Evidence map›Paper›PMID 34949575›Full record

ArticleBMJ open respiratory research2021

Predicting death from COVID-19 using pre-existing conditions: implications for vaccination triage.

Shujie Xiao, Neha Sahasrabudhe, Samantha Hochstadt, Whitney Cabral, Samantha Simons, Mao Yang, David E Lanfear, L Keoki Williams

Abstract read
In one paragraph

Article in BMJ open respiratory research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Shujie XiaoCenter for Individualized and Genomic Medicine Research (CIGMA), Henry Ford Health System, Detroit, Michigan, USA.
Neha SahasrabudheCenter for Individualized and Genomic Medicine Research (CIGMA), Henry Ford Health System, Detroit, Michigan, USA.
Samantha HochstadtCenter for Individualized and Genomic Medicine Research (CIGMA), Henry Ford Health System, Detroit, Michigan, USA.
Whitney CabralCenter for Individualized and Genomic Medicine Research (CIGMA), Henry Ford Health System, Detroit, Michigan, USA.
Samantha SimonsCenter for Individualized and Genomic Medicine Research (CIGMA), Henry Ford Health System, Detroit, Michigan, USA.
Mao YangCenter for Individualized and Genomic Medicine Research (CIGMA), Henry Ford Health System, Detroit, Michigan, USA.
David E LanfearCenter for Individualized and Genomic Medicine Research (CIGMA), Henry Ford Health System, Detroit, Michigan, USA.
L Keoki WilliamsCenter for Individualized and Genomic Medicine Research (CIGMA), Henry Ford Health System, Detroit, Michigan, USA kwillia5@hfhs.org.

Funding

PHARMACOGENOMICS OF INHALED CORTICOSTEROID RESPONSIVENESS IN PATIENTS WITH ASTHMAR01AI079139 · NIAID · HENRY FORD HEALTH SYSTEM · PI WILLIAMS, KEOKI · 2009 to 2013
$3.9M
Impact of Race and Genetic Factors on Beta-blocker Effectiveness in Heart FailureR01HL103871 · NHLBI · HENRY FORD HEALTH SYSTEM · PI LANFEAR, DAVID E · 2011 to 2015
$3.5M
Leveraging electronic medical records to perform large-scale diabetes pharmacogenomics among ancestrally diverse patient populationsR01DK113003 · NIDDK · HENRY FORD HEALTH SYSTEM · PI WILLIAMS, KEOKI · 2017 to 2021
$3.2M
Plasma Metabolomics and Myocardial Energetics in Heart FailureR01HL132154 · NHLBI · HENRY FORD HEALTH SYSTEM · PI LANFEAR, DAVID E, SABBAH, HANI N · 2017 to 2020
$3.1M
Poly-omic Study of Asthma Exacerbations in Diverse PopulationsR01HL141845 · NHLBI · HENRY FORD HEALTH SYSTEM · PI WILLIAMS, KEOKI · 2019 to 2022
$3.0M
Combined Transcriptomics and Genomics to Find Asthma Genes in Admixed PopulationsR01HL118267 · NHLBI · HENRY FORD HEALTH SYSTEM · PI WILLIAMS, KEOKI · 2014 to 2017
$2.9M
NHLBI NIH HHS R01 HL103871NHLBI NIH HHS R01 HL118267NHLBI NIH HHS R01 HL132154NHLBI NIH HHS R01 HL141845NIAID NIH HHS R01 AI079139NIDDK NIH HHS R01 DK113003
6 · The paper itself

Abstract

introductionGlobal shortages in the supply of SARS-CoV-2 vaccines have resulted in campaigns to first inoculate individuals at highest risk for death from COVID-19. Here, we develop a predictive model of COVID-19-related death using longitudinal clinical data from patients in metropolitan Detroit.

methodsAll individuals included in the analysis had a laboratory-confirmed SARS-CoV-2 infection. Thirty-six pre-existing conditions with a false discovery rate p<0.05 were combined with other demographic variables to develop a parsimonious prediction model using least absolute shrinkage and selection operator regression. The model was then prospectively validated in a separate set of individuals with confirmed COVID-19.

resultsThe study population consisted of 15 502 individuals with laboratory-confirmed SARS-CoV-2. The main prediction model was developed using data from 11 635 individuals with 709 reported deaths (case fatality ratio 6.1%). The final prediction model consisted of 14 variables with 11 comorbidities. This model was then prospectively assessed among the remaining 3867 individuals (185 deaths; case fatality ratio 4.8%). When compared with using an age threshold of 65 years, the 14-variable model detected 6% more of the individuals who would die from COVID-19. However, below age 45 years and its risk equivalent, there was no benefit to using the prediction model over age alone. DISCUSSION: Using a prediction model, such as the one described here, may help identify individuals who would most benefit from COVID-19 inoculation, and thereby may produce more dramatic initial drops in deaths through targeted vaccination.

Indexed as

COVID-19AgedCOVID-19 VaccinesHumansMiddle AgedSARS-CoV-2TriageVaccinationCOVID-19 VaccinesCOVID-19viral infection

Identifiers

PMID34949575
PMCPMC8705216

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.