Supplementary Appendix
Supplement to:
Normalization of Testosterone Level is Associated with Reduced
Myocardial Infarction and Mortality in Men
Rishi Sharma M.D. M.H.S.A, Olurinde A. Oni M.B.B.S., M.P.H., Kamal Gupta M.D., Guoqing
J. Chen, M.D., Ph.D., M.P.H., Mukut Sharma Ph.D., Buddhadeb Dawn M.D., Ram Sharma
Ph.D., Deepak Parashara M.D., Virginia J Savin M.D., John A. Ambrose M.D. Rajat S. Barua
M.D. Ph.D.
Supplementary Appendix
Contents
Methods
page 3
Study design
page 3
Study population
Inclusion criteria
Exclusion criteria
page 5
page 5-6
Statistical analysis
page 6-7
Figures 4-8
page 8-12
Tables 3-5
page 13-15
References
page 16
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METHODS:
This retrospective, cohort study of male veterans was conducted using data obtained from
Veterans Administrations Corporate Data Warehouse (CDW) through the Veterans
Administrations Informatics and Computing Infrastructure (VINCI).1 The Veterans Health
Administration (VHA) provides care to Veterans at over 1,400 establishments across the United
States and each Veteran has an unique identifiers in the CDW database. These identifiers were
used to cross-link with clinical data for each patient over the study years. The data of study
patients were retrieved from December 1999 to May 2014 including outpatient, inpatients,
pharmacy, vital status, and laboratory data. The quality of data from these sources is well
documented, and the data have been widely used by investigators for retrospective longitudinal
studies (www.virec.research.va.gov). The Institutional Review Board of Kansas City Veterans
Affairs Medical Center, USA, approved the study.
Study Design:
This retrospective cohort study was designed to determine the role of testosterone replacement
therapy (TRT) on cardiovascular outcomes by comparing the incidences of MI, stroke and allcause mortality among different sub-populations of treated and untreated patients. All patients’
CV events and co-existing conditions were based on the International Classification of Diseases
9th Revision (ICD-9) codes. All of our patients had total testosterone TT levels checked at least
on two separate occasions as recommended by guidelines2 for entry in the study.
Determination of TT level: Low TT was determined to be present when TT level was less than
the lower limit of normal laboratory reference range (NLRR) reported for the particular test
result. This method was adopted because the available test results lacked uniform laboratory
3
range for normal TT level and reporting units in the database. We chose to use this method rather
than a discrete cut off value as we found that different facilities used different test assays that had
different reference ranges and reporting units.2,3 Even in the same hospital, the assay used could
change over time Moreover, there is lack of standardization when it come to testosterone levels
and other tests using the stoichiometric measurements.4,5 Hence, we classified each test result as
low or normal based on their respective laboratory reference range reported with the test result as
this was the most accurate method to minimize the effect of use of multiple assays.
Outcome Measures: Primary outcome measures were (1) the incidence of myocardial infarction
(ICD-9 410.x0 and 410.x1), (2) the incidence of ischemic stroke (ICD-9 433.x1, 434 [excluding
434.x0], or 436), and (3) the all-cause mortality-determined using dates of death in CDW data
augmented with vital status files.
Ascertainment of TRT Exposure: Exposure to the use of TRT was ascertained from the
medication prescription of patient medical records. For this study, patients who received
any form of TRT (injection, gel or patch) were considered treated over the study period.
Confounding factor measures: Confounding measures includes patient demographics,
comorbidity, such as diabetes mellitus (DM), chronic obstructive pulmonary disease (COPD),
obstructive sleep apnea (OSA), congestive heart failure (CHF), peripheral vascular disease
(PVD), coronary artery disease (CAD), and other factors. The ICD-9-CM diagnosis codes were
used to capture the coexisting conditions. Other factors included the baseline body mass
index, low-density lipoprotein (LDL), and use of aspirin, b-blockers and statins.
Study Population:
We identified patients from the CDW lab database.
4
Inclusion criteria: We included patients whose first tested TT level was reported to be lower than
NLRR. These patients were as identified have low TT levels. There is some known variablity in
levels of T and the societal guidelines also recommend rechecking levels to confirm true low T
state prior to considering initiating TRT.2 In order to increase the likelihood of enrolling only
true low T patients we only enrolled those who had low TT on a second repeat sample prior to
any TRT. Those with normal levels on repeat test before initiation of TRT were excluded from
the final study cohort Figure 1.
Exclusion criteria. We excluded (1) female patients (2) those who received TRT before the first
available low-T (3) those who had myocardial infarction (MI, ICD-9 codes 410.x0 and 410.x1)
or ischemic stroke (ICD-9 codes 433.x1, 434 [excluding 434.x0], and 436) before the first day of
study and (4) those who on repeat testing had normal TT level before any treatment was started.
Patients who met the eligibility criteria were classified into treated or untreated based on their
TRT status. Treated patients were further classified based on the normalization TT level as
described below .
Treated Patients: Patients who received any form of TRT (such as injection, gel, and patch) after
being determined to have low-T were classified as treated. Of these, individuals who achieved
significant improvement in post-treatment TT levels resulting in levels within the NLRR were
classified as normalized TRT group (Gp1). Those patients whose levels remained less than lower
limit of NLRR despite receiving TRT were classified as non-normalized TRT group (Gp2). The
median time interval between commencing TRT and the repeat T measurements normalizedTRT was 9.6 months and for non-normalized TRT was 9.2 months.
Untreated Patients: Patients who did not receive any form of TRT during the follow-up period
were classified as untreated (Gp3).
5
The start day for all enrolled patients was the index date of their first reported low-TT. Those
who did not develop MI, stroke or die during the period of follow-up were censored on the last
day of utilization of VHA care. If the last day of utilization of VHA care was missing, they were
censored on the day we obtained the study data.
Statistical analysis:
Continuous variables were reported as means (SD), categorical variables as percentages. Chi
square test and Students’ t-test were used to compare normally distributed patients’ baseline
characteristics. Non-parametric tests were used for non-normally distributed variables.
For the incidence of MI, stroke, all-cause mortality and composite endpoint: Statistical analyses
were conducted using different modelling strategies in order to examine the association of TRT
with the study endpoints. For instance, in assessing the difference between sub-groups of treated
and untreated groups with regards to study endpoints, we performed univariable and
multivariable Cox proportional hazard regression analyses.
Propensity score analyses:
In an observational study such as ours, the potential bias is selection of TRT. To control for the
potential selection bias of using TRT, a propensity score approach was used in the analyses.
Propensity score, a balance score that estimates the probability that the patient was treated with
TRT was calculated after adjusting the potential observed risk or confounding factors. The scores
were used to correct for systematic differences between treated and untreated patients which can
potentially bias the estimated exposure effect. Each study patient’s propensity scores for
receiving the TRT were computed by fitting the baseline covariates in a logistic regression
analysis. Baseline covariates were determined based on factors known to be related to proposed
6
cardiovascular outcomes including age, body mass index, hypertension, DM, COPD, OSA, CHF,
PVD, CAD, LDL, use of aspirin, b-blockers and statins. To ensure robust analysis of our data,
we utilized stabilized inverse probability of treatment weights (IPTW),6 which allowed us to
keep all patients in the study while using the propensity scores to achieve balance between each
pair of sub-groups we studied. In this method, individuals are weighted by the inverse probability
of their treatment status. We have formed treatment assignment vectors by using logistic models
to generate propensity score vectors.
TRT was fitted as a time-varying covariate in this study.7 We applied the stabilized IPTW to
obtain Kaplan-Meier survival curves and to compare event-free survival time between the
groups, along with log-rank P-value. SAS 9.4 was used for statistical analyses while Stata 12 was
used to plot Kaplan Meier curves with TRT as a time-varying exposure variable. The study
hypotheses were tested at two-sided level of significance less than 0.05.
7
Figure 4.
Figure: 4a
Figure: 4b
Figure: 4c
Figure 4 (a-c): Kaplan-Meier curve depicting the incidence of ischemic stroke among different study groups. Stroke free survival
probability was significantly higher in the normalized treated group compared to the untreated and non-normalized treated groups as
shown in Figures 4a and 4b respectively. There was no significant difference between the non-normalized treated and the untreated
groups as shown in Figure 4c. Inverse probability treatment weights were applied to plot the graphs.
8
Graphical presentation of the effectiveness of stabilized IPTW
Figure 5a
Figure 5b
Figure 5 (a-b). Figure 5a shows the distribution of the computed propensity scores in the unmatched cohorts. Dashed line represents the
treated normalized group while the continuous line represents the untreated group. Figure 5b shows the distribution of propensity scores
after performing stabilized inverse probability of treatment weight (IPTW) matching. These figures further reassure the functionality of
stabilized IPTW.
9
Graphical presentation of the effectiveness of stabilized IPTW
Figure 6a
Figure 6b
Figure 6 (a-b). Figure 6a shows the distribution of the computed propensity scores in the unmatched cohorts. Dashed line represents the
treated normalized group while the continuous line represents the treated non-normalized group. Figure 6b shows the distribution of
propensity scores after performing stabilized inverse probability of treatment weight (SIPTW) matching. These figures further reassure
the functionality of stabilized IPTW.
10
Graphical presentation of the effectiveness of stabilized IPTW
Figure 7a
Figure 7b
Figures 7 (a – b): Figure 7a shows the distribution of the computed propensity scores in the unmatched cohorts. Dashed line represents
the treated non-normalized group while the continuous line represents the untreated group. Figure 6b shows the distribution of propensity
scores after performing stabilized inverse probability of treatment weight (SIPTW) matching. These figures further reassure the
functionality of stabilized IPTW.
11
Figure 8a
Figure 8b
Figure 8c
Figure 8 a-c: Kaplan-Meier curves for myocardial infarction free survival after truncating follow-up beyong 10 years.
12
Table 3. Distribution of types of testosterone supplementation among the propensity matched treated patients
Treated normalized
Treated non-normalized
Gel
Patch
Injection
4784 (11.7%)
2698 (11.3%)
16670 (40.8%)
9609 (40.1%)
19309 (47.3%)
11579 (48.3%)
Oral
(Tab/Cap)
89 (0.2%)
67 (0.3%)
TOTAL
40852 (100%)
23953 (100%)
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Table 4. Cox regression analysis adjusting for covariates
Model
Normalized treated vs untreated
Multivariate Cox model
Myocardial infarction
HR
95% CI
0.78
0.65 - 0.94
P=0.0103
Stroke
HR
95% CI
0.64 0.43 - 0.95
P=0.0286
All-cause mortality
HR
95% CI
0.44 0.42 - 0.46
P<0.0001
Normalized treated vs non-norm treated
Multivariate Cox model
HR
0.82
95% CI
0.70 - 0.95
P=0.0075
HR
0.69
95% CI
0.50 - 0.95
P=0.0217
HR
0.53
95% CI
0.50 - 0.55
P<0.0001
Non-norm treated vs untreated
Multivariate Cox model
HR
0.99
95% CI
0.81 - 1.22
P=0.9502
HR
0.96
95% CI
0.63 - 1.48
P=0.8578
HR
0.85
95% CI
0.80 - 0.90
P<0.0001
14
Table-5: Adjusted hazard ratios for myocardial infarction after truncating follow-up
beyond ten years:
Normalized-treated Vs. Untreated (ref = Untreated)
Model
HR
95% CI
0.85
0.70 – 1.03
(Stabilized IPTW)
N=34990 vs. 11105
Comparing Normalized-treated Vs. Non-normalized-treated (ref = Non-normalized treated)
Propensity Matched
HR
95% CI
Propensity Matched
0.85
0.73 - 0.99
(Stabilized IPTW)
N= 35126 vs. 22027
Comparing Non-normalized-treated Vs. Untreated (ref=Untreated)
Propensity Matched
(Stabilized IPTW)
N=22114 vs. 11117
P
0.097
P
0.036
HR
95% CI
P
1.04
0.85 - 1.30
0.711
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References:
1. http://vaww.vinci.med.va.gov/vincicentral/Data
2. Lazarou S, Reyes-Vallejo L, Morgentaler A. Wide variability in laboratory reference values
for serum testosterone. The journal of sexual medicine 2006;3:1085-9.
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pitfalls in measuring testosterone: an Endocrine Society position statement. The Journal of
clinical endocrinology and metabolism 2007;92:405-13.
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testosterone in adult men: comparison of current laboratory methods versus liquid
chromatography-tandem mass spectrometry. The Journal of clinical endocrinology and
metabolism 2004;89:534-43.
5. Vesper HW, Botelho JC. Standardization of testosterone measurements in humans. The
Journal of steroid biochemistry and molecular biology 2010;121:513-9.
6. Austin PC. The performance of different propensity score methods for estimating marginal
hazard ratios. Stat Med. 2013;32:2837-49.
7.
Xu S, Shetterly S, Powers D, Raebel MA, Tsai TT, Ho PM, Magid D. Extension of Kaplan-
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