Early PREdiction of sepsis using leukocyte surface biomarkers: the ExPRES-sepsis cohort study
Open accessAbstract
Purpose Reliable biomarkers for predicting subsequent sepsis among patients with suspected acute infection are lacking. In patients presenting to emergency departments (EDs) with suspected acute infection, we aimed to evaluate the reliability and discriminant ability of 47 leukocyte biomarkers as predictors of sepsis (Sequential Organ Failure Assessment score ≥ 2 at 24 h and/or 72 h following ED presentation). Methods In a multi-centre cohort study in four EDs and intensive care units (ICUs), we standardised flow-cytometric leukocyte biomarker measurement and compared patients with suspected acute infection (cohort-1) with two comparator cohorts: ICU patients with established sepsis (cohort-2), and ED patients without infection or systemic inflammation but requiring hospitalization (cohort-3). Results Between January 2014 and February 2016, we recruited 272, 59 and 75 patients to cohorts 1, 2, and 3, respectively. Of 47 leukocyte biomarkers, 14 were non-reliable, and 17 did not discriminate between the three cohorts. Discriminant analyses for predicting sepsis within cohort-1 were undertaken for eight neutrophil (cluster of differentiation antigens (CD) CD15; CD24; CD35; CD64; CD312; CD11b; CD274; CD279), seven monocyte (CD35; CD64; CD312; CD11b; HLA-DR; CD274; CD279) and a CD8 T-lymphocyte biomarker (CD279). Individually, only higher neutrophil CD279 [OR 1.78 (95% CI 1.23-2.57); P = 0.002], higher monocyte CD279 [1.32 (1.03-1.70); P = 0.03], and lower monocyte HLA-DR [0.73 (0.55-0.97); P = 0.03] expression were associated with subsequent sepsis. With logistic regression the optimum biomarker combination was increased neutrophil CD24 and neutrophil CD279, and reduced monocyte HLA-DR expression, but no combination had clinically relevant predictive validity. Conclusions From a large panel of leukocyte biomarkers, immunosuppression biomarkers were associated with subsequent sepsis in ED patients with suspected acute infection. Clinical trial registration NCT02188992.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Most of the investigated biomarkers failed to show discriminative power or clinical relevance.
“Additionally, most of the investigated biomarkers failed to show discriminative power or clinical relevance [42, 43].”
From Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy (2020) · cites this paper as [43]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Sepsis-induced immunosuppression: from cellular dysfunctions to immunotherapy
Hotchkiss, R. S., Monneret, G., & Payen, D. (2013) · Nature Reviews Immunology · doi:10.1038/nri3552 · Cited by 2,772 · see citing works
Modern modelling techniques are data hungry: a simulation study for predicting dichotomous endpoints
van der Ploeg, T., Austin, P. C., & Steyerberg, E. W. (2014) · BMC Medical Research Methodology · doi:10.1186/1471-2288-14-137 · Cited by 809 · see citing works
Flagged first, then by citations
Research works printed but not linked: 4
- [1]1. Datta D, Conway Morris A, Antonelli J, et al. Early PREdiction of Severe Sepsis (ExPRESSepsis) study: protocol for an observational derivation study to discover potential leucocyte cell surface biomarkers. BMJ Open. 2016;6(8):e011335.
- [3]3. Conway Morris A, Datta D, Shankar-Hari M, et al. Cell-surface signatures of immune dysfunction risk-stratify critically ill patients: INFECT study. Intensive Care Med. 2018;44(5):627-635.
- [4]4. Guerin E, Orabona M, Raquil MA, et al. Circulating immature granulocytes with T-cell killing functions predict sepsis deterioration*. Crit Care Med. 2014;42(9):2007-2018.
- [6]6. Conway Morris A, Anderson N, Brittan M, et al. Combined dysfunctions of immune cells predict nosocomial infection in critically ill patients. Br J Anaesth. 2013;111(5):778-787. STROBE Statement-checklist of items that should be included in reports of observational studies
Cited by
Sorted by citations
Fleuren, L. M., Klausch, T. L. T., Zwager, C. L., Schoonmade, L. J., Guo, T., Roggeveen, L. F., Swart, E. L., Girbes, A. R. J., Thoral, P., Ercole, A., et al. (2020) · Intensive Care Medicine · doi:10.1007/s00134-019-05872-y · Cited by 755 · see citing works
- Authors
- Shankar-Hari, M., Datta, D., Wilson, J., Assi, V., Stephen, J., Weir, C. J., Rennie, J., Antonelli, J., Bateman, A., Felton, J. M., Warner, N., Judge, K., Keenan, J., Wang, A., Burpee, T., Brown, A. K., Lewis, S. M., Mare, T., Roy, A. I., Wright, J., Hulme, G., Dimmick, I., Gray, A., Rossi, A. G., Simpson, A. J., Conway Morris, A., & Walsh, T. S.
- Affiliations
- Centre for Inflammation Research (GB)
- King's College London (GB)
- University of Edinburgh (GB)
- Becton Dickinson (United States) (US)
- Guy's and St Thomas' NHS Foundation Trust (GB)
- Royal Victoria Infirmary (GB)
- Centre for Life (GB)
- Edinburgh Royal Infirmary (GB)
- Newcastle University (GB)
- ORCID
- Venue
- Intensive Care Medicine
- Published
- 2018-10-05
- Volume
- 44
- Issue
- 11
- Pages
- 1836-1848
- ISSN
- 0342-4642 (ISSN-L)
- 1432-1238
- OpenAlex
- W2894670795
- Cited by
- 74
- References
- 2
- Citations by year
- 2026: 8 · 2025: 7 · 2024: 8 · 2023: 13 · 2022: 12 · 2021: 6 · 2020: 10 · 2019: 9 · 2018: 1
- FWCI
- 5.08
- Type
- journal article
- Language
- en
- Open access
- green
- kclpure.kcl.ac.uk
- Licence
- Locations
- Intensive Care Medicine (publishedVersion)
- PubMed (publishedVersion)
- Research Portal (King's College London) (submittedVersion, other-oa)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, other-oa)
- Apollo (University of Cambridge) (submittedVersion)
- Apollo
- Funders
- Innovate UK (Sepsis 2: 101193)
- Research Trainees Coordinating Centre (CS-2016-16-011)
- Wellcome Trust (WT 2055214/Z/16/Z)
- Topics
- Health Sciences › Medicine › Epidemiology › Sepsis Diagnosis and Treatment
- Life Sciences › Immunology and Microbiology › Immunology › Inflammation biomarkers and pathways
- Health Sciences › Medicine › Nephrology › Acute Kidney Injury Research
- Keywords
- sepsis prediction, acute infection, neutrophils, monocytes, CD24, HLA-DR, CD64, CD11b, complement receptor 1, PD-L1, SARS-CoV-2, SARS-CoV-1, emergency department
- MeSH terms
- Adult, Aged, Aged, 80 and over, Emergency Service, Hospital, Female, HLA-DR Antigens, Humans, Intensive Care Units, Leukocytes, Male, Middle Aged, Predictive Value of Tests, Reproducibility of Results, Cohort Studies, Biomarkers, Antigens, CD, Logistic Models, Sepsis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review