Multi-Dimensional Outcome Prediction Algorithm for Hospitalized COVID-19 Patients
Conditions: COVID-19, Post Acute Sequelae of COVID-19, Long COVID, Organ Dysfunction Syndrome, Multiple, Frailty Syndrome
Sponsor: University of California, Los Angeles

Multi-Dimensional Outcome Prediction Algorithm for Hospitalized COVID-19 Patients  
SPECIFIC AIMS 
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-mediated coronavirus disease (COVID-19) is 
an evolutionarily unprecedented natural experiment that causes major changes to the host immune system. 
While it is too soon to fully understand the impact of COVID-19 on overall health and well-being, there are already 
several reports of significant sequelae, including neurologic, cardiac, respiratory and inflammatory, which appear 
to correlate with disease severity. There is a clear and urgent need to develop prediction tests for adverse short- 
and long-term outcomes for COVID-19 in order to accurately assess prognosis and to more effectively deliver 
appropriate care during and after acute infection.  
We hypothesize that integrating genomics, transcriptomics and proteomics data into a combinatorial 
biomarker model along with clinical data will provide accurate predictors of COVID-19 short- and long-term health 
outcomes. A single parameter provides limited information and is incapable of adequately characterizing the 
complex biological responses in symptomatic COVID-19 to predict outcome. Since they were designed for other 
illnesses, it is unlikely that existing clinical tools, such as respiratory, cardiovascular, and other organ function 
assessment scores, will precisely assess the long-term prognosis of this novel disease. Our extensive 
experience in biomarker development suggests that integrating molecular and clinical data increases prediction 
accuracy of long-term outcomes. We propose that complementary multi-dimensional information gathered 
near the time of symptom onset can be used to predict new onset or worsening frailty, organ dysfunction 
and death within one year after COVID-19 onset.  
We will test our hypothesis in a mixed US population that will include hospitalized patients from a civilian 
population in the country’s most populous county (Los Angeles) and a representative National Veteran’s 
population from Los Angeles and three other major metropolitan areas: Atlanta, Georgia, Bronx, New York, and 
Houston, Texas. This consortium of sites is both demographically and geographically diverse. 
The main goal of the proposed work is to develop a prediction test that performs well in this hospitalized 
patient group that broadly reflecting US demographics. We anticipate this test will: 1) demonstrate precision 
prediction in the general US population, 2) meaningfully guide triaging and treatment decisions and, therefore, 
3) reduce morbidity and mortality rates, enhance patient quality of life, and improve healthcare cost-
effectiveness. More accurate prognostic information will also assist clinicians in goals of care discussions in 
situations of likelihood of futility and assist patients and families in this decision-making process. Finally, it will 
provide a logical means for allocating resources in short supply, such as ventilators or therapeutics with limited 
availability. A secondary goal of this research is to identify and better understand expression of genes and their 
biological role in patients with COVID-19.  
 
SPECIFIC AIM 1: CORRELATE MULTI-OMIC DATA WITH OUTCOMES  
We hypothesize that various clinical COVID-19 disease outcome phenotypes correlate with molecular data and 
clinical patterns and are associated with immunologic pathways. 
Aim 1.1: Single-omic identification of candidate genomic, transcriptomic, proteomic and clinical 
parameters that correlate with outcomes. Our objective is to identify circulating blood-based single-omic 
parameters (genomic, transcriptomic, proteomic) and clinical data that correlate with COVID-19 outcomes.  
Aim 1.2: Multi-omics correlation analysis. Our objective is to establish a correlation pattern between the multi-
omics data and the outcome data in symptomatic COVID-19 patients.  
Aim 1.3: Weighted Gene Co-Expression Network Analysis (WGCNA) and pathway analysis. To better 
understand co-expression of genes and their biological role in patients with symptomatic COVID-19, we will 
perform WGCNA and pathway analyses. 
 
SPECIFIC AIM 2: TRAIN AND VALIDATE ALGORITHM FOR OUTCOME PREDICTION  
We hypothesize that our algorithm will predict COVID-19 short- and long-term outcomes of new onset or 
worsening frailty, organ dysfunction and death within one year after COVID-19 onset more accurately than 
clinical parameters alone.  
Aim 2.1: Train algorithm for outcome prediction. Our objective is to train our algorithm in a mixed hospitalized 
COVID-19 population.  
Aim 2.2: Independently validate predictive algorithm for outcome prediction. Our objective is to validate 
the algorithm in an independent COVID-19 population that was not used for algorithm development.  

RESARCH STRATEGY 
SIGNIFICANCE  
Epidemiology. SARS-CoV-2-mediated coronavirus disease (COVID-19) causes major changes to the host 
immune system and can lead to injury and dysfunction in virtually every organ(1, 2). Several high risk COVID-
19 populations have been identified. A systematic review of over 10,900 COVID-19 cases found that preexisting 
chronic conditions and co-morbidities(3, 4), such as hypertension (20%), diabetes (10%), cardiovascular disease 
(8%), and chronic pulmonary disease (3%) were strongly correlated with disease severity and admission into the 
intensive care unit(5). Older persons, individuals with immunocompromised systems, non-Hispanic blacks, 
Latinos or Hispanics, and those with obesity and chronic kidney disease are at higher risk for hospitalization and 
mortality from COVID-19(6, 7). Some reports have found a significantly higher mortality rate in men versus 
women with COVID-19(8, 9). Recent information suggests that not only elderly persons with comorbidities are 
vulnerable to negative health outcomes. There have been several reports of younger COVID-19 patients without 
underlying diseases that have developed stroke, multisystem inflammatory syndrome and other 
complications(10-14). Among various US-high risk COVID-19 populations, Veterans have experienced a higher 
percentage of COVID-19-related deaths than the general population. As of October 23, 2020, the crude fatality 
rate within the Veterans Affairs (VA) Healthcare System was 5.4% versus 2.7% in the overall US population(15, 
16). Roughly half of all Veterans are over 65 years old and ~87% are male(17). Compared to the overall US 
population, Veterans have a greater incidence of chronic diseases and mental illnesses, as well as high rates of 
homelessness, which contribute to their elevated risk of developing severe COVID-19(18-28). Compared to 
infection with influenza, Veterans with COVID-19 have increased risk of several complications, including 
pneumonia, respiratory failure, acute respiratory distress syndrome (ARDS), pneumothorax, cardiogenic shock, 
myocarditis, deep vein thrombosis, pulmonary embolism, disseminated intravascular coagulation, cerebral 
ischemia or infarction, intracranial hemorrhage, acute kidney failure, dialysis initiation, acute hepatitis or liver 
failure, sepsis, bacteremia, and pressure ulcers (29). The sites of the consortium that will be participating 
in this project are both demographically and geographically diverse and will provide representation from 
these various populations discussed above. We anticipate that this will result in a predictive algorithm 
that is generalizable to the wider US population.   
Long-term outcomes. It is too soon in the experience with this novel disease to have a comprehensive 
understanding of long-term outcomes in patients recovering from COVID-19. As just described, COVID-19 is 
associated with high rates of complications that may continue to incur morbidity, disability, and delayed mortality 
in survivors. Nearly 20% of young adults aged 18-34 years with no chronic medical conditions diagnosed with 
COVID and not requiring hospitalization, had not returned to their usual state of health 21 days after testing(30). 
In an observational cohort, 78 of 100 patients who had recovered from COVID-19 had abnormal findings on 
cardiovascular MRI (median of 71 days after diagnosis) and 36 of those reported dyspnea and unusual 
fatigue(31). In a small cohort (n=174) of recovered COVID-19 patients, approximately half required medium to 
long-term specialist follow-up, and significant persistent physical, psychological, and cognitive impairments were 
identified despite clinical resolution of the infection(32). In the prior severe acute respiratory syndrome (SARS) 
epidemic of 2003, survivors were noted to have long-term impact on pulmonary function, functional capacity and 
quality of life(33). Functional disability was thought to be out of proportion to the degree of lung function 
impairment and may have been related to additional factors such as muscle deconditioning and steroid 
myopathy(33, 34). This will be an important consideration when looking at long-term outcomes in COVID-19 due 
to routine use of corticosteroids in severe disease. A significant proportion of SARS survivors at 2 years showed 
persistent impairment of exercise capacity and health status(35); 30% of healthcare workers (HCW) and 7% of 
non-HCW had not returned to work. A post-SARS syndrome of chronic fatigue, pain, weakness, depression and 
sleep disturbance, which overlaps with the clinical and sleep features of fibromyalgia and chronic fatigue 
syndrome has been reported(36). Over one-third of patients hospitalized with SARS had moderate to severe 
depression and anxiety one year after physical recovery(37). A constellation of physical, cognitive, and 
psychological disabilities has been described in those surviving critical illness(38). Patients experiencing post-
intensive care syndrome (PICS) often report cognitive and physical dysfunction, which can persist long-term(39)[. 
PICS can also lead to disability and moderate or severe pain(40). The physical and psychological difficulties 
experienced by critical care survivors result in frequent readmissions to hospital and subsequent healthcare 
resource utilization. Taken together, these studies suggest that COVID-19 will have significant long-term impact 
on many patients (41-43). Therefore, being able to predict these events with precision would be greatly beneficial 
in their management.  
 

Inflammatory Responses. COVID-19 and Clinical Organ Dysfunction. SARS-CoV-2 infection causes major 
changes to the host immune system. Although inflammation is necessary to fight infection, exaggerated 
inflammatory responses in symptomatic COVID-19 may lead to unfavorable short- and long-term outcomes, 
such as new onset or worsening of frailty, organ dysfunction and death. Like other viral respiratory tract 
infections, SARS-CoV-2 can cause pneumonia and acute respiratory distress syndrome(44, 45), but also injury 
and clinical level dysfunction in virtually every organ system, including the cardiovascular(46-49), 
neurological(10, 50-52), renal(53, 54), hepatic(55-57), gastro-intestinal(58, 59), clotting(10) and immune 
systems(58).  
Organ dysfunction and Cytokine Storm Syndrome. Organ dysfunction is linked to severity of sepsis(60). A 
subset of patients with SARS-CoV-2 infection develop profound inflammation and multi-organ dysfunction(61-
63). Symptomatic COVID-19 may result in “cytokine storm”(64, 65), which is characterized by excessive release 
of pro-inflammatory cytokines and may accelerate multi-organ failure(65-71). Interleukin (IL) 1β and macrophage 
colony-stimulating factor (M-CSF) have been identified as candidate target genes for inflammatory storm(72). 
IL-6, IL-12, and C-reactive protein are important modulators of inflammation and may play key roles in the 
progression of COVID-19(61, 73). Elevated levels of pro-inflammatory ferritin and IL-6 have been identified as 
potential markers for fatality in COVID-19 patients(65, 67).  
Circulating Cell-Free DNA (cfDNA). Release of mitochondrial DNA via organ-failure-related cell necrosis into 
the circulation may result in severe immune consequences, such as hyper-inflammation(74, 75). The spleen, 
liver and kidneys are responsible for eliminating circulating DNA and are often damaged in critical illness 
conditions, such as severe COVID-19(75-77). Thus, inflammation may be triggered not only by viral infection, 
but also by elevated cfDNA. During infection with SARS-CoV-2, high levels of cfDNA, composed of nuclear and 
mitochondrial DNA, can be produced and may induce cytokine storm and vascular abnormalities(70). cfDNA 
levels are associated with COVID-19 progression and severity including intubation and death (78, 79). 
Understanding Cytokine Storm Syndrome through Multi-Omic Systems Biology. Infection with SARS-CoV-
2 and other viruses may lead to significant alterations in the host transcriptome and modulated immune 
response(80, 81). In PBMCs of COVID-19 patients, more than 1,000 genes were reported to be differentially 
expressed compared to controls. Some of these genes are directly involved in immunity, such as humoral 
responses, lymphocyte-mediated immunity and complement activation. Thus, studying transcriptomic changes 
provides a high resolution understanding of SARS-CoV-2-induced immune activity in PMBCs. Up- and 
downregulated PBMC genes are also involved in other biological processes, such as protein translation(64). 
Immunophenotyping generates systemic and tissue specific immune profiles. Patients with severe COVID-19 
often present with profound lymphopenia, develop neutrophilia, and high hemoglobin levels(72, 82, 83). Immune 
cell ratios and activation, as well as inflammatory mediators, are associated with disease state and prognosis(46, 
56, 82, 84-86). Immunophenotyping analysis demonstrated decreased levels of lymphocytes, including NK, T, 
B, and Treg cells, as well as monocytes(55, 68, 72). A heterogeneous interferon-stimulated gene signature, 
downregulation of HLA class II, and a novel B cell-derived granulocyte population have also been identified(87). 
A recent multi-omic analysis by RNA-seq and high-resolution mass spectrometry on 128 blood samples from 
COVID-19-positive and -negative patients with diverse disease severities mapped 219 molecular features with 
high significance to COVID-19 status and severity involved in complement activation, dysregulated lipid 
transport, and neutrophil activation (88). While presenting the most comprehensive evidence of predictive 
potential of COVID-19-related outcomes based on multi-omic analysis, the authors conclude that limitations 
include the single-center restricted demographics of the population not representing “replicate factors related to, 
among others, geography or population socioeconomic status” and the fact that the “study was not powered to 
demonstrate the association of omic data with survival, which is the most impactful patient-centered outcome 
measure” (88). We intend to address both of these limitations in our study.  
Utility of Prediction Tests. There is a high priority to develop prediction tests for adverse short- and long-term 
outcomes. Adverse outcomes include frailty, single organ dysfunction (heart, lung, liver, gut, coagulation, kidney, 
immune and brain systems), multi-organ dysfunction, and death. Clinical tools alone, such as respiratory 
diagnostics, cardiovascular parameters, organ function assessment scores and co-morbidity scores are not 
informative enough to assess the prognosis of this novel disease (88). One systematic review identified 10 
clinical models of predictive outcomes. However, these models were “poorly reported and at high risk of bias, 
raising concerns that their prediction could be unreliable when applied in daily practice”(89). These models did 
not utilize molecular data, which we have shown can significantly increase prediction precision(90).  
Genomics has been widely employed in prediction algorithms with great success. In 2004, Oncotype DX 
was launched as the first assay that both quantifies the likelihood of breast cancer recurrence and predicts the 
magnitude of chemotherapy benefit(91). Low scores allow doctors to recommend patients to forego chemo- 

and/or radiation-therapy, and the associated side effects. Dr. Deng co-led the effort to develop the AlloMapTM test 
to rule-out heart transplant allograft rejection without invasive endomyocardial biopsy(92). Today, the test is part 
of the transplantation medicine protocol in >90% of US heart transplant centers(93, 94) OncotypeDXTM, 
AlloMapTM, and other genomic assays provide evidence that molecular tests add critical information to the shared 
decision-making process, improve quality of healthcare, and enhance cost-effectiveness(95). We have 
successfully developed and validated an algorithm that predicts mortality in advanced heart failure patients (see 
preliminary data section). Combining preoperative transcriptomic (mRNA sequencing) and clinical data into one 
model increased prediction accuracy compared to using clinical tools alone(90). Given our experience 
developing predictive tools for heart failure and transplant rejection, we hypothesize that incorporating molecular 
data into our proposed test will facilitate COVID-19 outcome prediction (96). 
Conclusion. We postulate that a good prediction tool that is integrated in clinical settings can enhance the 
decision-making process, decrease morbidity and mortality, and improve healthcare cost-effectiveness. COVID-
19-related organ injury can modulate cfDNA patterns, which may regulate changes in PBMC gene 
expression(22) and release of cytokines(23-26). These molecules are highly dynamic and interactive. A single 
parameter alone provides limited information and is incapable of adequately characterize biological response in 
symptomatic COVID-19. We hypothesize that integrating genomics, transcriptomics and proteomics data into a 
combinatorial biomarker model along with clinical data will provide accurate predictors of COVID-19 short- and 
long-term health outcomes. We propose that these data, gathered near the time of symptom onset, can be used 
to predict frailty, organ dysfunction and death within one year of COVID-19 onset. While different acute treatment 
options (e.g., antiviral therapy, anti-inflammatory therapy, ventilator support, etc.) are initiated based on clinical 
data, our test would help optimize short- and long-term management plans. Event prediction may also assist in 
decision-making in goals of care discussions due to likelihood of futility or for allocation of limited resources.  
 
INNOVATION  
The goal of this project is to develop a test that accurately predicts short- and long-term (within one-
year) outcomes in a hospitalized COVID-19 population using both clinical and molecular data. Our overall 
proposition is that new onset or worsening frailty, organ dysfunction and death from COVID-19 within one year 
after diagnosis can be most accurately predicted by an algorithm that incorporates genomic, transcriptomic and 
proteomic data in addition to clinical data. We hypothesize that the immunological profile of a patient at the time 
of COVID-19 onset contains information about their ability to recover from SARS-CoV-2 infection. The potential 
to recover from disease is likely related to the balance between innate and adaptive immunity(97) and reflects 
the effects of the primary immunological stressor (i.e., viral infection), secondary organ dysfunction, co-
morbidities, frailty, disabilities and age(98). Characterizing the comprehensive changes to cfDNA, PBMC 
transcriptome, and cytokines in patients presenting with COVID-19 will provide the framework for establishing a 
model for outcome prediction.  
We have chosen to test our hypothesis in a mixed hospitalized COVID-19 population that is at increased 
risk of adverse outcomes from COVID-19. To do so, we propose a dual strategy, combing a civilian population 
in Los Angeles County, one of the country’s largest metropolitan areas, and a representative National Veteran’s 
population who are at increased risk of adverse outcomes from COVID-19(19, 99-101). We anticipate that a 
prediction test that performs well in this mixed cohort will: 1) demonstrate precision prediction in the general 
population, 2) meaningfully guide triaging and treatment decisions and, therefore, 3) reduce morbidity and 
mortality rates, enhance patient quality of life, and improve healthcare cost-effectiveness. More accurate 
prognostic information will also assist clinicians in goals of care discussions in situations of likelihood of futility 
and assist patients and families in this decision-making process. Finally, it will provide a logical means for 
allocating resources in short supply, such as ventilators or therapeutics with limited availability. 
Patients with multiple diagnoses, chronic conditions, and lower health status require more intensive 
resource utilization upon hospital admission(102). Cost analyses suggest that a hospitalized COVID-19 patient 
may incur an average of $73,000. The pandemic is projected to cost $360 billion to $1.4 trillion, depending on 
the assumed incidence rate(103). Implementation of the proposed testing strategy will likely improve healthcare 
cost-effectiveness. 
 
RESEARCH PLAN  
Study Design. We propose to conduct an observational, non-randomized study to predict new onset or 
worsening frailty, single organ dysfunction, multi-organ dysfunction and survival within one year following 
diagnosis in Veterans and civilians with symptomatic COVID-19 (Figures 1 and 2).  

Multi-Center Organization. We are initiating a Multiple-PI 
study between UCLA (Contact-PI Dr. Mario Deng) and VA 
Greater Los Angeles Healthcare System (Co-PI Dr. David 
Beenhouwer). We will work in collaboration with Harbor-UCLA 
Medical Center (Co-Investigator Dr. Tim Hatlen) and Olive 
View-UCLA Medical Center (Co-Investigator Glenn Mathisen) 
as well as VA Medical Centers in Atlanta (Co-Investigator Dr. 
Vincent Marconi), Bronx (Co-Investigator Dr. Sheldon Brown) 
and Houston (Co-Investigator Dr. Maria Rodriguez-Barradas). 
Each of the four participating VA centers will recruit 75 VA 
patients over the 42-month recruitment period (n=300 Veteran 
patients). UCLA Westwood/Santa Monica, Harbor-UCLA 
Medical Center and Olive View-UCLA Medical Center together 
will recruit 300 civilian patients over the 42-month recruitment 
period.  
Patient Population. We plan to complete 1-year follow-up in 
500 patients from a large US-metropolitan area (LA-County) 
(n=250) and four national metropolitan VA centers (n=250). To 
account for a dropout rate of ~17%, we will recruit a total of 
600 patients. LA-County is one of the largest metropolitan 
areas in the US. As per October 30, 2020, there were 290,278 
laboratory confirmed cases, 24,670 hospitalized patients and 
6,999 deaths(104). Since the first reported case of COVID-19 
in the US, the four VA sites in Atlanta, Bronx, Houston and Los 
Angeles, have admitted 847 Veterans. While these sites vary 
in their COVID-19 experiences, combined they appear to be 
representative of the National VA population (Table 1, re-
submission Introduction). These four sites belong to the 
SUPERNOVA consortium, which is a CDC-funded study 
ongoing for over 5 years that performs active surveillance for 
acute gastroenteritis and respiratory infection, including 
COVID-19 (28). The local site investigators are infectious 
disease physicians and have study staff trained in enrolling 
and following patients with infections. Study staff are well 
versed in identifying patients admitted with COVID-19, using a 
combination of admission log screening and TheraDoc 
(clinical surveillance software that integrates with the 
electronic medical record). Coordinators at the sites are 
experienced with abstraction of clinical data into REDCap and 
obtaining, processing, storing and shipping biological 
specimens. Inclusion criteria. Adult COVID-19 patients 
admitted to the hospital with any symptoms, including 
pneumonia, ARDS, sepsis, and septic shock(44) will be 
approached for consent (UCLA Informed Consent Form 20-
000763, PI Dr. Deng). Since a majority of COVID-19-related 
deaths and hospitalizations are in male patients, we expect to 
enroll more men than women (9). Exclusion criteria. 1) Lack 
of 
consent 
by 
either 
patient 
or 
legally 
authorized 
representative, 2) <18 years old, 3) COVID-19 re-infection. 
Endpoint Definition. Clinical composite endpoints will include new onset or worsening frailty, single organ 
dysfunction, multi-organ dysfunction, and death within one year (primary endpoint) and at time of discharge 
(secondary endpoint).  
General Hospital
Quarantine
Critical Care
COVID-19 Test Positive
80%
Baseline Research Blood Draw
General Hospital
Critical Care
Hospice
Mild symptoms, pneumonia, acute respiratory 
distress syndrome, sepsis, septic shock 
Outcomes at baseline, discharge (secondary), and 1-year (primary):
1) Frailty
2) Single organ dysfunction
3) Multi-organ dysfunction
4) Death
20%
Figure 1. COVID-19 clinical decision making, study 
population and primary endpoint [modified after(96)]. 
Table 1. Routine laboratory values to be 
abstracted from electronic medical record. 
Hematologic/immunologic 
Renal 
White blood cell count 
Blood urea nitrogen 
Total lymphocyte count 
Creatinine 
Absolute neutrophil count 
 
Hematocrit  
Hepatic 
Platelets 
Bilirubin 
Aspartate transaminase 
SARS-CoV-2 antibody 
Alanine aminotransferase 
 
Lactate dehydrogenase 
Inflammatory 
 
C-reactive protein 
Coagulation 
Erythrocyte sedimentation rate 
Prothrombin time 
Procalcitonin 
Partial thromboplastin time 
Interleukin 6  
International normalized ratio 
Ferritin 
D-dimer 
 
 
Cardiac 
Chemistries 
Troponin  
Sodium  
Creatine kinase MB  
Glucose 
Brain natriuretic peptide 
Lactate 
Figure 2. Study design. 
UCLA Lab 
CPT 8 ml:
cfDNA Sequencing
mRNA Sequencing
-80 C Storage Until Shipping
Participating Centers
ACD 8 ml: 
Flow Cytometry
Cytokine Quantification
Long-Term Outcome Prediction in Symptomatic COVID-19 Patients
N=500 (250 Civilians, 250 Veterans)
Aim 1.3: 
WGCNA and 
Pathway 
Analysis
Aim 2.1: 
Train & Test 
Algorithm
AIM 1: 
CORRELATE MULTI-OMIC DATA 
WITH OUTCOMES
Aim 1.1: 
Single-Omics 
Analysis
N=500
AIM 2: 
TRAIN & VALIDATE 
ALGORITHM FOR 
OUTCOME PREDICTION 
Aim 2.2: 
Independently 
Validate 
Algorithm
Aim 1.2: 
Multi-Omics 
Analysis
N=300
N=200

Data Collection. The data collection method is simple, cost-effective, and minimally invasive, requiring only one 
research blood draw at enrollment, abstraction of clinical parameters from the electronic medical record, and a 
brief interview at enrollment, at discharge, and at one year. Clinical Data. Demographics, laboratory values, 
organ function parameters, frailty parameters, comorbidities, disability data as well as COVID-19 treatment data 
will be collected. Table 1 summarizes the routine clinical laboratory values to be collected. Validated organ 
function scores, such as SOFA score, MELD-XI score, the Seattle Heart Failure Model will be calculated(105-
107). Comorbidities will be assessed using Charlson and Elixhauser scoring systems(108, 109). Risk of alcohol 
use disorders will be assessed with AUDIT-C (110). National Health Service Frailty Scale scores will be 
assessed(111). All clinical data will be collected at baseline, at discharge and at one year after COVID-19 
enrollment, abstracted into REDCap, and later analyzed using R (Vienna, AU). All patients will be interviewed in 
person (if not available, then by televisit) at discharge and one year after enrollment. The National Death Index 
will be searched for subjects who are uncontactable(112). Blood Collection and Processing. Research blood 
will be collected within 72h of admission and COVID-19 diagnosis confirmation and processed within 2h of 
sampling, into one 8 ml CPT tube (BD Biosciences) for PBMC transcriptomic analysis and cfDNA analysis and 
one 8 ml ACD tube (BD Biosciences) for immunophenotyping and cytokine concentration analysis. Each study 
site will isolate plasma and PBMC using standard procedures, aliquot these 
samples in duplicates and store samples at -80°C. Samples will be shipped 
in batches to UCLA on dry ice by express delivery. cfDNA Sequencing. 
The Illumina NovaSeq platform will be used to sequence paired-end reads 
of cfDNA for 2x150 bp with 1% Phix control using S4 flow cell. For cfDNA 
concentration quantification, we will use Invitrogen QubitTM dsDNA HS 
Assay Kit and Invitrogen Qubit 2.0 Fluorometer. Then, cfDNA sequencing 
will be done on an Illumina HiSeq 3000. RNA Sequencing. Total RNA will 
be isolated from PBMC(25)(90). Purified RNA quality will be verified on an 
Agilent 2100 Bioanalyzer. RNA concentrations will be determined using a 
NanoDrop ND-1000 spectrophotometer. The mRNA library will be prepared 
with Illumina TruSeq RNA kit. The cDNA libraries will be quantitated using 
Qubit. Total mRNA will be amplified and sequenced on the whole-genome 
Illumina HiSeq 3000. Flow Cytometry. Immunophenotyping of PBMCs will 
be performed with 12-color monoclonal antibody panels to identify the 
subpopulations listed in Table 2. Cell fluorescence will be acquired on an 
LSR Fortessa™ cell analyzer, using standard acquisition templates. 
Cytokine Quantification. We will quantify plasma cytokine levels using a 
38 multiplex Luminex array. Fluorescence intensity is acquired using a 
Luminex 200 instrument and xPONENT software.  
Sample Size Requirements and Power Calculations. Current available 
literature suggests that at least 20% of the symptomatic COVID-19 infected 
population may develop frailty, organ dysfunction or death within one 
year(46, 47, 52, 56, 57, 61, 62, 113-116). Based on these assumptions, we 
will perform power calculations on the following numbers (250 civilians, 250 Veterans) (Figure 2): Aim 1.1 single-
omic identification (n=100, 80 endpoint negative/20 endpoint positive) and confirmation (n=200, 160 endpoint 
negative/40 endpoint positive) of candidate genomic, transcriptomic, proteomic and clinical parameters that 
correlate with outcomes; Aim 1.2 multi-omics correlation analysis (n=300 from Aim 1.1, 240 endpoint negative/60 
endpoint positive); Aim 1.3 WGCNA and pathway analysis (n=300 from Aim 1.1); Aim 2.1 training/testing of the 
predictive algorithm (n=300 from Aim 1.1); Aim 2.2 independent validation of predictive algorithm (independent 
n=200). Samples will be processed in 5 batches, approximately 120 samples per year within 5 years. Statistical 
analysis will begin after one-year outcomes of the first 100 patients have been collected. Patients will be stratified 
by outcomes, Veteran/civilian status, age, gender, ethnicity, comorbidities, frailty and other parameters that 
appear to be significant such as alcoholism.  
Timeline. The timeline of the entire project is summarized in Table 3. We anticipate that: 1) recruitment of 600 
COVID-19 patients will be begin 3 months after funding start, be completed within 42 months, 2) the 12-month 
follow-up period will be completed by month 57, and 3) the analysis will be completed by month 60.  
 
 
 
 
Table 2. Cell subsets discerned through use 
of multi-parameter panels. Our panels allow 
extensive 
peripheral 
blood 
leukocyte 
population analysis as demonstrated by the 
list above (details see text). 

PROJECT ACTIVITIES 
YEAR 1 
YEAR 2 
YEAR 3 
YEAR 4 
YEAR 5 
QUARTERS 
1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 
Finalize IRB approval, study protocol, staff training, and 
REDCAP  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Recruit participants for Aim 1.1 (n=100) 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Recruit participants for Aims 1.2, 1.3, and 2.1 (n=200) 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Recruit participants for Aim 2.2 (n=200) 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Collection of patient clinical variables from EMR 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Data checks and quality assurance 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Single-omics identification and confirmation of correlates 
(Aim 1.1) 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Multi-omics analysis (Aim 1.2) 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
WGCNA and pathway analysis (Aim 1.3) 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Train & test predictive algorithm (Aim 2.1) 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Independent validation of predictive algorithm (Aim 2.2) 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Data reporting and publication of study results  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
PRELIMINARY DATA. We have developed and validated a survival outcome prediction algorithm for patients 
evaluated for advanced heart failure therapies using preoperative clinical and transcriptomic data(90, 98). This 
algorithm accurately predicted both short-term recovery and long-term survival (Study 1). In a separate study, 
we characterized the temporal dynamics of the immune system under standardized exercise and resting 
conditions in both patients with heart failure and healthy volunteers. We performed integrated analysis of 
genomic, transcriptomic, proteomic, and clinical data from the multivariate cardiopulmonary exercise panel. Our 
results showed a difference in each of the parameters of healthy volunteers and heart failure patients (Study 2). 
We propose to expand on our previous work to develop a COVID-19 test to predict adverse short- and long-term 
outcomes. We hypothesize that transcriptomic and clinical biomarkers will accurately predict the short- and long-
term (within one year) outcome following SARS-CoV-2 infection. Integration of additional parameters, such as 
cfDNA, immunophenotyping and cytokine concentrations may increase prediction accuracy compared to using 
transcriptomic and clinical data alone. 
Development and Validation of a Survival Outcome Prediction 
Algorithm for Patients Evaluated for Advanced Heart Failure 
Therapies (Study 1). Clinical Prediction Model Development 
(n=96). First, we aimed to understand the relationship of clinical 
information with patients’ post-surgical outcomes. A retrospective study 
was conducted in 96 advanced heart failure patients undergoing 
mechanical circulatory support (MCS) surgery from 2012 to 2016 in a 
tertiary academic medical center. Clinical data was collected one day 
before and one year after MCS implantation. Twelve clinically 
established prognostic variables(107, 117-131) were selected, including 
serum bilirubin, serum creatinine, blood glucose, white blood cell count, 
alveolar oxygen pressure, fraction of inspired oxygen, systolic and 
diastolic blood pressures, International Normalized Ratio, heart rate, 
respiratory rate, temperature, and the Glasgow Coma Scale. We 
calculated Sequential Organ Failure Assessment (SOFA)(105) and 
Model for End Stage Liver Disease Except-INR (MELD-XI) scores(106). 
We also included chronological age as an important demographic 
parameter, which has been shown to correlate with immune system 
function, and overall health and survival(132-134). Of 12 variables included in our clinical prediction model, age 
showed the highest correlation with patients’ outcome after surgery, followed by respiratory rate, white blood cell 
count, and minimum diastolic blood pressure (Table 4)(135-137). Gene Discovery on Single-Intervention 
(MCS) Cohort (n=29). Next, we conducted a study with 29 patients undergoing MCS surgery from 2012 to 2014. 
PBMC samples were collected one day before surgery (day -1). Clinical data was collected on day -1, day 8 and 
one year postoperatively. Patients were classified by SOFA and MELD-XI scores into improving (both scores 
improved from day -1 to day 8, n=17) and not improving (either one or both scores did not improve from day -1 
Table 4. Twelve clinically established prognostic 
variables. Gray indicates four parameters; p-
value <0.10 used for model development. 
Parameter
Estimate
Std. Error
t value
Pr (>|t|)
Age
-0.0068
0.0027
-2.540
0.012
SOFA Score
-0.0141
0.0189
-0.744
0.4578
GCS
0.1874
0.3366
0.557
0.5785
Temperature
0.0037
0.0610
0.061
0.9514
Resp. Rate
-0.2517
0.1277
-1.972
0.0503
Bilirubin
-0.0608
0.0398
-1.527
0.1286
WBC
-0.1456
0.0726
-2.005
0.0467
Glucose
0.0011
0.0009
1.137
0.257
Heart Rate
0.0016
0.0020
0.815
0.4165
Max. Systolic
-0.0003
0.0022
-0.128
0.8984
Min. Diastolic
0.0046
0.0027
1.732
0.0851
INR
0.0561
0.0478
1.173
0.2426
Table 3. Overall Study Timeline. 

to day 8, n=12). RNA sequencing was performed on purified mRNA and analyzed 
using Next Generation Sequencing (NGS) Strand. Differentially expressed genes 
were identified by Mann-Whitney test with Benjamini-Hochberg false discovery 
rate (FDR) correction. Preoperative differentially expressed genes were used to 
construct a support vector machine algorithm to predict improving vs. non-
improving membership. Out of 28 patients alive 8 days postoperatively, one-year 
survival was 88% in the improving group and 27% in the non-improving group. We 
identified 28 preoperative differentially expressed genes between groups, with an 
average 93% prediction accuracy of group membership. Out of 105 genes 
identified preoperatively between one-year survivors and non-survivors, 12 genes 
overlapped with the 28 predictive genes (AGRN, SAP25, ANKRD22, BATF2, 
FRMD6, HEXA-AS1, DNM1P46, NAPSA, KIR2DL4, RHBDD3, TIMP3, and 
BCORP1). These predictive genes are all implicated in inflammation and immune 
system regulation (138-162). The 12 genes and 4 clinical parameters are 
summarized in Table 5. The 12 genes demonstrated similar gene expression 
within 
short-term 
recovery and one-year 
survivor groups and 
were 
consistently 
differentially expressed 
between both groups. 
Average 
gene 
expression profiles are 
displayed in Figure 3. 
We concluded that in 
advanced heart failure 
patients following MCS 
implantation, 
preoperative 
gene 
expression 
profiling 
predicts early changes 
in 
organ 
function 
scores and correlates 
with 
long- 
term 
outcomes and gene 
expression lends itself 
to 
outcome 
prediction(90). The 12, 
28, and 105 gene lists 
have been patented by 
UCLA. 
Algorithm 
Prototype 
Development. Based 
on these data, we 
constructed 
a 
prototype algorithm to 
convert 
gene 
expression values of 
the 12 genes and 4 
clinical parameters into 
a numerical survival 
prediction 
estimate. 
The algorithm is written  
as 
follows: 
 
P =
e(β + α1X1+ … αnX𝑛)
1+e(β + α1X1+ … αnX𝑛) , β 
and α estimates were 
Organ Recovery
1-Year Survival
Pre-Op Gene Expression
Figure 
3. 
Average 
gene 
expression 
profiles 
of 
12 
Parameter
Average of
Survivors
(n=18)
Average of
Non-Survivors
(n=11)
Description
Transcriptomic  Data
BCORP1
-0.22 2.25
0.64 1.61
BCORP1 is a pseudogene. Although not fully functional, pseudogenes may be functional,
similar to other kinds of noncoding DNA, which can perform regulatory functions.
BATF2
-1.24 1.41
-0.19 0.86
BATF2 belongs to a class of transcription factors that regulate various immunological
functions and control the development and differentiation of immune cells. BATF2 controls
Th2 cell functions and lineage development of T lymphocytes. Following infection, BATF2
participates in the development of and differentiation of CD8+ thymic conventional dendritic
cells. BATF2 is involved in IFN signaling and positive regulation of immune responses by
altering expression of cytokines and chemokines.
FRMD6
-0.81 1.10
-0.81 1.10
FRMD6 has been linked to various complex diseases, such as asthma, Alzheimer's
disease, lung cancer, and colorectal cancer. It regulates cell proliferation and apoptosis and
is thought to have tumor suppressor properties. FRMD6 mediates Vitamin D inhibition of
immune cell proliferation.
KIR2DL4
-0.63 1.73
0.62 1.13
KIR2DL4 codes for transmembrane glycoproteins expressed by natural killer (NK) cells and
subsets of T cells. KIR2DL4 inhibits the NK cell activity and reduces T cell death in Sezary
syndrome. KIR2DL4 recognizes human leukocyte antigen G and has been suggested as a
useful diagnostic biomarker of neoplastic NK-cell proliferations.
HEXA-AS1
0.46 0.85
-0.75 1.21
HEXA-AS1 is an antisense RNA Gene and is affiliated with the lncRNA class. Diseases
associated with HEXA-AS1 include Tay-Sachs Disease.
DNM1P46
-1.25 1.09
0.58 1.37
DNM1P46 is a pseudogene associated with diagnosing, treating, and monitoring chronic
inflammatory response syndrome. DNM1P46 is a long noncoding RNA that is mediated by
p53, a gene that codes for a protein that regulates the cell cycle and hence functions as a
tumor suppression. p53 was activated in DNM1P46 during apoptotic pathways.
RHBDD3
0.24 1.28
-1.43 1.19
RHBDD3 is a member of the rhomboid family of proteases that suppresses the activation of
dendritic cells (DCs) and production of interleukin 6 (IL-6) triggered by Toll-like receptors.
The rhomboid proteins are involved in signaling via the receptor for epidermal growth factor,
mitochondrial homeostasis and parasite invasio. RHBDD3 negatively controls the activation
of DCs and maintains the balance of regulatory T cells and TH17 cells by inhibiting the
production of IL-6 by DCs, thus contributing to the prevention of autoimmune diseases.
SAP25
0.79  1.05
-0.59 1.13
SAP25 is a member of the nucleocytoplasmic shuttling proteins that are located in
promyelocytic leukemia nuclear bodies. Promyelocytic leukemia
nuclear bodies are
implicated in diverse cellular functions, such as gene regulation, apoptosis, senescence,
DNA repair, and antiviral response.
NAPSA
0.51 1.16
-0.35 1.06
NAPSA is a pronapsin gene, which may have a considerable diagnostic value as a marker
for primary lung cancer. NAPSA was detected in a subset of poorly differentiated papillary
thyroid carcinomas and anaplastic carcinomas.
TIMP3
0.58 1.40
-0.60 1.01
TIMP3 is an extracellular matrix-bound protein, which regulates matrix composition and
affects tumor growth. TIMP3 suppresses tumor inactivation in cancer by mechanisms of
invasion and angiogenesis. TIMP-3 downregulation is associated with aggressive non-small
cell lung cancer and hepatocarcinoma cells, as compared with less invasive or normal lung
and liver cells. It mediates VEGF by blocking the binding of VEGF to VEGF receptor-2,
inhibiting downstream signaling, and prevents angiogenesis. These inhibitive properties
seem to be independent of its matrix metalloproteinases-inhibitory activity.
AGRN
-1.81 1.52
0.17 0.60
AGRIN is evolutionarily conserved in the extracellular matrix. Its intracellular processes
include proliferation, apoptosis, migration, motility, autophagy, angiogenesis, tumorigenesis,
and immunological responses. AGRIN interacts with the α/β-dystroglycan receptor in the
formation of immunological synapses with lymphocytes and aids in activation and
maintainence of monocyte cell survival downstream in an α-dystroglycan dependent
manner. The AGRIN LG3 domain has been used as a biomarker for detection of
prematurely ruptured fetal membranes.
ANKRD22
-0.81 1.87
0.17 0.49
ANKR22, involved in protein lipid modification, has been patented as a possible biomarker
for several types of cancers to identify patient responses to cancer immunotherapy.
Clinical Data
Age
54 15
61 14
Age is associated with immune system function, overall health and survival.
Min. DBP
49 8
51 13
Minimum diastolic blood pressure has been shown to have an inverse relationship with
mortality rates in patients ages ≥85 years old.
WBC
11.19 4.21
11.01 4.40
Total white blood cell count is a prognostic indicator of long-term survival, reflects immune
system inflammatory activation, and may indicate progressive multi organ dysfunction.
Resp. Rate
20 4
23 5
Respiratory rate serves as a key marker in patients experiencing sepsis or systemic
inflammation.
Table 5. 12 genes and 4 clinical parameters used in preliminary model development.  

calculated using a LASSO logistic regression model(163). 
Youden’s index was used to establish a cutoff point at 0.749, with 
values >0.749 predicting survivor status and ≤0.749 predicting 
non-survivor status. Predictive Power on Independent Cohort 
Undergoing Various Advanced Heart Failure Therapies (n=48). 
Next, we validated the predictive accuracy of the prototype 
algorithm that contains the previously identified 12 genes and 4 
clinical parameters, in independent samples from advanced heart 
failure patients undergoing various therapies (n=48, including 
guidelines-directed medical therapy, heart transplant, MCS, and 
transcatheter 
aortic 
valve 
replacement). 
Performance 
characteristics for the algorithm on the independent cohort are 
summarized in Table 6. On this larger, more heterogeneous 
cohort, our test had 71% sensitivity, 90% specificity, 56% positive 
predictive value, and 95% negative predictive value. Test 
Performance on Stage 1 Proof-of-Concept Pooled Cohort 
(n=77).  Next, we pooled the 29 patients used for gene selection 
with the 48 patients undergoing various advanced heart failure 
therapies to perform the following analysis (Table 7). One-year 
survival was 77% (59/77) in this combined cohort. ComBat was 
used for batch effect correction(164-166). Univariate analysis 
between 1-year survival status and the 12, 28, and 105 gene lists 
confirmed that all significantly differentially expressed genes in this 
combined cohort exhibited the same directionality as in the original 
cohort used for initial gene discovery(90). Various logistic 
regression 
models 
were 
constructed 
using 
the 
LASSO 
method(163) and measures of predictive performance such as 
AUCs are summarized in Table 8. Prediction of 1-year survival 
using 4 clinical parameters alone (age, white blood cell count, 
respiratory rate, and minimum diastolic blood pressure) achieved 
an AUC=0.69. Adding the 12-gene candidates to the clinical model 
without ComBat correction improved the 1-year survival prediction 
to AUC=0.90. The 12-gene list with ComBat correction and clinical 
data led to an observed AUC=0.86. We concluded that: 1) the 
direction of gene expression in the pooled 77 patient cohort was 
similar to that of the original 29 patients used for gene discovery, 
2) batch effects played a minor role in data performance and 3) the 
addition of 12 genes meaningfully improved 1-year survival 
prediction of outcomes in patients undergoing diverse advanced 
heart failure therapies compared to the clinical model alone. 
Prototype Score Construction (n=77). Next, from the prototype 
raw numerical survival prediction algorithm score, we calculated a 
prototype clinical score for each of the 77 patients using non-batch 
adjusted data and risk stratified into 5 quintiles (Table 9). Note that, 
consistent with the UCLA patent application language guided by 
the intended clinical use, higher prototype raw scores translate into 
higher clinical scores. Higher deciles indicated binning of higher 
clinical scores and a higher likelihood of 1-year survival post-
advanced heart failure therapy. The pooled survival rate for the top 
6 deciles was 0.96 (47/49), and the survival rate for the pooled 
lower 4 deciles was 0.43 (12/28).  
Multi-Omics Analysis of Exercise-Induced Changes in Health and Heart Failure (Study 2). In order to 
assess the relationship between cf-mtDNA and the PBMC transcriptome, PBMC immunophenotypes, cytokines 
and clinical exercise physiology parameters in health and disease, we characterized the temporal dynamics of 
the immune system under exercise and resting conditions in healthy volunteers (n=4) and patients with heart 
failure (n=16). We performed integrated analysis of genomic, transcriptomic, and proteomic data, as well as 
Table 8. Modeling and test algorithm 
predictive performance on combined cohort 
Model 
AUC 
% Increase 
in Accurate 
Survival 
Prediction 
Clinical (age, WBC, RR, MDBP) 
0.69 
-- 
12 genes (raw) + clinical 
0.90 
68% 
12 genes (COMBAT) + clinical 
0.86 
55% 
Table 6. Performance characteristics of 
prototype test. 
Cut Off 0.749 
(Youden’s Index)
29 Patients
(Gene Discovery)
48 Patients
(Independent 
Validation)
Sensitivity
100%
71%
Specificity
72%
90%
Positive Pred. Value
69%
56%
Negative Pred. Value
100%
95%
Table 7. Demographic data of 77 patients in 
Proof-of-Concept Study. 
Table 9. Predictive performance of risk-
stratified deciles.

clinical data from the multivariate cardiopulmonary exercise panel. All participants underwent exercise testing. 
Blood samples were collected at 3 time-points: before (TP1), during (TP2), and 1-hour after peak oxygen uptake 
(VO2) (TP3). Genome Data. To understand the cfDNA dynamics during exercise testing, we performed total 
cfDNA as well as cf-mtDNA fragment quantification correlation analysis with VO2 max and percent predicted of 
VO2 max for all 20 participants at 3 timepoints (Figure 4).  We observed a positive correlation between 
cardiorespiratory performance (as measured by peak VO2 and percent predicted of peak VO2) and cf-mtDNA 
at TP2. A negative correlation was found between cardiorespiratory performance and cf-mtDNA, beginning at 
the time of peak VO2 and more pronounced 1 hour after peak VO2. Numbers of cf-mtDNA fragments decreased 
after peak VO2 in healthy volunteers and increased in heart failure. Heart failure patients had pronounced cf-
mtDNA fragments one hour after peak VO2. Numbers of cf-mtDNA fragments decreased after peak VO2 in 
healthy volunteers and increased in heart failure. Transcriptome Data. In both groups, a differential relationship 
was observed between cf-mtDNA fragment number and expression of 9 PBMC transcripts associated with 
reactive oxygen species (Figure 5). Proteome Data. A differential relationship was found between the reactive 
oxygen species related PBMC transcripts and pro-inflammatory cytokines of both groups (Figure 6). These 
preliminary data suggest that temporal dynamics of cf-mtDNA correlates with gene expression and cytokine 
production and that these patterns differ in healthy individuals and those with chronic disease. 
 
SPECIFIC AIMS 
AIM 1: CORRELATE MULTIOMIC DATA WITH OUTCOMES. Hypothesis and Objectives. We hypothesize 
that various clinical COVID-19 disease outcome phenotypes correlate with molecular data and clinical patterns 
and are associated with immunologic pathways. Our first objective is to identify and confirm mono-dimensional 
genomic, transcriptomic, proteomic and clinical parameters that correlate with outcomes (Aim 1.1). Outcomes 
are defined as new onset or worsening frailty, single organ dysfunction, multiorgan dysfunction, and death within 
one year (primary endpoint) and at time of discharge (secondary endpoint). Since genomics, transcriptomics and 
proteomics often have complementary roles, our second objective is to perform multi-omics correlation analysis 
(Aim 1.2). Our third objective is to perform Weighted Gene Co-Expression Network Analysis (WGCNA) and 
pathway analysis to understand the role of the genes involved in the inflammatory response during SARS-CoV-
2 infection (Aim 1.3).  
Aim 1.1: Single-omic identification of candidate genomic, transcriptomic, proteomic and clinical 
parameters that correlate with outcomes. Objective. Our objective is to identify single-omic parameters 
(genomic, transcriptomic, proteomic) and clinical data that correlate with COVID-19 outcomes. We will perform 
exploratory analyses on the first 100 samples and confirm these parameters on the following 200 samples 
(Figure 2). Genomic Analysis. Raw cfDNA sequences will be aligned to HG38 and cf-mtDNA to HG38_MT_only 
(Strand NGS 3.3.1). ComBat will be used for batch effect correction(164-166). We will calculate the number of 
cf-mtDNA fragments in each sample. Transcriptomic Analysis. Raw transcriptome data will be aligned (HG19) 
and normalized (DESeq), batch effect corrected by ComBat(164-166) and filtered by expression (20th to 100th 
percentile) (Strand NGS (Avadis)). Differentially expressed genes between positive and negative endpoint 
groups will be identified by non-parametric statistics (Mann-Whitney test with Benjamini-Hochberg correction). 
We will use a more relaxed criteria of FDR=0.1 and fold change of at least 2.0 to identify significant genes as 
previously described by our group(90, 167, 168). Immunophenotyping Analysis. We will identify COVID-19 
HV (n=4)
HF (n=16)
PARS2
DUSP2
CCR2
TNFAIP3
NFKBIA
PER1
FPR2
HSPA12B
MAFF
1   2   3
1   2   3
Figure 5. Heat map of Pearson correlation of 9 
differentially expressed genes involved in oxidative 
stress signalling by reactive oxygen species and 
fragment number of cell free mitochondrial DNA at 
timepoint 1, 2, and 3 in healthy volunteers (HV) and 
heart failure patients (HF). 
TP1
TP2
TP3
Healthy Volunteers (n=4)
Heart Failure (n=16)
Figure 6. Heat map of Pearson correlation of 9 differentially 
expressed genes involved in oxidative stress signaling by 
reactive oxygen species (from top to bottom: PARS2, DUSP2, 
CCR2, TNFAIP3, NFKBIA, PER1, FPR2, HSPA12B, and 
MAFF) and 10 differentially expressed cytokines (from left to 
right: FGF-2, G-CSF, fractalkine, MCP-3, IL-13, IL-17A, IL-1a, 
IL-4, IL-6, TNFb, and VEGF) at timepoint (TP) 1, 2, and 3  in 
healthy volunteers and heart failure patients. 
Figure 4. Trends in VO2 
max, % predicted VO2 max, 
and genomic data. Median 
number 
of 
cf-mtDNA 
fragments 
in 
healthy 
volunteers 
(HV, 
white 
boxplots) and heart failure 
(HF, grey boxplots). 

antigen exposed PBMC subsets for comparative analysis to genome, transcriptome and cytokine data. Analyses 
will be performed using the FCS Express V4 analysis software. Cytokine Analysis. The median fluorescent 
intensity data will be processed using a 5-parameter logistic fitting method to calculate cytokine concentrations 
by Milliplex analyst 3 software. We expect to achieve test results with 3-4% intra-plate coefficient of variation and 
less than 15% inter-plate coefficient of variation. Interpretation of Results. We expect COVID-19 patients with 
new onset or worsening frailty, organ dysfunction and death within one year to have more pronouncedly altered 
inflammatory profiles than patients who recover. We expect to find differentially expressed genes and differences 
in cf-mtDNA patterns, leukocyte subpopulations and cytokine production between patients with positive and 
negative endpoint outcomes.  
Aim 1.2: Multi-omics correlation analysis. Objective. Genomics, transcriptomics and proteomics often have 
complementary roles to jointly perform a certain biological function and create a specific observable phenotype. 
Our objective in Aim 1.2 is to establish a correlation pattern between the multi-omics data and the outcome data 
in symptomatic COVID-19 patients. Bioinformatics. Multi-omics analyses will be performed on the first 300 
samples to correlate outcome with cfDNA patterns, PBMC coding and non-coding mRNA expression, PBMC 
subpopulations and cytokine profiles (Figure 2). We will construct mixed effects regression models for each 
candidate feature. For continuous markers (cytokines) we will construct linear models. All logistic regression 
models will be stratified by primary composite outcome (single organ dysfunction, multiorgan dysfunction, and 
death). For count markers (sequencing), we will run negative binomial models. We will use the Benjamini-
Hochberg procedure to control the FDR at alpha 0.05. If significant interaction effects are found after FDR 
adjustment, post hoc comparisons will be estimated using model contrasts and summarized with 95% confidence 
intervals estimated from the model. Interpretation of Results. Multi-omics correlations will add essential 
information in addition to the single omics analysis. We expect that the candidate genomic, transcriptomic, 
proteomic and clinical predictors will have distinct correlations within the two endpoint groups. This information 
can help characterize the biology of the immune response to COVID-19 and identify the most promising members 
for the prediction algorithm. 
Aim 1.3: WGCNA and pathway analysis. Objective. The role of the genes involved in the inflammatory 
response during SARS-CoV-2 infection is unclear. To better understand co-expression of genes and their 
biological role in patients with symptomatic COVID-19, we will perform WGCNA and pathway analysis. To dissect 
the pro- and anti-inflammatory responses during SARS-CoV-2 infection with an non-supervised strategy, we will 
use WGCNA(170). WGCNA provides a non-supervised systematic method to identify co-expressed genes and 
pathways linked to the primary outcome. Bioinformatics. Normalized transcriptome data from the first 300 
samples will be stratified by composite endpoint, batch effect corrected by ComBat and filtered by variance and 
entropy criteria to remove uninformative transcripts (Figure 2)(164-166, 169). This approach offers a powerful 
way to reduce dimensionality while enhancing biological interpretability. The co-expressed modules and their 
respective eigen-genes will be interpreted in their relationship to clinical and biological features. The clinical 
relevance of the modules will be inferred using a linear mixed effect model. The biological relevance of the 
modules will be deduced using gene ontology (GOSim(171, 172)) and pathway analysis (Strand NGS (Avadis)). 
Interpretation of Results. The results from WGCNA will offer a useful framework for relational and mechanistic 
reasoning. Our model will provide an integrative representation of leukocyte biology and related pathways during 
the COVID-19 inflammatory response. We anticipate that this strategy will enhance identification of candidate 
entities for the development of the prediction algorithm for adverse COVID-19 short- and long-term outcome 
events in Aim 2. 
 
AIM 2: TRAIN AND VALIDATE ALGORITHM FOR OUTCOME PREDICTION  
Hypothesis and Objectives. We hypothesize that our algorithm will predict COVID-19 short- and long-term 
outcomes more accurately than clinical parameters alone. Our objectives are to train the algorithm in a mixed 
US-high risk COVID-19 population (Aim 2.1) and validate the predictive algorithm on an independent mixed US-
high risk COVID-19 population (Aim 2.2).  
Aim 2.1: Train algorithm for outcome prediction. Objective. Our objective is to train and test our algorithm 
in a mixed US COVID-19 population, which will improve prediction of adverse short- and long-term outcomes(92, 
173). Bioinformatics. Parameter Selection. Model development will largely follow the modeling reported in the 
preliminary data section and will be constructed using significant entities identified in the marker screening phase 
(Aim 1). Since VA and civilian patients may have different co-morbidity and risk profiles, these models will also 
include a term for whether the patient is from the VA or civilian setting. This term, if significant, will allow flexibility 
in our models to make different predictions based upon known characteristic differences between VA and civilian 
patient populations. Power calculations. The first 300 samples (240 endpoint negative and 60 endpoint positive) 

will be used for model building (Figure 2). The overall sample size will support the following: 1) This sample size 
will be sufficient to include approximately 10 clinical features based on conventional rules of thumb suggesting 
that we should have 10-15 subjects in the less frequent outcome category per variable in a logistic regression 
model; 2) This will allow us to estimate the AUC for the model with a precision of approximately 0.032 (based on 
the 95% confidence interval for the AUC and an overall AUC of at least 0.8); 3) Provide 80% power for detecting 
delta AUCs between pairs of models (see below) of 0.1 assuming that the models will have a correlation of 0.5 
and AUCs of ~0.8. Logistic regression modeling. Logistic regression modeling. We will assess the 
algorithm by constructing three separate logistic regression models, one using clinical factors alone as our 
baseline model, one using clinical factors plus the significant omics markers from the screening phase 
(genomics, transcriptomics, and proteomics parameters), and one using clinical factors plus a simplified list of 
significant omics features (e.g. bedside, rapid test, easy to obtain). Each of the three models will have variable 
selection and coefficients estimated by the LASSO method and validation results and comparisons between the 
three models performance (AUC) will be validated in Aim 2.2. Model building optimization by cross-
validation. Ten-fold cross-validation will be used to estimate model performance in terms of AUC. The AUCs 
will be compared between models with DeLong’s test for paired AUCs. Interpretation of Results. We expect to 
derive a multi-omics algorithm that will predict outcomes of COVID-19 patients more accurately than clinical data 
alone.  
Aim 2.2: Independently validate predictive algorithm for outcome prediction. Objective. Our objective is 
to validate the algorithm in an independent randomly selected mixed US COVID-19 population who were not 
used for algorithm development(173). Bioinformatics. We will validate the performance of the prototype test 
algorithm to predict the composite endpoints in an independent cohort of 200 patients (Figure 2). Rigorous 
testing of the analytical performance of the test will identify and quantify the technical variations of the test 
performed on the patient samples and will be done in a blinded fashion, without knowledge of the clinical status 
or treatment outcomes of the patients whose data was obtained. For model testing, the sample size of 200 (160 
endpoint negative and 40 endpoint positive) will provide precision of +/- 0.066 for the testing AUC as well as an 
80% power to detect differences in AUCs between models of 0.12, when comparing models based on clinical 
features alone and models combining clinical and molecular features. Finally, with this sample size we will be 
able to estimate the precision of the sensitivity and specificity (based on the width of the 95% confidence 
intervals) between 0.0545 to 0.11 depending on the observed values of those performance characteristics. We 
plan to make the raw data and the algorithm publically available as a web-based free tool for further scientific 
development by the international scientific community. Interpretation of Results. We expect that the algorithm 
will confirm the desired clinical test performance, i.e. increase accuracy of one-year primary endpoint (new onset 
or worsening frailty, organ dysfunction or death) prediction of at least AUC 0.12. 
 
POTENTIAL PROBLEM AREAS & ALTERNATIVE METHODS/APPROACHES  
Pitfall #1: The arrival of evidence-based antiviral therapies (e.g., remdesivir) may alter the clinical course of the 
disease(174), however this has not yet been established and while some studies suggest remdesivir provides a 
modest benefit, interim analysis of the large WHO SOLIDARITY trial suggest no mortality benefit (175). 
Alternative: We will model clinical trajectories after initiation of anti-viral therapy by dichotomizing the study 
cohort into two groups: short versus long recovery time. By comparing these groups, we expect to identify an 
effective prediction algorithm from our baseline dataset for higher (shorter time to recovery) versus lower 
functional recovery potential (longer time to recovery). It will be important to analyze for corticosteroid treatment, 
which has become routine treatment for severe COVID-19(34), and was recognized as a potential contributor 
(e.g., steroid myopathy) to long-term sequelae in the SARS epidemic(176).  
Pitfall #2: The implementation of effective vaccine strategies may alter the recruitment modeling for our study. 
Alternative: Rapid vaccine implementation is a major initiative in the fight against COVID-19 and there are 
several parallel efforts(177-181). Thus, it is quite likely that an effective vaccine against COVID-19 will be 
implemented during the study period. While it is difficult to predict with precision when vaccination levels will 
provide effective herd immunity and thus lead to a significant reduction in cases, it is unlikely that this will happen 
in less than 1.5 years(178). It is also unlikely that a vaccine strategy will be so effective that it will prevent all 
disease or eliminate the disease entirely. The latter has happened with only a single human pathogen to date, 
and the worldwide effort to eliminate smallpox took well over two decades of sustained effort. It is currently not 
thought to be possible to effectively eradicate an infection that has an animal reservoir (like SARS-CoV-2). Our 
goal is to enroll 3.3 patients/week from 7 sites over 42 months (July 1, 2021 through December 31, 2024). Given 
the experience of the sites involved and the location of these sites in major metropolitan areas prone to high 
rates of COVID-19, we expect that the majority of subjects will be enrolled relatively quickly during the first half 

of the enrollment period. Thus, we expect to be able to complete the study as designed even if there is rapid 
implementation of an effective vaccine strategy that affects our ability to recruit subjects in the latter years of the 
project.  
Pitfall #3: Our predictive algorithm does not perform well on certain subgroups. Alternative: We think this is 
unlikely because the biological responses (or lack thereof) that lead to poor outcomes are likely to be similar 
among people with differences in demographic and underlying diseases (182, 183). However, in the validation 
process, we will look at important subgroups to see if there are issues with performance. While we have guarded 
against a sub-group imbalance by proposing a dual strategy, combining a civilian population in one of the 
country’s largest metropolitan areas and a representative National Veteran’s population, it is possible that our 
algorithm may not be as accurate in predicting outcome in subgroups (e.g., women versus men). If this is the 
case, we will train the algorithm on the specific subgroup only and determine whether we identify different 
parameters. Using this approach, we may also uncover clues pointing to underlying pathogenic mechanisms in 
SARS-CoV-2 infection. 
Pitfall #4: Currently, there is not enough data available to develop a 
clinical phenotype scale for COVID-19. Alternative: We will have a 
comprehensive outcome list and will be able to explore a more 
detailed way of characterizing COVID-19 outcomes by the end of our 
study. In order to better define clinical phenotypes, we will correlate 
molecular and clinical data from our 500 patients to a tentative 10-
point COVID-19 Functional Recovery Scale, modified from the WHO 
8-point ordinal scale (Figure 7)(44, 174). Since the COVID-19 
landscape is continually changing, we will modify this scale as 
necessary. The COVID-19 Functional Recovery Scale scores will be 
assessed by tele-medicine or in-person visits at baseline, discharge (secondary endpoint) and one-year (primary 
endpoint). An ordinal scale of continuous phenotypes may provide better power for outcome prediction than a 
scale that only detects presence or absence of adverse outcome.  
Pitfall #5: SARS-CoV-2 genetic drift may affect outcomes. Alternative: As SARS-CoV-2 spread geographically 
and temporally, its genetic diversity increased (184, 185), and several genetic subgroups have already been 
defined. Certain mutations have been associated with higher mortality (186, 187), while others demonstrate 
improved binding to the respiratory tract (188, 189). It is currently not clear how viral evolution has affected the 
development or prevalence of sequelae and outcomes in general. To examine this crudely, we will monitor trends 
over the study period (i.e., early versus late). However, other time-specific effects could confound this analysis. 
The SUPERNOVA study is collecting respiratory samples for SARS-CoV-2 genome analysis. By integrating this 
information, we could look for differences based on genetic lineages. 
Pitfall #6: Impact of influenza or other seasonal respiratory viruses on COVID-19. Alternative: While co-
infection with SARS-CoV-2 and influenza virus have been described (190, 191), it remains unclear how influenza 
or other seasonal respiratory viruses will impact COVID-19. Additional studies are needed to establish whether 
viral co-infections in SARS-CoV-2 patients could potentially drive viral interference or otherwise influence 
disease outcomes (192, 193). It is possible that steps taken to mitigate spread of SARS-CoV-2, may significantly 
reduce influenza rates (194), as appears to have happened in Australia (195). However, if both viruses are 
prevalent, co-infection is certainly possible and may be common. All patients admitted to the enrolling sites with 
suspecting respiratory viral infection, will be tested for both SARS-CoV-2 and influenza. We will include co-
infected patients in our analyses. If we enroll enough patients with infection with SARS-CoV-2 and influenza or 
another seasonal respiratory virus, we will further analyze this subset. 
 
SUMMARY  
We propose to develop a tool that predicts adverse short- and long-term (within one year) outcomes for 
hospitalized COVID-19 patients. We hypothesize that new onset or worsening frailty, organ dysfunction and 
death from COVID-19 within one year after COVID-19 diagnosis can be accurately predicted by our algorithm 
that incorporates baseline genomic, transcriptomic, proteomic and clinical data. We will test our hypothesis in a 
mixed US COVID-19 population. We anticipate that a prediction test that performs well in the demographically 
and geographically diverse patient population that we will recruit will also demonstrate powerful prediction 
precision in the general population. Event prediction will assist clinicians in making tailored treatment plans. 
Future implementation of our tool into clinical settings may decrease morbidity and mortality, enhance patient 
quality of life and improve healthcare cost-effectiveness.  
 
Figure 7 Tentative Ordinal COVID-19 Functional 
Recovery Scale. 

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