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Development of COVIDVax Model to Estimate the Risk of SARS-CoV-2-Related Death Among 7.6 Million US Veterans for Use in Vaccination Prioritization.
Ioannou, George N; Green, Pamela; Fan, Vincent S; Dominitz, Jason A; O'Hare, Ann M; Backus, Lisa I; Locke, Emily; Eastment, McKenna C; Osborne, Thomas F; Ioannou, Nikolas G; Berry, Kristin.
  • Ioannou GN; Division of Gastroenterology, Veterans Affairs Puget Sound Healthcare System, University of Washington, Seattle.
  • Green P; Research and Development, Veterans Affairs Puget Sound Health Care System, Seattle, Washington.
  • Fan VS; Research and Development, Veterans Affairs Puget Sound Health Care System, Seattle, Washington.
  • Dominitz JA; Division of Pulmonary, Critical Care, and Sleep, Veterans Affairs Puget Sound Healthcare System, University of Washington, Seattle.
  • O'Hare AM; Division of Gastroenterology, Veterans Affairs Puget Sound Healthcare System, University of Washington, Seattle.
  • Backus LI; Division of Nephrology, Veterans Affairs Puget Sound Healthcare System, University of Washington, Seattle.
  • Locke E; Department of Veterans Affairs, Population Health Services, Palo Alto Healthcare System, Palo Alto, California.
  • Eastment MC; Research and Development, Veterans Affairs Puget Sound Health Care System, Seattle, Washington.
  • Osborne TF; Division of Allergy and Infectious Diseases, Veterans Affairs Puget Sound Healthcare System, University of Washington, Seattle.
  • Ioannou NG; Veterans Affairs Palo Alto Healthcare System, Palo Alto, California.
  • Berry K; Department of Radiology, Stanford University School of Medicine, Stanford, California.
JAMA Netw Open ; 4(4): e214347, 2021 04 01.
Article in English | MEDLINE | ID: covidwho-1168797
Semantic information from SemMedBD (by NLM)
1. Vaccination PREVENTS 2019 novel coronavirus
Subject
Vaccination
Predicate
PREVENTS
Object
2019 novel coronavirus
2. Vaccination PREVENTS 2019 novel coronavirus
Subject
Vaccination
Predicate
PREVENTS
Object
2019 novel coronavirus
ABSTRACT
Importance A strategy that prioritizes individuals for SARS-CoV-2 vaccination according to their risk of SARS-CoV-2-related mortality would help minimize deaths during vaccine rollout.

Objective:

To develop a model that estimates the risk of SARS-CoV-2-related mortality among all enrollees of the US Department of Veterans Affairs (VA) health care system. Design, Setting, and

Participants:

This prognostic study used data from 7 635 064 individuals enrolled in the VA health care system as of May 21, 2020, to develop and internally validate a logistic regression model (COVIDVax) that predicted SARS-CoV-2-related death (n = 2422) during the observation period (May 21 to November 2, 2020) using baseline characteristics known to be associated with SARS-CoV-2-related mortality, extracted from the VA electronic health records (EHRs). The cohort was split into a training period (May 21 to September 30) and testing period (October 1 to November 2). Main Outcomes and

Measures:

SARS-CoV-2-related death, defined as death within 30 days of testing positive for SARS-CoV-2. VA EHR data streams were imported on a data integration platform to demonstrate that the model could be executed in real-time to produce dashboards with risk scores for all current VA enrollees.

Results:

Of 7 635 064 individuals, the mean (SD) age was 66.2 (13.8) years, and most were men (7 051 912 [92.4%]) and White individuals (4 887 338 [64.0%]), with 1 116 435 (14.6%) Black individuals and 399 634 (5.2%) Hispanic individuals. From a starting pool of 16 potential predictors, 10 were included in the final COVIDVax model, as follows sex, age, race, ethnicity, body mass index, Charlson Comorbidity Index, diabetes, chronic kidney disease, congestive heart failure, and Care Assessment Need score. The model exhibited excellent discrimination with area under the receiver operating characteristic curve (AUROC) of 85.3% (95% CI, 84.6%-86.1%), superior to the AUROC of using age alone to stratify risk (72.6%; 95% CI, 71.6%-73.6%). Assuming vaccination is 90% effective at preventing SARS-CoV-2-related death, using this model to prioritize vaccination was estimated to prevent 63.5% of deaths that would occur by the time 50% of VA enrollees are vaccinated, significantly higher than the estimate for prioritizing vaccination based on age (45.6%) or the US Centers for Disease Control and Prevention phases of vaccine allocation (41.1%). Conclusions and Relevance In this prognostic study of all VA enrollees, prioritizing vaccination based on the COVIDVax model was estimated to prevent a large proportion of deaths expected to occur during vaccine rollout before sufficient herd immunity is achieved.
Subject(s)

Full text: Available Collection: International databases Database: MEDLINE Main subject: Veterans / Mass Vaccination / COVID-19 Vaccines / COVID-19 / Health Planning / Health Priorities Type of study: Etiology study / Prognostic study / Risk factors Limits: Aged / Female / Humans / Male / Middle aged Country/Region as subject: North America Language: English Journal: JAMA Netw Open Year: 2021 Document Type: Article

Full text: Available Collection: International databases Database: MEDLINE Main subject: Veterans / Mass Vaccination / COVID-19 Vaccines / COVID-19 / Health Planning / Health Priorities Type of study: Etiology study / Prognostic study / Risk factors Limits: Aged / Female / Humans / Male / Middle aged Country/Region as subject: North America Language: English Journal: JAMA Netw Open Year: 2021 Document Type: Article