Modeling the 4D Study: statins and cardiovascular outcomes in long-term hemodialysis patients with diabetes.

Abstract

Randomized, controlled trials (RCTs) are the gold standard for defining causal inferences but are sometimes not feasible because of cost, ethical, or time considerations. We explored the accuracy and potential use of a "simulated trial" through the modeling of a previously published RCT, Die Deutsche Diabetes Dialyse Studie (4D Study), a landmark study that investigated the cardiovascular benefit of atorvastatin use in 1255 patients with ESRD.
Using a large historical database of interventions and outcomes in dialysis patients, we conducted an observational model of the 4D Study in dialysis patients who had type 2 diabetes and were prescribed a statin (5144 patients) and matched to a non-statin user (5144 control subjects) before multivariate modeling. Inclusion, exclusion, and outcome parameters of the study, as prespecified by the 4D Study, were strictly modeled in this analysis.
In covariate- and propensity-adjusted Cox regression, statin use (versus nonuse) was associated with a decrease in the composite primary outcome of cardiac death, nonfatal myocardial infarction, and stroke. Statin use was also associated with a decrease in cardiovascular mortality and all cardiac events combined. The hazard ratios in this observational model were numerically comparable to the hazard ratios reported in the 4D Study; however, because of the larger number of patients "enrolled," results in this simulated study achieved statistical significance.
Statin use was associated with some cardiovascular benefit in a simulated trial of patients with ESRD; however, the size of benefit was considerably smaller than that seen in the general population. Such simulated trials may represent an exploratory, cost-effective option when RCTs are not immediately feasible.

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Modeling the 4D Study: Statins and Cardiovascular
Outcomes in Long-Term Hemodialysis Patients with
Diabetes
Kevin E. Chan,*†Ravi Thadhani,†J. Michael Lazarus,* and Raymond M. Hakim*
*Fresenius Medical Care North America, Waltham, Massachusetts; and†Nephrology Division, Department of Medicine,
Massachusetts General Hospital, Boston, Massachusetts
Background and objectives: Randomized, controlled trials (RCTs) are the gold standard for defining causal inferences but
are sometimes not feasible because of cost, ethical, or time considerations. We explored the accuracy and potential use of a
“simulated trial” through the modeling of a previously published RCT, Die Deutsche Diabetes Dialyse Studie (4D Study), a
landmark study that investigated the cardiovascular benefit of atorvastatin use in 1255 patients with ESRD.
Design, setting, participants, & measurements: Using a large historical database of interventions and outcomes in dialysis
patients, we conducted an observational model of the 4D Study in dialysis patients who had type 2 diabetes and were
prescribed a statin (5144 patients) and matched to a non–statin user (5144 control subjects) before multivariate modeling.
Inclusion, exclusion, and outcome parameters of the study, as prespecified by the 4D Study, were strictly modeled in this
analysis.
Results: In covariate- and propensity-adjusted Cox regression, statin use (versus nonuse) was associated with a decrease in
the composite primary outcome of cardiac death, nonfatal myocardial infarction, and stroke. Statin use was also associated
with a decrease in cardiovascular mortality and all cardiac events combined. The hazard ratios in this observational model
were numerically comparable to the hazard ratios reported in the 4D Study; however, because of the larger number of patients
“enrolled,” results in this simulated study achieved statistical significance.
Conclusions: Statin use was associated with some cardiovascular benefit in a simulated trial of patients with ESRD;
however, the size of benefit was considerably smaller than that seen in the general population. Such simulated trials may
represent an exploratory, cost-effective option when RCTs are not immediately feasible.
Clin J Am Soc Nephrol 5: 856–866, 2010. doi: 10.2215/CJN.07161009
E
termining standards of care (1,2) and causal inference. Because
RCTs minimize potential confounding (3), such studies have
become a requirement for the approval of new therapies by
national regulatory bodies such as the US Food and Drug
Administration.
In the ESRD population, very few RCTs have been success-
fully carried out, mainly because of the prohibitive costs of
conducting a well-designed RCT (4). When cost, time, or ethical
factors prevent the blind randomization of participants, a well-
done epidemiologic study may represent a reasonable alterna-
tive for hypothesis testing and generation (5). Such analytic
approaches have become increasingly prevalent in the dialysis
research community with the organization of clinical databases
through US Renal Data System (6), Dialysis Outcomes and
Practice Patterns Study (DOPPS) (7), and large dialysis provid-
vidence-based medicine has promulgated a hierarchy
of best research evidence in which randomized, con-
trolled trials (RCTs) remain the gold standard for de-
ers. Although valuable, it remains uncertain how reliable a
rigorous ESRD observational study performs relative to an
RCT, especially with the evolution of newer design strategies
that minimize the effect of confounding (8).
Die Deutsche Diabetes Dialyze Studie (4D Study) (9) was a
landmark RCT of the ESRD population that examined the
impact of atorvastatin on cardiovascular outcomes. We mod-
eled the 4D Study using clinical data abstracted from a large,
national population of patients with ESRD. Our observational
model strictly adhered to the same criteria and time period of
the 4D Study (10), such that point estimates between the pro-
spective trial and its observational simulated model could be
implicitly compared to assess residual bias.
Materials and Methods
Database Description
Fresenius Medical Care North America is a large provider of long-
term renal replacement therapy for ?115,000 patients with ESRD in the
United States. All clinics and ancillary services are networked through
an information system that prospectively collects demographic, medi-
cation, comorbidity, outcome, laboratory, process, and patient encoun-
ter events for the purpose of health care delivery. Deidentified clinical
data sets can be retrospectively extracted from the system for research
purposes, as further described in previous journal publications (11–15).
Together, the setup and process provide the three pillars of a good
Received October 7, 2009. Accepted February 3, 2010.
Published online ahead of print. Publication date available at www.cjasn.org.
Correspondence: Dr. Kevin E. Chan, 920 Winter Street, Waltham, MA 02451.
Phone: 781-699-2546; Fax: 781-699-4047; E-mail: kevin.chan@fmc-na.com
Copyright © 2010 by the American Society of NephrologyISSN: 1555-9041/505–0856

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surveillance system: Data validity, informative results, and feasible
implementation (16,17).
Study Design and Cohort
To re-create the 4D Study, we strictly adhered to the same eligibility
criteria for the 4D Study as outlined in a previous publication (10)
(Table 1). We anticipated the same 8% reduction in the incidence of the
primary end point, as reported by the 4D Study, in our power calcu-
lations (? ? 0.90, ? ? 0.05), which estimated a sample size of ?8000
patients. To facilitate the attainment of this target, we placed no restric-
tions on vintage and extended the recruitment period to encompass
January 1, 1997, through December 31, 2003, during which time we
“enrolled” all new users of hepatic hydroxymethyl glutaryl–CoA
(HMG-CoA) reductase inhibitors (users) among a national population
of patients who had type 2 diabetes and were receiving long-term
hemodialysis (49% of population). To decrease the effect of change in
practice patterns, patient outcomes were followed only to October 31,
2004, which coincides with the date the 4D Study was presented at the
American Society of Nephrology Renal Week (18). All statin users and
nonusers were required to have at least 90 days of dialysis therapy in
a participating dialysis unit without any previous statin prescription.
Potential nonusers were also patients who had type 2 diabetes but no
previous statin prescription on the index date (the date a statin was
started for a statin user). For each user, a nonuser within the same
facility was randomly matched on the most recent total cholesterol level
and vintage to form a study cohort for subsequent propensity and
multivariate (46 parameters) modeling. Cholesterol levels that were
drawn before the initiation of statin therapy were matched to similar
untreated lipid profiles in non–statin users. Matching was done using a
modified “greedy macro” (19,20) that was structured so that each
patient could be enrolled only once in the analysis between the study
period of January 1, 1997, through December 31, 2003. We were unable
to match on LDL levels because only 6.4% of the study cohort had
received LDL testing (measured or calculated), whereas 80% of patients
had a total cholesterol level recorded.
Exclusion criteria paralleled those used in the 4D Study (10) with the
exception that patients with any LDL level or dialysis vintage were
eligible for the study. Exclusion for “prior sensitivity to HMG-CoA
reductase inhibitors” was defined as any discontinuation of statin use
before study enrollment. The study was reviewed by the Fresenius
Medical Care North America Office of Compliance and designated
exempt research under 45 CFR 46.101 (b).
End Points and Covariates
The primary end point was a composite of death from cardiac causes,
fatal stroke, nonfatal myocardial infarction (MI), and nonfatal stroke.
Secondary end points included all-cause mortality, all cardiac events
combined, all cerebrovascular events combined, and the absolute
change in albumin 12 months after statin initiation. Death, stroke, and
cardiovascular outcomes were classified on the basis of physician-
written hospitalization discharge summaries and death records charted
in the electronic medical records. Forty-six baseline covariates (listed in
Table 3) were defined as the most recent value on the date of study
enrollment (i.e., measured before a statin was started). Fasting blood
glucose was used to adjust for the degree of diabetic control and disease
severity, because only 65.7% of patients had glycosylated hemoglobin
readings at baseline.
Statistical Analysis
Baseline characteristics of the study patients by statin use are re-
ported in parallel with the characteristics of the 4D Study cohort (Table
2). Covariate (46 parameters) adjusted Cox regression models were
implemented using forward variable selection with variable exit criteria
set at P ? 0.20 to determine the relative hazard of statin use versus
nonuse. Patients were right-censored at the time they left the facility
(because of discharge, death, transfer, withdrawal, transplantation, or
attainment of outcome) or at the change of their statin status. Compar-
isons between the hazard ratios (HRs) of this study and the 4D Study
were done using t tests. For validation purposes, we also performed an
additional intention-to-treat (ITT) analysis in which patients were not
censored if their statin status changed after enrollment in the study. To
balance for potential confounding by indication, all Cox models were
weighted (21,22) according to their reciprocal probability of receiving
statin therapy (propensity score [23]) in the users, and 1 ? probability
of receiving statin therapy in the nonusers. Logistic models were used
to calculate a subject’s probability of receiving therapy as a function of
the baseline parameters as explained further in Table 2.
We also determined the relationship between the primary outcome
with the daily statin dosage (mg) in patients who remained enrolled in
the analysis at 90 days, the time point when statin dosage was ascer-
tained. We assumed that 90 days represented a reasonable period for a
physician to titrate the statin dosage to a steady-state level, after which
the analysis became ITT. All statin dosages were converted to a uniform
value on the basis of their published efficacy to reduce LDL (24–26).
Thus, 20 mg of atorvastatin was regarded as the equivalent to 80 mg of
simvastatin, because both reduce LDL by approximately 47%. Gener-
alized propensity scores by quintiles of statin dosage were calculated
using multinomial logistic regression and incorporated as inverse
weights into the Cox models to adjust for potential confounding by
indication (27).
The HR for the primary outcome by statin type was also calculated
through the inclusion of three separate categorical variables in the Cox
model (atorvastatin versus untreated; simvastatin versus untreated;
other statin versus untreated), with a generalized propensity scoring
method. Statistical difference between the type of statin (atorvastatin
versus simvastatin versus other statin) was determined using a Wald test
for joint hypothesis testing between multiple regression coefficients.
Subgroup analyses by baseline vintage, total cholesterol, and comor-
bidities were also performed. Because increased baseline medication
use was associated with statin prescription (Table 2), we also stratified
by angiotensin-converting enzyme inhibitor, angiotensin receptor
blocker, ? blocker, and antiplatelet use to determine whether concur-
rent cardioprotective medications confounded the effect statin therapy
on the primary outcome. Effect modification testing between statin use
and covariates was done through the addition of interaction terms to
the Cox model. All statistical analysis was done using SAS 9.1 (SAS
Institute, Cary, NC).
Results
Patients
A total of 5144 new statin users were randomly matched to an
equivalent nonuser (as shown in the schematic pathways in Fig-
ure1).Asexpected,severalbaselinedifferencesbetweenthestudy
groups were noted. These measured baseline differences became
insignificant after balancing with propensity adjustment (Table 2).
Among statin users, the average equivalent atorvastatin dosage
was 33 mg/d. The mean length of follow-up was 1.6 years in the
statin user group and 1.8 years in the non–statin user group.
Under an ITT assumption whereby patients were not censored if
their statin status deviated from their initial assignment, the mean
length of follow-up was 2.1 years in the statin user group and 2.0
years in the non–statin user group.
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Table 1. Comparison of study design between this study and the 4D Study
Parameter
This Study
4D Study
Time period
patient recruitment period
January 1997 to December 2003
March 1998 to October 2002
study end date
October 2004
March 2004
Inclusion criteria
Men and women who had type 2 diabetes and were on
HD for at least 90 days at a participating facilitya
Men and women who had type 2 diabetes and were on
HD for no more than 24 months
Age 18 to 80 years
Age 18 to 80 years
Exclusion criteria
Serum LDL cholesterol ?80 or ?190 mg/dl
Fasting serum triglycerides ?1000 mg/dl
Fasting serum triglycerides ?1000 mg/dl
Symptomatic hepatobiliary cholestatic disease, abnormal
liver function with SGOT or SGPT ?3? ULN
Symptomatic hepatobiliary cholestatic disease, abnormal
liver function with SGOT or SGPT ?3? ULN
Positive test for HbsAg or HIV or known hepatitis C
Positive test for HbsAg and HIV or known hepatitis C
Hematopoietic or systemic disease not related to ESRD
Hematopoietic or systemic disease not related to ESRD
Active pregnancy
Nonamenorrheic women who are of child-bearing potential
and do not use a medically relevant contraception
Revascularization, CHF, or MI during the previous 3
monthsa
Revascularization, CHF, or MI during the previous 3 months
Previous unsuccessful kidney transplantation
Previous unsuccessful kidney transplantation
Hypertension resistant to therapy (pre- or postdialysis BP
?200/110 mmHg)
Hypertension resistant to therapy (BP ?200/110 mmHg)
Hypo- or hyperthyroidism (TSH beyond normal range)
Hypo- or hyperthyroidism (TSH beyond normal range)
History of ethanol abuse
Excessive ethanol intake
Previous prescription of HMG-CoA reductase inhibitorsa
Previous sensitivity to HMG-CoA reductase inhibitors
Significant gastrointestinal disease or surgery, which may
interfere with drug absorption
Significant gastrointestinal disease or surgery, which may
interfere with drug absorption
Participation in another trial during the previous 30 days
Unwilling to consent
Exposure
New users of statinsbwere matched to a patient with no
history of statin use; matching criteria included
untreated cholesterol level and dialysis vintage, within
the same facility on the same day
Randomization to atorvastatin 20 mg once daily versus
placebo; randomization was stratified by facility
Outcomes
-primary outcome
Composite of death from cardiac causes, fatal stroke,
nonfatal MI, and nonfatal stroke
Composite of death from cardiac causes, fatal stroke,
nonfatal MI, and nonfatal stroke
-secondary outcomes
All cardiac events combined
All cardiac events combined
All cerebrovascular events combined
All cerebrovascular events combined
All-cause mortality
All-cause mortality
CHF, congestive heart failure; HD, hemodialysis; SGOT, serum glutamic oxaloacetic transaminase; SGPT, serum glutamic pyruvic transaminase; TSH, thyroid-
stimulating hormone; ULN, upper limit of normal.
aAll patients (statin users and nonusers) had to be admitted to a participating dialysis facility for at least 90 days before they were eligible for the analysis; the 90-
day period was necessary to document (1) any previous statin use (i.e., patients who were continuing a statin that was started before admission to the facility) or (2)
any revascularization, CHF, or MI events in the previous 90 days for possible exclusion from the study.
bPatients who started a statin after at least 90 days of dialysis in a participating facility and had no previous documentation of statin prescription,
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Lipid Levels
A total of 16% of patients had a baseline cholesterol level of
?200 mg/dl, which is above the level recommended by the
National Cholesterol Education Program (28). The difference in
baseline total cholesterol (i.e., cholesterol level before statin
treatment initiation) between the two groups was small (abso-
lute difference of 5 mg/dl as shown in Table 2), suggesting that
users and nonusers were well matched at the start. During
Table 2. Baseline patient characteristics
Characteristic
Statin User
(n ? 5144)
Non–Statin User
(n ? 5144)
PPa
4D Study Population
(n ? 1255)
Age (years)
Female gender (%)
Vintage (years)
BP (mmHg)
systolic
diastolic
Access (%)
fistula
catheter
graft
unknown
History of cardiovascular disease (%)
MI
CAD
CHF
peripheral vascular diseaseb
stroke or TIA
BMI (kg/m2)
Hemoglobin (g/dl)
Glycosylated hemoglobin (%)
Fasting blood glucose (mg/dl)
Albumin (g/dl)
Calcium (mg/dl)
Phosphorus (mg/dl)
Lipid values (mg/dl)
total cholesterol
direct LDL
HDL
triglycerides
Cardioprotective medications (%)
ACEI
ARB
? blocker
antiplatelet therapy
Intravenous medications
EPO (1000 U per session)
vitamin D use (%)
62.6 (0.1)
54.1 (0.5)
2.24 (0.03)
62.3 (0.2)
53.0 (0.5)
2.79 (0.03)
0.09
0.26
?0.0001
0.99
0.96
0.27
65.7
46.1
0.7
152.6 (0.3)
75.9 (0.2)
153.0 (0.3)
77.1 (0.2)
0.45
?0.0001
0.77
0.44
146
76
23.8 (0.6)
22.9 (0.6)
48.3 (0.7)
5.1 (0.3)
22.8 (0.6)
23.3 (0.6)
49.9 (0.7)
3.9 (0.3)
0.01 0.96 Not reported
4.6 (0.3)
22.3 (0.6)
26.0 (0.6)
19.1 (0.5)
9.6 (0.4)
33.2 (0.4)
11.6 (0.02)
7.1 (0.03)
180 (1)
3.8 (0.01)
9.2 (0.01)
5.6 (0.02)
4.2 (0.3)
18.0 (0.5)
28.1 (0.6)
19.3 (0.6)
10.4 (0.4)
32.2 (0.4)
11.6 (0.02)
6.8 (0.03)
169 (1)
3.8 (0.01)
9.2 (0.01)
5.8 (0.03)
0.23
?0.0001
0.02
0.78
0.19
0.07
0.57
?0.0001
?0.0001
0.08
0.98
?0.0001
0.69
0.78
0.93
0.67
0.86
0.98
0.39
c
17.6
29.4
35.4
44.7
17.8
27.6
10.9
6.8
0.82
0.34
0.41
0.53
3.8
9.2
6.0
182 (0.7)
83 (1.3)
40 (0.3)
225.5 (2.2)
177 (0.6)
76 (1.5)
42 (0.4)
188.0 (1.8)
?0.0001
0.0006
0.0007
?0.0001
0.98
c
219
126
36
264
c
c
36.6 (0.7)
12.6 (0.5)
44.6 (0.7)
51.2 (0.7)
28.9 (0.6)
9.2 (0.4)
30.2 (0.6)
31.6 (0.6)
?0.0001
?0.0001
?0.0001
?0.0001
0.54
0.89
0.86
0.74
48
12
41
51
5.41 (0.07)
52.1 (0.7)
6.13 (0.08)
77.9 (0.6)
?0.0001
?0.0001
0.22
0.94
6.21
Not reported
Data are means (SE). ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin receptor blocker; BMI, body mass
index; EPO, epogen.
aAfter adjustment for propensity scoring; components of the propensity score include age, gender, race (white, black, or
other), vintage, cholesterol, comorbidity (individual categorical variables for hypertension, atrial fibrillation, CAD, CHF,
chronic lung disease, MI, stroke, other cardiovascular disease, dementia, hemiplegia, past infection, peptic ulcer disease,
connective tissue disease, previous hospitalization for chest pain, amputation, peripheral vascular disease), dialysis adequacy,
EPO dosage, vitamin D usage, access (fistula, graft, catheter, or unknown), BMI, fasting blood glucose, baseline laboratory
values (hemoglobin, albumin, white blood cell count, ferritin, calcium, phosphorus, parathyroid hormone), cardiovascular
medication (ACEI, ARB, coumadin, ? blocker, clopidogrel, digoxin, aspirin), and BP (systolic and diastolic).
bPatients with a history of amputation were also defined as having peripheral vascular disease.
cVariables were not included in the analysis because of too many missing values.
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Table 3. Study outcomes by statin exposure
Outcome
Statin Usersa
(n ? 5144)
Non–Statin Usersa
(n ? 5144)
Unadjusted
[HR (95% CI)]
(n ? 10,288)
Adjusted
[HR (95% CI)]
(n ? 9016)b
Adjusted with ITT
[HR (95% CI)]
(n ? 9016)b
Primary outcome
1139 (14.1)
1425 (15.5)
0.91 (0.84 to 0.98)
0.90 (0.82 to 0.97)d
0.91 (0.84 to 0.98)
Death from cardiac causes
767 (9.1)
1041 (10.9)
0.84 (0.76 to 0.92)
0.83 (0.75 to 0.92)
0.86 (0.79 to 0.94)
sudden death
22 (0.3)
31 (0.3)
0.81 (0.47 to 1.39)
0.86 (0.47 to 1.57)
0.86 (0.49 to 1.50)
MI
677 (8.1)
922 (9.7)
0.84 (0.76 to 0.92)
0.85 (0.76 to 0.94)
0.87 (0.79 to 0.95)
CHF
49 (0.6)
64 (0.7)
0.88 (0.61 to 1.28)
0.68 (0.45 to 1.02)
0.67 (0.46 to 0.97)
CADc
18 (0.2)
23 (0.2)
0.89 (0.48 to 1.65)
0.84 (0.43 to 1.65)
1.34 (0.76 to 2.39)
Nonfatal MI
353 (4.3)
411 (4.4)
0.97 (0.84 to 1.12)
0.92 (0.79 to 1.08)
0.97 (0.85 to 1.12)
Fatal stroke
55 (0.7)
75 (0.8)
0.83 (0.59 to 1.17)
0.93 (0.64 to 1.35)
0.91 (0.66 to 1.27)
ischemic
34 (0.4)
51 (0.5)
0.75 (0.49 to 1.16)
0.93 (0.60 to 1.46)
0.90 (0.60 to 1.33)
hemorrhagic
21 (0.3)
24 (0.3)
0.99 (0.55 to 1.78)
0.90 (0.45 to 1.80)
0.92 (0.51 to 1.66)
Nonfatal stroke
237 (2.9)
230 (2.5)
1.17 (0.97 to 1.40)
1.25 (1.03 to 1.51)
1.19 (1.00 to 1.41)
ischemic
202 (2.5)
197 (2.1)
1.16 (0.95 to 1.41)
1.26 (1.03 to 1.56)
1.17 (0.97 to 1.41)
hemorrhagic
39 (0.5)
36 (0.4)
1.23 (0.78 to 1.93)
1.11 (0.68 to 1.82)
1.27 (0.83 to 1.93)
All cardiac events combined
1146 (14.4)
1362 (14.9)
0.97 (0.90 to 1.05)
0.90 (0.83 to 0.98)
0.93 (0.86 to 1.00)
death from cardiac causes
767 (9.1)
1041 (10.9)
0.84 (0.76 to 0.92)
0.83 (0.75 to 0.92)
0.86 (0.79 to 0.94)
nonfatal MI
353 (4.3)
411 (4.4)
0.97 (0.84 to 1.12)
0.92 (0.79 to 1.08)
0.97 (0.85 to 1.12)
CADc
272 (3.4)
223 (2.4)
1.39 (1.17 to 1.66)
1.14 (0.94 to 1.38)
1.14 (0.96 to 1.35)
All cerebrovascular events
320 (3.9)
317 (3.4)
1.14 (0.98 to 1.34)
1.15 (0.97 to 1.36)
1.12 (0.96 to 1.30)
stroke
264 (3.2)
274 (2.9)
1.09 (0.92 to 1.29)
1.16 (0.97 to 1.39)
1.10 (0.94 to 1.30)
ischemic
219 (2.7)
228 (2.4)
1.09 (0.90 to 1.31)
1.18 (0.97 to 1.43)
1.09 (0.91 to 1.29)
hemorrhagic
50 (0.6)
53 (0.6)
1.07 (0.72 to 1.57)
0.99 (0.64 to 1.52)
1.11 (0.77 to 1.59)
TIA
70 (0.8)
59 (0.6)
1.35 (0.95 to 1.90)
1.04 (0.72 to 1.51)
1.16 (0.82 to 1.63)
Death from all causes
1315 (15.6)
1831 (19.2)
0.82 (0.76 to 0.88)
0.82 (0.76 to 0.89)
0.87 (0.82 to 0.94)
Death, but not from CVA or CAD
493 (5.9)
715 (7.5)
0.78 (0.70 to 0.88)
0.80 (0.70 to 0.91)
0.88 (0.79 to 0.98)
other cardiovascular disease
68 (0.8)
106 (1.1)
0.72 (0.53 to 0.98)
0.73 (0.52 to 1.01)
0.85 (0.63 to 1.14)
infection
150 (1.8)
212 (2.2)
0.81 (0.66 to 1.00)
0.79 (0.62 to 1.01)
0.86 (0.70 to 1.06)
cancer
6 (0.07)
30 (0.3)
0.23 (0.09 to 0.54)
0.14 (0.50 to 0.42)
0.43 (0.21 to 0.85)
unknown
122 (1.5)
180 (1.9)
0.77 (0.61 to 0.97)
0.71 (0.55 to 0.92)
0.81 (0.65 to 1.01)
other
147 (1.7)
187 (2.0)
0.89 (0.72 to 1.11)
0.97 (0.76 to 1.24)
1.01 (0.82 to 1.25)
CAD, coronary artery disease; CHF, congestive heart failure; CVA, cerebrovascular accident; TIA, transient ischemic attack.
aData are presented as no. of patients (events per 100 patient years).
bModel adjusted for age, gender, race (white, black, or other), vintage, total cholesterol, Charlson comorbidity index (diabetes, MI, CHF, stroke, peptic ulcer disease,
cancer with or without metastasis, liver disease, hemiplegia, dementia, peptic ulcer disease, chronic lung disease, connective tissue disease, peripheral vascular
disease, AIDS), other comorbidity (hypertension, atrial fibrillation, CAD, past infection, previous hospitalization for chest pain, amputation, vascular disease), dialysis
adequacy, epogen dosage, vitamin D usage, access (fistula, graft, catheter, or unknown), body mass index, fasting blood glucose, baseline laboratory values
(hemoglobin, albumin, white blood cell count, ferritin, calcium, phosphorus, parathyroid hormone), cardiovascular medication (angiotensin-converting enzyme
inhibitor, angiotensin receptor blocker, warfarin, ? blocker, clopidogrel, digoxin, aspirin), BP (systolic and diastolic), and propensity score.
cAngina or revascularization.
dAdjusted HR for the primary outcome by statin type (versus nonuser): Atorvastatin (HR 0.86; 95% CI 0.76 to 0.98; n ? 2235), simvastatin (HR 0.84; 95% CI
0.74 to 0.95; n ? 1420), and other statin (HR 0.95; 95% CI 0.84 to 1.08; n ? 897).
860Clinical Journal of the American Society of NephrologyClin J Am Soc Nephrol 5: 856–866, 2010