Evidence map›Paper›PMID 37097523›Full record

ArticleAnnals of intensive care2023

Risk factors for severe COVID-19 in the young-before and after ICU admission.

Johanna Kämpe, Olof Bohlin, Martin Jonsson, Robin Hofmann, Jacob Hollenberg, Rebecka Rubenson Wahlin, Per Svensson, Per Nordberg

Open access · goldAbstract read
In one paragraph

Article in Annals of intensive care, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.2field-weighted citation impact, top 23% 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

2 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
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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 1 country.

Johanna KämpeDepartment of Clinical Science and Education, Södersjukhuset, Centre for Resuscitation Science, Karolinska Institutet, 11883, Stockholm, Sweden. johanna.kampe@ki.se.ORCID http://orcid.org/0000-0003-4060-4602
Olof Bohlin *Department of Clinical Science and Education, Södersjukhuset, Centre for Resuscitation Science, Karolinska Institutet, 11883, Stockholm, Sweden.
Martin JonssonDepartment of Clinical Science and Education, Södersjukhuset, Centre for Resuscitation Science, Karolinska Institutet, 11883, Stockholm, Sweden.
Robin HofmannDepartment of Clinical Science and Education, Södersjukhuset, Karolinska Institutet, 11883, Stockholm, Sweden.
Jacob HollenbergDepartment of Clinical Science and Education, Södersjukhuset, Centre for Resuscitation Science, Karolinska Institutet, 11883, Stockholm, Sweden.
Rebecka Rubenson WahlinDepartment of Clinical Science and Education, Södersjukhuset, Karolinska Institutet, 11883, Stockholm, Sweden.
Per Svensson *Department of Clinical Science and Education, Södersjukhuset, Karolinska Institutet, 11883, Stockholm, Sweden.
Per Nordberg *Department of Clinical Science and Education, Södersjukhuset, Centre for Resuscitation Science, Karolinska Institutet, 11883, Stockholm, Sweden.
Karolinska Institutet · SEKarolinska University Hospital · SE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundFactors associated with severe COVID-19 and death among young adults are not fully understood, including differences between the sexes. The aim of this study was to identify factors associated with severe COVID-19 requiring intensive care and 90-day mortality among women and men below 50 years of age.

methodsA register-based study using data from mandatory national registers, where patients with severe COVID-19 admitted to the ICU with need for mechanical ventilation (cases) between March 2020 and June 2021 were matched regarding age, sex, and district of residence with 10 population-based controls. Both the study population and the controls were divided into groups based on age (< 50 years, 50-64, and ≥ 65 years) and sex. Multivariate logistic regression models including socioeconomic factors were used to calculate odds ratios (OR) with 95% confidence intervals (CIs) for associations between severe COVID-19 in the population to compare the magnitude of the risk associations for co-morbidities in the different age categories, and subsequently factors associated with 90-day mortality among patients admitted to ICU.

resultsIn total, 4921 cases and 49,210 controls (median age 63 years, 71% men) were included. The co-morbidities with the strongest associations with severe COVID-19 for the young population compared to older patients were chronic kidney disease (OR 6.80 [3.61-12.83]), type 2 diabetes (OR 6.31 [4.48-8.88]), hypertension (OR 5.09 [3.79-6.84]), rheumatoid arthritis (OR 4.76 [2.29-9.89]), obesity (OR 3.76 [2.88-4.92]), heart failure (OR 3.06 [1.36-6.89]), and asthma (OR 3.04 [2.22-4.16]). When comparing women vs. men < 50 years of age, stronger associations were seen for women regarding type 2 diabetes (OR 11.25 [6.00-21.08] vs OR 4.97 [3.25-7.60]) and hypertension (OR 8.76 [5.10-15.01] vs OR 4.09 [2.86-5.86]). The factors associated with 90-day mortality in the young were previous venous thromboembolism (OR 5.50 [2.13-14.22]), chronic kidney disease (OR 4.40 [1.64-11.78]) and type 2 diabetes (OR 2.71 [1.39-5.29]). These associations with 90-day mortality were foremost driven by the female population.

conclusionChronic kidney failure, type 2 diabetes, hypertension, rheumatoid arthritis, obesity, heart failure, and asthma were the strongest risk factors associated with severe COVID-19 requiring ICU-care in individuals < 50 years compared to the older population. However, after ICU admission, previous thromboembolism, chronic kidney failure, and type 2 diabetes were associated with increased 90-day mortality. The risk associations for co-morbidities were generally stronger among younger individuals compared to older and in women compared to men.

Identifiers

PMID37097523
PMCPMC10127156
OpenAlexW4366990692

What OpenQuestion holds

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LicenceCC BY
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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.