as CKD advances.
Estimates of quality of life in CKD came from several sources. Gorodetskaya et al. published
“Health-related Quality of Life and Estimates of Utility in Chronic Kidney Disease” where they
described the burden associated with stages 3, 4, and 5 (ESRD) CKD (51). Because our model
further delineates between stages 3a and 3b, we imputed quality of life for these states. To do
this we assumed:
1) Quality of life in stage 3 CKD declined in a linear fashion
2) Quality of life associated with stage 4 CKD began at GFR of 30 (i.e. entry into stage 4)
We divided the interval of stage 3 CKD into thirds using the following equation:
(1 – QALYstage4)/3 = y. Then, 1 – y was equal to the QALY associated with stage 3a.
Meanwhile, 1 – 2y was equal to the QALY associated with stage 3b. With a Stage 4 QALY of
.875, this produced stage 3a QALY of 0.958 and stage 3b QALY of 0.917. The average, 0.9375
was nearly identical to 0.93, reported by Gorodetskaya et al. for the average QALY associated
with stage III CKD. QALY associated with stage 5 CKD (i.e. ESRD) was reported as 0.704.
This is also consistent with several indices published in “One Thousand Health Related Quality
of Life Estimates (27)”.
Quality of life loss in patients with ESRD (0.704) was derived from an analysis by
Gorodetskaya et al. under the “effects of kidney disease” for patients with a GFR under 15
ml/min/1.73m2 or on dialysis (51). This was comparable to the range reported by Tengs et al.
(0.50 to 0.84) (27) for various dialysis-related conditions.
Quality of life following myocardial infarction and stroke:
Quality of life following a myocardial infarction was derived from Tengs & Wallace for the
category “a history of coronary heart disease” and was 0.80 (27). This was lower than the
quality of life for survivors of myocardial infarction reported in another analysis (0.87), but the
latter analysis excluded patients with congestive heart failure(52). Consequently the higher
estimate motivated the upper range (0.90) used in the deterministic sensitivity analysis. The
lower range in the deterministic sensitivity was based on estimates reported by Tengs &
Wallace (27) for various coronary artery disease-related conditions and was 0.50.
For quality of life following a stroke, baseline assumptions and the range in the
deterministic sensitivity analysis were derived from Tengs & Wallace (27). The baseline
assumption was 0.80, while the deterministic sensitivity assumption included a range of 0.40 to
0.92.
Deterministic Sensitivity Analysis Results and Exploratory Analyses
Virtually all of the model’s assumptions were tested using one-way sensitivity analysis.
These analyses provide several insights. First, lower rates of CKD progression led to
improvement in the cost-effectiveness of statins in women; an annual GFR decline from 1.0 to
2.0 ml/min/1.73m2 led to statins costing between $49,500 to $77,000 per QALY gained in 50
year-old women. (Figure S26) In 65 year-old men and women, the cost of statin therapy
ranged from $13,800 to $40,100 per QALY gained over the range of relative risk reductions
described from statins in CKD stage 3. (Figure S27)
In a 2-way sensitivity analysis, cost-
effectiveness of statins therapy was more sensitive to the rate of rhabdomyolysis in younger
men if the relative risk reduction from statins is diminished (i.e., a diminished treatment effect
with higher rates of complications) than our baseline assumption. (Figure S28) If Statins were
to reduce CKD progression, they become more cost-effective. This occurs with relatively low
benefits in CKD progression such as a 5 percentage point reduction in rate of progression.
(Figure S6) The results do not vary markedly when the remaining cost, quality-of-life,
probability, and hazard assumptions are varied. (Figures S29-30)
Exploratory analyses of additional statin side-effects:
Due to recent reports of an increased risk of additional side-effects from statins, we conducted
sensitivity analyses where 1) patients had a risk of developing diabetes while on statins; 2)
patients had a risk of developing irreversible memory loss while on statins. (53,54)
The following estimates of the probability of diabetes, costs, quality of life, and increased
hazard of death with diabetes were obtained from literature:

Probability of diabetes while on statins: 2 cases per 1,000 person years (53).

Additional annual medical cost due to diabetes: $6,702 in 2010 dollars (55).

Quality adjusted life years associated with diabetes: 0.80 (56).

Increased hazard of death in all health states associated with diabetes: 2.31 (57).
It is important to note that data on increased risks of diabetes from statin use in CKD patients
are currently extremely limited. This exploratory analysis is conservative from the point of view
of statins’ cost-effectiveness in the CKD population because it potentially includes costs,
hazards or death, and quality of life decrements associated with CKD and CVD twice (once in
the model without diabetes, and again as a part of the cost, quality of life, and hazard estimates
due to diabetes).This implies that the decrements from each additional case of diabetes while
on statins applied in the model are likely higher than that which would be observed in an
empirical study.
The following estimates of the probability of irreversible memory loss while on statins
and quality of life associated with this condition were also obtained from literature (54,58):

Probability of irreversible memory loss while on statins: 1.1% of statin users (54).

Quality of life associated with mild memory loss: 0.82 reduction in QALYs (58).
If statins cause increases in type II diabetes rates for patients with CKD, the ICER from
statin increases to $74,911 per QALY gained in 65 year-old men (range from $60,662 to
$177,608 per QALY gained in men of different ages and CVD risks). In women (who have a
lower baseline cardiovascular risk), the ICER increases to $336,000/QALY gained in 65 yearolds, and a treatment strategy with statin therapy is dominated by one without statins at ages
below 60 (i.e. a strategy without statins yields more quality adjusted life years at a lower cost).
Irreversible memory loss increases the ICER to $61,347/QALY gained in 65 year old men and
$150,731 per QALY gained in 65 year old women. For younger men, the ICER is as high as
$103,795/QALY gained in 50 year olds, while in younger women, it is as high as
$704,000/QALY. (Figures S7-8)
Probabilistic Sensitivity Analysis
Distributions for probabilistic sensitivity analysis were obtained – when possible – from
uncertainty in estimates reported in the literature. (See Table S10) In instances where a wide
range of estimates have been reported in the literature, the distribution variance was increased
to reflect this. Assumptions regarding cost of ESRD were derived from national averages for
different age groups published by the USRDS.
The degree of uncertainty about ESRD costs for probabilistic sensitivity analysis was
estimated by determining a range of costs associated with varying mixes of transplant recipients
and privately versus publically insured recipients (upper 95%CI for cost assumed 10% fewer
transplants and 10% more privately insured). This uncertainty is likely correlated across age
groups. For instance, if there are 5% fewer transplant recipients and 5% fewer privately insured
individuals in our modeled cohort compared to prevalent ESRD patients in the United States in
one age group, a similar difference is likely to be present in the other age groups. However,
because older patients are less likely to receive transplants and are less likely to be privately
insured, the magnitude of the cost uncertainty (as a share of average ESRD costs) associated
with a given percentage point reduction (or increase) in the number of patients with transplants
or privately insured is smaller in older individuals. To account for this attenuation of uncertainty
at older ages while maintaining a realistic correlation of uncertainty across age groups, we
modeled ESRD cost uncertainty in the following manner:
1) We defined a single uncertainty distribution in terms of the percentage above/below the
base case estimates of ESRD costs for 50-59 year-olds. In this case, a normal
distribution with mean 1.0 and 95% confidence interval spanning 0.88-1.12. This
corresponded to +/-12% of the base case value for 50-59 year-olds.
2) We drew from the normal distribution defined in 1) and subtracted 1.0 to get the
percentage difference from the base case
3) We then multiplied this percentage difference by age specific multipliers defined in Table
S11, given the lower range of uncertainty in costs for older individuals with ESRD,
especially those on Medicare.
4) When then added 1.0 to the number in 3) and multiplied by the base case age-specific
ESRD costs, shown for convenience in Table S11 to produce a distribution of agespecific ESRD costs that were correlated across age groups and had lower uncertainty
for older individuals with ESRD.
Uncertainty regarding mortality in ESRD was obtained from reported mortality in the
setting of more or fewer kidney transplants in addition to differing racial and co-morbid mixes in
the ESRD population.
The analysis adopted a societal perspective, and discounted all healthcare costs and
benefits at 3% annually. We used TreeAge software to implement the decision model and to
perform the deterministic sensitivity analyses as well as the probabilistic sensitivity analyses.
Table S1: Studies on Risk Reduction from Statins in Mild-to-Moderate (Stage 3) CKD
Pravastatin Pooling Project (primary prevention)
JUPITER (primary prevention)
TNT (includes CHD)
HR
0.77
0.55
0.68
LCI
0.68
0.38
0.55
UCI
0.86
0.82
0.84
N
4,491
3,267
3,107
RR
LCI
UCI
N
0.8
0.75
0.74
0.66
0.89
0.85
18,781
19,363
0.78
0.76
0.68
0.73
0.89
0.8
35,417
45,362
Meta-analyses
Navaneethan et al.:
CV mortality Non-fatal CV event Palmer et al.:
CV mortality Non-fatal CV event -
Upadhyay et al.:1
CV mortality 0.82
0.74
0.91
13,211
Non-fatal CV event 0.78
0.71
0.86
18,407
Sources: (5-7,35,37). HR = Hazard Ratio; LCI and UCI = Lower and Upper 95% Confidence
Intervals
Table S2: Modeled Versus Reported Life Expectancy in Men under Different Scenarios
(prior to use of Statins)
50
55
60
65
70
75
80
85
Life Table
No CKD,
Hypertensive
CKD Not
Progressing
Beyond Stage III
Progressive
CKD
28.8
24.7
20.7
17.0
13.6
10.4
7.8
5.7
27.9
23.8
20.0
16.4
13.1
10.2
7.7
5.7
22.6
19.1
15.8
12.9
10.2
7.9
6.0
4.5
17.9
15.9
13.8
11.6
9.5
7.5
5.8
4.4
Table S3: Modeled Versus Reported Life Expectancy in Women under Different Scenarios
(prior to use of Statins)
50
55
60
65
70
75
80
85
Life
Table
No CKD,
Hypertensive
CKD Not
Progressing
Beyond Stage III
Progressive
CKD
32.5
28.0
23.8
19.7
15.9
12.3
9.3
6.8
32.1
27.7
23.5
19.5
15.7
12.3
9.3
6.8
27.1
23.1
19.3
15.8
12.5
9.7
7.3
5.4
20.0
18.1
16.0
13.7
11.4
9.1
7.0
5.3
Table S4: Statins at Average Retail Prices – Health Benefits, Costs, and Incremental CostEffectiveness Ratio from Statin Therapy for Patients with Different Age, Sex, and
Cardiovascular Risk Profiles
Starting
Age
10-Year
Probability
of MI
%
Increased
Cost
($)
Gain in
Increased life
QALYs
expectancy
(discounted) (undiscounted)
(months)
Reduced risk
of MI or
stroke
(percent)
Incremental
costeffectiveness
ratio
($/QALY)
Men
50
6
7,000
0.09
1.6
4.4
82,600
55
10
6,700
0.09
1.7
4.8
71,800
60
12
6,200
0.10
1.7
5.0
63,500
65
16
5,600
0.10
1.6
5.1
57,200
70
17
4,800
0.09
1.5
5.0
52,600
75
20
4,000
0.08
1.2
4.7
51,800
80
20
3,200
0.06
0.9
4.1
55,400
85
20
2,400
0.04
0.6
3.3
62,600
Women
50
1
7,700
0.03
0.7
2.1
257,700
55
2
7,500
0.04
0.9
2.6
191,700
60
3
7,100
0.05
1.0
3.1
148,000
65
5
6,600
0.06
1.1
3.5
117,000
70
8
5,800
0.06
1.1
3.7
97,800
75
14
4,800
0.06
1.1
3.8
82,800
80
14
3,800
0.05
0.9
3.6
74,300
85
14
2,900
0.04
0.6
3.0
79,300
Because costs are rounded to the nearest $100, incremental cost-effectiveness ratios may be
slightly different than the incremental costs and QALYs in the table suggest.
Table S5: Detailed Results from the Base Case
Statins
Cost ($)
No statins
Difference
Discounted QALYs
Statins No statins
Difference
ICER
($/QALY)
Men
50
204,300
202,500
1,700
10.74
10.66
0.09
20,500
55
186,300
184,500
1,800
9.59
9.50
0.09
19,600
60
176,200
174,400
1,800
8.44
8.35
0.10
18,900
65
172,100
170,300
1,800
7.31
7.21
0.10
18,000
70
161,500
160,000
1,600
6.14
6.05
0.09
16,900
75
145,700
144,400
1,300
5.04
4.96
0.08
16,300
80
123,200
122,300
900
4.02
3.96
0.06
16,100
85
94,500
94,000
600
3.12
3.09
0.04
15,400
Women
50
230,100
228,400
1,700
11.46
11.43
0.03
56,800
55
216,000
214,200
1,800
10.50
10.46
0.04
46,200
60
209,300
207,400
1,900
9.43
9.38
0.05
39,200
65
206,200
204,300
1,900
8.26
8.20
0.06
33,400
70
194,000
192,200
1,700
6.98
6.92
0.06
29,300
75
174,100
172,700
1,400
5.70
5.65
0.06
25,000
80
146,400
145,400
1,100
4.56
4.51
0.05
21,300
85
111,700
111,000
700
3.58
3.54
0.04
19,800
Because costs are rounded to the nearest $100, cost differences and incremental costeffectiveness ratios may be slightly different than the incremental costs and QALYs in the table
suggest.
Table S6: Annual Mortality Rate with ESRD by Incident Age
Incident
ESRD
90 days
From:
90 days
1 year
2 years
To:
1 year
2 years
3 years
Age:
50-59
0.18
0.09
0.11
0.12
60-64
0.24
0.11
0.15
0.17
65-69
0.32
0.14
0.18
0.20
70-79
0.48
0.19
0.25
0.26
80-84
0.65
0.26
0.34
0.34
85+
0.93
0.28
0.44
0.44
Note: Time intervals include specified interval plus 90 days
3 years
5 years
5 years
indefinitely
0.12
0.16
0.20
0.30
0.42
0.50
0.13
0.20
0.27
0.34
0.44
0.49
Table S7: Studies on Annual Rate of CKD Progression
Base Case SD: 3.75
SD from AASK --- 3.3, MDRD 4.2, VA approx 3.75
VA study (1)
MDRD (2)
European (4)
AASK (3)
Average annual
GFR decline
-1.152
-3.800
-1.030
-1.640
Sum:
Persons
4171
840
3047
1094
9152
Follow-up
(years)
2.6
2.2
3.7
4.0
12.5
Personyears
10844.6
1873.5
11172.3
4376.0
28266.4
Study
Weight
0.38
0.07
0.40
0.15
1.00
Weighted
GFR
Decline
-0.44
-0.25
-0.41
-0.25
-1.36
Table S8: Framingham-Based Probability of Stroke in One Year by Age in Patients with
Moderate Hypertension
Age
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
Men
0.0022
0.0023
0.0024
0.0025
0.0027
0.0028
0.0030
0.0031
0.0033
0.0034
0.0036
0.0038
0.0040
0.0042
0.0044
0.0047
0.0049
0.0052
0.0054
0.0057
0.0060
0.0063
0.0066
0.0070
0.0073
Women
0.0010
0.0010
0.0011
0.0012
0.0013
0.0013
0.0014
0.0015
0.0016
0.0017
0.0019
0.0020
0.0021
0.0023
0.0024
0.0026
0.0028
0.0030
0.0032
0.0034
0.0036
0.0038
0.0041
0.0044
0.0047
Age
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
Note: Probability constant after age 84 when the study stopped
Men
0.0077
0.0081
0.0085
0.0090
0.0094
0.0099
0.0104
0.0110
0.0115
0.0121
0.0121
0.0121
0.0121
0.0121
0.0121
0.0121
0.0121
0.0121
0.0121
0.0121
0.0121
0.0121
0.0121
0.0121
0.0121
0.0121
Women
0.0050
0.0053
0.0057
0.0061
0.0065
0.0069
0.0074
0.0079
0.0084
0.0090
0.0090
0.0090
0.0090
0.0090
0.0090
0.0090
0.0090
0.0090
0.0090
0.0090
0.0090
0.0090
0.0090
0.0090
0.0090
0.0090
Table S9: Framingham-Based Probability of Myocardial Infarction in 1 Year by Age in
Patients with Moderate Hypertension
Age
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
Men
0.0046
0.0049
0.0052
0.0055
0.0059
0.0063
0.0067
0.0071
0.0075
0.0080
0.0085
0.0090
0.0096
0.0102
0.0108
0.0115
0.0122
0.0130
0.0138
0.0146
0.0155
0.0165
0.0175
0.0186
0.0198
Women
0.0006
0.0006
0.0007
0.0007
0.0008
0.0009
0.0010
0.0011
0.0012
0.0014
0.0015
0.0017
0.0019
0.0021
0.0023
0.0025
0.0028
0.0031
0.0034
0.0038
0.0041
0.0046
0.0051
0.0056
0.0062
Age
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
Men
0.0210
0.0221
0.0221
0.0221
0.0221
0.0221
0.0221
0.0221
0.0221
0.0221
0.0221
0.0221
0.0221
0.0221
0.0221
0.0221
0.0221
0.0221
0.0221
0.0221
0.0221
0.0221
0.0221
0.0221
0.0221
0.0221
Women
0.0069
0.0076
0.0084
0.0093
0.0102
0.0113
0.0125
0.0138
0.0150
0.0150
0.0150
0.0150
0.0150
0.0150
0.0150
0.0150
0.0150
0.0150
0.0150
0.0150
0.0150
0.0150
0.0150
0.0150
0.0150
0.0150
Table S10: Probabilistic Sensitivity Analysis Inputs
Intervention effects:
(Targeted Mean
Value)
Range (95% CI)
Distribution
CV relative risk reduction from statins (stage 3 CKD and no CKD)
0.80
0.68
0.90
beta
CV relative risk reduction from statins (stage 4 CKD)
0.83
0.68
0.94
beta
Rate of rhabdomyolysis (per 10,000 person-years)
4.64
2.38
7.66
gamma
Probability of death from rhabdomyolysis
0.08
0.06
0.09
beta
Probability of myopathy on statins
0.09
0.06
0.13
beta
Non-CKD Natural History Parameters:
Probability of MI and stroke
(Framingham model)
(+-20%)
lognormal
Probability of death from acute MI
0.07
0.06
0.08
beta
Increased hazard of death after MI - hazard ratio
1.40
1.00
1.98
lognormal
Probability of death from acute stroke
0.12
0.11
0.14
beta
Increased hazard of death after Stroke - hazard ratio
2.36
1.95
2.76
normal
Increased all-cause mortality (CKD stage 3a) - hazard ratio
1.20
1.10
1.30
normal
Increased all-cause mortality (CKD stage 3b) - hazard ratio
1.80
1.70
1.90
normal
Increased all-cause mortality (CKD stage 4) - hazard ratio
3.20
3.00
3.40
normal
(see appendix text)
0.77
1.27
lognormal
Increased risk of MI and Stroke (CKD stage 3a) - hazard ratio
1.40
1.30
1.50
normal
Increased risk of MI and Stroke (CKD stage 3b) - hazard ratio
2.00
1.90
2.10
normal
Increased risk of MI and Stroke (CKD stage 4) - hazard ratio
2.80
2.65
2.95
normal
Increased hazard of death from acute MI due to CKD (all stages)
1.40
1.20
1.60
normal
Increased hazard of death from acute stroke due to CKD (all stages)
1.86
1.45
2.26
normal
1.36
1.14
1.60
lognormal
CKD-Specific Natural History Parameters:
Probability of death from ESRD
Rate of CKD progression
(ml/min/1.73m2/yr)
Table S10 (continued):
Quality of life assumptions
(Targeted Mean
QALY)
CKD stage 3a
0.96
0.85
0.11
normal
CKD stage 3b
0.92
0.81
0.98
beta
CKD Stage 4
0.88
0.69
0.98
beta
CKD stage 5
0.70
0.50
0.87
beta
Following MI
0.80
0.76
0.84
beta
Following Stroke
0.80
0.68
0.90
beta
Myalgia
0.80
0.68
0.90
beta
Cost assumptions ($2010)
(Targeted Mean $)
Annual Cost Following MI
4,343
3,534
5,217
gamma
Annual Cost Following Stroke
6,060
3,100
9,984
gamma
Annual Cost of Stage 3a CKD
1,833
1,493
2,201
gamma
Annual Cost of Stage 3b CKD
4507
3,675
5,408
gamma
Annual Cost of Stage 4 CKD
5,844
1,201
14,054
gamma
Acute cost of MI
11,070
5,551
18,401
gamma
First year after MI
7,747
6,850
8,675
gamma
Acute cost of Stroke
18,516
15,309
21,955
gamma
First year after Stroke
7,770
4,940
11,200
gamma
Cost of Rhabdomyolysis
55,794
45,650
66,825
gamma
4
2
7
lognormal
Hepatic panel
11.7
1
48
lognormal
Level 1 office visit
19.7
9
39
lognormal
Monthly cost of Statin
Table S11: Costs and Distributions for ESRD
Age
Age-Based Multiplier applied to the percentage
difference drawn from a single uncertainty
distribution in the Probabilistic Sensitivity Analysis1
ESRD Cost2
50-59
1.000
66883.6
60-64
1.025
65671.9
65-69
1.041
70077.8
70-79
0.812
77574.5
80-84
0.371
84573.7
85+
0.246
85509.9
1
Multiplied by the cost drawn from the probabilistic sensitivity analysis
2
Range (95% CI)
Includes an estimate of Medicare Part D Costs
Range (95% CI)
Figure S1a: Model Schematic – Cardiovascular Disease Model
Probability of myocardial infarction and stroke are derived from probabilities from a Framingham
model multiplied by an additional hazard of cardiovascular (CV) event due to chronic kidney
disease (CKD) stage. Independent mortality hazards associated CKD stages are multiplied by
mortality rates from U.S. life tables to obtain mortality in the “healthy” state. Mortality following
myocardial infarction and stroke are derived from combining mortality from U.S. life tables,
mortality hazards following CV events in the general population, and hazards of death in
patients with CKD.
Figure S1b: Model Schematic – Chronic Kidney Disease Model
Not shown in the schematic, the model subdivides stage III CKD into IIIa and IIIb.
Figure S1c: Model Schematic – Statin Toxicities Model
Rhabdo. refers to rhabdomyolysis. Scenarios where patients develop diabetes or memory loss
were modeled separately. We assume patients with diabetes or memory loss remain on statins.
Figure S2: Cost-effectiveness of Statins under Different Assumptions about CKD
Progression
Figure S3: Statins at Average Retail Prices – Differing Cardiovascular Risk Groups in
Women
Figure S4: Statins at Average Retail Prices – Differing Cardiovascular Risk Groups in
Men
Figure S5: Sensitivity Analysis – Severity of CKD upon Statin Initiation
Figure S6: Sensitivity Analysis – Relative Risk Reduction in CKD Progression from
Statins in the Base Case
Figure S7: Sensitivity Analysis – Assuming Statins Cause Diabetes
*Treatment strategy without statins is the “dominant” strategy (i.e. leads to more quality-adjusted
life years at a lower cost)
Incremental cost-effectiveness ratio for 60 year old women is $1.5 million /QALY gained, while
for 65 year old women it is $338,000/QALY gained
Figure S8: Sensitivity Analysis – Assuming Irreversible Memory Loss from Statins
Figure S9: Probabilistic Sensitivity Analysis in 65 Year Old Men
Figure S10: Probabilistic Sensitivity Analysis in 50 Year Old Men
Figure S11: Probabilistic Sensitivity Analysis in 65 Year Old Women
Figure S12: Probabilistic Sensitivity Analysis in 50 Year Old Women
Figure S13: Survival after Developing ESRD by Age of Onset
Figure S14: Proportion of Simulated Cohort in Each CKD Stage assuming Constant GFR
Decline for Each Individual
Figure S15: Proportion of Simulated Cohort in Each CKD Stage assuming New Rate of
Decline for Each Individual per Year
Figure S16: Modeled CKD Progression in 50 Year-olds under Different Assumptions
about Within-person Variability in Rate of Progression:
Figure S17: Modeled CKD Progression in 65 Year-olds with Different Assumptions About
Within-person Variability in Rate of Progression:
Figure S18: Modeled CKD Progression in 80 Year-olds under Different Assumptions
about Within-person Variability in Rate of Progression:
Figure S19: 10-year Probability of MI in Men with Baseline Characteristics by Age
Figure S20: 10-year Probability of MI in Women with Baseline Characteristics by Age
Figure S21: Relative Increase in Mortality in Men with Stage 3a CKD vs. No CKD by Age
Figure S22: Relative Increase in Mortality in Men with Stage 3b CKD vs. No CKD by Age
Figure S23: Relative Increase in Mortality in Men with Stage 4 CKD vs. No CKD by Age
Figure S24: Estimated Cost of Healthy Year
Estimates based on reported costs in $2010 of $3,474 for people 50-64, 10,948 for ages 65-74, and 18,968 for 75+.
Figure S25: Quality of Life in Healthy Individuals with Age
Note: data imputed from Beaver Dam Study where for men aged 45-54, 55-64, 65-74, 75-84, and 84+, QALYs are:
0.0941, 0.874, 0.841, 0.838, 0.817 respectively. For women aged 45-54, 55-64, 65-74, 75-84, and 84+, QALYs are:
0.901, 0.871, 0.833, 0.792, 0.8 respectively.
Figure S26: Sensitivity Analysis of Rate of CKD Progression
Figure S27: Sensitivity Analysis – Relative Risk Reduction in CV Events from Statins in
Stage 3 CKD
Figure S28: Sensitivity to Rate of Rhabdomyolysis for 50 Year-old Men Experiencing Different
Relative Risk Reductions from Statins.
Figure S29: Deterministic Sensitivity Analyses in Women
Figure S30: Deterministic Sensitivity Analysis in Men
Table S12: Statins at Average Retail Prices – Detailed Results from the Base Case
Statins
Cost ($)
No statins
Difference
Discounted QALYs
Statins No statins
Difference
ICER
($/QALY)
Men
50
209,600
202,500
7,000
10.74
10.66
0.09
82,600
55
191,200
184,500
6,700
9.59
9.50
0.09
71,800
60
180,600
174,400
6,200
8.44
8.35
0.10
63,500
65
175,900
170,300
5,600
7.31
7.21
0.10
57,200
70
164,800
160,000
4,800
6.14
6.05
0.09
52,600
75
148,400
144,400
4,000
5.04
4.96
0.08
51,800
80
125,500
122,300
3,200
4.02
3.96
0.06
55,400
85
96,300
94,000
2,400
3.12
3.09
0.04
62,600
Women
50
236,100
228,400
7,700
11.46
11.43
0.03
257,700
55
221,700
214,200
7,500
10.50
10.46
0.04
191,700
60
214,500
207,400
7,100
9.43
9.38
0.05
148,000
65
210,900
204,300
6,600
8.26
8.20
0.06
117,000
70
198,000
192,200
5,800
6.98
6.92
0.06
97,800
75
177,500
172,700
4,800
5.70
5.65
0.06
82,800
80
149,100
145,400
3,800
4.56
4.51
0.05
74,300
85
113,800
111,000
2,900
3.58
3.54
0.04
79,300
Because costs are rounded to the nearest $100, cost differences and incremental costeffectiveness ratios may be slightly different than the incremental costs and QALYs in the table
suggest.
Figure S31: Statins at Average Retail Prices – Cost-effectiveness of Statins under
Different Assumptions about CKD Progression
1.
Al-Aly Z, Zeringue A, Fu J et al. Rate of kidney function decline associates with mortality. Journal
of the American Society of Nephrology 2010;21:1961-9.
2.
Hunsicker LG, Adler S, Caggiula A et al. Predictors of the progression of renal disease in the
Modification of Diet in Renal Disease Study. Kidney International 1997;51:1908-19.
3.
Lewis J, Greene T, Appel L et al. A comparison of iothalamate-GFR and serum creatinine-based
outcomes: acceleration in the rate of GFR decline in the African American Study of Kidney
Disease and Hypertension. Journal of the American Society of Nephrology 2004;15:3175-83.
4.
Eriksen BO, Ingebretsen OC. The progression of chronic kidney disease: a 10-year populationbased study of the effects of gender and age. Kidney International 2006;69:375-82.
5.
Navaneethan SD, Pansini F, Perkovic V et al. HMG CoA reductase inhibitors (statins) for people
with chronic kidney disease not requiring dialysis. Cochrane Database of Systematic Reviews
2009:CD007784.
6.
Upadhyay A, Earley A, Lamont JL, Haynes S, Wanner C, Balk EM. Lipid-Lowering Therapy in
Persons With Chronic Kidney Disease: A Systematic Review and Meta-analysis. Annals of Internal
Medicine 2012;157:251-62.
7.
Palmer SC, Craig JC, Navaneethan SD, Tonelli M, Pellegrini F, Strippoli GFM. Benefits and Harms
of Statin Therapy for Persons With Chronic Kidney Disease: A Systematic Review and Metaanalysis. Annals of Internal Medicine 2012;157:263-75.
8.
Hallan SI, Coresh J, Astor BC et al. International comparison of the relationship of chronic kidney
disease prevalence and ESRD risk. Journal of the American Society of Nephrology 2006;17:227584.
9.
Dalrymple LS, Katz R, Kestenbaum B et al. Chronic kidney disease and the risk of end-stage renal
disease versus death. Journal of General Internal Medicine 2011;26:379-85.
10.
Ishani A, Grandits GA, Grimm RH et al. Association of single measurements of dipstick
proteinuria, estimated glomerular filtration rate, and hematocrit with 25-year incidence of endstage renal disease in the multiple risk factor intervention trial. Journal of the American Society
of Nephrology 2006;17:1444-52.
11.
Expert Panel on Detection E, Treatment of High Blood Cholesterol in A. Executive Summary of
The Third Report of The National Cholesterol Education Program (NCEP) Expert Panel on
Detection, Evaluation, And Treatment of High Blood Cholesterol In Adults (Adult Treatment
Panel III). JAMA 2001;285:2486-97.
12.
Wolf PA, D'Agostino RB, Belanger AJ, Kannel WB. Probability of stroke: a risk profile from the
Framingham Study. Stroke 1991;22:312-8.
13.
Burt VL, Whelton P, Roccella EJ et al. Prevalence of hypertension in the US adult population.
Results from the Third National Health and Nutrition Examination Survey, 1988-1991.
Hypertension 1995;25:305-13.
14.
Go AS, Chertow GM, Fan D, McCulloch CE, Hsu C-y. Chronic kidney disease and the risks of
death, cardiovascular events, and hospitalization.[Erratum appears in N Engl J Med.
2008;18(4):4]. New England Journal of Medicine 2004;351:1296-305.
15.
Muntner P, He J, Hamm L, Loria C, Whelton PK. Renal insufficiency and subsequent death
resulting from cardiovascular disease in the United States. Journal of the American Society of
Nephrology 2002;13:745-53.
16.
USRDS. Annual Data Report: Atlas of Chronic Kidney Disease and End-Stage Renal Disease in the
United States. United States Renal Data System. Bethesda, MD: National Institutes of Health,
National Institute of Diabetes and Digestive and Kidney Diseases, 2011.
17.
Rogers WJ, Frederick PD, Stoehr E et al. Trends in presenting characteristics and hospital
mortality among patients with ST elevation and non-ST elevation myocardial infarction in the
National Registry of Myocardial Infarction from 1990 to 2006. American Heart Journal
2008;156:1026-34.
18.
Myerson M, Coady S, Taylor H, Rosamond WD, Goff DC, Jr., Investigators A. Declining severity of
myocardial infarction from 1987 to 2002: the Atherosclerosis Risk in Communities (ARIC) Study.
Circulation 2009;119:503-14.
19.
McGovern PG, Jacobs DR, Jr., Shahar E et al. Trends in acute coronary heart disease mortality,
morbidity, and medical care from 1985 through 1997: the Minnesota heart survey. Circulation
2001;104:19-24.
20.
Roger VL, Jacobsen SJ, Weston SA et al. Trends in the incidence and survival of patients with
hospitalized myocardial infarction, Olmsted County, Minnesota, 1979 to 1994.[Summary for
patients in Ann Intern Med. 2002 Mar 5;136(5):I16; PMID: 11874327]. Annals of Internal
Medicine 2002;136:341-8.
21.
Petty GW, Brown RD, Jr., Whisnant JP, Sicks JD, O'Fallon WM, Wiebers DO. Survival and
recurrence after first cerebral infarction: a population-based study in Rochester, Minnesota,
1975 through 1989. Neurology 1998;50:208-16.
22.
Hollander M, Koudstaal PJ, Bots ML, Grobbee DE, Hofman A, Breteler MMB. Incidence, risk, and
case fatality of first ever stroke in the elderly population. The Rotterdam Study. Journal of
Neurology, Neurosurgery & Psychiatry 2003;74:317-21.
23.
Dennis MS, Burn JP, Sandercock PA, Bamford JM, Wade DT, Warlow CP. Long-term survival after
first-ever stroke: the Oxfordshire Community Stroke Project. Stroke 1993;24:796-800.
24.
Gruppetta M, Calleja N, Fava S. Long-term survival after acute myocardial infarction and relation
to type 2 diabetes and other risk factors. Clinical Cardiology 2010;33:424-9.
25.
Thompson PD, Clarkson P, Karas RH. Statin-associated myopathy. JAMA 2003;289:1681-90.
26.
Services DoHaHRCfMaM. Clinical Laboratory Fee Schedule. 2010.
27.
Tengs TO, Wallace A. One thousand health-related quality-of-life estimates. Medical Care
2000;38:583-637.
28.
McClure DL, Valuck RJ, Glanz M, Murphy JR, Hokanson JE. Statin and statin-fibrate use was
significantly associated with increased myositis risk in a managed care population. Journal of
Clinical Epidemiology 2007;60:812-8.
29.
Graham DJ, Staffa JA, Shatin D et al. Incidence of hospitalized rhabdomyolysis in patients treated
with lipid-lowering drugs. JAMA 2004;292:2585-90.
30.
Hedenmalm K, Alvan G, Ohagen P, Dahl M-L. Muscle toxicity with statins.
Pharmacoepidemiology & Drug Safety 2010;19:223-31.
31.
Alsheikh-Ali AA, Ambrose MS, Kuvin JT, Karas RH. The safety of rosuvastatin as used in common
clinical practice: a postmarketing analysis. Circulation 2005;111:3051-7.
32.
Ballantyne CM, Corsini A, Davidson MH et al. Risk for myopathy with statin therapy in high-risk
patients. Archives of Internal Medicine 2003;163:553-64.
33.
Omar MA, Wilson JP. FDA adverse event reports on statin-associated rhabdomyolysis. Annals of
Pharmacotherapy 2002;36:288-95.
34.
Skrabal MZ, Stading JA, Cannella CA, Monaghan MS. Two cases of rhabdomyolysis associated
with high-dose simvastatin.[Erratum appears in Am J Health Syst Pharm. 2003 Sep
1;60(17):1733]. American Journal of Health-System Pharmacy 2003;60:578-81.
35.
Shepherd J, Kastelein JJP, Bittner V et al. Intensive lipid lowering with atorvastatin in patients
with coronary heart disease and chronic kidney disease: the TNT (Treating to New Targets)
study. Journal of the American College of Cardiology 2008;51:1448-54.
36.
Heart Protection Study Collaborative G. MRC/BHF Heart Protection Study of cholesterol
lowering with simvastatin in 20,536 high-risk individuals: a randomised placebo-controlled
trial.[Summary for patients in Curr Cardiol Rep. 2002 Nov;4(6):486-7; PMID: 12379169]. Lancet
2002;360:7-22.
37.
Tonelli M, Isles C, Curhan GC et al. Effect of pravastatin on cardiovascular events in people with
chronic kidney disease. Circulation 2004;110:1557-63.
38.
Ridker PM, MacFadyen J, Cressman M, Glynn RJ. Efficacy of rosuvastatin among men and
women with moderate chronic kidney disease and elevated high-sensitivity C-reactive protein: a
secondary analysis from the JUPITER (Justification for the Use of Statins in Prevention-an
Intervention Trial Evaluating Rosuvastatin) trial. Journal of the American College of Cardiology
2010;55:1266-73.
39.
Baigent C, Landray MJ, Reith C et al. The effects of lowering LDL cholesterol with simvastatin
plus ezetimibe in patients with chronic kidney disease (Study of Heart and Renal Protection): a
randomised placebo-controlled trial. Lancet 2011;377:2181-92.
40.
Meara E, White C, Cutler DM. Trends in medical spending by age, 1963-2000. Health Aff
(Millwood) 2004;23:176-83.
41.
D. H. Smith GAN, C. M. Gullion, E.S. Johnson,
D. Keith. Predicting Costs Of Care In Chronic
Kidney Disease: The Role Of Comorbid Conditions. The Internet Journal of Nephrology 2007;4.
42.
Kauf TL, Velazquez EJ, Crosslin DR et al. The cost of acute myocardial infarction in the new
millennium: evidence from a multinational registry. Am Heart J 2006;151:206-12.
43.
Tsevat J, Kuntz KM, Orav EJ, Weinstein MC, Sacks FM, Goldman L. Cost-effectiveness of
pravastatin therapy for survivors of myocardial infarction with average cholesterol levels. Am
Heart J 2001;141:727-34.
44.
Samsa GP, Bian J, Lipscomb J, Matchar DB. Epidemiology of recurrent cerebral infarction: a
medicare claims-based comparison of first and recurrent strokes on 2-year survival and cost.
Stroke 1999;30:338-49.
45.
Leibson CL, Hu T, Brown RD, Hass SL, OFallon WM, Whisnant JP. Utilization of acute care services
in the year before and after first stroke: A population-based study. Neurology 1996;46:861-869.
46.
Taylor TN, Davis PH, Torner JC, Holmes J, Meyer JW, Jacobson MF. Lifetime cost of stroke in the
United States. Stroke 1996;27:1459-66.
47.
Drugs CRHBB. Statin Drugs to Treat High Cholesterol and Heart Disease. 2010.
48.
Research Z. Prices for Pravastatin. March 7, 2012.
49.
Services DoHaHRCfMaM. Physician Fee Schedule. 2011.
50.
Fryback DG, Dasbach EJ, Klein R et al. The Beaver Dam Health Outcomes Study: initial catalog of
health-state quality factors. Med Decis Making 1993;13:89-102.
51.
Gorodetskaya I, Zenios S, McCulloch CE et al. Health-related quality of life and estimates of
utility in chronic kidney disease. Kidney Int 2005;68:2801-8.
52.
Tsevat J, Goldman L, Soukup JR et al. Stability of time-tradeoff utilities in survivors of myocardial
infarction. Med Decis Making 1993;13:161-5.
53.
Preiss D, Seshasai SRK, Welsh P et al. Risk of incident diabetes with intensive-dose compared
with moderate-dose statin therapy: a meta-analysis. JAMA 2011;305:2556-64.
54.
Glasser SP, Wadley V, Judd S et al. The association of statin use and statin type and cognitive
performance: analysis of the reasons for geographic and racial differences in stroke (REGARDS)
study. Clinical Cardiology 2010;33:280-8.
55.
Dall TM, Zhang Y, Chen YJ, Quick WW, Yang WG, Fogli J. The economic burden of diabetes.
Health Affairs 2010;29:297-303.
56.
Sullivan PW, Ghushchyan V. Preference-Based EQ-5D index scores for chronic conditions in the
United States. Med Decis Making 2006;26:410-20.
57.
Gregg EW, Gu Q, Cheng YJ, Narayan KMV, Cowie CC. Mortality trends in men and women with
diabetes, 1971 to 2000.[Summary for patients in Ann Intern Med. 2007 Aug 7;147(3):I10; PMID:
17577000]. Annals of Internal Medicine 2007;147:149-55.
58.
Ekman M, Berg J, Wimo A, Jonsson L, McBurney C. Health utilities in mild cognitive impairment
and dementia: a population study in Sweden. International Journal of Geriatric Psychiatry
2007;22:649-55.