Effect of Dexamethasone on Inpatient Mortality Among Hospitalized COVID-19 Patients
Conditions: Coronavirus
Sponsor: Aetion, Inc.

Protocol: Effect of dexamethasone on inpatient mortality among hospitalized COVID-19 patients 
06.10.2021 
 
 
Protocol: 
Effect of dexamethasone on inpatient mortality among hospitalized  
COVID-19 patients 
 
Protocol ID: RQ004-1-20210610 
 
 
 
Principal Investigators: 
 
Nicolle Gatto, PhD, MPH 
Senior Vice President, Aetion, Inc., New York, NY 
nicolle.gatto@aetion.com  
 
Elizabeth M. Garry, PhD, MPH 
Senior Principal Scientist, Aetion, Inc., Boston, MA 
liz.garry@aetion.com 
 
Aloka Chakravarty, PhD, MStat 
US Food and Drug Administration, Silver Spring, MD 
Aloka.Chakravarty@fda.hhs.gov 
 
 
 
 
Disclaimer: This protocol reflects the views of the authors and should not be construed to 
represent FDA’s views or policies. 
 

Protocol: Effect of dexamethasone on inpatient mortality among hospitalized COVID-19 patients 
06.10.2021 
 
Protocol: 
Effect of dexamethasone on inpatient mortality among hospitalized  
COVID-19 patients 
Protocol ID: RQ004-1-20210610 
CONTENTS 
I.  ABBREVIATIONS 
1 
II.  BACKGROUND 
1 
A. Emergence of COVID-19 and treatment 
1 
B. Randomized Evaluation of COVID-19 Therapy (RECOVERY): Efficacy of dexamethasone 
2 
C. Aetion Research Collaboration Agreement (RCA) with the US Food and Drug Administration (FDA) 
3 
III.  RESEARCH AIM AND OBJECTIVES 
4 
IV.  DATA SOURCE 
5 
V.  STUDY DESIGN 
6 
A. Study design 
6 
B. Exposure definition 
8 
C. Study population 
8 
1. Cohort entry 
8 
2. Treatment index 
8 
3. Exclusion criteria 
10 
D. Subgroups: COVID-19 severity at treatment index 
10 
E. Primary outcome definition and follow-up (28-day mortality) 
11 
F. Covariates and their corresponding assessment windows 
12 
G. Statistical Software 
13 
VI.  STATISTICAL METHODS 
13 
A. Propensity score model fitting 
13 
B. Diagnostic phase and checklist 
14 
C. Inferential phase 
15 
VII.  SENSITIVITY ANALYSES 
16 
VIII.  POTENTIAL LIMITATIONS 
18 
A. Addressed in Design and Analyses 
18 
B. To Be Considered in Interpretation Only 
20 
IX.  STRENGTHS 
20 
X.  HUMAN SUBJECTS 
20 
XI.  REFERENCES 
20 
XII.  APPENDICES 
23 
A. APPENDIX A: Explorations to justify design decisions 
23 
1. Exploration 1: Defining the COVID-19 study population (cohort) 
23 
2. Exploration 2: Comparison of our study cohort and design to the RECOVERY trial 
25 
3. Exploration 3: Defining the time period for measuring a modified version of the COVID-19 severity score 
published by the World Health Organization (mWHO score) at treatment index 
29 
4. Exploration 4: Determining the definitions for mWHO COVID-19 severity components 
30 
5. Exploration 5: Determining the cohort entry date truncation 
33 
B. APPENDIX B: Study design diagrams for each sensitivity analysis 
36 

Protocol: Effect of dexamethasone on inpatient mortality among hospitalized COVID-19 patients 
06.10.2021 
 
 
CONFIDENTIAL: This material is part of the Aetion-FDA research collaboration entitled “COVID-19 Data Sharing and Analyses.”   page 1 
 
I.  ABBREVIATIONS 
ARDS 
AT 
CED 
CPT 
CSI 
DEX 
ECMO 
EHR 
FDA 
HCPCS 
HCRU 
ICD 
IMV 
IT 
KRT 
MPRED 
mWHO 
NAAT 
NDC 
NIV 
O2 
PS 
RCA 
RWD 
RSS 
SA 
SES 
WHO 
Acute respiratory distress syndrome 
As-treated censoring approach 
Cohort entry date 
Current procedural terminology 
Corticosteroids of interest (DEX, hydrocortisone, Methylprednisolone, Prednisone) 
Dexamethasone 
Extracorporeal membrane oxygenation 
Electronic health records 
US Food and Drug Administration 
Healthcare common procedure coding system 
Healthcare resource utilization 
International classification of disease  
Invasive mechanical ventilation 
Initial treatment censoring approach 
Kidney replacement therapy 
Methylprednisolone 
Modified World Health Organization severity score 
Nucleic Acid Amplification Test 
National drug code 
Non-invasive ventilation 
Oxygen 
Propensity score 
Research collaboration agreement 
Real-world data 
Risk set sampling 
Sensitivity analysis 
Socioeconomic status 
World Health Organization 
 
II.  BACKGROUND 
 
A. Emergence of COVID-19 and treatment  
 
As of May 25, 2021, there have been over 32.7 million cases of COVID-19 in the United States 
(US) since the emergence of SARS-CoV-2 in late 2019 [WHO 2020(a)]. COVID-related 
hospitalizations are at an estimated cumulative rate of 553.8 per 100,000 in the US population, 
with COVID-19 related deaths in the US reaching over 587,000 [CDC 2020]. Despite the 
availability of vaccines across the US, the burden of disease remains high and determining the 
effectiveness of potential COVID-19 therapeutics continues to be an urgent concern. 
 
On June 22, 2020, the RECOVERY Collaborative Group published preliminary findings 
demonstrating a significant reduction in 28-day mortality among hospitalized COVID-19 
patients in the UK treated with the corticosteroid dexamethasone (DEX) [RECOVERY 2020]. 
As a result of this trial, the NIH Treatment Guidelines Panel updated their guidelines to 
recommend use of DEX in hospitalized patients receiving supplemental oxygen (O2) therapy or 
mechanical ventilation and the World Health Organization (WHO) updated their guidelines to 
recommend DEX in severe and critically ill hospitalized patients [NIH 2020(a); WHO 2020(b)]. 
In early February 2020, results from the DEXA-ARDS trial among patients in Spain in intensive 

Protocol: Effect of dexamethasone on inpatient mortality among hospitalized COVID-19 patients 
06.10.2021 
 
 
CONFIDENTIAL: This material is part of the Aetion-FDA research collaboration entitled “COVID-19 Data Sharing and Analyses.”   page 2 
 
care units (ICUs) with established moderate-to-severe acute respiratory distress syndrome 
(ARDS) and invasive mechanical ventilation (IMV), which occurs among patients with severe 
COVID-19, reported a decrease in 60-day mortality for patients randomized to intravenous DEX 
compared to continued routine intensive care [21% (29/139) vs. 36% (50 /138)] [Villar 2020]. 
However, since the trial was stopped by the data safety monitoring board due to low enrollment 
rate, we focus here on the RECOVERY trial, but have incorporated a sensitivity analysis to align 
with the mortality endpoint used in the DEXA-ARDS trial [see section VII. Sensitivity Analyses, 
SA5].  
 
B. Randomized Evaluation of COVID-19 Therapy (RECOVERY): Efficacy of 
dexamethasone 
 
The RECOVERY trial, which first reported preliminary results on June 22, 2020, was an open-
label, 1:2 randomized controlled trial of 6,425 patients randomized to either 6 mg of DEX daily 
for 10 days (n = 2,104) or usual care (n = 4,321). Overall, 22.9% of patients assigned to the DEX 
group died within 28 days of randomization vs. 25.7% of the control arm (rate ratio [RR] of 0.83, 
95% CI [0.75, 0.93]). The difference in survival between the two groups varied noticeably when 
stratified by baseline COVID-19 severity level. While the incidence of death was lower in the 
DEX group, compared to the usual care group, among patients receiving IMV (RR: 0.64, 95% CI 
[0.51, 0.81]) and among those receiving supplemental O2 therapy but not IMV (RR: 0.82, 95% 
CI [0.72, 0.94]), there was no discernable difference among patients who were not receiving any 
supplemental O2 (RR: 1.19, 95% CI [0.91 to 1.55]) [RECOVERY 2020].  
 
The effectiveness of DEX in preventing death among moderate to severe COVID-19 patients has 
not yet been confirmed or established in routine care, yet DEX utilization has substantially 
increased in the US since June 2020 [Aetion 2021]. Thus, to evaluate the effectiveness of DEX 
in preventing death under routine use in a timely manner, we sought to conduct an observational 
cohort study using US-based real-world data (RWD).  
 
In one of the explorations conducted prior to the finalization of this protocol [see Appendix A.2. 
Exploration 2], we compared the distribution of key patient characteristics between a version of 
our hospitalized US-based RWD study cohort to those reported for the hospitalized UK-based 
patients randomized in the RECOVERY trial. The RECOVERY trial was a large, pragmatic 
randomized trial that simplified the data collection process in an effort to minimize the already 
heavy burden of the pandemic on frontline workers. Exploration of baseline clinical differences 
between the real-world study and trial populations was therefore limited by the degree of 
available information for the trial patients. However, among the available RECOVERY trial 
data, we identified important clinical and demographic differences in the trial population versus 
this study’s populations at baseline.  
 
 
 
 

Protocol: Effect of dexamethasone on inpatient mortality among hospitalized COVID-19 patients 
06.10.2021 
 
 
CONFIDENTIAL: This material is part of the Aetion-FDA research collaboration entitled “COVID-19 Data Sharing and Analyses.”   page 3 
 
C. Aetion Research Collaboration Agreement (RCA) with the US Food and Drug 
Administration (FDA) 
 
Aetion entered a research collaboration agreement (RCA) with the US Food and Drug 
Administration (FDA) to use RWD to advance the understanding and the natural history of 
COVID-19 in specific patient populations, as well as treatment and diagnostic patterns during 
the coronavirus disease (COVID-19) pandemic. Aetion is collaborating with the FDA to identify 
and analyze fit-for-purpose data sources (i.e., near-real-time information, meaningful population 
size, and sufficient capture of key study parameters and longitudinal patient experience [Gatto 
2019; FDA 2018]) to characterize COVID-19 patient populations, identify risk factors for 
COVID-19-related complications, and contribute to the scientific evaluation of potential 
interventions.  
 
We are using a stepwise approach to conduct this research, separating the research process into 
three distinct sequential phases: Exploratory (data explorations done in advance to facilitate key 
design decisions, as noted above), Diagnostic (requirements that must be satisfied prior to 
viewing treatment-specific outcomes, e.g., sample size and covariate balance), and Inferential 
(final comparative analyses) [see Figure 1 below].  
 
Figure 1. RWD Research Process phase schematic 
 
 
In the Exploratory Phase, the Aetion research team engaged with the FDA RCA team to better 
understand the data and make decisions about the study design to inform the development of this 
protocol. The following explorations, which are further detailed in Appendix A, were completed 
prior to protocol finalization: 
1. Exploration 1: Defining a COVID-19 study population 
2. Exploration 2: Comparison of our study cohort and design to the RECOVERY trial  
3. Exploration 3: Defining the time period for measuring a modified version of the 
COVID-19 severity score published by the World Health Organization (mWHO 
score) at treatment index 
4. Exploration 4: Determining the definitions for mWHO COVID-19 severity 
components 
5. Exploration 5: Determining the cohort entry date truncation 
 
Given the periodic data updates during the Exploratory Phase, explorations were carried out on 
earlier cuts of the data. Therefore, although the cohort design remained relatively consistent 
across explorations, the cohort samples used for each exploration differed from each other, and 
from the final cohort that will be used for the diagnostic and inferential phases of the analysis 
[See IV. Data Source].  
 

Protocol: Effect of dexamethasone on inpatient mortality among hospitalized COVID-19 patients 
06.10.2021 
 
 
CONFIDENTIAL: This material is part of the Aetion-FDA research collaboration entitled “COVID-19 Data Sharing and Analyses.”   page 4 
 
For the Diagnostic Phase, we developed a checklist of diagnostic criteria [see section VI.B, 
Table 4] that must be satisfactorily completed prior to beginning the implementation of the 
Inferential Phase. As such, the relationship between the treatment and outcome of interest will 
not be described or evaluated in the analytic dataset until we reach consensus that the diagnostic 
criteria are satisfied. Upon each data update, the Diagnostic results will be presented to the FDA 
RCA team until there is consensus that we have satisfied the necessary criteria to progress to the 
Inferential Phase [see section VI.C]. 
  
III.  RESEARCH AIM AND OBJECTIVES  
 
As part of the RCA with the FDA, this study informs the overall project to establish a system for 
rapid-cycle evaluation, using best practice scientific and operational methods, as applied to 
relevant and meaningful clinical questions. In this study, we seek to assess the effectiveness of 
DEX to reduce the risk of inpatient mortality within 28 days among US patients hospitalized 
with COVID-19 diagnosis or SARS-CoV-2 infection, overall and stratified by COVID-19 
severity subgroups [defined according to the presence of encounters indicating supplemental 
oxygen or ventilation; see section V.D for additional subgroup detail], using the HealthVerity 
database. 
  
The principles outlined by Hernan et al. emphasize the importance of explicitly emulating a 
hypothetical target trial when planning a RWD analysis to yield RWE [Hernan, Robins 2016; 
Hernan, Sauer 2016]. Although we reference the UK RECOVERY trial as a basis for 
comparison [RECOVERY 2020], we sought to emulate a hypothetical target trial with varied 
design elements, and research questions tailored for US practice and US patients. In a sensitivity 
analysis, we further sought to replicate the RECOVERY trial design elements (to the extent 
possible) [see section VII. Sensitivity Analyses, SA8]. 
 
Specifically, among US patients hospitalized with COVID-19 diagnosis or SARS-CoV-2 
infection without prior use of any corticosteroid of interest (CSI; DEX, methylprednisolone, 
prednisone, hydrocortisone) estimate the hazard ratio and corresponding 95% confidence interval 
for the risk of inpatient mortality within 28 days after treatment initiation, comparing new use of 
DEX with: 
● non-users of corticosteroids (Primary objective I; DEX+ vs. CSI-), to evaluate the 
potential benefit of DEX above ‘standard of care’ inpatient treatment for COVID-19, 
where ‘standard of care’ excludes all CSIs (DEX, MPRED, PRED, HC).  
● non-users of DEX (Secondary objective I; DEX+ vs. DEX-), to evaluate the potential 
benefit of DEX above ‘standard of care’ inpatient treatment for COVID-19, where 
‘standard of care’ excludes DEX but may include those newly treated with a non-DEX 
CSI (MPRED, PRED, HC). This comparison is most similar to the RECOVERY trial that 
compared DEX to ‘standard of care’ [RECOVERY 2020]. 
● methylprednisolone active comparator (Secondary objective II; DEX+ vs. MPRED+), 
to evaluate the potential benefit of DEX compared to a clinically meaningful treatment 
alternative (here, another corticosteroid), among patients without prior use of DEX or 
MPRED (traditional new user design). This comparison excludes those treated with a CSI 
other than DEX and MPRED.  
Under the assumption that all of the CSIs are protective  [NIH 2020(c)], we would expect a 
stronger effect for CSI- comparison (Primary objective), some protective effect for the DEX- 
comparison (Secondary objective I), and little or no effect for the MPRED+ comparison 
(Secondary objective II).  

Protocol: Effect of dexamethasone on inpatient mortality among hospitalized COVID-19 patients 
06.10.2021 
 
 
CONFIDENTIAL: This material is part of the Aetion-FDA research collaboration entitled “COVID-19 Data Sharing and Analyses.”   page 5 
 
 
Key subgroups:  
Findings will be presented for DEX versus each of the 3 referent groups (CSI-, DEX-, MPRED+) 
among each of the following subgroups of patients to evaluate the potential benefit of DEX 
compared to ‘standard of care’ or a clinically meaningful treatment alternative stratified by 
COVID-19 disease severity at the time of treatment index [defined according to the presence of 
encounters indicating oxygen or ventilation; see section V.D for additional subgroup detail]: 
(1) no O2; 
(2) any O2 or non-invasive ventilation (O2/NIV); 
(3) any IMV; and 
(4) composite of O2/NIV/IMV. 
 
Table 1. Summary of comparisons and populations of interest for each objective  
Objective 
Exposure 
Referent Group 
Population(s) of interest 
Primary 
DEX+ 
CSI- 
Overall 
Secondary-I 
DEX- 
Secondary-II 
MPRED+ 
Key 
subgroups 
CSI- 
Each of the following subsets of patients: 
(1) no O2; 
(2) O2/NIV;  
(3) IMV; and 
(4) composite O2/NIV/IMV  
DEX- 
MPRED+ 
 
IV.  DATA SOURCE 
 
This study uses de-identified RWD from HealthVerity comprising open and closed Medical 
claims and includes major payer types (commercial and federal Medicaid and Medicare 
programs) from all US states and territories, open and closed Medical and Outpatient Pharmacy 
claims, Laboratory Data including results, and Hospital Chargemaster transactional records that 
include diagnoses, procedures and treatments for billable inpatient and outpatient hospital 
encounters. The data elements derived from chargemaster data are listed in Appendix C along 
with their percent missingness. The database also includes clinical observations as recorded in 
electronic health records (EHR), where available. The open claims are sourced in near-real-time 
from practice management systems, billing systems and claims clearinghouses, and the closed 
claims are sourced from insurance providers and payers. All ages are included in the full 
database with ages 65 or older over-sampled to account for the under-representation of Medicare 
aged patients with commercial coverage. The HealthVerity Medical Claims, Pharmacy, 
Laboratory and Hospital Chargemaster dataset has been used for scientific publications of 
COVID-19 research [Burn 2020; Gordon 2020; Harvey 2021; Murk 2020].  
 
As of 12-March-2021, the data spans the time period 01-December-2018 to 22-February-2021 
(the last date of available data) and included 58,233,249 patients. The data will be updated 
periodically as new data become available until we are able to achieve sufficient sample size, as 
defined in the diagnostic checklist of the statistical analysis [see section VI.B].  
 
The primary endpoint of 28-day mortality is defined using Inpatient Chargemaster claims with a 
discharge status of ‘expired’ indicating death. Given that the HealthVerity data source is 
relatively new, there are no validation studies of this data source yet for mortality or other 
endpoints. However, use of discharge status to determine mortality endpoints among hospitalized 

Protocol: Effect of dexamethasone on inpatient mortality among hospitalized COVID-19 patients 
06.10.2021 
 
 
CONFIDENTIAL: This material is part of the Aetion-FDA research collaboration entitled “COVID-19 Data Sharing and Analyses.”   page 6 
 
COVID-19 patients has been used in both in a similar chargemaster data source [Rosenthal 
2020] and for national surveillance reporting [CDC-NCHS 2021].   
 
We further explored the data given the importance of this variable to our study. When we 
compared use of discharge status of death to the separate data files from the vendor providing 
month of death (date of death was redacted for privacy concerns), we found that all patients with 
a death month indicated also met our endpoint definition. Further, when we compared weekly 
deaths over time in our overall cohort of patients hospitalized with COVID-19 (reminder that we 
will not look at death stratified by treatment until after the diagnostic phase is completed), we 
identified similar trends to that of two external national benchmarks, the Centers for Disease 
Control {CDC-NCHS 2021} and data sourced from State and local health agencies {NYT 2021}. 
providing further assurance of the accuracy of the capture of our endpoint. 
 
 
V.  STUDY DESIGN 
 
Key elements of the study design are described in the text below and additional measure 
definition detail is available upon request.  
 
A. Study design 
 
This study is an observational cohort study using secondary healthcare data. The study diagram 
and its components are illustrated and further detailed below [see Figure 2]. 
 
 

Protocol: Effect of dexamethasone on inpatient mortality among hospitalized COVID-19 patients 
06.10.2021 
 
 
CONFIDENTIAL: This material is part of the Aetion-FDA research collaboration entitled “COVID-19 Data Sharing and Analyses.”   page 7 
 
Figure 2. Study diagram 
 
Inclusion/Exclusion Criteria 
COVID-19 confirmation 
Hospitalized patients with confirmed COVID-19 (ICD 10 diagnosis: U07.1 or positive or presumptive positive SARS-
CoV-2 diagnostic laboratory test result; Day -21 to Day T).  
Baseline Exclusion 
Exclude patients without activity in the 183 day baseline period (Day -183 to Day -1). 
Missing Demo- graphics 
Exclusion 
Exclude patients with missing age, sex, or region on the hospital admission date (Day 0; CED).  
COVID-19 Vaccine 
Exclusion 
Exclude patients with any record of a COVID-19 vaccine on or any time prior to the treatment index date given that 
these patients are assumed to be selectively different (Day S to Day -1) 
Treatment Exposure 
Hospitalized COVID-19 patients with new use of systemic dexamethasone (DEX+) or referent groups (CSI-, DEX-, 
MPRED+). Treatment index date (Day T) must occur between hospital admission date (Day 0; CED) and 1 day prior 
to hospital discharge date (Day D-1).  
Treatment Washout 
Exclude patients with prior systemic CSI use from all comparisons (Day -90 to Day T-1).   
Covariate Assessment 
Baseline 
Baseline (pre-admission) comorbidities, co-medications, and healthcare resource utilization (HCRU; Day -183 to Day -
1). 
Pre-admission  
COVID-19-related symptoms, complications, and HCRU leading up to the hospital admission date (Day -21 to Day -1). 
On admission 
Demographics, facility characteristics, and covariates related to admitting status (Day 0; CED).  
Pre-treatment 
(Inclusive) 
Treatments administered prior to or concurrent with treatment index date, pre-treatment clinical status, and time 
from admission to treatment (Day 0 to Day T). 
mWHO Severity 
COVID-19 severity per modified version of WHO ordinal scale (mWHO), used for matching and subgrouping of DEX+ 
and referent patients (Day T-1 to Day T).  
Outcome Assessment 
Follow-up 
Follow-up window for primary outcome of 28-day inpatient mortality (Day T+1 to the earlier of Day T+28 or D).  
CED, Cohort Entry Date; D, Hospital discharge date; S, Start of available data; T, Treatment index date 
 
 
 
 

Protocol: Effect of dexamethasone on inpatient mortality among hospitalized COVID-19 patients 
06.10.2021 
 
 
CONFIDENTIAL: This material is part of the Aetion-FDA research collaboration entitled “COVID-19 Data Sharing and Analyses.”   page 8 
 
B. Exposure definition  
 
The primary exposure of interest is new (incident) initiation of systemic DEX. DEX exposure is 
defined with procedure codes (current procedural terminology, CPT-4/healthcare common 
procedure coding system, HCPCS [CMS 2020(b)]), hospital charge codes for the corresponding 
text strings (e.g., ‘dexamethasone’, ‘decadron’), and drug names referenced from national drug 
codes (NDC), where there is an absence of evidence of a non-systemic route of administration 
(e.g., ciprofloxacin 0.3%/dexamethasone 0.1% ear drops are excluded). All available data types 
with treatment-specific information are used for assessment of DEX exposures, including 
medical claims, pharmacy claims, and chargemaster records. DEX use is considered “new” if the 
patient had no record of systemic DEX dispensing or remaining outpatient pharmacy supply 
during the 90-day Treatment Washout Window prior to the cohort entry and up until 1 day prior 
to treatment index (Day 0, hospital admission date). 
 
 
C. Study population 
 
1. Cohort entry  
Hospitalized Patients with COVID-19: 
Patients will enter the cohort upon the admission date of the first qualifying inpatient 
hospitalization observed in the hospital chargemaster data. The first eligible cohort entry date is 
01-April-2020, the date when the ICD-10 code for COVID-19 diagnosis was added. The last 
possible cohort entry date will be 60 days prior to the last date of available data [see Appendix 
A.5 Exploration 5 for additional detail justifying this design decision]. 
 
COVID-19 confirmation was defined as either of the following occurring between 21 days prior 
to the hospital admission date and the treatment index date, which is described in section C.2. 
below (inclusive; Day -21 to Day T): 
● Outpatient medical encounter prior to or on the admission date or diagnosis ‘present 
at admission’ with ICD-10-CM diagnosis of U07.1 for COVID-19, virus identified; 
OR  
● positive or presumptive positive SARS-CoV-2 diagnostic laboratory test result 
 
The decision to include either diagnosis or positive laboratory test results was based on an initial 
exploration [see justification detail in Appendix A.1. Exploration 1].  
 
For the DEX+ versus MPRED+ comparison (Secondary Objective II), given the increased use 
of MPRED earlier on in the COVID-19 pandemic, we may consider delaying the cohort entry to 
start in May or June pending data exploration.  
 
2. Treatment index 
Primary Objective -- New users of DEX (DEX+) versus matched non-users of CSI (CSI-): 
From the patients who enter the cohort, new users of DEX between the admission date (Day 0) 
and 1 day prior to discharge date (Day D-1) or a maximum period of 21 days after admission 
(Day 21) will be selected. Greater than 80% of the patients who initiated DEX did so within 1 or 
2 days of their hospitalization. A sensitivity analysis (SA2) will be conducted to explore 
initiation dates further. For each new user of DEX, at least 1 patient who has not (yet) used any 
CSI as of the DEX patient's index date or within the 90-day washout period prior matched on the 

Protocol: Effect of dexamethasone on inpatient mortality among hospitalized COVID-19 patients 
06.10.2021 
 
 
CONFIDENTIAL: This material is part of the Aetion-FDA research collaboration entitled “COVID-19 Data Sharing and Analyses.”   page 9 
 
criteria defined in Table 2 below will be randomly risk-set sampled (RSS). DEX+ versus CSI- 
matching ratios of 1:1, 1:2, 1:3, and 1:4 will be considered to select up to 4 non-user referent 
patients for each exposed, and the ideal ratio will be selected based on the diagnostic criteria 
[defined below in section VI.B. Diagnostic phase and checklist]. 
 
 
RSS matching allows us to proxy the ‘standard of care’ by randomly selecting 1 or more patients 
who have not initiated DEX from the risk set of patients who are eligible for initiating DEX at 
the same time [Li 2001]. Since the COVID-19 pandemic and treatment of patients with COVID-
19 is rapidly changing over time, there is much potential for calendar-time bias. Furthermore, 
patients prescribed DEX may appreciably differ from those not prescribed DEX. Employing an 
RSS that matches DEX users and non-users on the date of DEX treatment (in the users) and time 
since admission will minimize the potential for this bias. Cohort entry month will also be 
included in the propensity score (PS) calculation to further adjust for time-related biases. 
Geographic region was considered for the RSS matching criteria. However, during our data 
explorations, the comparison of baseline balance showed that matching without region yielded 
better baseline balance than matching with region. Therefore, we removed region from the RSS 
matching. However, given known regional differences, we still account for region in our 
propensity score model. 
 
The treatment index date (Day T) for patients who are users and for the matched non-user 
referent patients will be defined by the index date of the matched users.  
 
 
Secondary Objective I -- New users of DEX (DEX+) versus matched non-users of DEX 
(DEX-): 
From the patients who enter the cohort, new users of DEX between the admission date (Day 0) 
and 1 day prior to discharge date (Day D-1) or a maximum period of 21 days after admission 
(Day 21) will be selected. For each new user of DEX, at least 1 patient who has not (yet) used 
DEX as of the DEX patient's index date or within the 90-day washout period prior matched on 
the criteria defined in Table 2 below will be randomly RSS. DEX+ versus DEX- matching ratios 
of 1:1, 1:2, 1:3, and 1:4 will be considered to select up to 4 non-user referent patients for each 
exposed, and the ideal ratio will be selected based on the diagnostic criteria [defined below in 
section VI.B. Diagnostic phase and checklist]. 
 
Secondary Objective II -- New users of DEX (DEX+) versus new users of MPRED 
(MPRED): 
From the patients who enter the cohort, all patients who begin use of DEX or MPRED between 
the admission date (Day 0) and 1 day prior to discharge date (Day D-1) or a maximum period of 
21 days after admission (Day 21). New use will be determined using a 90-day washout period to 
exclude patients with prior use of either DEX or MPRED during the 90 days prior. If a patient 
uses both treatments over the course of the admission, they will be assigned to their first 
qualifying treatment arm. If a patient receives both DEX and MPRED on the treatment index, 
they will be excluded since the effects or the drugs cannot be disentangled among these patients.    
 
 
 

Protocol: Effect of dexamethasone on inpatient mortality among hospitalized COVID-19 patients 
06.10.2021 
 
 
CONFIDENTIAL: This material is part of the Aetion-FDA research collaboration entitled “COVID-19 Data Sharing and Analyses.”   page 10 
 
Table 2: Risk set sampling (RSS) matching criteria 
RSS matching criteria 
Variable type 
Categories (for categorical) / calipers (for 
continuous)  
Calendar date 
continuous 
±3 days 
Age 
continuous 
±3 years 
Sex 
categorical 
male, female 
Combined comorbidity score over 
183d baseline period [Gagne 2011] 
categorical 
0-1, 2-3, 4-5, 6+ 
Time since hospital admission 
categorical 
0-1, 2-3, 4-5, 6 - 9, 10 - 14, 15 - 19, 20+ days 
COVID-19 severity at treatment 
index date, per modified WHO 
(mWHO) score 
categorical 
No O2, O2/NIV, IMV (highest recorded from 
days T-1 to T) [see additional subgroup detail 
below in section V.D. Subgroups] 
 
3. Exclusion criteria  
Patients who meet any of the following criteria will be excluded from the study cohort:  
● No interaction with the healthcare system (medical encounter) in the 183 days prior to 
and including hospital admission (Day -183 to Day 0) to minimize the potential for 
misclassification of baseline covariates and/or new use 
● No age or sex recorded in the 183 days prior to and including hospital admission 
(Day -183 to Day 0) as required for matching 
● No geographic region recorded in 183 days prior to and including hospital admission 
(Day -183 to Day 0) as necessary to adjust for regional variation  
● Any recorded use of any CSI (dispensing or remaining supply) in the 90-day washout 
prior to the treatment index date (Day T-90 to Day T-1) to satisfy new use definition 
● Any record of a COVID-19 vaccine recorded on or any time prior to the treatment 
index date given that these patients are assumed to be selectively different (Day S to 
Day T) 
 
The following will be evaluated (one at a time) in sensitivity analyses in order to tighten or 
loosen some of the exclusion criteria:  
● Two sensitivity analyses will restrict the study population by adding the following further 
exclusions: (1) patients who entered the cohort prior to the release of the initial 
RECOVERY trial results; and (2) patients who initiated DEX use more than 1 day after 
the hospital admission date [see section VII. SENSITIVITY ANALYSES, SA1, SA2].  
● Two sensitivity analyses will broaden the study population by loosening exclusions: (1) 
extend the lookback period to confirm a prior healthcare encounter to 365 days to allow 
for better capture of covariates among patients who do not have as frequent medical 
encounters (office visit or otherwise); and (2) allow patients with confirmed COVID-19 
only after treatment index during the index hospitalization (no confirmation on/prior to 
treatment index or within 21 days prior to admission) [see section VII. SENSITIVITY 
ANALYSES, SA3, SA4].  
 
 
D. Subgroups: COVID-19 severity at treatment index 
 
The World Health Organization (WHO) Clinical Characterisation and Management Working 
Group proposed the WHO Clinical Progression Scale, which was created to classify COVID-19 
patient outcomes according to disease progression and severity among ambulatory and 
hospitalized patients [WHO Working Group 2020]. We developed an adaptation of the scale that 
is a modified version of the WHO scale, referred to here as the mWHO score, to categorize 

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COVID-19 severity at treatment index among hospitalized patients. This mWHO score restricts 
to the WHO Scale categories specific to hospitalized COVID-19 patients (categories 4-9 in the 
original scale), which are then collapsed into three mutually exclusive categories based on 
supplemental oxygen and other ventilation support requirements (no oxygen, oxygen or NIV, 
IMV). The scale was operationalized by incorporating data from Day T-1 to Day T to account 
for the lag time in recording ventilation and oxygen supplementation data in the medical claims 
and chargemaster data. Patients will be assigned to one of the following mWHO categories based 
on the highest severity feature recorded from 1 day prior to treatment index to the treatment 
index date (Day T-1 to Day T): 
● No oxygen at treatment index or within 1 day prior (No O2) 
● Any oxygen or non-invasive ventilation with no record of invasive ventilation at 
treatment index or within 1 day prior (O2/NIV) 
● Any use of invasive mechanical ventilation at treatment index or within 1 day prior 
(IMV) 
● Composite of any O2/NIV or IMV at treatment index or within 1 day prior  
 
Explorations were conducted to (1) determine the assessment period and justify the additional 
lookback to day T-1 for capture of the mWHO categories [see Appendix A.3 Exploration 3] and 
(2) justify the components of the mWHO algorithm definition [Appendix A.4. Exploration 4]. 
respectively. The definitions of these severity categories included procedure codes (O2 and NIV 
procedure codes for O2/NIV; intubation, IMV, and extracorporeal membrane oxygenation 
[ECMO] procedure codes for IMV) and diagnoses indicating a clinical need for O2 (diagnosis of 
hypoxia or hypoxemia) or IMV (diagnosis of ARDS). While the primary algorithms used to 
define O2/NIV and IMV refer to both procedural and diagnostic encounters in the data, a 
sensitivity analysis was also added to evaluate the robustness of findings among the mWHO 
categories using an algorithm based on procedures alone [see section VII. SENSITIVITY 
ANALYSES, SA7].  
 
Another COVID-19 severity score was developed by the FDA Sentinel Initiative to classify 
patients as asymptomatic/very mild, mild, moderate, severe, and critical [Yih 2020]. However, 
this score requires day-level diagnoses that are not always available via the HealthVerity 
inpatient chargemaster data (which is generally limited to diagnoses reported on the admission 
and discharge date). Therefore, use of this FDA Sentinel Initiative COVID-19 severity score is 
not feasible in this implementation. 
 
Additional subgroups were added for patients who were in the intensive care unit upon treatment 
index and those who were not as a sensitivity analysis [see section VII. SENSITIVITY 
ANALYSES, SA6].   
 
 
E. Primary outcome definition and follow-up (28-day mortality) 
 
The primary outcome will be inpatient mortality over a 28-day period, sourced from the 
chargemaster discharge status field. In the primary analysis, follow-up will begin 1 day after 
treatment index (Day T+1) and will continue until the earliest occurrence of the mortality 
endpoint, 28 days of follow-up reached (Day T+28) or discharge from hospital (Day D). This 
approach, referred to here as the “initial-treatment” (IT) approach, assumes patients continue the 
initial treatment without censoring upon treatment changes, similar to the intent-to-treat 
approach used in the RECOVERY trial and other randomized controlled trials.  
 

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We will also implement an as-treated (AT) censoring approach that additionally censors upon 
treatment changes. For the DEX+ versus CSI- comparison (Primary Objective), this will censor 
the follow-up of non-users of CSI upon start of any CSI use. For the DEX+ versus DEX- 
comparison (Secondary Objective I), this will censor the follow-up of non-users of DEX upon 
start of any DEX use. For the DEX+ vs. MPRED+ comparison (Secondary Objective II), this 
will censor the follow-up of patients who start DEX use upon start of any MPRED use and the 
follow-up of patients who start MPRED use upon start of any DEX use. 
 
A sensitivity analysis will also broaden the follow-up period for capturing inpatient mortality to 
consider deaths within 60 days after treatment index (T+60) if they occur during the index 
hospitalization. [see section VII. SENSITIVITY ANALYSES, SA5]. 
 
 
F. Covariates and their corresponding assessment windows 
 
Table 3 provides an overview of the covariates and their corresponding assessment windows 
considered for inclusion in the PS model. These variables were selected a priori based on prior 
literature, including published studies [Izurieta 2020] and  [Yih 2020], and discussions with 
clinicians, and conditions listed in the ‘COVID-19 associated hospitalization related to 
underlying medical conditions’ of the National Strategy for the COVID-19 Response and 
Pandemic Preparedness [Biden 2021, p.105]. Although all covariates will be considered, not all 
covariates will ultimately be included in the final model as PS model assumptions will also be 
checked via the diagnostic checklist [see section VI.B Diagnostic phase and checklist]. 
Missingness among covariates of interest within our study cohort will be enumerated and 
reported in our findings. 
 
 
Table 3: Description of patient characteristics and covariate assessment windows 
Covariate Category 
[Assessment Window] 
Details 
Patient Characteristics 
Baseline Covariates 
 
(Day -183 to Day -1) 
 
 
Baseline (pre-
admission) 
comorbidities, 
co-medications, 
and health 
resource 
utilization. 
- Lifestyle factors: History of smoking/tobacco use*, Insurance 
type (commercial versus Medicare/Medicaid coverage) 
- Comorbidities and comedications: asthma, hematological 
cancers, solid cancers, chronic lung disease, cardiovascular 
disease (any), diabetes, immunosuppressive conditions, kidney 
or liver disease, overweight, obesity (diagnosis in medical 
claims/chargemaster or body mass index ≥25-<40 in EHR), 
severe obesity (diagnosis in medical claims/chargemaster or 
body mass index ≥40 in EHR), dependence on respiratory 
support, frailty score, neurological/cognitive impairment, 2+ or 
3+ high-risk conditions (asthma, obesity, diabetes, chronic 
kidney disease, severe obesity, coronary artery disease, stroke, 
and chronic obstructive pulmonary disease), hypertension, 
statin use, steroid use (any, including inhaled), any 
immunosuppressant use, anticoagulant and/or antiplatelet use 
as well as the Combined comorbidity score [Gagne 2011] 
- Health resource utilization: Number of days hospitalized, 
number of outpatient visits, number of pharmacy claims, 
number of distinct medications dispensed, any encounter 
indicating skilled nursing facility, influenza vaccination status 

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Pre-admission 
Covariates 
(Day -21 to Day -1) 
AND 
 
Pre-treatment 
Covariates 
 
(Day 0 to Day T, 
inclusive) 
COVID-19 
characteristics 
and treatments  
- Pre-treatment COVID-19 status: dyspnea or hypoxia, 
pneumonia, ARDS, shock, non-respiratory organ failure, other 
organ support procedures (ECMO, KRT, Pressors), positive 
SARS-CoV-2 lab test result 
- Pre-treatment medications including those that overlap 
with treatment index (Day T): Anticoagulant and/or 
antiplatelet agents, antibiotics, other experimental COVID-19 
therapy listed in NIH guidelines 
(hydroxychloroquine/chloroquine, remdesivir, 
lopinavir/ritonavir and other HIV protease inhibitors, 
ivermectin, IL-6 inhibitors, IL-1 inhibitors, BTK inhibitors, 
JAK inhibitors, interferons, convalescent plasma, IVIG-SARS-
CoV-2, Mesenchymal stem cells, COVID-19 mAbs [NIH 
2020(b) as of the date of protocol finalization and may be 
updated as deemed necessary]) 
Pre-admission 
Covariates 
(Day -21 to Day -1) 
COVID-19 
symptoms and 
HCRU prior to 
the admission 
date. 
- COVID-19 symptoms: pre-admission COVID-19 symptoms 
- HCRU: Inpatient hospitalization, emergency room encounter 
Admission 
Covariates 
(Day 0; CED) 
Demographics, 
hospital facility 
characteristics, 
and COVID-19 
severity on the 
admission date.  
- Hospital and admitting characteristics: Hospital setting 
type, hospital teaching status, hospital number of beds, 
admission source, emergency room encounter, ambulance 
encounter, admission type 
- COVID-19 severity at admission: mWHO severity 
categorization on the admission date 
Pre-treatment 
Covariates 
 
(Day 0 to Day T, 
inclusive) 
Treatments 
administered 
prior to or 
concurrent with 
treatment index 
date, pre-
treatment 
clinical status, 
and time from 
admission to 
treatment. 
- Pre-treatment COVID-19 status: Highest recorded mWHO 
severity 
- Pre-treatment utilization characteristics: Number of days 
hospitalized, number of days with critical/ICU care, in ICU at 
treatment index, number of unique department codes, number 
of unique procedure codes 
- Demographics: Month of treatment index, age, sex, insurance 
type, US region 
*Smoking/tobacco use may be under-reported [see section VIII. Limitations for additional detail] 
 
G. Statistical Software 
 
Primary analyses will be conducted using the Aetion Evidence Platform® (2020) software for 
real-world data analysis, which has been validated for a range of studies [Wang 2016]. 
Supplemental analyses and additional plots will be created using R 4.0.3 (2020-10-10). 
 
 
VI.  STATISTICAL METHODS 
 
A. Propensity score model fitting 
 
Propensity score (PS) matching will be used to control for confounding by identifying referent 
patients who are comparable to DEX+ patients with respect to several covariates identified a 

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priori for their potential to introduce confounding. Multivariable logistic regression will be used 
to estimate the propensity to initiate DEX+ versus each referent group (CSI-; DEX-; MPRED+), 
based on main effects of the independent covariates without interaction terms. PS model 
coefficients will be estimated using the overall study cohort. Appropriate referent groups will be 
identified using nearest neighbor matching with a caliper width of ±1%. Referent groups for 
subgroup analyses will be identified using the overall PS model [Wang 2018, Strategy A].  
 
Model fit and performance will be evaluated using the criteria outlined in Table 4. The PS model 
for the primary objective will be deemed acceptable if all the criteria described in Table 4 are 
achieved. We will further attempt to satisfy all criteria for secondary objectives, but strategies 
such as covariate adjustment to control for residual imbalance may be used in the event that 
some criteria are not satisfied.    
 
As a secondary modeling strategy, we may consider estimating PS model coefficients within 
each subgroup and pooling subgroup matches to form the overall cohort. This modeling strategy 
may be used for the final analysis if diagnostic criteria outlined in Table 4 are improved [Wang 
2018, Strategy C].  
 
In our primary variable selection strategy, we will 1) define a list of potential covariates a priori 
as described in Section V.F; 2) specify one or several models using all or a manually selected 
subset of the covariates; 3) assess model diagnostics, including but not limited to diagnostic 
assessments described in Table 4; and 4) refit models in response to diagnostic results providing 
information about over-fitting, positivity violations, or covariate instability. Possible strategies 
for improving model fits may include collapsing sparse or collinear variables into composite 
variables where appropriate, or removing variables from the model if covariate balance is 
maintained between DEX+ and referent groups. We may also consider automated variable 
selection strategies such as HDPS or LASSO if they improve model fits relative to manual 
variable selection.  
 
B. Diagnostic phase and checklist 
 
The diagnostic phase checklist developed for this study is shown in Table 4 below. Upon each 
data update, the diagnostic results will be executed and discussed with the FDA RCA team until 
there is consensus that we have satisfied the necessary criteria for the primary objective in the 
overall cohort to progress to the Inferential Phase. Sample size calculations are part of the 
iterative cycle of the Diagnostic Phase rather than fully completed in advance to allow 
consideration of new study findings (in addition to the RECOVERY trial [RECOVERY 2020]) 
that impact the assumptions underlying the sample size calculations. Treatment effects will not 
be estimated during the Diagnostic Phase. All treatment effects will be examined during the 
Inferential Phase after the data is locked and propensity score models are finalized.  
 
Table 4: Diagnostic checklist to complete prior to progression to the inferential phase 
# Diagnostic 
Description 
! 
1 Confirm 
adequate 
sample size 
Adequate sample size will be defined as the minimum sample size required to 
achieve 80% power using a 2-sided 95% confidence interval (0.05-alpha) 
assuming a hazard ratio of 0.8 and the baseline risk of 28-day mortality in the 
primary referent CSI- group estimated as the risk in the RSS-matched CSI- 
group prior to PS matching (since the baseline risk is otherwise unknown in the 
 

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US patients hospitalized for COVID-19). Further confirm which subgroups of 
interest achieved minimum sample size for formal hypothesis testing.  
2 Confirm 
positivity of 
variables 
PS distributions will be visually inspected, and overlap in all areas of the PS 
distributions will be confirmed.  
 
3 Confirm 
baseline 
confounder 
balance  
We will confirm that the distributions of all potential confounders are balanced 
for DEX+ vs. referent. Covariate balance will be defined as absolute 
standardized differences (ASDs) < 0.10 [Austin 2009].  
Although variables with balance prior to PS matching may be removed from the 
PS model, balance of these variables will still be confirmed after PS matching. 
If there is a variable with a small residual imbalance (0.10 ≤ ASD  ≤ 0.20), 
balance may be deemed acceptable if the variable does not predict the outcome 
among the referent group (defined as an ASD <0.10 when comparing the risk of 
the outcome in those with the variable vs. those without it). Evaluation of 
imbalance in outcome prediction will only be conducted once at the end of the 
diagnostic phase if all other diagnostic criteria are met. 
 
4 Confirm 
models are not 
overfit  
We will confirm that all models contain approximately 12 exposed subjects or 
more per covariate  
 
 
C. Inferential phase 
 
The Inferential Phase begins after all diagnostic criteria are satisfied or the protocol is publicly 
posted, whichever is later. To prevent changes to the underlying patient or event-level data from 
occurring when new data are available, the data cut will be locked at the start of the Inferential 
Phase.  
 
For the primary outcome of 28-day mortality, the following will be reported in the overall cohort 
and within each subgroup of interest before PS matching and after PS matching: 
● Rates reported per 1,000 person-days, overall and for each subgroup.  
● Hazard ratios (HR) and corresponding 95% CIs for the effect of DEX+ compared to 
each referent (CSI-; DEX-; MPRED+) overall and for each subgroup using PS-
matched cohorts. If residual imbalance (0.10 ≤ ASD  ≤ 0.15) remains after PS 
matching, Cox proportional hazard models may be specified to adjust for any 
imbalanced confounders. Otherwise, HRs will be unadjusted.  
 
In subgroups that do not achieve the minimum sample size requirements, results will be 
presented in the context of an estimation framework rather than a formal hypothesis testing 
framework, and findings should be interpreted with caution. 
 
 
 

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VII.  SENSITIVITY ANALYSES  
 
The following eight sensitivity analyses (SA1-SA8) in Table 5 below were determined a priori. 
Additional sensitivity analyses may be considered post hoc upon review of preliminary results 
with the FDA RCA team. Unless otherwise specified, all sensitivity analyses will be applied in 
response to the primary objective but not the secondary objectives. 
 
Table 5: Summary of all sensitivity analyses planned a priori  
Category 
Brief 
Description 
Detail / Purpose 
Inclusion/ 
exclusion 
criteria (more 
specific) 
 
 
SA1: Restrict 
study population 
to 
hospitalizations 
occurring after 
release of initial 
RECOVERY 
findings* 
Detail: Restrict cohort entry to on or after June 16, 2020, given the 
release of the RECOVERY results in early June 2020. 
 
Purpose: Evaluate whether there is (qualitative) heterogeneity of the 
HR (which may be due to treatment pattern changes) after the 
substantial increase in DEX use in the US.  
SA2: Restrict 
study population 
to patients with 
immediate 
treatment 
initiation* 
Detail: Restrict DEX treatment index to DEX initiation on, or within 
1d after, admission date. 
 
Purpose: Evaluate whether there is (qualitative) heterogeneity of the 
HR (which may be due to covariate differences in patients treated 
earlier vs. later in the hospital stay) when restricting to DEX use 
upon admission, noting that the 1 day after admission was added to 
account for patients who may be admitted late in the evening who do 
not initiate treatment until the next morning. 
Inclusion/ 
exclusion 
criteria (more 
sensitive) 
SA3: Broaden 
population to 
include patients 
with less 
frequent medical 
encounters* 
 
 
Detail: Lengthen baseline window to lookback 365 days to allow for 
better capture of covariates among patients who do not have as 
frequent medical encounters (office visit or otherwise). Evaluate 
whether covariate status using 183 day versus 365 day lookback is 
differential by treatment.  
 
Purpose: Evaluate whether HR is robust within a broader cohort that 
may include people with less healthcare utilization (patients without 
a healthcare encounter within 183 days that do have one within 365 
days prior to admission) prior to hospitalization for COVID-19. 
Determine if the lookback period impacts the distribution of baseline 
characteristics. 
SA4: Broaden 
patient 
population to 
include patients 
with confirmed 
COVID after 
treatment index* 
Detail: Additionally include patients with positive or presumed 
positive lab values or COVID-19 diagnosis occurring anytime during 
hospitalization, including discharge diagnoses. 
 
Purpose: Evaluate whether HR is robust within a broader cohort 
including people without a COVID-19 diagnosis prior to or at 
admission.  Will increase the sample size by approximately 10%, but 
may have the potential for misclassification of patients who were 
hospitalized for reasons other than COVID-19 who had a secondary 
diagnosis of COVID-19 or contracted COVID-19 during 
hospitalization as patients hospitalized for COVID-19.  

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Category 
Brief 
Description 
Detail / Purpose 
Follow up 
censoring 
SA5: Broaden 
the follow-up 
period for 
capturing 
inpatient 
mortality 
endpoints* 
Detail: Determine whether treatment with DEX reduces the risk of 
inpatient mortality within 60 days during the initial hospitalization. 
 
Purpose: Evaluate effect of DEX on risk of inpatient death over a 
longer time frame. Aligns with mortality endpoint definition used in 
dexamethasone-ARDS trial [Villar 2020].  
Additional 
subgroup 
SA6: Stratify by 
ICU at 
Treatment index 
Detail: Estimate HR within ICU subgroup, defined based on the 
presence of a medical encounter with a revenue or department code 
indicating ICU at treatment index. 
 
Purpose: Evaluate whether patients who initiate treatment while in 
the ICU differ from those who do not (within the overall cohort), an 
alternative proxy for COVID-19 severity. 
Alternative 
subgroup 
definition 
SA7: Define 
COVID-19 
severity based 
on procedures 
alone 
Detail: Estimate HR within each of the mWHO subgroups using an 
algorithm that relies on procedural encounters only for defining 
O2/NIV and IMV (without the additional diagnostic encounters 
included in the primary definitions). 
 
Purpose: Evaluate the impact of the robustness of findings among 
patients identified as O2/NIV and IMV and those identified as 
having neither (no O2) and understand the impact of the shift from 
lower to higher COVID-19 severity on the effect of DEX+ on 28-day 
mortality.  
Inclusion/ 
exclusion 
criteria 
related to 
RECOVERY 
SA8: One 
comprehensive 
sensitivity 
analysis that 
attempts to more 
closely resemble 
RECOVERY 
population 
Detail: 
1. Remove new user washout 
2. Exclude patients aged 80 years or older 
3. Exclude patients with systemic fungal infections 
 
Purpose: To more closely align with RECOVERY design and 
baseline population 
1. RECOVERY included any use of DEX. 
2. Our study population has half the number of patients 80+ as 
RECOVERY due to under-report of older patients in our 
commercial data source, and these patients may therefore be 
selectively different. 
3. RECOVERY trial excluded patients contraindicated “in the 
opinion of the attending clinician.” Systemic fungal infection is 
the only contraindication listed in the package insert for DEX, so 
excluding these patients is an attempt to satisfy this criterion. 
* Alternative versions of the study diagrams for these sensitivity analyses are illustrated in Appendix B.  
 
 
 

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VIII.  POTENTIAL LIMITATIONS  
 
A. Addressed in Design and Analyses 
 
The following limitations have been addressed via study design or analyses, but will still be 
taken into consideration when interpreting and disseminating the findings from the 
implementation of this protocol: 
● Difficulty determining COVID-19 severity for the modified WHO categorization: 
Oxygen supplementation is critical to the mWHO COVID-19 severity categorization 
(IMV, O2/NIV, no O2) upon admission and upon treatment initiation. Although it seems 
IMV is generally well captured via procedural claims data alone, it is unclear if oxygen 
support is fully captured via procedural claims. We have developed algorithms to 
determine IMV or O2/NIV support based on the presence of relevant procedures or 
diagnoses, but determination of no O2 will rely on the absence of recorded procedures or 
diagnoses for IMV or O2/NIV, which may increase the potential for misclassification in 
the no O2 group. Furthermore, given the lack of day-level diagnostic information (i.e., 
only admitting and discharge diagnoses are available), there is potential for under-
reporting of  severity on days during the hospital stay. Lastly, reporting of ARDS used to 
define IMV might not have been as well captured early in the pandemic, so there is 
increased potential for misclassification among patients hospitalized earlier in the study 
period. The inclusion of ECMO in the IMV definition may minimize the potential for 
under-report of ARDS since ECMO has been given as a short-term rescue therapy in 
patients with ARDS. In SA1, we restrict cohort entry to on or after June 16, 2020, given 
the release of the RECOVERY results in early June 2020 [see section VII. Sensitivity 
Analyses, SA1]. Through this analysis, we will be able to see how ARDS is reported 
among patients after the early months to better understand this concern. Further, SA7 
allows us to evaluate the impact of the shift from lower to higher COVID-19 severity on 
the effect of DEX+ on 28-day mortality using an algorithm that relies on procedural 
encounters only for defining O2/NIV and IMV [see section VII. Sensitivity Analyses, 
SA7]. 
● COVID-19 diagnosis: The unique ICD-10 code for COVID-19 diagnosis (U07.1: 
COVID-19, virus identified) was created by the World Health Organization (WHO) in 
April 2020. Prior to the widespread use of the new code, other respiratory-related codes 
were used for billing purposes. We chose to delay the start of our cohort selection to 
April when the code was available. There is a possibility that early on, some facilities 
were not yet using the new code. However, given our findings from exploration 1 [see 
Appendix A.1. Exploration 1], concerns about the specificity of the ICD-10 codes other 
than the designated U07.1 code, we agreed to proceed with the definition of confirmed 
COVID-19 via diagnosis of U07.1 or positive/presumptive positive SARS-CoV-2 
diagnostic laboratory test result. 
● Unstructured laboratory encounter data: In addition to using the unique diagnosis 
code for COVID-19 (ICD-10: U07.1), we will include patients without a diagnosis claim 
if they have laboratory results identifying positive or presumed positive SARS-CoV-2 
diagnostic test via nucleic acid amplification test (NAAT). The laboratory data has 
unstructured free-text for test type and results, which present a challenge. Although we 
anticipate that we will not be able to categorize all COVID-19 laboratory results, we rely 
on an algorithm developed by the data vendor to discern the majority of test types and 
results.  

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● Open claims: Data from open claims can be captured within days of a healthcare 
encounter to provide near-real-time data. However, the composition and completeness 
may be less stable for the most recent calendar dates. Based on our findings from 
exploration 5, we have truncated cohort entry to remove the last 60 days, which should 
minimize this concern [see Appendix A.5 Exploration 5].  
● Lack of enrollment information in open claims to determine denominator of 
patients at risk with the same potential to contribute baseline data: Without 
beneficiary enrollment information like that included with closed claims data, use of 
“open” datasets limits the ability to clearly determine when a patient is observable or “at 
risk.” However, we are confident that our cohort will include all patients hospitalized 
available in the data and will additionally require the presence of any encounter during 
the 183-day baseline period for inclusion as a proxy for enrollment and extend this period 
to 365 days in a sensitivity analysis. 
● Unstructured inpatient medication data: Unlike outpatient pharmacy data, inpatient 
medication data in the HealthVerity data is largely captured via vendor charge code fields 
(e.g., ‘DEXAMETHASONE 4 MG OR TABS’) in addition to medical procedural claims 
such as HCPCS for medication administration (e.g., J1095 - INJECTION, 
DEXAMETHASONE ACETATE, PER 8 MG) that have non-standardized free-text 
descriptions rather than standardized fields (e.g., NDC codes). Therefore, text search 
strings will be used to query the data when procedure codes are not available. For this 
reason, it is possible that some of the patients included using our primary definition for 
systemic DEX may have non-systematic use that was not excluded due to a lack of 
indication in the search string. However, we assume this to be rare. Our team has 
developed a series of search string algorithms to minimize the potential for error and will 
incorporate additional manual review for key measures. 
● Potential for residual confounding: Although best practices will be applied to control 
for confounding, the potential for residual confounding still exists in observational cohort 
analyses. We will apply RSS to match on time-related biases, which are assumed to be 
strong given the evolving healthcare landscape of the COVID-19 pandemic that will 
include other matching factors of importance (i.e., time since admission, age, sex, and 
comorbidity score). Furthermore, we will use PS matching on top of RSS matching and 
will not conduct inferential comparative analyses until we have satisfied the balancing 
criteria specified in the diagnostic checklist. Finally, a number of a priori specified 
sensitivity analyses will evaluate the robustness of our findings. If warranted, post hoc 
sensitivity analyses may also be conducted to evaluate the potential impact of 
unmeasured confounding. Although we recognize that factors causing residual 
confounding may manifest after index, this study does not account for changes in disease 
severity and other COVID-19 treatment received after index date to avoid additional 
potential bias that may be induced by conditioning on mediating variables in the causal 
pathway, or on colliders. 
● Completeness of tobacco/smoking covariate: Smoking/tobacco use will be defined 
according to the presence of the indicator for current or past user in the electronic health 
record (EHR), a diagnosis indicating use or abuse of tobacco, or presence of a procedural 
consultation or medication dispensed for cessation. The addition of EHR data makes this 
definition more complete than algorithms based on claims alone, but misclassification via 
under-reporting of smoking behavior is likely. 

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B. To Be Considered in Interpretation Only 
 
The following limitations will be taken into consideration when interpreting and disseminating 
the findings from the implementation of this protocol: 
 
● Lack of indicators of socio-economic status (SES): The HealthVerity data leveraged 
for this study does not include SES. We will include insurance type in the PS model for 
covariate adjustment as may be indicative of SES (e.g., patients with a PPO generally 
have higher SES than those with HMO), but no other proxies are available to adjust for 
SES. 
● Lack of race/ethnicity information: The HealthVerity data leveraged for this study does 
not include race or ethnicity demographic data. Therefore, we are unable to evaluate 
whether the effect of DEX on 28-day mortality varies by race or ethnicity, explore 
potential racial and ethnic disparities related to COVID-19 severity and treatment, or 
control for confounding by race or ethnicity.  
● Incomplete dose information: Inpatient medication dose is not explicitly captured in the 
HealthVerity data. Dose can be abstracted from the free-text descriptions of the vendor 
codes, but requires manual review and does not allow for all medications to be assigned a 
dose. Thus, since a subgroup analysis by dose for DEX (or other CSIs) is not possible for 
all patients  
 
IX.  STRENGTHS  
 
As noted throughout this protocol and incorporated into the limitation section above, there are 
many strengths of this study. As recommended to determine fit-for-purpose [Gatto 2019; FDA 
2018], we selected a RWD source with near-real-time information, a reasonable population size, 
and sufficient capture of key study parameters and longitudinal patient experience to evaluate 
our research question. Further, our stepwise approach enforces best practices for application of 
principled RWD epidemiology. This is done first by exploring the data to reinforce data fit-for-
purpose and inform key study design choices, such as the COVID-19 severity category 
definitions and the assessment period used to assign them, and then by using a diagnostic 
checklist to ensure that all necessary criteria, such as sample size and confounding control, have 
been fully satisfied prior to estimating and interpreting treatment effects and finalizing the 
inferential analysis plan. Lastly, sensitivity analyses have been developed a priori with 
documented justifications to evaluate the robustness of findings. 
 
X.  HUMAN SUBJECTS  
The use of this de-identified data source was approved for exemption by the New England 
Independent Review Board. 
 
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Wang SV, Jin Y, Fireman B, Gruber S, He M, Wyss R, Shin H, Ma Y, Keeton S, Karami S, Major JM, Schneeweiss 
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Epidemiol. 2018 Aug 1;187(8):1799-1807. doi: 10.1093/aje/kwy049. PMID: 29554199. 
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Organization, 2020. Available online: https://covid19.who.int/. Accessed March 24, 2021. 
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Yih WK, Hua W, Draper C, Dutcher S, Fuller C, Kempner M, Kit B, Lyons J, Driscoll MR, Toh SD, Lo Re V. 
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XII.  APPENDICES  
 
A. APPENDIX A: Explorations to justify design decisions 
 
In the Exploratory Phase, researchers engaged in investigations to better understand the data and 
make decisions about the study design. The following explorations were completed in advance 
of the development of this protocol: 
1. Exploration 1: Defining the COVID-19 study population (cohort) 
2. Exploration 2: Comparison of our study cohort and study design to the RECOVERY 
trial  
4. Exploration 3: Defining the time period for measuring mWHO COVID-19 severity 
category at treatment index 
6. Exploration 4: Determining the definitions for mWHO COVID-19 severity 
components 
7. Exploration 5: Determining the cohort entry date truncation 
 
The explorations were carried out in advance of subsequent Diagnostic Phase and Inferential 
Phase. Given the periodic data updates during the Exploratory Phase, explorations were carried 
out on earlier versions of the study cohort prior to the most recent data update and finalized 
design decisions. Therefore, the cohorts used for each exploration may differ from each other 
and the final cohort used for the Diagnostic Phase and Inferential Phase.  
 
1. Exploration 1: Defining the COVID-19 study population (cohort) 
Challenge/goal: 
Describe ways in which the available data elements could be used for 
COVID-19 cohort identification (lab results and diagnoses). 
Description of 
exploration: 
After identifying relevant definition components (i.e., positive/presumed 
positive SARS-CoV-2 laboratory results and ICD-10 U07.1 – COVID-19, 
virus identified), we explored the distribution and overlap of each 
component/feature within our COVID-19 population, specifically: 
● 
Distribution/overlap of components: SARS-CoV-2 
positive/presumed positive lab results and ICD-10 U07.1 
● 
Distribution/overlap of assessment windows considered for cohort 
inclusion: pre-admission, at admission, at discharge 
● 
Trends over time in SARS-CoV-2 positive/presumed positive lab 
results, ICD-10 U07.1, other coronavirus NOS (‘not otherwise 
specified’) codes 
 
In addition, we also explored the distribution and overlap of the narrow 
definition of COVID-19 (U07.1 code or positive/presumed positive  
SARS-CoV-2 laboratory results) with a broader definition of COVID-19 
that additionally included the following interim ICD-10 codes that may 
have been used for COVID-19 diagnosis prior to release and widespread 
adoption of the specific U07.1 code: 
● 
B97.21 – SARS-associated coronavirus as the cause of diseases 
classified elsewhere 
● 
B97.29 – Other coronavirus as the cause of diseases classified 
elsewhere 

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● 
J12.81 - Pneumonia due to SARS-associated coronavirus 
● 
B34.2 - Coronavirus infection, unspecified 
Summary of key 
findings: 
Figure A.1 illustrates the overlap and non-overlap of COVID-19 
confirmation via diagnosis and laboratory results. Almost all patients 
(99.5%) had the ICD-10 diagnosis code (U07.1), 8.8% had both the 
diagnosis code and the positive or presumed positive lab result, and 0.5% 
had only the lab result. 
 
Using the narrow and broad definitions yielded cohorts of patients with 
similar demographic characteristics, comorbidities, and COVID-19 
symptoms, but some differences in baseline medication utilization were 
seen; use of medications such as antihypertensives, antibiotics, and 
antifibrinolytics decreased over time in the broadly defined cohort, 
whereas medication use appeared to be more stable over time using the 
narrowly-defined cohort. The narrow definition alone identified 
approximately 84% of patients identified using the broad cohort.  
Design decision: 
Although findings confirmed that the majority of patients have the ICD-
10 diagnosis code (U07.1), given the supporting coding guidance from 
CMS [CMS 2020(a)], we opted to define COVID-19 confirmation as 
either an ICD-10 diagnosis or a positive or presumptive diagnostic lab 
result. 
 
Given concerns about the validity of the ICD-10 codes other than the 
designated U07.1 code, we agreed to proceed with the narrower cohort 
definition to increase specificity of our case definition (decrease the 
percentage of false positives). 
 
Figure A.1. Exploration of COVID-19 confirmation via diagnosis and laboratory results 
 
 

Protocol: Effect of dexamethasone on inpatient mortality among hospitalized COVID-19 patients 
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2. Exploration 2: Comparison of our study cohort and design to the RECOVERY trial  
Challenge/goal: 
Understand how our US study population and real-world study design 
compares to the UK RECOVERY trial on DEX.  
Description of 
exploration: 
Compare the distribution of key patient characteristics between our study 
cohort at the time of exploration to those reported for the DEX 
RECOVERY trial. 
Summary of key 
findings: 
Table A.1 summarizes the overall comparison of our study to the DEX 
RECOVERY trial. Table A.2 and Table A.3 present the distribution of key 
patient characteristics, overall and stratified by COVID-19 severity, 
respectively.  
 
Similar to the RECOVERY trial [RECOVERY 2020], the primary 
objective for the current study aims to determine if treatment with DEX 
among patients hospitalized in the US with COVID-19 reduces the risk of 
inpatient mortality within 28 days among patients with supplemental 
oxygen and IMV respiratory support. 
 
Compared with our US RWD patients, the UK RECOVERY trial patients 
were older and more likely to be male, and appeared to have a lower 
frequency of diabetes and a higher frequency of heart disease, pulmonary 
disease, severe liver and kidney disease, noting that our real-world study 
relies on coding to capture comorbidities and that it was not possible to 
compare the study populations with respect to baseline characteristics not 
measured in RECOVERY (e.g., medication history, comorbidity scores). 
RECOVERY trial patients were also much more likely to start treatment 
on O2/NIV or IMV. 
Design decision: 
Given important differences identified via this exploration for the source 
populations, data types, and study designs used for our RWD study as 
compared to the RECOVERY trial, we prioritized application robust 
methodology for use of RWD and sought to replicate RECOVERY trial 
design elements where possible (e.g., use of an “initial-treatment” (IT) 
censoring approach that assumes patients continue the initial treatment 
without censor upon treatment changes, similar to the intent-to-treat 
approach used in the RECOVERY trial) in a sensitivity analysis [see 
Section VI1, SA8]. Differences will be taken into consideration when 
interpreting findings as they compare to those from the trial.  
 
 
 

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Table A.1: Summary of comparison of our real-world study to the RECOVERY trial 
Design 
parameter 
RECOVERY trial 
HealthVerity study population 
Study 
population 
Compared to our study population, the RECOVERY trial population was more likely to be 
male, of older age, on higher doses of DEX, and more likely to be receiving O2/NIV or IMV 
at randomization (treatment index).  
 
Geographical differences and distinctions in US versus UK care systems may also influence 
the populations under study. 
 
There were study population aspects of the trial that we were not able to account for: 
1. We assume that ‘usual care’ in the unexposed of the trial may differ from our 
matched non-users of DEX. 
2. We were unable to account for the following trial exclusion criteria given its 
subjective nature “Does the patient have any medical history that might, in the 
opinion of the attending clinician, put the patient at significant risk if they were to 
participate in the trial?” 
3. Since the RECOVERY trial was a large, practical trial that, while randomized, 
simplified the data collection process in an effort to minimize the already heavy 
burden of the pandemic on frontline workers, exploration of baseline clinical 
differences between the real-world study and trial populations to determine patient 
selection was limited by the degree of available information for the trial patients. 
Data 
collection 
Collected baseline and follow-up data 
through web-based forms filled out by 
attending physicians and/or research staff 
at each participating hospital. Follow-up 
information was identified through 
communications with clinical staff and 
review of medical notes, routine 
healthcare system data, and registry data.  
Our study used secondary data from hospital 
chargemaster transactional records, open and 
closed medical and pharmacy claims, laboratory 
data, and EHR records to measure baseline, 
exposure, and follow-up variables.  
 
We cannot measure all variables reported in the 
RECOVERY trial in the HealthVerity dataset 
(e.g., cannot reliably measure dose, viral load, 
eGFR) due to differences in data collection 
methods and available data types. Additionally, 
we cannot compare many baseline 
characteristics that are measurable in the 
HealthVerity dataset but were not measured in 
RECOVERY.  
Primary 
exposure 
Randomized to oral/IV DEX (6 mg once 
daily for up to 10 days) or ‘usual care’. 
Over the course of the study follow-up, 
8% of referent patients used DEX in 
routine care. 
 
All use was considered without requiring 
no prior use of DEX, although we assume 
no use prior to the trial. 
New use of oral/IV DEX (any dose for any 
duration) or risk-set matched patients without 
DEX (as of the day the DEX patient initiated 
use). 
 
 
New use required no prior use of DEX during 
90d washout. 
COVID-19 
inclusion 
criteria 
Lab-confirmed Nucleic Acid 
Amplification Test (NAAT) 
SARS-CoV-2 infection.  
Either of the following: 
● positive or presumptive positive COVID-19 
diagnostic laboratory test result; OR 

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● medical encounter or diagnosis ‘present at 
admission’ with ICD-10-CM diagnosis of 
U07.1 – COVID-19, virus identified  
Medical 
history 
exclusion 
“No medical history that might, in the 
opinion of the attending clinician, put the 
patient at significant risk.”  
Unable to proxy this subjective criteria. 
Follow-up 
used to 
capture 
death 
endpoint 
Patients were followed from 
randomization until death or discharge or 
at 28 days after first randomization 
(whichever is sooner). 
In the primary analysis, follow-up will begin 1 
day after treatment index (Day T+1) and 
continue until the earliest of 28 days of follow-
up reached (Day T+28) or discharge from 
hospital (Day D).  
 
 
Table A.2: Comparison of our real-world study population to the RECOVERY trial, Overall 
 
 
 
 
 

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Table A.3: Comparison of our real-world study population to the RECOVERY trial, stratified by 
COVID-19 severity   
 
 
 
 
 

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3. Exploration 3: Defining the time period for measuring a modified version of the COVID-
19 severity score published by the World Health Organization (mWHO score) at treatment 
index 
Challenge/ 
goal: 
Describe how changing the assessment period for determining mWHO 
severity categorization used for RSS matching and subgroups (no O2, 
O2/NIV, IMV) to additionally include days prior to treatment index date 
(Day T) changes the distribution of severity categorization.  
Description 
of 
exploration: 
We aimed to account for situations where patients received respiratory 
support in other medical settings (e.g., emergency room), in transit prior to 
admission, or cases where the billing date for a procedure was captured at a 
later date than the procedure was performed. We began without an additional 
lookback evaluating only on treatment index (Day T), and additionally 
including a lookback of 1 day (Day T-1 to Day T), 2 days (Day T-2 to Day 
T) or 3 days (Day T-3 to Day T) prior to treatment index. We also considered 
looking forward in time to account for instances where procedure dates were 
coded on a day after the procedure had been performed, but did not wish to 
introduce concern for immortal person-time bias.  
Summary of 
key findings: 
Figure A.2 illustrates how the distribution of mWHO COVID severity 
categorization shifts when the assessment period looks further back from 
treatment index date (Day T). When the assessment period additionally 
included a 1-day lookback, a small distribution of patients categorized as no 
oxygen when the assessment window was only on the day of treatment index 
(Day T) would shift to O2/NIV (2.5-3.2%) and IMV (0.2%) when the 
assessment period additionally included a 1-day lookback, and 0.3-0.4% of 
patients categorized as oxygen would shift to IMV.  
Design 
decision: 
Although shifts identified via this exploration were small, they were 
considered meaningful. Given the trade-off for reducing misclassification 
without introducing bias, it was decided that we would extend the lookback to 
1 day but not further, assigning the mWHO categorization based on max 
score between T-1 to T. 
Figure A.2: Distribution of mWHO COVID severity when the assessment period is varied 
 
All plots follow the same cohort of patients RSS-matched according to the mWHO score on day T, changing the assessment 
window to T-1, T-2, and T-3 for the analysis subgroups to see how patients would have been categorized differently if the window 
changed.  

Protocol: Effect of dexamethasone on inpatient mortality among hospitalized COVID-19 patients 
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CONFIDENTIAL: This material is part of the Aetion-FDA research collaboration entitled “COVID-19 Data Sharing and Analyses.”   page 30 
 
 
4. Exploration 4: Determining the definitions for mWHO COVID-19 severity components 
Challenge/ 
goal: 
The WHO Clinical Characterisation and Management Working Group 
proposed the WHO Clinical Progression Scale, which was created to classify 
COVID-19 patients according to disease progression and severity [WHO 
working group 2020]. Given that the WHO Clinical Progression Scale was 
devised for use in studies using primary data sources, such as prospective 
cohort studies and randomized clinical trials, we devised a modified WHO 
Clinical Progression Scale (mWHO scale) to capture similar constructs of 
COVID-19 severity using only information available in RWD (no O2, 
O2/NIV, IMV) that also aligns with the COVID-19 severity categories used for 
the RECOVERY trial [RECOVERY 2020].  
Description 
of 
exploration: 
We compared procedure-based definitions alone to those that utilized 
diagnoses and quantified the percent of patients in each category before and 
after the addition of the diagnoses.  
Summary of 
key 
findings: 
Additional detail related to the mWHO definition components are shown in 
Table A.4. 
 
When mWHO categorization with additional clinical diagnoses [see Figure 
A.3, v3] was compared to mWHO categorization without additional clinical 
observations [see Figure A.3, v1], there were substantial shifts from the least 
severe category (no O2) to more severe categories (25-48% to O2/NIV and 1-
2% to IMV) and from the O2/NIV to IMV category (4-6%). The shifts were 
differential according to treatment, as patients who initiated DEX had greater 
shifts and therefore differentially more misclassification/under-reporting of 
severity. 
 
Figure A.4 and Figure A.5 are Venn diagrams illustrating the overlap and non-
overlap of patients with corresponding encounters for the procedures, 
chargemaster diagnoses, and medical claim diagnoses for O2/NIV and IMV, 
respectively, to better visualize the percent of patients that would or would not 
be included if any of the encounter components were to be left out of the 
composite definitions used to define these subgroups. From these figures, we 
can conclude that the shifts from no O2 to O2/NIV were primarily driven by 
the inclusion of clinical diagnoses in the Chargemaster data and Medical 
Claims, e.g., the addition of hypoxia or hypoxemia diagnosis, as 47.6% of 
O2/NIV patients had Chargemaster or Medical Claims but did not have other 
procedure-based components [see Figure A.4]. Similarly, 4.9% of IMV 
patients had a diagnosis of ARDS via Chargemaster data or Medical Claims 
but did not have other procedure-based components [see Figure A.5]. 
Design 
decision: 
Given the substantial shifts, especially from no O2 to O2/NIV, we incorporated 
both the procedure-based encounters and the diagnoses into our final 
algorithm. 
 
 

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Table A.4: mWHO definition components 
mWHO 
Subgroup 
 
Components used to define mWHO subgroups 
Additional Details on Component 
definitions in HealthVerity data 
No O2 
Absence of evidence of a higher category 
! 
O2/NIV 
Standardized Procedure-related Diagnosis Codes  
!- ICD-10 dx for O2/ventilation use 
Standardized Clinical Diagnosis Codes 
!- ICD-10 dx hypoxia or hypoxemia 
Standardized Procedure Codes  
! - HCPCS, CPT, Revenue Codes 
Free-text 
! - Charge Codes 
IMV 
Standardized Procedure-related Diagnosis Codes  
!- ICD-10 dx for intubation/IMV 
Standardized Clinical Diagnosis Codes 
!- ICD-10 dx for ARDS 
Standardized Procedure Codes 
!- HCPCS, CPT, ICD-10 
Free-text 
!- Charge Codes 
 
Figure A.3. mWHO categorization before and after additional clinical observations were 
included (cohort RSS-matched according to v1) 
 
 
 
 

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Figure A.4. O2/NIV drivers in the shift in mWHO categorization (cohort RSS-matched according 
to v3, n=8306) 
 
 
 
Figure A.5. IMV drivers in the shift in mWHO categorization (cohort RSS-matched according to 
v3, n=834) 
 
 
 
 
 

Protocol: Effect of dexamethasone on inpatient mortality among hospitalized COVID-19 patients 
06.10.2021 
 
 
CONFIDENTIAL: This material is part of the Aetion-FDA research collaboration entitled “COVID-19 Data Sharing and Analyses.”   page 33 
 
5. Exploration 5: Determining the cohort entry date truncation 
Challenge/ 
goal: 
Although the lag (time between the latest data encounter and now) of this 
HealthVerity “open” claims RWD source is much shorter to provide near-real-
time data, the composition and completeness may be less stable for the most 
recent calendar dates, creating what we refer to here as an ‘artificial 
truncation.’ This is due to two issues: 
1. Variation of the update schedule cadence of each medical provider 
source, as some are daily while others are weekly or monthly; and 
2. Hospital stay information is not included in the data until at least 10 
days after a patient is discharged, so patients who are currently still 
hospitalized (i.e., patients with longer hospital stays) or recently 
discharged are not yet included in the data most proximal to the date of 
the data cut. This attribute is likely to skew the most recent period 
towards patients with shorter hospital stays.  
 
The goal of this exploration is to understand the impact of “open” claims on 
the most recent data and determine the optimal latest cohort entry date in which 
data are reliable for comparative studies for the hospitalized COVID-19 
population. 
Description 
of 
exploration: 
We selected our hospitalized cohort of patients with confirmed COVID-19 
from 5 separate data cuts received on the following dates: 14-September-2020, 
05-October-2020, 02-November-2020, 16-November-2020, 7-December-2020, 
11-January-2021, 08-February-2021, and 22-February-2021.  
 
First, we plotted and evaluated the weekly median length of hospital stays 
(percentiles) in the newest data cut received 7-December. 
 
Next, we plotted and compared weekly average length of stay (LOS; i.e., 
duration of hospital stays) trends in an older data cut from 16-November-2020 
and the newest data cut from 22-February-2021 to determine the appropriate 
number of days prior to the end of data to consider omitting from cohort 
selection due to data completeness concerns. 
 
Lastly, we plotted and compared monthly calendar time trends for the number 
of admissions and deaths among each of the data cuts. 
Summary of 
key 
findings: 
Within our hospitalized COVID-19 cohorts, for patients in the data cut 
received 22-February-2021, the latest hospital admission was 07-February-
2021 and the median LOS was 6 days. We see a data lag of ~15-20 days 
between the latest available admission date and the day of data receipt, which 
was also seen via other data cuts. Considering a data lag of 20 days and the 
95th percentile of 27 days LOS, we assume the end of the study period should 
be a minimum of 47 days prior to the end of the data to account for at least 
95% of the observed hospital stays [see Figure A.6]. 
 
In the weekly LOS plots comparing an older data cut from 16-November-2020 
to a newer data cut from 22-February-2021, we began to see the lines diverge 
the week of 17-September-2020, approximately 60 days before the latest 

Protocol: Effect of dexamethasone on inpatient mortality among hospitalized COVID-19 patients 
06.10.2021 
 
 
CONFIDENTIAL: This material is part of the Aetion-FDA research collaboration entitled “COVID-19 Data Sharing and Analyses.”   page 34 
 
admission date observed within the data cut received 16-November-2020. [see 
Figure A.7] 
 
For each data cut, the number of monthly admissions drops quickly, and likely 
artificially, as the cohort entry date approaches the data cutoff [see Figure A.8]. 
Death events also drop quickly, and likely artificially, as the cohort entry date 
approaches the data cutoff [see Figure A.9]. For both admissions and deaths, 
data from the more recent data cuts converge with data from earlier data cuts 
approximately 60 days prior to the data cutoff. 
Design 
decision: 
After evaluating time trends in LOS, and numbers of admissions and deaths 
across data cuts, we determined that the cohort entry date selection period 
should end 60 days earlier than the end of the data to ensure complete data for 
reporting the 28-day primary mortality endpoint. The truncation of the cohort 
entry period will further delay the Inferential Phase as we wait for sufficient 
data to accumulate in order to satisfy the diagnostic criteria.   
Figure A.6. Length of hospital stay (percentiles) in the newest data cut received 22-February-2021 
 
Figure A.7. Trends in chargemaster hospitalizations and average length of stay comparing older 
and newer data cuts 
 
 

Protocol: Effect of dexamethasone on inpatient mortality among hospitalized COVID-19 patients 
06.10.2021 
 
 
CONFIDENTIAL: This material is part of the Aetion-FDA research collaboration entitled “COVID-19 Data Sharing and Analyses.”   page 35 
 
Figure A.8. Number of admissions by week in a hospitalized COVID-19 cohort
 
 
Figure A.9. Number of deaths by week in a hospitalized COVID-19 cohort 
 

Protocol: Effect of dexamethasone on inpatient mortality among hospitalized COVID-19 patients 
06.10.2021 
 
 
CONFIDENTIAL: This material is part of the Aetion-FDA research collaboration entitled “COVID-19 Data Sharing and Analyses.”   page 36 
 
B. APPENDIX B: Study design diagrams for each sensitivity analysis 
 
SA1: Restrict study population to hospitalizations occurring after release of initial 
RECOVERY findings 
 
Detail: Restrict cohort entry to on or after June 16 given the release of the RECOVERY results 
in early June. 
 
Purpose: Evaluate whether there is (qualitative) heterogeneity of the HR, which may be due to 
treatment pattern changes, after the substantial increase in DEX use in the US.  
 
Figure B.1. Study diagram (SA1) 
 
 
 
 

Protocol: Effect of dexamethasone on inpatient mortality among hospitalized COVID-19 patients 
06.10.2021 
 
 
CONFIDENTIAL: This material is part of the Aetion-FDA research collaboration entitled “COVID-19 Data Sharing and Analyses.”   page 37 
 
SA2: Restrict study population to patients with immediate treatment initiation 
 
Detail: Restrict DEX treatment index to DEX initiation on, or within 1d after, admission date. 
 
Purpose: Evaluate whether there is (qualitative) heterogeneity of the HR (which may be due to 
covariate differences in patients treated earlier vs. later in the hospital stay) when restricting to 
DEX use early in the hospital stay. 
 
Figure B.2. Study diagram (SA2) 
 
 
 
 

Protocol: Effect of dexamethasone on inpatient mortality among hospitalized COVID-19 patients 
06.10.2021 
 
 
CONFIDENTIAL: This material is part of the Aetion-FDA research collaboration entitled “COVID-19 Data Sharing and Analyses.”   page 38 
 
SA3: Broaden population to include patients with less frequent medical encounters 
 
Detail: Lengthen baseline window to lookback 365 days to allow for better capture of covariates 
among  patients who do not have as frequent medical encounters (office visit or otherwise). 
Evaluate whether covariate status using 183 day versus 365 day lookback is differential by 
treatment.  
 
Purpose: Evaluate whether HR is robust within a broader cohort that may include people with 
less healthcare utilization (patients without a healthcare encounter within 183 days that do have 
one within 365 days prior to admission) prior to hospitalization for COVID-19. Determine if the 
lookback period impacts the distribution of baseline characteristics. 
 
Figure B.3. Study diagram (SA3) 
 
 
 
 

Protocol: Effect of dexamethasone on inpatient mortality among hospitalized COVID-19 patients 
06.10.2021 
 
 
CONFIDENTIAL: This material is part of the Aetion-FDA research collaboration entitled “COVID-19 Data Sharing and Analyses.”   page 39 
 
SA4: Broaden patient population to include patients with confirmed COVID after 
treatment index 
 
Detail: Additionally include patients with positive or presumed positive lab values or COVID-19 
diagnosis occurring anytime during hospitalization, including discharge diagnoses. 
 
Purpose: Evaluate whether HR is robust within a broader cohort including people without a 
COVID-19 diagnosis prior to or at admission. Will increase the sample size by approximately 
10%, but may have the potential for misclassification of patients who were hospitalized for 
reasons other than COVID-19 who had a secondary diagnosis of COVID-19 or contracted 
COVID-19 during hospitalization as patients hospitalized for COVID-19.  
 
Figure B.4. Study diagram (SA4) 
 
 
 
 
 
 

Protocol: Effect of dexamethasone on inpatient mortality among hospitalized COVID-19 patients 
06.10.2021 
 
 
CONFIDENTIAL: This material is part of the Aetion-FDA research collaboration entitled “COVID-19 Data Sharing and Analyses.”   page 40 
 
SA5: Broaden the follow-up period for capturing inpatient mortality endpoints 
 
Detail: Determine whether treatment with DEX reduces the risk of inpatient mortality within 60 
days during the initial hospitalization. 
 
Purpose: Evaluate effect of DEX on risk of inpatient death over a longer time frame. Aligns with 
mortality endpoint definition used in dexamethasone-ARDS trial [Villar 2020].  
 
Figure B.5. Study diagram (SA5)