Evidence map›Paper›PMID 32793923›Full record

ArticlemedRxiv : the preprint server for health sciences2021

A phenome-wide association study (PheWAS) of COVID-19 outcomes by race using the electronic health records data in Michigan Medicine.

Maxwell Salvatore, Tian Gu, Jasmine A Mack, Swaraaj Prabhu Sankar, Snehal Patil, Thomas S Valley, Karandeep Singh, Brahmajee K Nallamothu, Sachin Kheterpal, Lynda Lisabeth and 2 more

Open access · greenAbstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in medRxiv : the preprint server for health sciences, 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, 4 citations in OpenAlex.

  1. Observational
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

12 authors at 1 institution in 1 country.

Maxwell Salvatore *Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, United States.
Tian Gu *Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, United States.
Jasmine A MackDepartment of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, United States.
Swaraaj Prabhu SankarRogel Cancer Center, University of Michigan Medicine, Ann Arbor, MI 48109, United States.
Snehal PatilDepartment of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, United States.
Thomas S ValleyDivision of Pulmonary and Critical Care Medicine and Department of Internal Medicine, University of Michigan Medicine, Ann Arbor, MI 48109, United States.
Karandeep SinghInstitute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI 48109, United States.
Brahmajee K NallamothuDivision of Cardiovascular Medicine and Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI 48109, United States.
Sachin KheterpalInstitute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI 48109, United States.
Lynda LisabethDepartment of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI 48109, United States.
Lars G FritscheDepartment of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, United States.
Bhramar MukherjeeDepartment of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, United States.
University of Michigan · US

Funding

XenograftP30CA046592 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Alexey I Nesvizhskii · 1988 to 2026
$178.2M
University of Michigan Training Program in Genomic ScienceT32HG000040 · NHGRI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Sebastian Zoellner · 1995 to 2026
$16.4M
Preventing avoidable deaths from pneumonia by improving use of intensive careK23HL140165 · NHLBI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI VALLEY, THOMAS SEBASTIAN · 2018 to 2022
$915k
NCI NIH HHS P30 CA046592NHGRI NIH HHS T32 HG000040NHLBI NIH HHS K23 HL140165
6 · The paper itself

Abstract

backgroundWe perform a phenome-wide scan to identify pre-existing conditions related to COVID-19 susceptibility and prognosis across the medical phenome and how they vary by race.

methodsThe study is comprised of 53,853 patients who were tested/positive for COVID-19 between March 10 and September 2, 2020 at a large academic medical center.

resultsPre-existing conditions strongly associated with hospitalization were

conclusionsUnderstanding pre-existing clinical diagnoses related to COVID-19 outcomes informs the need for targeted screening to support specific vulnerable populations to improve disease prevention and healthcare delivery.

Indexed as

biobankEHRhealth disparitiesodds ratiophenomerisk profile

Identifiers

PMID32793923
PMCPMC7418740
OpenAlexW3038401147

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.