Comparative Effectiveness and Safety of Four Second Line Pharmacological Strategies in Type 2 Diabetes Study
Conditions: Cardiovascular Events, Type2 Diabetes, Renal Disease
Sponsor: Brigham and Women's Hospital

1 
 
Comparative Effectiveness and Safety of Four Second line Pharmacological Strategies in Type 
2 Diabetes (CER-4-T2D) Study: Programming Protocol 
 
Investigators: Elisabetta Patorno, Deborah J. Wexler, Laurent Azoulay, Julie M. Paik, Brendan M. Everett, 
Sebastian Schneeweiss, Seoyoung C. Kim, Robert J. Glynn, Richard Wyss, Shirley V. Wang 
 
Other members of the research team: Elvira D’Andrea, Devin Abrahami, Phyo Htoo, Helen Tesfaye, 
Edouard Fu, Julianna Mastrorilli, Cassandra York 
 
IRB Protocol #: 2021P001784 
 
Note  
For a list of the main design and analytical revisions to the original CER-4-T2D study proposal, please see 
paragraph 12, page 17. 
 
Date: 2/17/2022 
 
1. OBJECTIVE: To design an observational analysis to emulate a target trial (i.e., a hypothetical 
pragmatic trial that would have answered the causal question of interest) comparing the 
effectiveness and safety of sodium-glucose cotransporter-2 inhibitors (SGLT2i), glucagon-like 
peptide 1 receptor agonists (GLP-1RA), dipeptidyl peptidase-4 inhibitors (DPP-4i), and sulfonylureas 
(SU), at the class and individual agent level, in head-to-head comparisons in patients with type 2 
diabetes (T2D) and low or moderate cardiovascular risk.  
 
Table 1.1 Specification and emulation of a target trial of second-line antidiabetic agents using real-
world data from the US and the UK 
Component 
Target trial  
Emulated trial using real-world data  
- CER-4-T2D Study - 
Aim 
To compare the effectiveness and safety 
of SGLT2i, GLP-1 RA, DPP-4i and SU at the 
class and individual agent level, in head-
to-head comparisons  
Same 
Eligibility 
Adults with continuous enrollment in 
database, who are at least 18 years old 
with a diagnosis of T2D at low or 
moderate risk of cardiovascular disease, 
who use metformin and have no history 
of type 1 diabetes, secondary or 
gestational diabetes, end-stage renal 
disease, pancreatitis, cirrhosis, MEN-2, 
organ transplant or insulin use. 
Same, except criteria are assessed within 
one year on or before cohort entry (see 
Section “3. STUDY COHORT”)  
Treatment strategies 
1. initiate SGLT2i 
2. initiate GLP-1 RA 
3. initiate DPP-4i 
4. initiate SU 
Same (see section “4. EXPOSURE”) 
Treatment assignment 
Patients are randomly assigned to any of 
the 4 treatment strategies 
Patients are assigned to treatment based 
on prescriptions filled (or issued by 
general practitioners). Randomization is 
emulated through adjustment for an 

2 
 
extensive list of baseline covariates and 
statistical adjustment using propensity 
scores. 
Follow-up 
Follow-up starts at treatment assignment 
and ends at diagnosis of 
safety/effectiveness outcome, death, or 
loss to follow-up.  
Follow-up starts at the date of initiation of 
treatment and ends at diagnosis of 
safety/effectiveness outcome, death, end 
of continuous health plan enrollment/end 
of registration with general practitioner, 
discontinuation of index exposure, 
addition/switch to other anti-diabetic 
medications, or end of study period 
(administrative end of follow-up), 
occurrence of bariatric surgery, whichever 
occurs first (see section “7. STUDY 
FOLLOW-UP AND CENSORING REASONS”). 
Outcome 
List of efficacy and safety outcomes 
List of effectiveness and safety outcomes 
(see section “5. OUTCOMES”) 
Causal contrast 
Intention-to-treat effect, i.e., effect of 
being assigned to treatment with SGLT2i 
vs. GLP-1 RA vs DPP4i vs SU at baseline, 
regardless of whether individuals 
received treatment assigned after 
baseline. 
On-treatment exposure definition in 
primary analyses to limit exposure 
misclassification during follow-up which is 
common in real-world evidence studies 
(see section “8. STATISTICAL ANALYSIS”) 
Statistical analysis  
Intention-to-treat analysis, i.e., 
comparison of risk of efficacy/safety 
outcomes under each treatment strategy 
under the assumption that loss to follow-
up did not introduce bias 
On-treatment exposure definition with 
adjustment for baseline characteristics 
(see sections “6. COVARIATES” and “8. 
STATISTICAL ANALYSIS”). 
 
 
2. DATA SOURCES: 
To emulate the specified target trial, we will use the following databases: 
 
2.1. Optum Clinformatics – April 1, 2012 to latest available data 
 
See description in paragraph 2.2. 
 
2.2. IBM MarketScan – April 1, 2012 to latest available data 
 
Optum and MarketScan databases are two U.S. research claims databases that primarily 
include adults with employer-based health plans, with nationwide coverage for over 60 million 
Americans, and meaningful numbers of patients ≥65 years from Medicare Advantage plans, 
employer-sponsored plans covering seniors, and Medicare supplemental insurance plans. 
Information is available on demographics, health plan enrollment status, inpatient and outpatient 
diagnoses and procedures, and pharmacy dispensing records, including medication start and refill, 
strength, quantity, and days’ supply. Laboratory test results (e.g., A1C) are available for 40-45% of 
patients in Optum and 5-10% in MarketScan. Mortality data are available in Optum from CMS, 
Social Security Administration Master Death Files, in-hospital deaths, and death as a reason for 
insurance discontinuation, and in MarketScan from in-hospital deaths. Both have been extensively 
used in pharmacoepidemiologic research. 
 

3 
 
2.3. Medicare fee-for-service (FSS) – April 1, 2012 to latest available data  
 
A U.S. federal health insurance program providing medical and prescription drug coverage to 
individuals aged 65 years and older and to younger individuals with disabilities. The Medicare 
program currently covers approximately 50 million Americans. The Medicare FFS claims database 
includes longitudinal, individual-level data on healthcare utilization, inpatient and outpatient 
diagnoses, diagnostic tests and procedures, and pharmacy filled prescriptions. Information on the 
date and cause of death is available through linkage with the Vital Status and the National Death 
Index (NDI) files. These data are widely used to study real‐world drug effectiveness and safety. 
 
2.4. Medicare FFS-RPDR – April 1, 2012 to latest available data  
 
The Partners Research Patient Data Repository (RPDR) captures longitudinal EHR data for all 
patients that receive care at 2 large health care provider networks in the Boston metropolitan area. 
It contains information on BMI, blood pressure, smoking status, laboratory, and radiology test 
results. Members of our research team have deterministically linked about 550,000 patients by 
beneficiary numbers, date of birth, and sex with Medicare claims (success rate, 99.2%), and have 
used this infrastructure for epidemiologic research. 
 
2.5. UK Clinical Practice Research Datalink (CPRD) – Jan 1, 2013 to latest available data 
 
The CPRD is comprised of two large, computerized databases of longitudinal primary care 
records, GOLD and Aurum, for >50 million patients, shown to be representative of the general U.K. 
population. The CPRD includes data on diagnoses, procedures, prescription drugs, laboratory 
values, clinical measurements, e.g., blood pressure and BMI, and lifestyle characteristics, e.g., 
smoking status and alcohol use. These variables have been validated and data and practices are 
audited regularly to ensure high data quality. Information on hospital admissions, including 
diagnoses and procedures, is available through linkage with the U.K. Hospital Episode Statistics 
database. Information on mortality, including causes of death, is available through linkage with the 
Office for National Statistics. 
 
2.6. U.S. National Veterans Health Administration (VHA) – April 1, 2012 to latest available data 
 
The VHA is the largest integrated national health system, serving over 12 million U.S. Veterans. 
The VHA database includes demographic, diagnostic and procedure information from inpatient and 
outpatient encounters. Pharmacy data include medication name, date filled, days supplied, and 
number of pills dispensed. Laboratory results and vital signs data (e.g., outpatient measurements of 
height, weight, and blood pressure) are available from VHA clinical sources. Information on dates 
and cause of death are available through linkage with the vital status and the NDI files. The VHA 
database has provided data for several high-impact studies on diabetes treatment. 
 
Note  
We will conduct sequential analyses in year 1, 2 and 3 of the research project where we will update the data 
to maximize the sample size by the end of the funding period. 
 
3. STUDY COHORT: 
 
3.1. Design diagram 

4 
 
 
Figure 1. General study design of the CER-4-T2D study. 
 
 
Note  
Covariate assessment window for CRPD data is defined using all available lookback from on or before 
cohort entry. 
 
3.2. Cohort entry (Day 0) is the day of the first fill or prescription with a second-line T2D medication. 
Follow-up for study outcomes will begin on the day after cohort entry (Figure 1).  
 
3.3. Inclusion criteria (detailed definitions are reported in the Table a1 of the Appendix): 
 
1) Age ≥ 18 years for Optum Cliniformatics, IBM Marketscan, CPRD, and VHA, and ≥ 65 years for 
Medicare FFS at cohort entry 
2) At least 12 months of continuous health plan enrollment (only claims) or registration with a 
general practitioner (CPRD) before and including cohort entry 
3) Diagnosis of T2D within 12 months before (or ever before in CPRD) and including cohort entry 
4) Low or moderate cardiovascular (CV) risk at cohort entry * 
5) Metformin maintenance therapy, defined as 2 fills (or prescriptions in CPRD) of metformin 
recorded within 6 months before and including cohort entry  
 
Note 
* In an initial stage, we will restrict to patients at low/moderate CV risk (relatively to a population with 
T2D) by removing patients with a diagnosis code of established CV diseases recorded within 12 months prior 
to (or ever before in CPRD) and including cohort entry (see Table a1 in the Appendix for definitions of CV 
diseases). In parallel, we will build a prediction model to capture the granularity of CV risk. In a second stage, 
after completion and validation of the prediction model, we will use the predicted risks to identify and include 
patients at low/moderate CV risk. See paragraph 11, page 17, for further details on the prediction model. 
 

5 
 
3.4. Exclusion criteria (detailed definitions are reported in Appendix Table a1): 
 
1) Missing age or gender information 
2) Nursing care admission within 12 months before and including cohort entry (criteria ignored in 
CPRD) 
3) Diagnosis of type 1 diabetes within 12 months before and including cohort entry 
4) Diagnosis of secondary or gestational diabetes within 12 months before and including cohort 
entry 
5) Any insulin fill or prescription within 12 months before and including cohort entry 
6) Diagnosis of end stage renal disease (stage ≥ 5) within 12 months before and including cohort 
entry 
7) Diagnosis of acute or chronic pancreatitis within 12 months before and including cohort entry 
8) Diagnosis of cirrhosis or acute hepatitis within 12 months before and including cohort entry 
9) Diagnosis of MEN-2 within 12 months before and including cohort entry 
10) Recorded solid organ transplant code within 12 months before and including cohort entry 
11) Patients with recorded initiation of more than one agent within a comparator class at cohort 
entry  
 
Note 
For CPRD data, the assessment window for exclusion criteria 3) to 10) is defined using all available 
lookback from on cohort entry. 
 
4. EXPOSURE: 
 
Definitions of new initiation and washout period described in the comparison #4.1 will apply to 
all the comparisons listed in the “EXPOSURE” section. The final definitions for each drug class might 
change based on feasibility findings on the frequency of use of individual agents. 
 
 
ONE-TO-ONE COMPARISONS AMONG SLGT-2 INHIBITORS (SGLT-2i), DPP-4 INHIBITORS (DPP4i) AND 
GLUCAGON-LIKE PEPTIDE-1 RECEPTOR AGONISTS (GLP-1 RA) [#4.1, #4.2, #4.3] 
 
4.1. SGLT-2i vs DPP4i 
 
4.1.1. Exposure: 
New initiation of SGLT-2i listed in Table 1. New initiation is defined as no fill or prescription 
for any SLGT-2i within 12 months prior to cohort entry (washout period). New SGLT-2i users are 
not allowed to receive any DPP4i fill or prescription within 12 months before the new SGLT-2i 
initiation.  
 
Table 1. List of SGLT-2 inhibitors 
CANAGLIFLOZIN 
CANAGLIFLOZIN/METFORMIN HCL 
DAPAGLIFLOZIN PROPANEDIOL/METFORMIN HCL 
DAPAGLIFLOZIN PROPANEDIOL  
EMPAGLIFLOZIN 
EMPAGLIFLOZIN/METFORMIN HCL  

6 
 
ERTUGLIFLOZIN PIDOLATE/METFORMIN HCL 
ERTUGLIFLOZIN PIDOLATE 
EMPAGLIFLOZIN/LINAGLIPTIN 
EMPAGLIFLOZIN/LINAGLIPTIN/METFORMIN HCL 
DAPAGLIFLOZIN PROPANEDIOL/SAXAGLIPTIN HCL 
ERTUGLIFLOZIN PIDOLATE/SITAGLIPTIN PHOSPHATE 
 
4.1.2. Referent: 
New initiation of DPP4i listed in Table 2. New initiation is defined as no prescription fill for 
any DPP4i within 12 months prior to cohort entry (washout period). New DPP4i users are not 
allowed to receive any SGLT-2i fill or prescription within 12 months before the new DPP4i 
initiation.  
 
Table 2. List of DPP4 inhibitors 
ALOGLIPTIN BENZOATE/METFORMIN HCL 
ALOGLIPTIN BENZOATE 
ALOGLIPTIN BENZOATE/PIOGLITAZONE HCL 
SAXAGLIPTIN HCL 
SAXAGLIPTIN HCL/METFORMIN HCL 
LINAGLIPTIN 
LINAGLIPTIN/METFORMIN HCL 
SITAGLIPTIN PHOSPHATE/METFORMIN HCL 
SITAGLIPTIN PHOSPHATE 
SITAGLIPTIN PHOSPHATE/SIMVASTATIN 
DAPAGLIFLOZIN PROPANEDIOL/SAXAGLIPTIN HCL 
EMPAGLIFLOZIN/LINAGLIPTIN 
EMPAGLIFLOZIN/LINAGLIPTIN/METFORMIN HCL 
ERTUGLIFLOZIN PIDOLATE/SITAGLIPTIN PHOSPHATE 
 
4.2. SGLT-2i vs GLP-1 RA 
Please replace the referent group with initiators of GLP-1 RA listed in Table 3.  
 
Table 3. List of GLP-1 RA 
INSULIN DEGLUDEC/LIRAGLUTIDE* 
INSULIN GLARGINE, HUMAN RECOMBINANT ANALOG/LIXISENATIDE* 
LIXISENATIDE 
LIRAGLUTIDE 
DULAGLUTIDE 
SEMAGLUTIDE 
ALBIGLUTIDE 
EXENATIDE MICROSPHERES 
EXENATIDE 
* Combinations with insulin might be added to the definition of GLP-1ra for the 
sensitivity analyses of comparative safety evaluations.  
 
4.3. GLP-1RA vs. DPP-4i 
Please replace the exposure group with initiators of GLP-1 RA listed in Table 3 and the referent 
group with initiators of DPP4i listed in Table 2.  
 

7 
 
 
ONE-TO-ONE COMPARISONS WITH SULFONYLUREA (SU) [#4.4, #4.5, #4.6] 
 
4.4. SGLT-2i vs SU 
Please replace the referent group with initiators of 2nd generation SU listed in Table 4.  
 
Table 4. List of 2nd generation SU 
PIOGLITAZONE HCL/GLIMEPIRIDE 
ROSIGLITAZONE MALEATE/GLIMEPIRIDE 
GLIPIZIDE/METFORMIN HCL 
GLYBURIDE,MICRONIZED 
GLYBURIDE/METFORMIN HCL 
GLIMEPIRIDE 
GLYBURIDE 
GLIPIZIDE 
 
4.5. GLP1RA vs. SU 
Please replace the exposure group with initiators of GLP-1 RA listed in Table 3 and the referent 
group with initiators of SU listed in Table 4.  
 
4.6. DPP41 vs. SU 
Please replace exposure group with initiators of DPP4i listed in Table 2 and referent group with 
initiators of SU listed in Table 4.  
 
 
N-WAY COMPARISONS [#4.7, #4.8, #4.9, #4.10] 
 
4.7. SGLT2i vs. GLP-1RA vs. DPP-4i vs. SU (4-way comparison) 
Initiators of DPP4i, listed in Table 2, are considered the referent group for the 4-way 
comparison. Further details are reported in the statistical analysis (section b of the paragraph 8.1.2) 
 
4.8. SGLT2i vs. GLP-1RA vs. DPP-4i (3-way comparison) 
Initiators of DPP4i, listed in Table 2, are considered the referent group for the 3-way 
comparison. Further details are reported in the statistical analysis (section b of the paragraph 8.1.2) 
 
4.9. Canagliflozin vs. Dapagliflozin vs. Empagliflozin (within-SGLT2i class n-way comparison) 
The referent and exposure groups will be selected through a feasibility analysis on the 
frequencies of index drugs and outcome events. Further details are reported in the statistical 
analysis (section b of the paragraph 8.1.2) 
 
4.10. 
Dulaglutide vs. Exenatide vs. Liraglutide vs. Semaglutide (within-GLP-1RA class n-way 
comparison) 
The referent and exposure groups will be selected through a feasibility analysis on the 
frequencies of index drugs and outcome events. Further details are reported in the statistical 
analysis (section b of the paragraph 8.1.2) 
 

8 
 
Note 
Inter-class comparisons of individual agents will be informed by findings from both 1:1 pairwise comparisons 
between classes and within-class comparisons of individual agents. Pre-specified contrasts of interest include 
comparison between the individual agents belonging to SGLT2i and GLP-1RA (e.g., empagliflozin vs. liraglutide). 
Further comparisons between individual agents, that are not currently listed in the protocol, might be investigated 
whether it is needed. 
 
5. OUTCOMES 
 
5.1. Effectiveness outcomes 
 
Primary effectiveness outcomes are MACE, modified MACE, and hospitalization for heart failure (see 
Table a2 in the Appendix for detailed definitions). Secondary effectiveness outcomes are myocardial 
infarction, stroke, CV mortality, all-cause mortality, coronary revascularization, chronic kidney disease 
(CKD) progression, kidney replacement therapy, kidney death, kidney failure, early kidney disease, 
glycemic control, weight loss or gain (see Table a3 of the Appendix for detailed definitions). 
 
Outcome 
Databases 
Optum 
MarketScan 
Medicare 
FFS 
CPRD 
VHA 
MACE 
Myocardial Infarction, Stroke, CV 
mortality 
 
 
Yes 
Yes 
Yes 
Modified MACE  
Myocardial Infarction, Stroke, All-
Cause mortality 
Yes 
Yes 
Yes 
Yes 
Yes 
Hospitalization for heart failure 
Yes 
Yes 
Yes 
Yes 
Yes 
Myocardial Infarction 
Yes 
Yes 
Yes 
Yes 
Yes 
Stroke  
Yes 
Yes 
Yes 
Yes 
Yes 
CV mortality  
 
 
Yes 
Yes 
Yes 
All-cause mortality  
Yes 
Yes 
Yes 
Yes 
Yes 
Coronary revascularization  
Yes 
Yes 
Yes 
Yes 
Yes 
CKD progression *  
Sustained decrease in eGFR, KRT 
(maintenance dialysis and kidney 
transplantation), kidney death 
 
 
 
Tentative 
Yes 
Sustained decrease in eGFR * 
Tentative 
 
 
Tentative 
Yes 
KRT * 
Yes 
Yes 
Yes 
Yes 
Yes 
Kidney death * 
 
 
Yes 
Yes 
Yes 
Kidney failure * (sustained eGFR <15 
ml/min/1.73m2, maintenance dialysis 
and kidney transplant)  
Tentative 
 
 
Tentative 
Yes 
Early kidney disease * 
Defined by change in eGFR in patients 
with baseline eGFR > 60  
Tentative 
 
 
Tentative 
Yes 
Glycemic control 
Defined by HbA1c change in patients  
with available baseline HbA1c 
Tentative 
 
 
Yes 
Yes 

9 
 
Insulin initiation 
Yes 
 
Yes 
Yes 
Yes 
Weight loss or gain * 
Defined by weight change in patients  
with available baseline weight 
 
 
 
Yes 
Yes 
Outcome analyses noted as “tentative” will require ad hoc investigation in corresponding databases to determine the likelihood 
of validity and thus the capacity of these databases to contribute to overall pooled estimates. 
* exploratory outcome since no validated claim-based outcome definition is currently available. We will consider additional 
components/measures whether necessary.  
Abbreviations: CKD, chronic kidney disease; KRT, kidney replacement therapy 
 
5.2. Safety outcomes 
 
Detailed definitions are reported in the Table a4 of the Appendix. 
 
Outcome 
Exposure of 
interest 
Databases 
Optum 
MarketScan 
Medicare 
FFS 
CPRD 
VHA 
Diabetic ketoacidosis  
SGLT-2i 
Yes 
Yes 
Yes 
Yes 
Yes 
Bone fractures  
SGLT-2i 
Yes 
Yes 
Yes 
Yes 
Yes 
Lower-limb amputations  
SGLT-2i 
Yes 
Yes 
Yes 
Yes 
Yes 
Acute kidney injury 
All drug classes 
Yes 
Yes 
Yes 
Yes 
Yes 
Urinary infections  
SGLT-2i 
Yes 
Yes 
Yes 
Yes 
Yes 
Genital infections 
SGLT-2i 
Yes 
Yes 
Yes 
Yes 
Yes 
Acute pancreatitis  
GLP1 RA, DPP4i 
Yes 
Yes 
Yes 
Yes 
Yes 
Biliary events 
GLP1 RA, DPP4i 
Yes 
Yes 
Yes 
Yes 
Yes 
Severe hypoglycemia  
SU 
Yes 
Yes 
Yes 
Yes 
Yes 
Short-term retinopathy 
progression * 
GLP1 RA 
Yes 
Yes 
Yes 
Yes 
Yes 
Safety signals identified via 
TreeScan ^ 
 
 
 
 
 
 
* exploratory outcomes since no validated claim-based outcome definition is currently available. 
 ^ see section 9 of the protocol. 
 
5.3. Other outcomes 
 
Outcome 
Databases 
Optum 
MarketScan 
Medicare 
FFS 
CPRD 
VHA 
Home time 
Time spent out of hospital 
and skilled nursing facility ^ 
Time to Nursing Home Placement ^^ 
 
 
Yes 
 
 
Medication persistence 
Time to discontinuation 
Yes 
Yes 
Yes 
Yes 
Yes 
Switching patterns 
Treatment trajectories: patterns of use 
following initiation of treatment under study. 
To be illustrated using concentric circle 
diagrams or Sankey diagrams as appropriate. 
Yes 
Yes 
Yes 
Yes 
Yes 

10 
 
^ Lee H, Shi SM, Kim DH. Home Time as a Patient-Centered Outcome in Administrative Claims Data. J Am Geriatr Soc. 2019 
Feb;67(2):347-351 
^^ Kim DH, Li X, Bian S, Wei LJ, Sun R. Utility of Restricted Mean Survival Time for Analyzing Time to Nursing Home Placement 
Among Patients with Dementia. JAMA Netw Open. 2021 Jan 4;4(1):e2034745.  
 
6. COVARIATES 
 
The overall list of covariates is reported in Table a5 of the Appendix. Specific set of covariates 
will be selected from the overall list based on the outcome investigated. Covariates will be assessed 
at baseline (i.e., within 12 months prior to and including cohort entry date) for all databases, except 
for CPRD, which will consider all available lookback available within the database. Definitions are 
available upon request.  
 
7. STUDY FOLLOW-UP AND CENSORING REASONS 
 
Using an “on-treatment approach” as main analysis of the comparisons listed in paragraph 4, 
please follow eligible individuals from the day after cohort entry until the first occurrence of: 
1) Effectiveness/safety study outcome, 
2) End of the study period (administrative end of follow-up),  
3) End of continuous health plan enrollment (only claims) or end of registration with general PR 
actioners (CRPD), 
4) Index exposure/referent discontinuation (grace period of 60 days, unless otherwise noted), 
5) Addition/switching to the other treatment group, 
6) Switching to anti-diabetic medications other than the study drugs, 
7) Bariatric surgery, 
8) Death.  
 
8. STATISTICAL ANALYSIS 
  
8.1. Primary analyses 
 
All the steps listed in this paragraph will be followed for each of the study cohort created based 
on eligible criteria and comparison of interest (See sections “3. STUDY COHORT” and “4. 
EXPOSURE”). 
 
8.1.1. Descriptive analysis (before adjustment) 
 
- 
Please create the study cohort following inclusion and exclusion criteria stated above (See 
paragraph “3. STUDY COHORT”) and selecting the appropriate comparison of interest (See 
paragraph “4. EXPOSURE”). 
- 
Please summarize the baseline patient characteristics (See paragraph 6) by index drug using 
descriptive statistics (frequencies, means, medians) before adjustment. Please create 
separate summary tables for each data source.  
- 
Please calculate and report numbers of events, person-years, incidence rates with 95% 
confidence intervals (CI) and rate differences with 95% CI of the outcome of interest.  

11 
 
 
8.1.2. Achieving balance in patient covariates (adjustment) 
 
Please use propensity score (PS) methodology to address confounding by indication. 
 
a. Pairwise comparisons of T2D drug classes: 
 
- 
Please calculate PS for each pairwise comparison as the predicted probability of receiving 
one class vs. another, conditional upon a set of potential confounders (See Table a5 in the 
Appendix) using a multivariable logistic regression model.  
- 
Please use the resulting PS to match patients in a 1:1 ratio using a nearest-neighbor 
algorithm with a maximum caliper of 0.01 of the PS (restricting analyses to those patients 
who share common distribution with respect to potential indications and contraindications). 
- 
When exposure prevalence is low and outcomes are rare, we will consider using PS-based 
fine stratification creating unequally sized propensity-score strata, after ranking only the 
exposed patients based on the PS and assigning unexposed patients to these strata based 
on their PS (propensity score strata exposed approach). SAS macros for propensity score 
stratification are available at: http://www.drugepi.org/dope-downloads/. 
 
b. N-way comparisons of T2D drug classes or agents: 
 
- 
We will consider using weighting methods to reweight both exposed and unexposed groups 
to balance patient characteristics. Weighting methods can naturally generalize to a non-
dichotomous treatment variable, including three or more treatment groups.1,2 Please use 
the example code available on https://github.com/kaz-yos/mw 
 
8.1.3. Diagnostics of achieved balance (after adjustment) 
 
- 
Please create a summary table stratified by index drug of the baseline patient characteristics 
listed in “6. COVARIATES”, using descriptive statistics (frequencies, means, medians) after 
adjustment. Please create separate summary tables for each data source. 
- 
Please inspect covariate balance before and after PS-adjustment by calculating standardized 
differences for each covariate (including characteristics only measured in a subset of the 
claims-only populations and thus not included in main PS model, see Table a5 in the 
Appendix). 
- 
Please inspect overlap in PS distributions before and after adjustment (plots) and assess the 
post-matching c-statistic from the PS model refit in the matched sample, which is expected 
to be closer to 0.5 if balance has been achieved.3 
 
8.1.4. Statistical analysis in the balanced study cohort 
 
- 
Please calculate PS-matched numbers of events, person-years, incidence rates, hazard 
ratios (HRs), and rate differences (RDs), each with 95% CIs for the outcome of interest. 
- 
Please use Cox proportional hazards models to estimate hazard ratios and 95% CI  

12 
 
- 
Please plot Kaplan-Meier curves of cumulative incidence and compare rates between 
treatment groups with log-rank tests 
- 
For recurrent events of selected CV outcomes (e.g., HHF), we will consider using 
semiparametric proportional rates method of Lin and a joint gamma frailty model will be 
used to quantify the association between 2nd-line T2D agents and recurrent outcome 
events. 
 
8.1.5. Pooling of database-specific estimates 
 
- 
Please pool estimates from all databases using the DerSimonian and Laird random-effects 
model with inverse variance.4 Please also pool estimate from all databases using a fixed-
effects model as a sensitivity analysis. 
- 
Please investigate between-dataset heterogeneity calculating the I2 statistic and 95% CI.5 
Values above 50% will be considered evidence of substantial heterogeneity. If heterogeneity 
across datasets exceeds 50% as measured by I2 statistic, we will investigate contribution to 
the overall heterogeneity of each database by removing one dataset in turn from the pooled 
analysis. 
 
8.2. Sensitivity analyses 
 
8.2.1. Assess and correct for residual confounding in main analyses 
 
a. Assess balance and address potential imbalances  
 
- 
Search for balance. Using available laboratory and EHR data in a subset of patients in the 
large claims databases (i.e., laboratory values in Optum and MarketScan; EHR data in 
Medicare FFS-RPDR), please evaluate the extent of imbalance after PS adjustment following 
the same methodology described in paragraph 8.1.3. If no imbalances remain, we will 
conclude that the main adjustment approach in claims data sufficiently addresses 
confounding.  
- 
In case of imbalance, search for differences in the results. If imbalances remain, please 
repeat analyses within the subset with and without the additional laboratory information in 
the PS model. If inclusion of these variables in the model does not materially change the 
results, we will again conclude that the main adjustment approach sufficiently addresses 
confounding.  
- 
In case of differences in the results, consider applying PS-calibration. If inclusion of these 
variables changes the results, please use PS-calibration to address unmeasured confounding 
by calibrating the PS in the main study population based on a “gold-standard” PS built in the 
subset of the population that includes the unmeasured confounders. 6-8 
 
b. Negative and positive tracer outcomes 
 
To increase confidence that the main analysis sufficiently addresses confounding and other 
biases, we will consider using: 

13 
 
i. 
Positive tracer outcomes, for which we would expect a positive or negative 
association with the exposure, 
ii. 
Negative tracer outcomes, for which we would expect a null finding. 
 
c. Quantitative bias analyses (i.e., defining the strength of a hypothetical unmeasured 
confounder which, if present, would explain the observed effect across a range of 
confounder prevalence measures in the treatment groups) to appraise the impact of any 
additional suspected source of unmeasured confounding.9 
 
Note 
If we cannot control for unmeasured confounding, we will disregard the database associated 
with higher likelihood for confounding. 
 
8.2.2. High-dimensional PS  
 
For databases that lack information for laboratory values (i.e., Optum and MarketScan) or 
EHR data (i.e., Medicare FFS-RPDR), we will consider using high dimensional PS approach to 
improve confounding adjustment by estimating the potential confounding for a large number 
(usually hundreds or thousands) of codes in the database. 10,11 This approach can adjust for 
variables that are proxies for confounders and that were not pre-specified risk factors for the 
outcomes of interest. 
 
8.2.3. Testing robustness of on-treatment approach 
 
To assess sensitivity of primary on-treatment estimated effects to potential informative 
censoring, we will conduct additional sensitivity analyses using: 
 
i. 
Varying grace period after index exposure/referent discontinuation. We will 
consider applying shorter or longer grace periods (e.g., 30 or 90 days), after 
treatment discontinuation.  
ii. 
Time-limited intention-to-treat (ITT) effect carrying forward the effect of the 
initiated T2D medication independently of discontinuation or switching. Please 
follow individuals from the day after cohort entry until the first occurrence of: 
1) Study outcome, 
2) End of the study period (or available data),  
3) End of continuous health plan enrollment,  
4) Death, 
5) 12 months after drug initiation. 
iii. 
Inverse probability censoring weights (IPCW). To investigate the impact of 
informative censoring from drug switching/discontinuation, and to investigate death 
as a competing risk, we will use inverse probability of censoring weights to reweigh 
the cohorts. These weights will be calculated by subdividing the follow-up period 
into 30-day intervals and using logistic regression models to predict the probability 
of remaining uncensored in each interval, using time-varying variables measured in 

14 
 
the previous interval. Stabilized IPCWs will be combined with treatment weights 
generated in the primary analysis for a final weight to be used in the outcome 
model. 
 
If sensitivity analyses (i) or (ii) indicate primary analyses are prone to informative 
censoring (e.g., 95% CI of primary estimates produced under the primary on-protocol scheme 
are non-overlapping with 95% CI of estimates produced under an ITT scheme or after the 
implementation of IPCW), then we will consider prioritization of results from ITT or IPCW 
analyses above primary on-treatment results to inform clinical decision making. 
 
8.3. Secondary analyses 
 
8.3.1. GRADE-like study population 
 
To closely mimic the population included in the GRADE trial, please build a new cohort 
following inclusion and exclusion criteria listed in the paragraph “3. STUDY COHORT” except for 
inclusion criteria n. 4 and 5 which are replaced with: 
 
a. Please modify criterion n. 4 removing from the list of the CV codes in Table a1 of the 
Appendix: ACS unstable angina, stable angina, coronary atherosclerosis. This 
modification will be applied to the cohort definition until completion and validation od 
the CV prediction model (see paragraph 11)  
b. Please modify criterion n. 5, metformin maintenance therapy will be defined in the 
GRADE-like cohorts as 2 fills (or prescriptions in CPRD) of metformin monotherapy 
recorded within 6 months before and including cohort entry 
 
 
8.3.2. Secondary analysis for safety outcomes 
 
To test the informativeness of drug-related harms, please build a new cohort following 
inclusion and exclusion criteria listed in the paragraph “3. STUDY COHORT” except for: 
 
a. Please remove inclusion criterion n. 4), thus the cohort is not restricted to patients 
with low or moderate CV risk  
b. Please remove inclusion criterion n. 5), thus the cohort is not restricted to patients on 
baseline metformin  
c. Please remove exclusion criterion n. 5) “Any insulin fill or prescription within 12 
months before and including cohort entry”, thus baseline use of insulin or other T2D 
medications is allowed as long as not art of the exposure definition.  
 
8.4. Subgroup analyses  
 
- 
Definition of potential effect modifiers. To assess potential effect modification, please conduct 
subgroup analyses for selected outcomes stratified by each subgroup of interest listed in Table 

15 
 
5. The variables defining the subgroups are measured at baseline (12 months prior to and 
including cohort entry date or, for CPRD data, any time before and including cohort entry date) 
or at cohort entry. Other subgroups might be considered based on further stakeholders’ 
feedback. 
- 
Achieving balance in patients’ covariates and diagnostics of achieved balance. Within each 
category of the subgroup of interest (for example, within “male” and “female” categories of the 
subgroup “gender”), please re-estimate the PS for the exposure and referent drugs and re-
perform the PS matching following all the steps reported in paragraphs 8.1.2 and 8.1.3. 
- 
Statistical analysis in the balanced subgroup cohort. For each category of the subgroup of 
interest, please provide number of outcome events, person-years, incidence rates and final 
findings in both relative (i.e., hazard ratio, HR and 95% CI) and absolute scales (i.e., rate 
difference, RD and 95% CI) before and after adjustment following the steps described in 
paragraph 8.1.4. 
- 
Testing treatment heterogeneity within subgroups. Finally, please estimate the presence of 
treatment heterogeneity across categories of the subgroup of interest by performing the Wald 
test for homogeneity on the relative and absolute scale.  
 
Table 5. Proposed pre-specified patient subgroups of interest 
 
Subgroup of 
interest 
Categories 
References 
Age 
65-74 years, 75+ years (Medicare) 
18-64 years, 65+ years (Other databases) 
-- 
Gender 
Female, male 
-- 
Race 
White, black, others (Medicare and VA) 
-- 
Baseline CV risk 
In an initial stage, we will identify the 
presence of low/moderate vs. high CV risk 
in the study population and accordingly 
stratify the analysis, based on diagnosis 
codes of CV diseases measured at baseline. 
After completion and validation of a CV 
prediction model, we will use predicted 
risks to identify finer CV risk levels. 
See paragraph 11 for further information on the 
development and validation of the CV prediction model 
Chronic kidney 
disease (CKD) 
We will stratify by CKD stages by using eGFR 
values or claims-based validated algorithms. 
- Paik JM et al. Accuracy of identifying diagnosis of 
chronic kidney disease in administrative claims data. 
Manuscript accepted for publication in 
Pharmacoepidemiology and Drug Safety. Dec 12, 2021. 
In press. 
- Iwagami M et al. Validity of estimated prevalence of 
decreased kidney function and renal replacement 
therapy from primary care electronic health records 
compared with national survey and registry data in the 
United Kingdom. Nephrol Dial Transplant. 
2017;32(suppl_2):ii142-ii150.  
Frailty 
We will stratify by frailty levels by using 
validated frailty index scores  
- Kim DH et al. Measuring Frailty in Administrative 
Claims Data: Comparative Performance of Four Claims-
Based Frailty Measures in the U.S. Medicare Data. J 
Gerontol A Biol Sci Med Sci. 2020;75(6):1120-1125.  

16 
 
- Cheng D et al. Updating and Validating the Veterans 
Affairs Frailty Index: Transitioning from ICD-9 to ICD-10. 
J Gerontol A Biol Sci Med Sci. 2021;76(7):1318-1325. 
- Orkaby AR et al. The Burden of Frailty among US 
Veterans and its Association with Mortality, 2002-2012. 
J Gerontol A Biol Sci Med Sci. 2019;74(8):1257-1264.  
- Clegg A, et al. Development and validation of an 
electronic frailty index using routine primary care 
electronic health record data. Age Ageing. 
2016;45(3):353-60. 
Socioeconomical 
conditions 
We will consider socioeconomical status 
categories available in Medicare and CPRD 
databases. 
- Herrett E et al. Data Resource Profile: Clinical Practice 
Research Datalink (CPRD). Int J Epidemiol. 
2015;44(3):827-836. 
- Gopalakrishnan C et al. Evaluation of Socioeconomic 
Status Indicators for Confounding Adjustment in 
Observational Studies of Medication Use. CPT 2019; 
105:1513-1521. 
Abbreviations: CV, cardiovascular 
Note: Based on feedback collected during the Stakeholder Advisory meeting on Dec 8th, 2021, additional groups that may be 
considered for inclusion are: duration of diabetes (CPRD only), number of medications used since time of diabetes diagnosis 
(CPRD only), time on metformin (CPRD only), glycemic control/HbA1c (CPRD and Optum only), BMI (CPRD and potentially claims-
based databases using BMI algorithm). 
 
8.5 Missing data 
 
Missingness in EHR and laboratory data will be examined in terms of frequency and patterns of 
missingness and addressed via complete-case analysis strategy or missing indicator variable or 
multiple imputation methods, depending on the extent of missing information.12,13 PS-calibration, as 
described above, will also be considered to assess the impact of missing data.6-8 
 
9. TREES-BASED SCAN STATISTICS (TreeScan™)  
 
In Medicare and one commercial database, we will consider identifying potential safety signals 
using tree-based scan statistics, a data mining approach implemented by the free TreeScan™ 
software (www.treescan.org). The wide range of health outcomes is arranged in a hierarchical tree 
constructed based on international classification of disease coding (ICD). The results will be adjusted 
for multiple testing.14-18 
 
10. PREDICTION RULES 
Guided by the results of the safety and Treescan analyses, we will estimate the individual 
patients’ risk of selected drug-related harms associated with second-line T2D medications by 
developing and validating treatment-specific prediction rules following the steps below. Input from 
the Advisory Panel and the research team will be considered in prioritizing the prediction of specific 
harms over others. 
1) Select potential predictors of drug-related adverse events based on previous literature, clinical 
experience, and expert opinion.   
2) Build predictive models of drug-related adverse events considering several machine learning 
approaches, including least absolute shrinkage and selection operator (LASSO), and potential 
other approaches, e.g., gradient boosted model.  

17 
 
3) Train the models in bootstrap samples without replacement and test them in subjects not 
included in the bootstrap sample. 19 
4) Assess the performance of the machine learning modeling approaches using several metrics, 
such as Brier score, area under the receiver operating characteristic curve, and calibration 
plots.20  
5) Build proportional hazards models including the outcome predictors identified by the most 
efficient machine learning modeling approach to produce coefficients that could be used to 
generate targeted scoring systems for assisting decision-making.  
6) We will consider validating the prediction rules on a different database. 
 
11. PREDICTION MODEL TO STRATIFY RISK OF CV DISEASE 
In addition to using diagnosis codes of CV diseases measured at baseline, we plan to also stratify the 
study populations into levels of CV risk on the basis of their predicted risk of atherosclerotic CV 
disease and/or heart failure as estimated by prediction models. In order to do so, we plan to use the 
following approach: 
1) Identify patients with type 2 diabetes mellitus patients, who have information from claims and 
electronic heath records (EHR) from the Medicare FFS-RPDR database. The cohort entry date 
will be any physician office or outpatient visit date.  
2) Identify outcome of interest defined as atherosclerotic CV disease or hospitalization for heart 
failure (see definitions in Table a1 and a2 of the Appendix) during follow up (e.g., two years) 
starting from cohort entry. 
3) Divide the study population into two subgroups: (i) one with baseline CV diseases (CVD) and (ii) 
one without baseline CVD, based on diagnosis codes listed in Table a1 of the Appendix.  
4) Select potential predictors based on clinical knowledge using information from (i) claims + EHR 
data, and (ii) claims only.   
5) Build predictive models using machine learning models, shown to work well in high-dimensional 
claims and in the presence of missing data: LASSO and gradient boosted model (XG-boost).  
6) Train the models using 10-fold cross validation based on training and testing samples.19 
7) Assess the performance of the machine learning models using Brier score, area under the 
receiver operating characteristic curve, and calibration plots.20  
8) Compare the performance between approaches based on claims-only vs. claims + EHR variables, 
using precision-recall curves and decision curves to contrast the net benefit of the selected 
approaches, and reporting the observed probability of events by predicted risk deciles.21,22  
9) Select the most influential predictors from these claims-based machine learning modelling 
approaches by relative influence measures or ranking the magnitude of coefficients and build 
proportional hazards models to produce coefficients that could be used to generate CV risk 
score. 
10) Apply the risk prediction score on target databases to identify populations at different levels of 
CV risk. 
 
12. CER-4-T2D revised analytical plan 
We summarize below the main revisions to the original CER-4-T2D study proposal: 

18 
 
• 
To increase the representativeness of the study population included in the CER-4-T2D study, we 
plan not to exclude patients with a history of malignancies. 
• 
To comply with the accelerated timeline of the CER-4-T2D study, we will prioritize the 
identification and inclusion in the analyses of patients at low/moderate CV risk on the basis of 
the absence of diagnosis codes indicative of established CV disease at baseline (i.e., pre-
exposure). In a second stage, we will build a prediction model to capture the granularity of CV 
risk and will use the predicted risks to identify finer levels of CV risk.  
• 
To account for the fact that individuals who undergo bariatric surgery during follow-up may no 
longer be eligible for type 2 diabetes (T2D) treatment, we plan to censor patients who undergo 
bariatric surgery during follow-up. 
• 
To assess sensitivity of primary on-treatment estimated effects to potential informative 
censoring, we will consider varying the primary grace period. 
 
 
 

19 
 
References 
 
1. Yoshida K, Hernandez-Diaz S, Solomon DH, et al. Matching Weights to Simultaneously Compare 
Three Treatment Groups: Comparison to Three-way Matching. Epidemiology 2017;28:387-95. 
2. Yoshida K, Solomon DH, Haneuse S, et al. Multinomial Extension of Propensity Score Trimming 
Methods: A Simulation Study. Am J Epidemiol 2019;188:609-16. 
3. Franklin JM, Rassen JA, Ackermann D, Bartels DB, Schneeweiss S. Metrics for covariate balance in 
cohort studies of causal effects. Stat Med 2014;33:1685-99. 
4. DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials 1986;7:177-88. 
5. Deeks JJ HJ, Altman DG. Identifying and measuring heterogeneity.  Cochrane Handbook for 
Systematic Reviews of InterventionsVersion 6.1, 2000. 
6. Sturmer T, Schneeweiss S, Avorn J, Glynn RJ. Adjusting effect estimates for unmeasured confounding 
with validation data using propensity score calibration. Am J Epidemiol 2005;162:279-89. 
7. Sturmer T, Schneeweiss S, Rothman KJ, Avorn J, Glynn RJ. Performance of propensity score 
calibration--a simulation study. Am J Epidemiol 2007;165:1110-8. 
8. Sturmer T, Schneeweiss S, Rothman KJ, Avorn J, Glynn RJ. Propensity Score Calibration and its 
Alternatives. Am J Epidemiol 2007;165:1122-3. 
9. Schneeweiss S. Sensitivity analysis and external adjustment for unmeasured confounders in 
epidemiologic database studies of therapeutics. Pharmacoepidemiol Drug Saf 2006;15:291-303. 
10. Rassen JA, Schneeweiss S. Using high-dimensional propensity scores to automate confounding 
control in a distributed medical product safety surveillance system. Pharmacoepidemiol Drug Saf 
2012;21 Suppl 1:41-9. 
11. Schneeweiss S, Rassen JA, Glynn RJ, Avorn J, Mogun H, Brookhart MA. High-dimensional propensity 
score adjustment in studies of treatment effects using health care claims data. Epidemiology 
2009;20:512-22. 
12. Sterne JA, White IR, Carlin JB, Spratt M, Royston P, Kenward MG, Wood AM, Carpenter JR. Multiple 
imputation for missing data in epidemiological and clinical research: potential and pitfalls. BMJ. 
2009;338:b2393.  
13. Hughes RA, Heron J, Sterne JAC, Tilling K. Accounting for missing data in statistical analyses: multiple 
imputation is not always the answer. Int J Epidemiol. 2019;48(4):1294-1304. 
14. Kulldorff M, Dashevsky I, Avery TR, et al. Drug safety data mining with a tree-based scan statistic. 
Pharmacoepidemiol Drug Saf. 2013;22(5):517-523. 
15. Brown JS, Petronis KR, Bate A, et al. Drug adverse event detection in health plan data using the 
gamma poisson shrinker and comparison to the tree-based scan statistic. Pharmaceutics. 
2013;5(1):179-200. 
16. Li R, Weintraub E, McNeil MM, et al. Meningococcal conjugate vaccine safety surveillance in the 
Vaccine Safety Datalink using a tree-temporal scan data mining method. Pharmacoepidemiol Drug 
Saf. 2018;1–7. 
17. Yih WK, Maro JC, Nguyen M, et al. Assessment of quadrivalent human papillomavirus vaccine safety 
using the self-controlled tree-temporal scan statistic signal-detection method in the sentinel system. 
Am J Epidemiol. 2018;187(6):1269-1276. 
18. Kulldorff M, Fang Z, Walsh SJ. A tree-based scan statistic for database disease surveillance. 
Biometrics. 2003;59(2):323-331. 

20 
 
19. Steyerberg EW, Harrell FE, Jr., Borsboom GJ, Eijkemans MJ, Vergouwe Y, Habbema JD. Internal 
validation of predictive models: efficiency of some procedures for logistic regression analysis. J Clin 
Epidemiol 2001;54:774-81. 
20. Steyerberg EW, Vickers AJ, Cook NR, et al. Assessing the performance of prediction models: a 
framework for traditional and novel measures. Epidemiology 2010;21:128-38. 
21. Saito T, Rehmsmeier M. The precision-recall plot is more informative than the ROC plot when 
evaluating binary classifiers on imbalanced datasets. PLoS One 2015;10:e0118432. 
22. Vickers AJ, Elkin EB. Decision curve analysis: a novel method for evaluating prediction models. Med 
Decis Making 2006;26:565-74. 
 
 

21 
 
Appendix  
*****Note***** 
• 
The lowercase letter (x) acts as a general wildcard. It will replace a set of codes characterized by the same 
numbers or letters before or after the x (for example, 250.x includes all codes starting with 250.; 402.x1 
includes 402.01, 402.11, 402.91; etc.) 
• 
Common abbreviations: MI, myocardial infarction; ACS, acute coronary syndrome; CV, cardiovascular; 
MACE, major adverse cardiovascular events; ICD, international classification of diseases.  
 
Table a1. Inclusion/exclusion criteria definitions 
 
Inclusion criteria 
Codes 
Setting/Position 
Type 2 diabetes 
mellitus 
ICD 9 diagnosis: 250.00, 250.02, 250.10, 250.12, 250.20, 250.22, 
250.30, 250.32, 250.40, 250.42, 250.50, 250.52, 250.60, 250.62, 
250.70, 250.72, 250.80, 250.82, 250.90, 250.92 
ICD 10 diagnosis: E11.x 
Any setting,  
any position 
Low or moderate CV 
risk  
 
N.B.  
- TO DEFINE OUR 
PRIMARY COHORT 
PLEASE EXCLUDE 
PATIENTS WITH THE 
FOLLOWING CV CODES 
(see paragraph 3.3) 
 
- TO DEFINE GRADE-
LIKE POPULATION 
PLEASE DO NOT 
INCLUDE IN THE LIST 
OF CV CODES: ACS 
UNSTABLE ANGINA, 
STABLE ANGINA, 
CORONARY 
ATHEROSCLEROSIS 
(see paragraph 8.3.1) 
Acute MI 
ICD-9 diagnosis: 410.x 
ICD-10 diagnosis:  I21.x, I22.x 
Old MI 
ICD-9 diagnosis: 412 
ICD-10 diagnosis:  I25.2 
MI sequelae 
ICD-9 diagnosis: 429.79 
ICD-10 diagnosis:  I23.x 
ACS unstable angina 
ICD-9 diagnosis: 411.1, 411.8x 
ICD-10 diagnosis:  I20.0, I24.8, I24.9, I25.110, I25.7x0 
Stable angina 
ICD-9 diagnosis: 413.xx 
ICD-10 diagnosis:  I20.1, I20.8, I20.9, I25.11x, I25.7x1, I25.7x8, I25.7x9 
Coronary atherosclerosis 
ICD-9 diagnosis: 414.xx, 429.2 
ICD-10 diagnosis:  I25.10, I25.3, I25.4x, I25.5, I25.6, I25.8x, I25.9 
Coronary procedure 
ICD-9 PX: 00.66, 36.03, 36.06, 36.07, 36.09, 36.1x, 36.2x, 36.3x 
ICD-10 PX: 0210.xxx, 0211.xxx, 0212.xxx, 0213.xxx, 021K0Z5, 021K4Z5, 
021L0Z5, 021L4Z5, 0270.xxx, 0271.xxx, 0272.xxx, 0273.xxx, 02C0.xxx, 
02C1.xxx, 02C2.xxx, 02C3.xxx, 02QA.xxx, 02QB.xxx, 02QC.xxx 
CPT/HCPCS: 33140, 33141, 33510-33536, 33545, 33572, 92920, 
92921, 92924, 92925, 92928, 92929, 92933, 92934, 92937, 92938, 
92941, 92943, 92944, 92973, 92980, 92980, 92981, 92984, 92995, 
92996 
History of coronary procedure 
ICD-9 diagnosis: V45.81, V45.82 
ICD-10 diagnosis: Z95.1, Z95.5, Z98.61, I97.410, I97.411, I97.610, 
I97.611, I97.630, I97.631, I97.640, I97.641, T82.211x, T82.212x, 
T82.213x, T82.218x  
Congestive heart failure 
ICD-9 diagnosis: 428.xx, 398.91, 402.x1, 404.x1, 404.x3 
ICD-10 diagnosis: I09.81, I11.0, I13.0, I13.2, I50.xxx, I97.13x 
Any setting,  
any position 

22 
 
Stroke 
ICD-9 diagnosis: 433.xx, 434.xx, 436 
ICD-10 diagnosis: I63.xxx, I65.xx, I66.xx, G43.6x9, G46.3, G46.4 
Peripheral arterial disease 
ICD-9 diagnosis: 440.2x, 440.3x, 440.4, 443.9 
ICD-10 diagnosis: I70.x, I73.89, I73.9, T82.310x, T82.312x, T82.320x, 
T82.322x, T82.330x, T82.332x, T82.390x, T82.392x, T82.856x, Z98.62 
ICD-9 procedure: 38.08, 38.18, 38.38, 38.48, 39.25, 39.29, 39.5x 
(excluding 39.53), 39.90, 39.91, 39.99 
ICD-10 procedure: 0410096-99,0410496-99, 0470046, 0470056, 
0470066, 0470076, 0470346, 0470356, 0470366, 0470376,0470446, 
0470456, 0470466, 0470476, 04700E6, 04703E6, 04704E6,047E046, 
047E056, 047E066, 047E076; 041009.x, 04100A.x, 04100J.x, 04100K.x, 
04100Z.x, 041049.x, 04104A.x, 04104J.x, 04104K.x, 04104Z.x (where 
x=B,C,D,F,G,H,J,K,Q,R,6,7,8,9); 041C09.x, 041C0A.x,, 041C0J.x, 
041C0K.x, 041C0Z.x, 041C49.x, 041C4A.x, 041C4J.x, 041C4K.x, 
041C4Z.x, 041D09.x, 041D0A.x, 041D0J.x, 041D0K.x, 041D0Z.x, 
041D49.x, 041D4A.x, 041D4J.x, 041D4K.x, 041D4Z.x, 041E09.x, 
041E0A.x, 041E0J.x, 041E0K.x, 041E0Z.x, 041E49.x, 041E4A.x, 
041E4J.x, 041E4K.x, 041E4Z.x, 041F09.x, 041F0A.x, 041F0J.x, 
041F0K.x, 041F0Z.x, 041F49.x, 041F4A.x, 041F4J.x, 041F4K.x, 
041F4Z.x, 041H09.x, 041H0A.x, 041H0J.x, 041H0K.x, 041H0Z.x, 
041H49.x, 041H4A.x, 041H4J.x, 041H4K.x, 041H4Z.x, 041J09.x, 
041J0A.x, 041J0J.x, 041J0K.x, 041J0Z.x, 041J49.x, 041J4A.x, 041J4J.x, 
041J4K.x, 041J4Z.x (where x=J,K,H); 041K09.x, 041K0A.x, 041K0J.x, 
041K0K.x, 041K0Z.x, 041K49.x, 041K4A.x, 041K4J.x, 041K4K.x, 
041K4Z.x, 041L09.x, 041L0A.x, 041L0J.x, 041L0K.x, 041L0Z.x, 041L49.x, 
041L4A.x, 041L4J.x, 041L4K.x, 041L4Z.x (where x=H,J,K,L,M,N,P,Q,S); 
041M09.x, 041M0A.x, 041M0J.x, 041M0K.x, 041M0Z.x, 041M49.x, 
041M4A.x, 041M4J.x, 041M4K.x, 041M4Z.x, 041N09.x, 041N0A.x, 
041N0J.x, 041N0K.x, 041N0Z.x, 041N49.x, 041N4A.x, 041N4J.x, 
041N4K.x, 041N4Z.x (where x=L,M,P,Q,S); 04700.xZ, 04703.xZ, 
04704.xZ, 047C0.xZ, 047C0.x6, 047C3.x6, 047C3.xZ, 047C4.x6, 
047C4.xZ, 047D0.x6, 047D0.xZ, 047D3.x6, 047D3.xZ, 047D4.x6, 
047D4.xZ, 047E0.xZ, 047E3.x6, 047E3.xZ, 047E4.x6, 047E4.xZ, 
047F0.x6, 047F0.xZ, 047F3.x6, 047F3.xZ, 047F4.x6, 047F4.xZ, 
047H0.x6, 047H0.xZ, 047H3.x6, 047H3.xZ, 047H4.x6, 047H4.xZ, 
047J0.x6, 047J0.xZ, 047J3.x6, 047J3.xZ, 047J4.x6, 047J4.xZ, 047K0.x6, 
047K0.xZ, 047K3.x6, 047K3.xZ, 047K4.x6, 047K4.xZ, 047L0.x6, 
047L0.xZ, 047L3.x6, 047L3.xZ, 047L4.x6, 047L4.xZ, 047M0.x6, 
047M0.xZ, 047M3.x6, 047M3.xZ, 047M4.x6, 047M4.xZ, O47N0.x6, 
O47N0.xZ, O47N3.x6, O47N3.xZ, O47N4.x6, O47N4.xZ, O47P0.x6, 
O47P0.xZ, O47P3.x6, O47P3.xZ, O47P4.x6, O47P4.xZ, O47Q0.x6, 
O47Q0.xZ, O47Q3.x6, O47Q4.xZ, O47R0.xZ, O47R3.x6, O47R4.xZ, 
O47S0.xZ, O47S3.x6, O47S4.xZ, O47T0.xZ, O47T3.x6, O47T4.xZ, 
O47U0.xZ, O47U3.x6, O47U4.xZ, O47V0.xZ, O47V3.x6, O47V4.xZ, 
O47W0.xZ, O47W3.x6, O47W4.xZ, O47Y0.xZ, O47Y3.x6, O47Y4.xZ 
(where x =4,5,6,7,D,E,F,G,Z); 047K0.x1, 047K3.x1, 047K4.x1, 047L0.x1, 
047L3.x1, 047L4.x1, 047M0.x1, 047M3.x1, 047M4.x1, O47N0.x1, 
O47N3.x1, O47N4.x1 (where x = 4,D,Z); 04700.x6, 04703.x6, 04704.x6, 
047E0.x6 (where x = D,E,F,G,Z); 04CK0.Zx, 04CK3.Zx, 04CK4.Zx, 
04CL0.Zx, 04CL3.Zx, 04CL4.Zx, 04CM0.Zx, 04CM3.Zx, 04CM4.Zx, 
04CN0.Zx, 04CN3.Zx, 04CN4.Zx, 04CP0.Zx, 04CP3.Zx, 04CP4.Zx, 

23 
 
04CQ0.Zx, 04CQ3.Zx, 04CQ4.Zx, 04CR0.Zx, 04CR3.Zx, 04CR4.Zx, 
04CS0.Zx, 04CS3.Zx, 04CS4.Zx, 04CT0.Zx, 04CT3.Zx, 04CT4.Zx, 
04CU0.Zx, 04CU3.Zx, 04CU4.Zx, 04CV0.Zx, 04CV3.Zx, 04CV4.Zx, 
04CW0.Zx, 04CW3.Zx, 04CW4.Zx, 04CY0.Zx, 04CY3.Zx, 04CY4.Zx 
(where x = Z,6); 04HC.xDZ, 04HD.xDZ, 04HE.xDZ, 04HF.xDZ, 04HH.xDZ, 
04HJ.xDZ, 04HK.xDZ, 04HL.xDZ, 04HM.xDZ, 04HN.xDZ, 04HP.xDZ, 
04HQ.xDZ, 04HR.xDZ, 04HS.xDZ, 04HT.xDZ, 04HU.xDZ, 04HV.xDZ, 
04HW.xDZ, 04HY.xDZ, 04NC.xZZ , 04ND.xZZ , 04NE.xZZ , 04NF.xZZ , 
04NH.xZZ , 04NJ.xZZ , 04NK.xZZ , 04NL.xZZ , 04NM.xZZ , 04NN.xZZ , 
04NP.xZZ , 04NQ.xZZ , 04NR.xZZ , 04NS.xZZ , 04NT.xZZ , 04NU.xZZ, 
04NV.xZZ,04NW.xZZ,04NY.xZZ (where x = 0,3,4). 
CPT/HCPCS: 35256, 35286, 35351, 35355, 35361, 35363, 35371-72, 
35381, 35454, 35456, 35459, 35470, 35473-74, 35482-83, 35485, 
35492-93, 35495, 35521, 35533, 35541, 35546, 35548-49, 35551, 
35556, 35558, 35563, 35565, 35558, 35563, 35565, 35570-71, 35582-
83, 35585, 35587, 35621, 35623, 35637-38, 35641, 35646-47, 35651, 
35654, 35656, 35661, 35663, 35666, 35671, 35681-83, 35879, 37207-
08, 37220-35 
Metformin 
 
NB. TO DEFINE GRADE-
LIKE POPULATION 
PLEASE USE ONLY THE 
NDC generic name 
“METFORMIN HCL” 
(see paragraph 8.3.1) 
NDC generic name: METFORMIN HCL, ALOGLIPTIN 
BENZOATE/METFORMIN HCL, REPAGLINIDE/METFORMIN HCL, 
CANAGLIFLOZIN/METFORMIN HCL, DAPAGLIFLOZIN 
PROPANEDIOL/METFORMIN HCL, LINAGLIPTIN/METFORMIN HCL, 
SAXAGLIPTIN HCL/METFORMIN HCL, ERTUGLIFLOZIN 
PIDOLATE/METFORMIN HCL, EMPAGLIFLOZIN/METFORMIN HCL, 
SITAGLIPTIN PHOSPHATE/METFORMIN HCL, ROSIGLITAZONE 
MALEATE/METFORMIN HCL, PIOGLITAZONE HCL/METFORMIN HCL, 
GLIPIZIDE/METFORMIN HCL, GLYBURIDE/METFORMIN HCL, 
METFORMIN HCL, EMPAGLIFLOZIN/LINAGLIPTIN/METFORMIN HCL 
-- 
Exclusion criteria 
Codes 
 
Nursing home 
Claims in SNF dataset 
CPT codes: 99301, 99302, 99303, 99311, 99312, 99313, 99315, 99316, 
99379, 99380, G0066 
Place of service code: 31 (skilled nursing facility), 32 (nursing facility), 
33 (custodial care facility) 
Any setting,  
any position 
Type 1 diabetes 
mellitus 
ICD 9 diagnosis: 250.01, 250.03, 250.11, 250.13, 250.21, 250.23, 
250.31, 250.33, 250.41, 250.43, 250.51, 250.53, 250.61, 250.63, 
250.71, 250.73, 250.81, 250.83, 250.91, 250.93 
ICD 10 diagnosis: E10.x 
Any setting,  
any position 
Secondary and 
gestational diabetes 
ICD 9 diagnosis: 249.x, 648.8x 
ICD 10 diagnosis: E08.x, E09.x, O24.4x, O99.81 
Any setting, any 
position 
Insulin  
ICD 9 diagnosis: V58.67 
ICD 10 diagnosis: Z79.4 
NDC generic name: INSULIN DEGLUDEC/LIRAGLUTIDE; INSULIN 
GLARGINE,HUMAN RECOMBINANT ANALOG/LIXISENATIDE; INSULIN 
INHALATION CHAMBER; INSULIN ISOPHANE,BEEF PURE; INSULIN NPH 
HUMAN SEMI-SYNTHETIC; INSULIN PROTAMINE ZINC,BEEF; INSULIN 
PROTAMINE ZN,PORK (P); INSULIN REG HUMAN SEMI-SYN; INSULIN 
REGULAR,HUMAN BUFFERED; INSULIN RELEASE UNIT; INSULIN ZINC 
EXT,BEEF (P); INSULIN ZINC EXTENDED HUMAN RECOMBINANT; 
INSULIN ZINC EXTENDED,BEEF; INSULIN ZINC HUMAN SEMI-SYN; 
INSULIN ZINC PROMPT,BEEF; INSULIN ZINC PROMPT,BF-PK; INSULIN 
ZINC PROMPT,PORK PURE; INSULIN,BEEF; INSULIN,PORK 
 

24 
 
PURIFIED/INSULIN ISOPHANE,PORK PURE; INSULIN GLULISINE; 
INSULIN POWDER INHALER/INSULIN INHALATION CHAMBER; INSULIN 
PROTAMINE ZN,BEEF (P); INSULIN PUMP CONTROLLER; INSULIN 
PUMP/INFUSION SET/BLOOD-GLUCOSE METER; INSULIN REGULAR, 
HUMAN/INSULIN RELEASE UNIT/CHAMBER/INHALER; INSULIN 
ZINC,BEEF PURIFIED/INSULIN ZINC,PORK PURIFIED; INSULIN,PORK 
REG. CONCENTRATE; INSULIN ASPART (NIACINAMIDE); INSULIN 
DEGLUDEC; INSULIN ISOPHANE,BEEF; INSULIN NPH HUMAN AND 
INSULIN REGULAR HUMAN SEMI-SYNTHETIC; INSULIN REG, HUM S-S 
BUFF; INSULIN REGULAR, HUMAN/INSULIN RELEASE UNIT; INSULIN 
ZINC BEEF; INSULIN ZINC,PORK PURIFIED; INSULIN,PORK; INSULIN 
ISOPHANE NPH,BF-PK; INSULIN LISPRO-AABC; INSULIN PROTAMINE 
ZN,BF-PK; INSULIN ZINC EXTENDED,BF-PK; INSULIN ZINC HUMAN 
RECOMBINANT; INSULIN ZINC,BEEF PURIFIED; INSULIN ZINC,BEEF-
PORK; INSULIN ISOPHANE,PORK PURE; INSULIN PUMP SYRINGE, 1.8 
ML; INSULIN REGULAR,BEEF-PORK; INSULIN DETEMIR; INSULIN 
ASPART PROTAMINE HUMAN/INSULIN ASPART; INSULIN,PORK 
PURIFIED; INSULIN PUMP SYRINGE, 3 ML; INSULIN ASPART; INSULIN 
PUMP CARTRIDGE; INSULIN LISPRO PROTAMINE AND INSULIN LISPRO; 
INSULIN GLARGINE,HUMAN RECOMBINANT ANALOG; INSULIN NPH 
HUMAN ISOPHANE; INSULIN LISPRO; INSULIN NPH HUMAN 
ISOPHANE/INSULIN REGULAR, HUMAN; INSULIN REGULAR, HUMAN 
End-stage renal disease 
(including dialysis or 
renal transplant) 
ICD-9 diagnosis: 585.5, 585.6, 996.81, V42.0, V45.1x, V56.xx 
ICD-9 procedure: 39.95, 54.98, 55.6x 
ICD-10 diagnosis: N18.5, N18.6, R88.0, T82.41x, T82.42x, T82.43x, 
T82.49x, T85.611x, T85.621x, T85.631x, T85.71x, T86.1x, Y84.1, 
Z48.22, Z49.xx, Z91.15, Z94.0, Z99.2 
ICD-10 procedure: 0TY00Zx, 0TY10Zx, 3E1M39Z, 5A1Dx0Z 
HCPCS/CPT: 50360, 50365, 90920, 90921, 90924, 90925, 90935, 
90937, 90939, 90940, 90945, 90947, 90957, 90958, 90959, 90960, 
90961, 90962, 90965, 90966, 90969, 90970, 90989, 90993, 90999, 
90997, 99512, 99559, 99512, G0257, G0314, G0315, G0316, G0317, 
G0318, G0319, G0322, G0323, G0326, G0327, S9335, S9339  
Any setting, any 
position 
Acute or chronic 
pancreatitis 
ICD 9 diagnosis: 577.0, 577.1 
ICD 10 diagnosis: K85.x, K86.0, K86.1 
Any setting, any 
position 
Cirrhosis or acute 
hepatitis 
 
Cirrhosis 
ICD-9 diagnosis: 571.2, 571.5, 571.6 
ICD-10 diagnosis: K70.11, K70.2, K70.3x, K70.4x, K71.7, K74.x 
(excluding K74.0x, K74.1, K74.2) 
Acute hepatitis 
ICD-9 diagnosis: 070.20, 070.21, 070.30, 070.31, 070.41, 070.51, 571.1 
ICD-10 diagnosis: B16.0, B16.1, B16.2, B16.9, B17.0, B17.10, B17.11, 
B17.2, B17.8, B17.9, K71.2 
Any setting, any 
position 
MEN-2 or history of 
medullary thyroid 
cancer 
ICD-9 diagnosis: 258.02, 258.03 
ICD-10 diagnosis: E31.22, E31.23 
Any setting, any 
position 
Organ transplant 
ICD-9 diagnosis:  V42.1x, V42.6x, V42.7x, V42.8x (except for V42.81 or 
V42.82), V42.9x, V58.44, E878.0x 
ICD-9 procedure: 33.5x, 33.6x, 37.51, 46.97, 50.5x, 52.8x, 55.6x, 
996.8x (except for 996.85 or 996.88), V42.0x 
Any setting, any 
position 

25 
 
ICD-10 diagnosis: T86.1xx-T86.4xx, T86.81x, T86.85x, T86.89x, 
T86.9xx, Y83.0x, Z48.2xx (except for Z48.290), Z94.0x-Z94.4x, Z94.82, 
Z94.83, Z94.89, Z94.9x 
ICD-10 procedure: 02YAxxx, 0BYCxxx-0BYMxxx, 0DY5xxx, 0DY6xxx, 
0DY8xxx, 0DYExxx, 0FSGxxx, 0FY0xxx, 0FYGxxx, 0TY0xxx, 0TY1xxx, 
3E030Ux, 3E033Ux, 3E0J3Ux, 3E0J7Ux, 3E0J8Ux 
CPT/HCPCS: 32851-32854, 33935, 33945, 44135, 44136, 47135, 
47136, 48554, 48556, 50360, 50365, 50370, 50380 
 
 
 
 
 

26 
 
Table a2. Primary effectiveness outcomes 
 
Outcome 
Components 
Diagnosis and/or Procedure Codes 
Setting/Position 
MACE 
MI 
ICD-9 diagnosis: 410.x 
ICD-10 diagnosis: I21.x (excluding I21.9, I21.Ax) 
Inpatient, primary or 
secondary position 
Stroke  
ICD-9 diagnosis: 430, 431, 433.x1, 434.x1, 436  
ICD-10 diagnosis: I60.x, I61.x, I63.x, I67.89  
Inpatient, primary 
position 
CV mortality 
Medicare & VHA 
NDI ICD-10 Cause of CV Death Code: I00.x - I99.x 
CPRD 
Read/SNOMED codes and ICD codes 
Primary cause of 
death 
Modified 
MACE 
MI 
Same definition reported for the MACE outcome 
 
Stroke 
Same definition reported for the MACE outcome 
 
All-cause 
mortality 
Medicare 
Vital Status File & NDI ICD-10 Cause of Death when available 
CPRD 
Read/SNOMED codes and ICD codes 
VHA 
NDI ICD-10 Cause of Death 
 
Hospitalized 
Heart Failure 
(HHF) 
-- 
ICD-9 diagnosis: 428.xx, 398.91, 402.x1, 404.x1, 404.x3 
ICD-10 diagnosis: I09.81, I11.0, I13.0, I13.2, I50.xxx 
Inpatient, primary 
position 
 
Note. Please provide also results for HHF outcome defined as above but with diagnosis codes in any position. 
 
 
 
 
 

27 
 
Table a3. Secondary effectiveness outcomes 
 
Outcomes 
Diagnosis and/or Procedure Codes 
Setting/Position 
MI 
Definition provided in Table a1 
Stroke  
Definition provided in Table a1 
CV mortality 
Definition provided in Table a1 
All-cause mortality 
Definition provided in Table a1 
 
Coronary revascularization 
ICD-9 procedure: 00.66, 36.03, 36.06, 36.07, 36.09, 
36.1x, 36.2x, 36.3x 
ICD-10 procedure: 0210.xxx, 0211.xxx, 0212.xxx, 
0213.xxx, 021K0Z5, 021K4Z5, 021L0Z5, 021L4Z5, 
0270.xxx, 0271.xxx, 0272.xxx, 0273.xxx, 02C0.xxx, 
02C1.xxx, 02C2.xxx, 02C3.xxx, 02QA.xxx, 02QB.xxx, 
02QC.xxx 
CPT/HCPCS: 33140, 33141, 33510-33536, 33545, 33572, 
92920, 92921, 92924, 92925, 92928, 92929, 92933, 
92934, 92937, 92938, 92941, 92943, 92944, 92973, 
92980, 92981, 92984, 92995, 92996 
Inpatient, any position 
 
 
 

28 
 
Table a4. Safety outcomes 
 
Outcomes 
Component 
Diagnosis and/or Procedure Codes 
Setting/Position 
Diabetic 
ketoacidosis 
(DKA) 
-- 
ICD-9 diagnosis: 250.1x 
ICD-10 diagnosis: E10.1x, E11.1x, E13.1x  
Inpatient, 
primary position 
Bone 
fractures  
Humerus 
Case qualifying (CQ) = 1 Diagnosis (ICD-9: 812.x, 733.11; ICD-10: M80.02xA, 
M80.82xA, M84.42xA, M84.62xA, S42.xxxA, S42.xxxB, S42.xxxC)  
OR 
CQ = 2 Diagnosis (ICD-9: 812.x, 733.11; ICD-10: M80.02xA, M80.82xA, 
M84.42xA, M84.62xA, S42.xxxA, S42.xxxB, S42.xxxC) AND (overlapping) 
Procedure (ICD-9: 78.52, 79.01, 79.11, 79.21, 79.31, 79.61; ICD-10: 0PHCx, 
0PHDx, 0PHFx 0PHGx, OPSDx, OPSEx, OPSFx, OPSGx; CPT-4: 23600, 23605, 
23610, 23615, 23620, 23625, 23630, 23665, 23670, 23680, 24500, 24505, 
24510, 24515, 24530, 24531, 24535, 24536, 24538, 24540, 24542, 24545, 
24560, 24565, 24575, 24586, 24587, 24588, 24516) 
Inpatient, any 
position 
 
 
Non-inpatient, 
any position 
Radius/ulna  
CQ = 1 Diagnosis (ICD-9: 813.x, 733.12; ICD-10: M80.03xA, M80.83xA, 
M84.43xA, M84.63xA, S52.xxxA, S52.xxxB, S52.xxxC) 
OR 
CQ = 2 Diagnosis (ICD-9: 813.x, 733.12; ICD-10: M80.03xA, M80.83xA, 
M84.43xA, M84.63xA, S52.xxxA, S52.xxxB, S52.xxxC) AND (overlapping) 
Procedure (ICD-9: 78.53, 79.02, 79.12, 79.22, 79.32, 79.62; ICD-10:   0PHHx, 
0PHKx, 0PHJx, 0PHLx, 0PSHx, 0PSKx, 0PSJx, 0PSLx; CPT-4: 24620, 24635, 
24650, 24655, 24660, 24665, 24666, 24670, 24680, 24685, 25500, 25505, 
25510, 25515, 25530, 25535, 25540, 25545, 25560, 25565, 25570, 25575, 
25600, 25605, 25610, 25611, 25615, 25620, 25650) 
Inpatient, any 
position 
 
Non-inpatient, 
any position 
Hip 
CQ = 1 Diagnosis [ICD-9: 820.x (excl. 820.01, 820.11), 821.x (excl. 821.32, 
820.11), 733.14, 733.15, 733.96, 733.97; ICD 10: M80.05xA, M80.85xA, 
M84.35xA (excl. M84.350x), M84.45xA (excl. M84.454x), M84.65xA (excl. 
M84.650x), M84.75xA, S72.xxxA, S72.xxxB, S72.xxxC (excl. S72.02x, S72.44x)]   
OR 
CQ = 2 Diagnosis [ICD-9: 820.x (excl. 820.01, 820.11), 821.x (excl. 821.32, 
820.11), 733.14, 733.15, 733.96, 733.97; ICD 10: M80.05xA, M80.85xA, 
M84.35xA (excl. M84.350x), M84.45xA (excl. M84.454x), M84.65xA (excl. 
M84.650x), M84.75xA, S72.xxxA, S72.xxxB, S72.xxxC (excl. S72.02x, S72.44x)] 
AND (overlapping) Procedure (ICD-9: 78.55, 79.05, 79.15, 79.25, 79.35, 
79.65; ICD-10: 0QH6x, 0QH7x, 0QH8x, 0QH9x, 0QHBx, 0QHCx, 0QS6x, 0QS7x, 
0QS8x, 0QS9x, 0QSBx, 0QSCx; CPT: 27230, 27232, 27235, 27236, 27238, 
27240, 27244, 27245, 27246, 27248, 27267, 27268, 27269, 27125, 27130, 
27500, 27503, 27508, 27509, 27513, 27501, 27502, 27506, 27507, 27514, 
27254) 
Inpatient, any 
position 
 
 
 
 
Non-inpatient, 
any position 
Pelvis 
CQ = 1 Diagnosis (ICD-9: 808.x, 733.98; ICD-10: ICD 10 diagnosis: M84.350xA, 
M84.454xA, M84.650xA, S32.3xxA, S32.3xxB, S32.4xxA, S32.4xxB, S32.5xxA, 
S32.5xxB, S32.6xxA, S32.6xxB, S32.8xxA, S32.8xxB, S32.9xxA, S32.9xxB) 
OR 
CQ = 2 Diagnosis (ICD-9: 808.x, 733.98; ICD-10: ICD 10 diagnosis: M84.350xA, 
M84.454xA, M84.650xA, S32.3xxA, S32.3xxB, S32.4xxA, S32.4xxB, S32.5xxA, 
S32.5xxB, S32.6xxA, S32.6xxB, S32.8xxA, S32.8xxB, S32.9xxA, S32.9xxB) AND 
(overlapping) Procedures (CPT/ HCPCS: 27193, 27194, 27200, 27202, 27215, 
27216, 27217, 27218, 27220, 27222, 27226, 27227, 27228, G0412, G0413, 
G0414, G0415) 
Inpatient,  
any position 
 
 
 
Non-inpatient, 
any position 

29 
 
Lower-limb 
amputations  -- 
ICD-9 procedure: 84.1x (excluding 84.18, 84.19) 
ICD-10 procedure: 0Y.6x (excluding 0Y.62, 0Y.63, 0Y.64) 
CPT: 27590, 27591, 27592, 27880, 27881, 27882, 27884, 27886, 27888, 
27889, 28800, 28805, 28810, 28820, 28825, 27594, 27596, 27598 
Inpatient or non-
inpatient,  
any position 
Acute 
kidney 
injury (AKI) 
-- 
ICD-9 diagnosis: 584.x 
ICD-10 diagnosis: N17.x 
Inpatient,  
any position 
Urinary tract 
infections 
(UTI)  
Primary UTI 
ICD-9 diagnosis: 590.xx, 595.xx, 597.xx, 599.0x 
ICD-10 diagnosis: N10-N12, N13.6, N30.x, N34.x, N39.0  
Inpatient,  
primary position 
Sepsis and UTI 
ICD-9 diagnosis: 590.x, 595.x, 597.x, 599.0x  
ICD-10 diagnosis: N10-N12, N13.6, N30.x, N34.x, N39.0 
AND (within the same inpatient discharge) 
ICD-9 diagnosis: 038.x, 785.52, 790.7, 995.9x 
ICD-10 diagnosis: A40.x, A41.x, R65.21, R78.81, R65.x 
Inpatient,  
any position 
Pyelonephritis 
ICD-9 diagnosis: 590.xx 
ICD-10 diagnosis: N10-N12, N13.6 
Inpatient,  
any position 
Genital 
infections^ 
-- 
ICD-9 diagnosis: 112.1, 616.1x, 112.2, 607.1, 112.2, 605 
ICD-10 diagnosis: B37.3, N77.1, N76.0-N76.3, B37.49, B37.42, N48.1, N47.6, 
B37.49, N47.x (except N47.0, N47.6) 
Any setting,  
any position 
Acute 
pancreatitis   
-- 
ICD-9 diagnosis: 577.0 
ICD-10 diagnosis: K85.x 
Inpatient, 
primary position 
Biliary 
events  
-- 
ICD-9 diagnosis: 574.x, 575.x, 576.x, 560.31, 571.6, 155.1, 156.x, 235.3, 230.8 
ICD-10 diagnosis: K80.x, K81.x, K82.x, K83.x, K85.1x, K87, K56.3, K74.3, C22.1, 
C23, C24.x, D37.6, D01.5 
 
Severe 
hypoglycemi
a  
-- 
ICD-9 diagnosis: 251.0, 251.1, 251.2, 962.3 
ICD-10 diagnosis: E10.641, E10.649, E11.641, E11.649, E13.641, E13.649, 
E15, E16.0, E16.1, E16.2, T38.3X1A, T38.3X1D, T38.3X1S, T38.3X2A, 
T38.3X2D, T38.3X2S, T38.3X3A, T38.3X3D, T38.3X3S, T38.3X4A, T38.3X4D, 
T38.3X4S, T38.3X5A, T38.3X5D, T38.3X5S 
OR 
Inpatient, 
primary position 
 
ICD-9 diagnosis: 251.0, 251.1, 251.2, 962.3  
ICD-10 diagnosis: E10.641, E10.649, E11.641, E11.649, E13.641, E13.649, 
E15, E16.0, E16.1, E16.2, T38.3X1A, T38.3X1D, T38.3X1S, T38.3X2A, 
T38.3X2D, T38.3X2S, T38.3X3A, T38.3X3D, T38.3X3S, T38.3X4A, T38.3X4D, 
T38.3X4S, T38.3X5A, T38.3X5D, T38.3X5S 
Emergency 
Department 
(ED), any 
position 
 
Short-term 
retinopathy  
Intravitreal anti-
VEGF injection 
CPT: 67028 
AND (within the same day) 
HCPCS: C9291, J0178, J2778, Q2046, C9257, Q5107, J9035, C9296, J9400 
Any setting, any 
position 
Panretinal photo-
coagulation 
CPT: 67228 
Onset of vitreous 
hemorrage 
ICD-9 diagnosis: 379.23 
ICD-10 diagnosis: H43.1x 
Proliferative 
diabetic 
retinopathy 
ICD-9 diagnosis: 362.02 
ICD-10 diagnosis: E11.35x 
^ findings for genital infections might be stratified by gender in a secondary analysis 
Note. Please provide also results for DKA and AKI outcomes defined as above but with diagnosis codes in any 
position and primary position respectively. 

30 
 
Definitions of effectiveness and safety outcomes are based on the following studies: 
 
MACE and its components 
- 
Kiyota Y, Schneeweiss S, Glynn RJ, Cannuscio CC, Avorn J, Solomon DH. Accuracy of Medicare claims-
based diagnosis of acute myocardial infarction: estimating positive predictive value on the basis of 
review of hospital records. Am Heart J 2004;148:99–104. 
- 
Wahl PM, Rodgers K, Schneeweiss S, et al. Validation of claims-based diagnostic and procedure codes 
for cardiovascular and gastrointestinal serious adverse events in a commercially-insured population. 
Pharmacoepidemiol Drug Saf 2010;19:596–603. 
- 
Tirschwell DL, Longstreth WT Jr. Validating administrative data in stroke research. Stroke 
2002;33:2465–2470. 
- 
Olubowale OT, Safford MM, Brown TM, et al. Comparison of expert adjudicated coronary heart 
disease and cardiovascular disease mortality with the national death index: results from the REasons 
for Geographic And Racial Differences in Stroke (REGARDS) study. J Am Heart Assoc 2017;6:e004966. 
- 
Patorno E, Pawar A, Franklin JM, Najafzadeh M, Déruaz-Luyet A, Brodovicz KG, Sambevski S, Bessette 
LG, Santiago Ortiz AJ, Kulldorff M, Schneeweiss S. Empagliflozin and the Risk of Heart Failure 
Hospitalization in Routine Clinical Care. Circulation. 2019 Jun 18;139(25):2822-2830.  
 
Hospitalized Heart Failure 
- 
Saczynski JS, Andrade SE, Harrold LR, et al. A systematic review of validated methods for identifying 
heart failure using administrative data. Pharmacoepidemiol Drug Saf 2012;21(Suppl. 1):129–140. 
- 
Patorno E, Pawar A, Franklin JM, Najafzadeh M, Déruaz-Luyet A, Brodovicz KG, Sambevski S, Bessette 
LG, Santiago Ortiz AJ, Kulldorff M, Schneeweiss S. Empagliflozin and the Risk of Heart Failure 
Hospitalization in Routine Clinical Care. Circulation. 2019 Jun 18;139(25):2822-2830.  
 
Coronary revascularization 
- 
Wahl PM, Rodgers K, Schneeweiss S, et al. Validation of claims-based diagnostic and procedure codes 
for cardiovascular and gastrointestinal serious adverse events in a commercially-insured population. 
Pharmacoepidemiol Drug Saf 2010;19:596–603. 
- 
Patorno E, Pawar A, Franklin JM, Najafzadeh M, Déruaz-Luyet A, Brodovicz KG, Sambevski S, Bessette 
LG, Santiago Ortiz AJ, Kulldorff M, Schneeweiss S. Empagliflozin and the Risk of Heart Failure 
Hospitalization in Routine Clinical Care. Circulation. 2019 Jun 18;139(25):2822-2830.  
 
Diabetic ketoacidosis 
- 
Fralick M, Schneeweiss S, Patorno E. Risk of Diabetic Ketoacidosis after Initiation of an SGLT2 Inhibitor. 
N Engl J Med. 2017 Jun 8;376(23):2300-2302. 
- 
Bobo WV, Cooper WO, Epstein RA Jr., Arbogast PG, Mounsey J, Ray WA. Positive predictive value of 
automated database records for diabetic ketoacidosis (DKA) in children and youth exposed to 
antipsychotic drugs or control medications: a Tennessee Medicaid Study. BMC Med Res Methodol 
2011;11:157. 
 
Bone fractures  
- 
Wright NC, Daigle SG, Melton ME, Delzell ES, Balasubramanian A, Curtis JR. The Design and Validation 
of a New Algorithm to Identify Incident Fractures in Administrative Claims Data. J Bone Miner Res. 
2019;34(10):1798-1807. 
- 
Ray WA, Griffin MR, Fought RL, Adams ML. Identification of fractures from computerized Medicare 
files. Journal of clinical epidemiology 1992;45:703-14. 

31 
 
- 
Hudson M, Avina-Zubieta A, Lacaille D, Bernatsky S, Lix L, Jean S. The validity of administrative data to 
identify hip fractures is high--a systematic review. Journal of clinical epidemiology 2013;66:278-85. 
 
Lower-limb amputations 
- 
Fralick M, Kim SC, Schneeweiss S, Everett BM, Glynn RJ, Patorno E. Risk of amputation with 
canagliflozin across categories of age and cardiovascular risk in three US nationwide databases: cohort 
study. BMJ. 2020 Aug 25;370:m2812.  
- 
Newton KM, Wagner EH, Ramsey SD, et al. The use of automated data to identify complications and 
comorbidities of diabetes: a validation study. J Clin Epidemiol1999;52:199-207.  
 
Acute kidney injury 
- 
Patorno E, Pawar A, Bessette LG, Kim DH, Dave C, Glynn RJ, Munshi MN, Schneeweiss S, Wexler DJ, 
Kim SC. Comparative Effectiveness and Safety of Sodium-Glucose Cotransporter 2 Inhibitors Versus 
Glucagon-Like Peptide 1 Receptor Agonists in Older Adults. Diabetes Care. 2021 Mar;44(3):826-835. 
[see validation study 2006] 
 
Urinary tract infections  
- 
Dave CV, Schneeweiss S, Kim D, Fralick M, Tong A, Patorno E. Sodium-Glucose Cotransporter-2 
Inhibitors and the Risk for Severe Urinary Tract Infections: A Population-Based Cohort Study. Ann 
Intern Med. 2019 Aug 20;171(4):248-256.  
 
Genital infections 
- 
Dave CV, Schneeweiss S, Patorno E. Comparative risk of genital infections associated with sodium-
glucose co-transporter-2 inhibitors. Diabetes Obes Metab. 2019 Feb;21(2):434-438.  
 
Acute pancreatitis   
- 
Moores K, Gilchrist B, Carnahan R, Abrams T. A systematic review of validated methods for identifying 
pancreatitis using administrative data. Pharmacoepidemiol Drug Saf. 2012 Jan;21 Suppl 1:194-202.  
 
Biliary events 
- 
Faillie JL, Yu OH, Yin H, Hillaire-Buys D, Barkun A, Azoulay L. Association of Bile Duct and Gallbladder 
Diseases With the Use of Incretin-Based Drugs in Patients With Type 2 Diabetes Mellitus. JAMA Intern 
Med. 2016 Oct 1;176(10):1474-1481.  
 
Severe hypoglycemia 
- 
Min JY, Presley CA, Wharton J, Griffin MR, Greevy RA Jr, Hung AM, Chipman J, Grijalva CG, Hackstadt 
AJ, Roumie CL. Accuracy of a composite event definition for hypoglycemia. Pharmacoepidemiol Drug 
Saf. 2019 May;28(5):625-631.  
- 
Karter AJ, Warton EM, Moffet HH, Ralston JD, Huang ES, Miller DR, Lipska KJ. Revalidation of the 
Hypoglycemia Risk Stratification Tool Using ICD-10 Codes. Diabetes Care. 2019 Apr;42(4):e58-e59 
 
Short-term retinopathy 
- 
Due to the lack of a validated claims-based algorithm to identify patients with short-term 
retinopathy, the current definition was ultimately built after extensive discussion within the 
research group and experts’ consultation (mainly ophthalmologists that studied retinopathy).  
 
 

32 
 
Table a5. Overall list of covariates. 
Demographics 
Age 
Gender 
Calendar year of cohort entry 
Geographic region (i.e., Midwest, Northeast, South, West, others) 
Race (i.e., white, black, others) 
Alcohol dependence 
Drug dependence 
Obesity  
Overweight 
Smoking status  
Diabetes related variables 
Diabetic nephropathy 
Diabetic retinopathy 
Diabetes ophthalmic manifestation 
Diabetic neuropathy 
Diabetic peripheral circulatory disorders 
Diabetic foot 
Infection of lower extremities 
Lower limb amputation 
Erectile dysfunction 
Hypoglycemia 
Hyperglycemia 
Diabetic ketoacidosis 
Hyperosmolar hyperglycemic nonketotic syndrome 
Diabetes with other complications 
Diabetes without mention of complications 
Duration of diabetes (when available) 
Number of HbA1c tests ordered  
Number of glucose tests or monitoring ordered 
Number of antidiabetic drugs used at cohort entry 
No previous use of other antidiabetic drugs 
Other comorbidities 
Cancer 
Acute myocardial infarction 
Old myocardial infarction 
Myocardial infarction sequelae 
Unstable angina 
Stable angina 
Coronary atherosclerosis 
Coronary procedure 
History of coronary procedure 
Congestive heart failure 
Stroke 
Cerebrovascular procedure 
Generalized and unspecified atherosclerosis 

33 
 
Atherosclerotic cerebrovascular disease 
Peripheral arteriopathy 
Peripheral arterial procedure 
Lower-limb amputations 
Cardiomyopathy 
Cardiac valve disorder 
Atrial fibrillation 
Chronic kidney disease 
     stage 1-2 
     stage 3-4 
     unspecified 
Acute kidney injury 
Hypertensive nephropathy 
Proteinuria 
Urinary tract infection 
Miscellaneous renal disease 
Kidney or urinary stone 
Disorders of electrolyte 
Disorders of fluid balance 
Liver diseases (including cirrhosis, non-alcoholic steatohepatitis or 
fatty liver disease, other liver diseases) 
COPD 
Pneumonia 
Asthma 
Dementia 
Hyperlipidemia 
Hypertension 
Ischemic heart disease 
Coronary revascularization 
Other cardiac dysrhythmias 
Conduction disorder 
Transient ischemic attack 
Major bleeding 
Edema 
Pneumonia 
Obstructive sleep apnea 
Osteoarthritis 
Osteoporosis 
Fractures 
Falls 
Hypothyroidism 
Other disorders of thyroid gland 
Depression 
Anxiety or sleep disorder 
Venous thromboembolism 
Indexes of general comorbidity and frailty 
Combined comorbidity score 
Frailty score Index 

34 
 
Measures of health care utilization 
Number of hospitalizations 
Number of days spent hospitalized 
Number of emergency department visits  
Number of outpatient visits 
Number of unique non-antidiabetic medication classes 
Number of antidiabetic medications used at cohort entry (days’ supply 
overlap with cohort entry date) 
Number of visits to endocrinologist 
Number of visits to cardiologist 
Number of visits to internist  
Number of visits to nephrologist  
Number of electrocardiograms received 
Number of echocardiograms received 
Number of stress tests received 
Number of preventive services received 
Number of creatinine tests ordered 
Number of lipid tests ordered 
Number of microalbuminuria tests ordered 
Number of metabolic or renal/creatinine tests ordered 
Measures of socioeconomic status 
Inpatient total costs 
Outpatient total costs 
Ratio of brand vs generic medications 
Dual eligibility with Medicare (e.g., Medicare Advantage program) 
Low-income subsidies (CMS) 
Out of pocket pharmacy cost 
Medications 
Metformin 
Sulfonylurea 
Thiazolidinediones  
Meglitinides 
α-glucosidase inhibitors 
DPP-4i  
GLP-1 ra 
SGLT-2i 
Insulin 
ACE inhibitors  
Angiotensin II receptor blockers 
Beta blockers 
Calcium channel blockers 
Thiazides 
Loop diuretics 
Mineralocorticoid receptor antagonists 
Nitrates 
Other antihypertensives 
Digoxin 
Antiarrhythmics 

35 
 
COPD/asthma medications (beta 2 agonist inhalant, anticholinergic 
inhalant, glucocorticoid inhalant) 
Oral corticosteroids 
Osteoporosis medications 
Statins 
Other lipid-lowering drugs 
Anticoagulants 
Antiplatelets 
NSAIDs 
Opioids 
Gabapentinoids 
Urinary tract infection antibiotics 
Antidepressants 
Benzodiazepines 
Other anxiolytics or hypnotics 
Antipsychotics 
Antiparkinsonian medications 
Dementia medication 
Laboratory values (when available) 
HbA1c 
Glucose 
Urine Albumin-Creatinine Ratio 
Proteinuria 
eGFR 
Total cholesterol 
LDL 
HDL 
Triglyceride level 
Abbreviations: COPD, chronic obstructive pulmonary disease; DPP-4i, 
dipeptidyl peptidase 4 inhibitors; GLP-1 ra, glucagon-Like Peptide 1 Receptor 
Agonists; SGLT-2i, sodium-glucose cotransporter 2 inhibitors; ACEi, 
angiotensin-converting enzyme inhibitor; eGFR, estimated Glomerular 
Filtration Rate; LDL, low-density lipoprotein; HDL, high-density lipoprotein.