Evidence map›Paper›PMID 38270311›Full record

Observational studyImmunity, inflammation and disease2024

An integrative model with HLA-DR, CD64, and PD-1 for the diagnostic and prognostic evaluation of sepsis.

Guosheng Chen, Huimin Chong, Peng Zhang, Dalin Wen, Juan Du, Chu Gao, Shi Zeng, Ling Zeng, Jin Deng, Kejun Zhang and 1 more

Open access · goldAbstract readObservational Study
In one paragraph

Observational study in Immunity, inflammation and disease, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
3.5field-weighted citation impact, top 7% 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

8 citing papers in PubMed, 1 synthesis or guideline pooled it, 9 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Observational
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

11 authors at 4 institutions in 2 countries.

Guosheng ChenState Key Laboratory of Trauma, Burns and Combined Injury, Institute of Surgery Research, Daping Hospital, Army Medical University (Third Military Medical University), Chongqing, China.
Huimin ChongState Key Laboratory of Trauma, Burns and Combined Injury, Institute of Surgery Research, Daping Hospital, Army Medical University (Third Military Medical University), Chongqing, China.
Peng ZhangYubei District Hospital of TCM, Chongqing, China.
Dalin WenState Key Laboratory of Trauma, Burns and Combined Injury, Institute of Surgery Research, Daping Hospital, Army Medical University (Third Military Medical University), Chongqing, China.
Juan DuState Key Laboratory of Trauma, Burns and Combined Injury, Institute of Surgery Research, Daping Hospital, Army Medical University (Third Military Medical University), Chongqing, China.
Chu GaoState Key Laboratory of Trauma, Burns and Combined Injury, Institute of Surgery Research, Daping Hospital, Army Medical University (Third Military Medical University), Chongqing, China.
Shi ZengDepartment of Neurosurgery, The People's Hospital of Chongqing Banan District, Chongqing, China.
Ling ZengState Key Laboratory of Trauma, Burns and Combined Injury, Institute of Surgery Research, Daping Hospital, Army Medical University (Third Military Medical University), Chongqing, China.
Jin DengState Key Laboratory of Trauma, Burns and Combined Injury, Institute of Surgery Research, Daping Hospital, Army Medical University (Third Military Medical University), Chongqing, China.
Kejun ZhangState Key Laboratory of Trauma, Burns and Combined Injury, Institute of Surgery Research, Daping Hospital, Army Medical University (Third Military Medical University), Chongqing, China.
Anqiang ZhangState Key Laboratory of Trauma, Burns and Combined Injury, Institute of Surgery Research, Daping Hospital, Army Medical University (Third Military Medical University), Chongqing, China.ORCID 0000-0002-0375-2078
Army Medical University · CNGuiyang Medical University · CNCM Hospital · KRFirst People's Hospital of Chongqing · CN

Funding

National Natural Science Foundation of China 81971830Natural Science Foundation of Chongqing, China cstc2021jcyj-msxmX0445Open Project Program of the State Key Laboratory of Trauma, Burn and Combined Injury SKLYQ202102Research project of clinical medical, Army Medical University 2019XLC304
6 · The paper itself

Abstract

backgroundSepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection and progressive immunosuppression with high mortality. HLA-DR, CD64, and PD-1 were assumed to be useful biomarkers for sepsis prediction. However, the ability of a combination of these biomarkers has not been clarified.

methodsAn observational case-control study was conducted that included 30 sepsis patients, 30 critically ill patients without sepsis admitted to the intensive care unit (ICU), and 32 healthy individuals. The levels of HLA-DR, CD64, and PD-1 expression in peripheral blood immune cells and subsets was assayed on Days 1, 3, and 5, and the clinical information of patients was collected. We compared these biomarkers between groups and evaluated the predictive validity of single and combined biomarkers on sepsis mortality.

resultsThe results indicate that PD-1 expression on CD4

conclusionThese findings indicate that the combinations of HLA-DR, CD64, and PD-1 outperformed each of the single indicator in diagnosis and predicting prognosis of sepsis.

Indexed as

Programmed Cell Death 1 ReceptorSepsisCase-Control StudiesCritical IllnessHLA-DR AntigensHumansPrognosisHLA-DR AntigensProgrammed Cell Death 1 ReceptorCD64HLA-DRPD-1predictsepsis

Identifiers

PMID38270311
PMCPMC10777881
OpenAlexW4390704669

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.