Evidence map›Paper›PMID 35366439›Full record

Observational studyThe American journal of emergency medicine2022

Validation of a simplified comorbidity evaluation predicting clinical outcomes among patients with coronavirus disease 2019 - A multicenter retrospective observation study.

James P d'Etienne, Naomi Alanis, Eric Chou, John S Garrett, Jessica J Kirby, David P Bryant, Sajid Shaikh, Chet D Schrader, Hao Wang

Open access · greenAbstract readMulticenter StudyObservational Study
In one paragraph

Observational study in The American journal of emergency medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact, top 97% 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

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

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

9 authors at 2 institutions in 1 country.

James P d'EtienneDepartment of Emergency Medicine, JPS Health Network, 1500 S. Main St., Fort Worth, TX 76104, United States of America. Electronic address: jdetienne@ies.healthcare.
Naomi AlanisDepartment of Emergency Medicine, JPS Health Network, 1500 S. Main St., Fort Worth, TX 76104, United States of America. Electronic address: nalanis@ies.healthcare.
Eric ChouDepartment of Emergency Medicine, Baylor University Medical Center, 3305 Worth St, Dallas, TX 75246, United States of America. Electronic address: echou@ies.healthcare.
John S GarrettDepartment of Emergency Medicine, Baylor University Medical Center, 3305 Worth St, Dallas, TX 75246, United States of America. Electronic address: jgarrett@baylorhealth.edu.
Jessica J KirbyDepartment of Emergency Medicine, JPS Health Network, 1500 S. Main St., Fort Worth, TX 76104, United States of America. Electronic address: jkirby@ies.healthcare.
David P BryantDepartment of Emergency Medicine, JPS Health Network, 1500 S. Main St., Fort Worth, TX 76104, United States of America. Electronic address: dbryant@ies.healthcare.
Sajid ShaikhDepartment of Information Technology, JPS Health Network, 1500 S. Main St., Fort Worth, TX 76104, United States of America. Electronic address: sshaikh@jpshealth.org.
Chet D SchraderDepartment of Emergency Medicine, JPS Health Network, 1500 S. Main St., Fort Worth, TX 76104, United States of America. Electronic address: cschrader@ies.healthcare.
Hao WangDepartment of Emergency Medicine, JPS Health Network, 1500 S. Main St., Fort Worth, TX 76104, United States of America. Electronic address: hwang@ies.healt.
JPS Health Network · USBaylor University Medical Center · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesWe compared and validated the performance accuracy of simplified comorbidity evaluation compared to the Charlson Comorbidity Index (CCI) predicting COVID-19 severity. In addition, we also determined whether risk prediction of COVID-19 severity changed during different COVID-19 pandemic outbreaks.

methodsWe enrolled all patients whose SARS-CoV-2 PCR tests were performed at six different hospital Emergency Departments in 2020. Patients were divided into three groups based on the various COVID-19 outbreaks in the US (first wave: March-May 2020, second wave: June-September 2020, and third wave: October-December 2020). A simplified comorbidity evaluation was used as an independent risk factor to predict clinical outcomes using multivariate logistic regressions.

resultsA total of 22,248 patients were included, for which 7023 (32%) patients tested COVID-19 positive. Higher percentages of COVID-19 patients with more than three chronic conditions had worse clinical outcomes (i.e., hospital and intensive care unit admissions, receiving invasive mechanical ventilations, and in-hospital mortality) during all three COVID-19 outbreak waves.

conclusionsThis simplified comorbidity evaluation was validated to be associated with COVID clinical outcomes. Such evaluation did not perform worse when compared with CCI to predict in-hospital mortality.

Indexed as

COVID-19ComorbidityHumansPandemicsRetrospective StudiesSARS-CoV-2Clinical outcomesComorbidityCOVID-19Pandemic outbreaks

Identifiers

PMID35366439
PMCPMC8907112
OpenAlexW4220836833

What OpenQuestion holds

Textmetadata
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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.