R01AI159946
Project Grant
Overview
Grant Description
Multi-Dimensional Outcome Prediction Algorithm for Hospitalized COVID-19 Patients - Project Summary
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. Several high-risk COVID-19 populations have been identified. Older adults, males, persons of color, and those with certain underlying health conditions (e.g., diabetes mellitus, obesity, etc.) are at higher risk for severe disease from COVID-19.
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, 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, especially for high-risk COVID-19 populations.
We hypothesize 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. 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 have chosen to test our hypothesis in a population reflecting US demographics that is at increased risk of adverse outcomes from COVID-19. We will enroll patients, broadly reflecting US demographics, from a hospitalized civilian population in one of the country's largest metropolitan areas and a representative national veteran's population.
We anticipate that a prediction test that performs well in this hospitalized patient group will: help guide triaging and treatment decisions and, therefore, reduce morbidity and mortality rates, enhance patient quality of life, and improve healthcare cost-effectiveness. More accurate prognostic information will also assist clinicians in framing goals of care discussions in situations of likely 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.
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. Several high-risk COVID-19 populations have been identified. Older adults, males, persons of color, and those with certain underlying health conditions (e.g., diabetes mellitus, obesity, etc.) are at higher risk for severe disease from COVID-19.
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, 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, especially for high-risk COVID-19 populations.
We hypothesize 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. 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 have chosen to test our hypothesis in a population reflecting US demographics that is at increased risk of adverse outcomes from COVID-19. We will enroll patients, broadly reflecting US demographics, from a hospitalized civilian population in one of the country's largest metropolitan areas and a representative national veteran's population.
We anticipate that a prediction test that performs well in this hospitalized patient group will: help guide triaging and treatment decisions and, therefore, reduce morbidity and mortality rates, enhance patient quality of life, and improve healthcare cost-effectiveness. More accurate prognostic information will also assist clinicians in framing goals of care discussions in situations of likely 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.
Funding Goals
TO ASSIST PUBLIC AND PRIVATE NONPROFIT INSTITUTIONS AND INDIVIDUALS TO ESTABLISH, EXPAND AND IMPROVE BIOMEDICAL RESEARCH AND RESEARCH TRAINING IN INFECTIOUS DISEASES AND RELATED AREAS, TO CONDUCT DEVELOPMENTAL RESEARCH, TO PRODUCE AND TEST RESEARCH MATERIALS. TO ASSIST PUBLIC, PRIVATE AND COMMERCIAL INSTITUTIONS TO CONDUCT DEVELOPMENTAL RESEARCH, TO PRODUCE AND TEST RESEARCH MATERIALS, TO PROVIDE RESEARCH SERVICES AS REQUIRED BY THE AGENCY FOR PROGRAMS IN INFECTIOUS DISEASES, AND CONTROLLING DISEASE CAUSED BY INFECTIOUS OR PARASITIC AGENTS, ALLERGIC AND IMMUNOLOGIC DISEASES AND RELATED AREAS. PROJECTS RANGE FROM STUDIES OF MICROBIAL PHYSIOLOGY AND ANTIGENIC STRUCTURE TO COLLABORATIVE TRIALS OF EXPERIMENTAL DRUGS AND VACCINES, MECHANISMS OF RESISTANCE TO ANTIBIOTICS AS WELL AS RESEARCH DEALING WITH EPIDEMIOLOGICAL OBSERVATIONS IN HOSPITALIZED PATIENTS OR COMMUNITY POPULATIONS AND PROGRESS IN ALLERGIC AND IMMUNOLOGIC DISEASES. BECAUSE OF THIS DUAL FOCUS, THE PROGRAM ENCOMPASSES BOTH BASIC RESEARCH AND CLINICAL RESEARCH. SMALL BUSINESS INNOVATION RESEARCH (SBIR) PROGRAM EXPANDS AND IMPROVES PRIVATE SECTOR PARTICIPATION IN BIOMEDICAL RESEARCH. THE SBIR PROGRAM INTENDS TO INCREASE AND FACILITATE PRIVATE SECTOR COMMERCIALIZATION OF INNOVATIONS DERIVED FROM FEDERAL RESEARCH AND DEVELOPMENT, TO INCREASE SMALL BUSINESS PARTICIPATION IN FEDERAL RESEARCH AND DEVELOPMENT, AND TO FOSTER AND ENCOURAGE PARTICIPATION OF SOCIALLY AND ECONOMICALLY DISADVANTAGED SMALL BUSINESS CONCERNS AND WOMEN-OWNED SMALL BUSINESS CONCERNS IN TECHNOLOGICAL INNOVATION. THE SMALL BUSINESS TECHNOLOGY TRANSFER (STTR) PROGRAM STIMULATES AND FOSTERS SCIENTIFIC AND TECHNOLOGICAL INNOVATION THROUGH COOPERATIVE RESEARCH AND DEVELOPMENT CARRIED OUT BETWEEN SMALL BUSINESS CONCERNS AND RESEARCH INSTITUTIONS, TO FOSTER TECHNOLOGY TRANSFER BETWEEN SMALL BUSINESS CONCERNS AND RESEARCH INSTITUTIONS, TO INCREASE PRIVATE SECTOR COMMERCIALIZATION OF INNOVATIONS DERIVED FROM FEDERAL RESEARCH AND DEVELOPMENT, AND TO FOSTER AND ENCOURAGE PARTICIPATION OF SOCIALLY AND ECONOMICALLY DISADVANTAGED SMALL BUSINESS CONCERNS AND WOMEN-OWNED SMALL BUSINESS CONCERNS IN TECHNOLOGICAL INNOVATION. RESEARCH CAREER DEVELOPMENT AWARDS SUPPORT THE DEVELOPMENT OF SCIENTISTS DURING THE FORMATIVE STAGES OF THEIR CAREERS. INDIVIDUAL NATIONAL RESEARCH SERVICE AWARDS (NRSAS) ARE MADE DIRECTLY TO APPROVE APPLICANTS FOR RESEARCH TRAINING IN SPECIFIED BIOMEDICAL SHORTAGE AREAS. IN ADDITION, INSTITUTIONAL NATIONAL RESEARCH SERVICE AWARDS ARE MADE TO ENABLE INSTITUTIONS TO SELECT AND MAKE AWARDS TO INDIVIDUALS TO RECEIVE TRAINING UNDER THE AEGIS OF THEIR INSTITUTIONAL PROGRAM.
Grant Program (CFDA)
Awarding / Funding Agency
Place of Performance
Los Angeles,
California
90095
United States
Geographic Scope
Single Zip Code
Related Opportunity
Analysis Notes
Amendment Since initial award the total obligations have increased 357% from $747,559 to $3,417,860.
Los Angeles University Of California was awarded
COVID-19 Outcome Prediction Algorithm for High-Risk Patients
Project Grant R01AI159946
worth $3,417,860
from the National Institute of Allergy and Infectious Diseases in July 2021 with work to be completed primarily in Los Angeles California United States.
The grant
has a duration of 5 years and
was awarded through assistance program 93.855 Allergy and Infectious Diseases Research.
The Project Grant was awarded through grant opportunity NIH Research Project Grant (Parent R01 Clinical Trial Not Allowed).
Status
(Ongoing)
Last Modified 7/3/25
Period of Performance
7/8/21
Start Date
6/30/26
End Date
Funding Split
$3.4M
Federal Obligation
$0.0
Non-Federal Obligation
$3.4M
Total Obligated
Activity Timeline
Subgrant Awards
Disclosed subgrants for R01AI159946
Transaction History
Modifications to R01AI159946
Additional Detail
Award ID FAIN
R01AI159946
SAI Number
R01AI159946-650691651
Award ID URI
SAI UNAVAILABLE
Awardee Classifications
Public/State Controlled Institution Of Higher Education
Awarding Office
75NM00 NIH National Institute of Allergy and Infectious Diseases
Funding Office
75NM00 NIH National Institute of Allergy and Infectious Diseases
Awardee UEI
RN64EPNH8JC6
Awardee CAGE
4B557
Performance District
CA-36
Senators
Dianne Feinstein
Alejandro Padilla
Alejandro Padilla
Budget Funding
Federal Account | Budget Subfunction | Object Class | Total | Percentage |
---|---|---|---|---|
National Institute of Allergy and Infectious Diseases, National Institutes of Health, Health and Human Services (075-0885) | Health research and training | Grants, subsidies, and contributions (41.0) | $1,326,905 | 100% |
Modified: 7/3/25