Evidence map›Paper›PMID 40831520›Full record

ArticleJournal of inflammation research2025

Immune Signatures Distinguish Pure and Mixed Sepsis in Critical COVID-19: A Retrospective Cohort Study.

Li Xiong, Huan Tang, Qian Xie, Hong Fang, Da Jing, Lin Chen

Registry-linked trialAbstract read
In one paragraph

Article in Journal of inflammation research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06491966 (Comparative Analysis of Clinical Outcomes and Inflammatory Responses in Viral Versus Bacterial Sepsis), which is not on this map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
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.

NCT06491966 unknown statusnot on this map

Comparative Analysis of Clinical Outcomes and Inflammatory Responses in Viral Versus Bacterial Sepsis: A Retrospective Cohort Study in ICU Patients

TypeobservationalSponsorSichuan Provincial People's HospitalRan2024 to 2024Enrolled300ConditionsSepsis, Sepsis Bacterial, Viral Sepsis, Inflammatory ResponseArmsobservation
3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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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

6 authors.

Li Xiong *Department of Pulmonary and Critical Care Medicine. Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, People's Republic of China.
Huan Tang *Department of Pulmonary and Critical Care Medicine. Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, People's Republic of China.
Qian XieDepartment of Pulmonary and Critical Care Medicine. Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, People's Republic of China.
Hong FangDepartment of Pulmonary and Critical Care Medicine. Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, People's Republic of China.
Da JingDepartment of Pulmonary and Critical Care Medicine. Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, People's Republic of China.
Lin ChenDepartment of Pulmonary and Critical Care Medicine. Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, People's Republic of China.ORCID 0000-0002-9196-3640

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: COVID-19-associated sepsis poses unique challenges in intensive care units (ICUs), where patients are often immunocompromised and prone to secondary bacterial infections. Differentiating immune phenotypes between pure viral and viral-bacterial sepsis is essential for timely diagnosis and personalized treatment. Objective: To compare clinical characteristics, immune profiles, and outcomes between patients with pure COVID-19 sepsis and those complicated by confirmed secondary bacterial infection, and to identify immune markers capable of differentiating sepsis phenotypes. Methods: This retrospective cohort study enrolled ICU patients with severe COVID-19 pneumonia between July 2021 and December 2023. Patients were classified into viral sepsis (V group, n=53) and viral-bacterial sepsis (VB group, n=28) based on microbiological confirmation of secondary infection. Clinical data, inflammatory markers, cytokine levels, and neutrophil CD64 expression (via flow cytometry) were analyzed. Logistic regression, ROC curves, and Kaplan-Meier survival analysis were used to evaluate phenotypes, identify discriminative markers, and assess outcomes. Results: The VB group exhibited significantly higher SOFA scores (median 9.5 vs 7, P<0.001), serum creatinine (103.2 vs 80.85 µmol/L, P=0.001), and LDH levels (531 vs 392 U/L, P=0.009), indicating more severe organ dysfunction. ICU stay was longer in the VB group (median 16 vs 13 days, P=0.027), and ICU mortality was slightly higher (85.7% vs 83.0%, P=0.785). Immune profiling showed significantly higher IL-2 (36.8 vs 22.1 pg/mL, P<0.001), IL-10, IFN-γ, and MYD88 in the V group, whereas TNF-α (62.3 vs 43.7 pg/mL, P=0.002) and PCT (2.51 vs 1.12 ng/mL, P=0.001) were higher in the VB group. IL-2, MYD88, and PCT independently discriminated phenotypes. Neutrophil CD64 showed strong predictive value for superinfection (AUC=0.969). Conclusion: COVID-19 patients with secondary bacterial sepsis exhibit distinct immune and clinical profiles. Immune phenotyping may enable early recognition of bacterial superinfection and guide phenotype-driven therapy in severe viral sepsis. Trial Registration: ClinicalTrials.gov, NCT06491966. Registered 2 April 2024, https://clinicaltrials.gov/ct2/show/NCT06491966.

Indexed as

COVID-19 sepsiscytokinesICUimmune profilingorgan dysfunctionsecondary bacterial infection

Identifiers

PMID40831520
PMCPMC12360393

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Registered trials

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.