Evidence map›Paper›PMID 35457497›Full record

ArticleInternational journal of environmental research and public health2022

COVID-19 Phenotypes and Comorbidity: A Data-Driven, Pattern Recognition Approach Using National Representative Data from the United States.

George D Vavougios, Vasileios T Stavrou, Christoforos Konstantatos, Pavlos-Christoforos Sinigalias, Sotirios G Zarogiannis, Konstantinos Kolomvatsos, George Stamoulis, Konstantinos I Gourgoulianis

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in International journal of environmental research and public health, 2022. 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
0.1field-weighted citation impact, top 55% 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

1 citing paper in PubMed, 1 citations in OpenAlex.

  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 at 2 institutions in 2 countries.

George D VavougiosDepartment of Neurology, University of Cyprus, 75 Kallipoleos Street, Lefkosia 1678, Cyprus.ORCID 0000-0002-0413-4028
Vasileios T StavrouLaboratory of Cardio-Pulmonary Testing and Pulmonary Rehabilitation, Department of Respiratory Medicine, Faculty of Medicine, University of Thessaly, Biopolis, 41500 Larissa, Greece.ORCID 0000-0002-2437-5339
Christoforos KonstantatosDepartment of Business Administration, University of Patras, University Campus-Rio, 26504 Patras, Greece.ORCID 0000-0002-3312-0186
Pavlos-Christoforos SinigaliasDepartment of Mechanical Engineering and Aeronautics, University of Patras, 26504 Patras, Greece.
Sotirios G ZarogiannisDepartment of Respiratory Medicine, Faculty of Medicine, School of Health Sciences, University of Thessaly, Biopolis, 41500 Larissa, Greece.ORCID 0000-0002-3083-3244
Konstantinos KolomvatsosDepartment of Electrical and Computer Engineering, University of Thessaly, 37 Glavani-28th October Str., Deligiorgi Building, 4th Floor, 38221 Volos, Greece.
George StamoulisDepartment of Electrical and Computer Engineering, University of Thessaly, 37 Glavani-28th October Str., Deligiorgi Building, 4th Floor, 38221 Volos, Greece.
Konstantinos I GourgoulianisLaboratory of Cardio-Pulmonary Testing and Pulmonary Rehabilitation, Department of Respiratory Medicine, Faculty of Medicine, University of Thessaly, Biopolis, 41500 Larissa, Greece.
University of Thessaly · GRUniversity of Patras · GR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The aim of our study was to determine COVID-19 syndromic phenotypes in a data-driven manner using the survey results based on survey results from Carnegie Mellon University’s Delphi Group. Monthly survey results (>1 million responders per month; 320,326 responders with a certain COVID-19 test status and disease duration <30 days were included in this study) were used sequentially in identifying and validating COVID-19 syndromic phenotypes. Logistic Regression-weighted multiple correspondence analysis (LRW-MCA) was used as a preprocessing procedure, in order to weigh and transform symptoms recorded by the survey to eigenspace coordinates, capturing a total variance of >75%. These scores, along with symptom duration, were subsequently used by the Two Step Clustering algorithm to produce symptom clusters. Post-hoc logistic regression models adjusting for age, gender, and comorbidities and confirmatory linear principal components analyses were used to further explore the data. Model creation, based on August’s 66,165 included responders, was subsequently validated in data from March−December 2020. Five validated COVID-19 syndromes were identified in August: 1. Afebrile (0%), Non-Coughing (0%), Oligosymptomatic (ANCOS); 2. Febrile (100%) Multisymptomatic (FMS); 3. Afebrile (0%) Coughing (100%) Oligosymptomatic (ACOS); 4. Oligosymptomatic with additional self-described symptoms (100%; OSDS); 5. Olfaction/Gustatory Impairment Predominant (100%; OGIP). Our findings indicate that the COVID-19 spectrum may be undetectable when applying current disease definitions focusing on respiratory symptoms alone.

Indexed as

COVID-19ComorbidityCoughHumansPhenotypeSARS-CoV-2United Statesbig datacomorbidityCOVID-19epidemiologypattern recognitionphenotypes

Identifiers

PMID35457497
PMCPMC9029400
OpenAlexW4223645161

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.