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Record W3182386410 · doi:10.1097/ccm.0000000000005195

Coronavirus Disease 2019 as Cause of Viral Sepsis: A Systematic Review and Meta-Analysis*

2021· review· en· W3182386410 on OpenAlexaboutno aff
Eleni Karakike, Evangelos J. Giamarellos‐Bourboulis, Miltiades Kyprianou, Carolin Fleischmann-Struzek, Mathias W. Pletz, Mihai G. Netea, Konrad Reinhart, Evdoxia Kyriazopoulou

Bibliographic record

VenueCritical Care Medicine · 2021
Typereview
Languageen
FieldMedicine
TopicCOVID-19 Clinical Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMeta-analysisCoronavirus disease 2019 (COVID-19)SepsisCoronavirus2019-20 coronavirus outbreakDiseaseSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Intensive care medicineVirologyPandemicImmunologyInfectious disease (medical specialty)Internal medicineOutbreak

Abstract

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OBJECTIVE: Coronavirus disease 2019 is a heterogeneous disease most frequently causing respiratory tract infection, which can induce respiratory failure and multiple organ dysfunction syndrome in its severe forms. The prevalence of coronavirus disease 2019-related sepsis is still unclear; we aimed to describe this in a systematic review. DATA SOURCES: MEDLINE (PubMed), Cochrane, and Google Scholar databases were searched based on a prespecified protocol (International Prospective Register for Systematic Reviews: CRD42020202018). STUDY SELECTION: Studies reporting on patients with confirmed coronavirus disease 2019 diagnosed with sepsis according to sepsis-3 or according to the presence of infection-related organ dysfunctions necessitating organ support/replacement were included in the analysis. The primary end point was prevalence of coronavirus disease 2019-related sepsis among adults hospitalized in the ICU and the general ward. Among secondary end points were the need for ICU admission among patients initially hospitalized in the general ward and the prevalence of new onset of organ dysfunction in the ICU. Outcomes were expressed as proportions with respective 95% CI. DATA EXTRACTION: Two reviewers independently screened and reviewed existing literature and assessed study quality with the Newcastle-Ottawa Scale and the Methodological index for nonrandomized studies. DATA SYNTHESIS: Of 3,825 articles, 151 were analyzed, only five of which directly reported sepsis prevalence. Noting the high heterogeneity observed, coronavirus disease 2019-related sepsis prevalence was 77.9% (95% CI, 75.9-79.8; I2 = 91%; 57 studies) in the ICU, and 33.3% (95% CI, 30.3-36.4; I2 = 99%; 86 studies) in the general ward. ICU admission was required for 17.7% (95% CI, 12.9-23.6; I2 = 100%) of ward patients. Acute respiratory distress syndrome was the most common organ dysfunction in the ICU (87.5%; 95% CI, 83.3-90.7; I2 = 98%). CONCLUSIONS: The majority of coronavirus disease 2019 patients hospitalized in the ICU meet Sepsis-3 criteria and present infection-associated organ dysfunction. The medical and scientific community should be aware and systematically report viral sepsis for prognostic and treatment implications.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gptno category
Domain: not available · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Systematic reviewhigh
grokno category
Domain: not available · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Meta-analysishigh
opusMeta-epidemiology (broad)
Domain: not available · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Meta-analysismedium
models splitAgreement compares identical category sets and study designs across arms.

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.016
metaresearch head score (Gemma)0.042
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.017
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.042
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0170.034
Bibliometrics0.0080.009
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.361
GPT teacher head0.595
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Labeled directly by 3 models reading the full record.

Meta-epidemiology (broad)

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designSystematic review · Meta-analysis
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations179
Published2021
Admission routes1
Has abstractyes

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