Evidence map›Paper›PMID 37752433›Full record

Observational studyBMC infectious diseases2023

Validation of sTREM-1 and IL-6 based algorithms for outcome prediction of COVID-19.

Mathias Van Singer, Thomas Brahier, Jana Koch, Pr Olivier Hugli, Andrea M Weckman, Kathleen Zhong, Taylor J Kain, Aleksandra Leligdowicz, Enos Bernasconi, Alessandro Ceschi and 5 more

Abstract readMulticenter StudyObservational Study
In one paragraph

Observational study in BMC infectious diseases, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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.

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

1 citing paper in PubMed.

  1. Article
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

15 authors.

Mathias Van Singer *Infectious Diseases Service, University Hospital of Lausanne, Lausanne, Switzerland. mathias.van-singer@chuv.ch.
Thomas Brahier *Infectious Diseases Service, University Hospital of Lausanne, Lausanne, Switzerland.
Jana KochDivision of Infectious Diseases and Hospital Epidemiology, Cantonal Hospital St.Gallen, St.Gallen, Switzerland.
Pr Olivier HugliEmergency Department, University Hospital of Lausanne, Lausanne, Switzerland.
Andrea M WeckmanTropical Disease Unit, Department of Medicine, Sandra Rotman Centre for Global Health, University of Toronto, University Health Network-Toronto General, Toronto, ON, Canada.
Kathleen ZhongTropical Disease Unit, Department of Medicine, Sandra Rotman Centre for Global Health, University of Toronto, University Health Network-Toronto General, Toronto, ON, Canada.
Taylor J KainTropical Disease Unit, Department of Medicine, Sandra Rotman Centre for Global Health, University of Toronto, University Health Network-Toronto General, Toronto, ON, Canada.
Aleksandra LeligdowiczDepartment of Medicine, Western University, London, Ontario, Canada.
Enos BernasconiDivision of infectious diseases, Ente Ospedaliero Cantonale, University of Geneva and University of Southern Switzerland, Lugano, Lugano, Switzerland.
Alessandro CeschiDivision of infectious diseases, Ente Ospedaliero Cantonale, University of Geneva and University of Southern Switzerland, Lugano, Lugano, Switzerland.
Sara ParolariDepartment of Infectious Diseases and Hospital Epidemiology, Cantonal Hospital Muensterlingen, Thurgau Hospital Group, Muensterlingen, Switzerland.
Danielle Vuichard-GysinDepartment of Infectious Diseases and Hospital Epidemiology, Cantonal Hospital Muensterlingen, Thurgau Hospital Group, Muensterlingen, Switzerland.
Kevin C KainTropical Disease Unit, Department of Medicine, Sandra Rotman Centre for Global Health, University of Toronto, University Health Network-Toronto General, Toronto, ON, Canada.
Werner C Albrich *Division of Infectious Diseases and Hospital Epidemiology, Cantonal Hospital St.Gallen, St.Gallen, Switzerland.
Noémie Boillat-Blanco *Infectious Diseases Service, University Hospital of Lausanne, Lausanne, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundA prospective observational cohort study of COVID-19 patients in a single Emergency Department (ED) showed that sTREM-1- and IL-6-based algorithms were highly predictive of adverse outcome (Van Singer et al. J Allergy Clin Immunol 2021). We aim to validate the performance of these algorithms at ED presentation.

methodsThis multicentric prospective observational study of PCR-confirmed COVID-19 adult patients was conducted in the ED of three Swiss hospitals. Data of the three centers were retrospectively completed and merged. We determined the predictive accuracy of the sTREM-1-based algorithm for 30-day intubation/mortality. We also determined the performance of the IL-6-based algorithm using data from one center for 30-day oxygen requirement.

results373 patients were included in the validation cohort, 139 (37%) in Lausanne, 93 (25%) in St.Gallen and 141 (38%) in EOC. Overall, 18% (93/373) patients died or were intubated by day 30. In Lausanne, 66% (92/139) patients required oxygen by day 30. The predictive accuracy of sTREM-1 and IL-6 were similar compared to the derivation cohort. The sTREM-1-based algorithm confirmed excellent sensitivity (90% versus 100% in the derivation cohort) and negative predictive value (94% versus 100%) for 30-day intubation/mortality. The IL-6-based algorithm performance was acceptable with a sensitivity of 85% versus 98% in the derivation cohort and a negative predictive value of 60% versus 92%.

conclusionThe sTREM-1 algorithm demonstrated good reproducibility. A prospective randomized controlled trial, comparing outcomes with and without the algorithm, is necessary to assess its safety and impact on hospital and ICU admission rates. The IL-6 algorithm showed acceptable validity in a single center and need additional validation before widespread implementation.

Indexed as

COVID-19AdultAlgorithmsHumansInterleukin-6Prospective StudiesReproducibility of ResultsRetrospective StudiesInterleukin-6BiomarkersClinical support decision toolCOVID-19sTREM-1Validation study

Identifiers

PMID37752433
PMCPMC10523774

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.