Evidence map›Paper›PMID 33011809›Full record

Observational studyThe Journal of infectious diseases2021

Comparing Clinical Characteristics of Influenza and Common Coronavirus Infections Using Electronic Health Records.

Dadong Li, Donna M Wolk, Michael N Cantor

Open access · bronzeAbstract readComparative StudyObservational Study
In one paragraph

Observational study in The Journal of infectious diseases, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.1field-weighted citation impact, top 44% of its field
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

4 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors at 2 institutions in 1 country.

Dadong LiRegeneron Genetics Center, Tarrytown, New York, USA.
Donna M WolkGeisinger Health System, Danville, Pennsylvania, USA.
Michael N CantorRegeneron Genetics Center, Tarrytown, New York, USA.ORCID 0000-0002-1074-1203
Regeneron (United States) · USGeisinger Health System · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWe compared outcomes in inpatients and outpatients, pre-COVID-19, who were infected with either coronavirus or influenza.

methodsUsing deidentified electronic health records data from the Geisinger-Regeneron partnership, we compared patients with RT-PCR-positive tests for the 4 common coronaviruses (229E, HKU1, NL63, OC43) or influenza (A and B) from June 2016 to February 2019.

resultsOverall, 52 833 patients were tested for coronaviruses and influenza. For patients ≥21 years old, 1555 and 3991 patient encounters had confirmed positive coronavirus and influenza tests, respectively. Both groups had similar intensive care unit (ICU) admission rates (7.2% vs 6.1%, P = .12), although patients with coronavirus had significantly more pneumonia (15% vs 7.4%, P < .001) and higher death rate within 30 days (4.9% vs 3.0%, P < .001). After controlling for other covariates, coronavirus infection still had a higher risk of death and pneumonia than influenza (odds ratio, 1.64 and 2.05, P < .001), with no significant difference in ICU admission rates.

conclusionsCommon coronaviruses cause significant morbidity, with potentially worse outcomes than influenza. Identifying a subset of patients who are more susceptible to poor outcomes from common coronavirus infections may help plan clinical interventions in patients with suspected infections.

Indexed as

Electronic Health RecordsAdultAgedAge FactorsCoronavirus InfectionsHospitalizationHumansInfluenza, HumanIntensive Care UnitsMiddle AgedRetrospective StudiesRisk Factorscommon coronaviruselectronic health recordsepidemiologyinfluenza

Identifiers

PMID33011809
PMCPMC7797744
OpenAlexW3091247468

What OpenQuestion holds

Textmetadata
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.