Evidence map›Paper›PMID 37935793›Full record

ArticleCommunications medicine2023

Platelet aggregates detected using quantitative phase imaging associate with COVID-19 severity.

Christian Klenk, Johanna Erber, David Fresacher, Stefan Röhrl, Manuel Lengl, Dominik Heim, Hedwig Irl, Martin Schlegel, Bernhard Haller, Tobias Lahmer and 3 more

Open access · goldAbstract read
In one paragraph

Article in Communications medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
4.7field-weighted citation impact, top 5% 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

7 citing papers in PubMed, 18 citations in OpenAlex.

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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

13 authors at 1 institution in 1 country.

Christian Klenk *Heinz-Nixdorf-Chair of Biomedical Electronics, School of Computation, Information and Technology, Technical University of Munich, TranslaTUM, 81675, Munich, Germany.ORCID http://orcid.org/0000-0002-4664-8107
Johanna Erber *TUM School of Medicine and Health, Department of Clinical Medicine - Clinical Department for Internal Medicine II, University Medical Centre, Technical University of Munich, 81675, Munich, Germany.
David FresacherHeinz-Nixdorf-Chair of Biomedical Electronics, School of Computation, Information and Technology, Technical University of Munich, TranslaTUM, 81675, Munich, Germany.ORCID http://orcid.org/0000-0002-7650-8033
Stefan RöhrlChair for Data Processing, School of Computation, Information and Technology, Technical University of Munich, 80333, Munich, Germany.ORCID http://orcid.org/0000-0001-6277-3816
Manuel LenglChair for Data Processing, School of Computation, Information and Technology, Technical University of Munich, 80333, Munich, Germany.
Dominik HeimHeinz-Nixdorf-Chair of Biomedical Electronics, School of Computation, Information and Technology, Technical University of Munich, TranslaTUM, 81675, Munich, Germany.ORCID http://orcid.org/0000-0001-8463-1544
Hedwig IrlTUM School of Medicine and Health, Department of Clinical Medicine - Clinical Department of Anaesthesiology and Intensive Care Medicine, University Medical Centre, Technical University of Munich, 81675, Munich, Germany.ORCID http://orcid.org/0000-0001-8042-1910
Martin SchlegelTUM School of Medicine and Health, Department of Clinical Medicine - Clinical Department of Anaesthesiology and Intensive Care Medicine, University Medical Centre, Technical University of Munich, 81675, Munich, Germany.ORCID http://orcid.org/0000-0001-7054-9566
Bernhard HallerTUM School of Medicine and Health, Department of Clinical Medicine - Institute of AI and Informatics in Medicine, University Medical Centre, Technical University of Munich, 81675, Munich, Germany.ORCID http://orcid.org/0000-0002-9723-393X
Tobias LahmerTUM School of Medicine and Health, Department of Clinical Medicine - Clinical Department for Internal Medicine II, University Medical Centre, Technical University of Munich, 81675, Munich, Germany.
Klaus DiepoldChair for Data Processing, School of Computation, Information and Technology, Technical University of Munich, 80333, Munich, Germany.
Sebastian RaschTUM School of Medicine and Health, Department of Clinical Medicine - Clinical Department for Internal Medicine II, University Medical Centre, Technical University of Munich, 81675, Munich, Germany.ORCID http://orcid.org/0000-0002-7917-8017
Oliver HaydenHeinz-Nixdorf-Chair of Biomedical Electronics, School of Computation, Information and Technology, Technical University of Munich, TranslaTUM, 81675, Munich, Germany. oliver.hayden@tum.de.ORCID http://orcid.org/0000-0002-2678-8663
Technical University of Munich · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe clinical spectrum of acute SARS-CoV-2 infection ranges from an asymptomatic to life-threatening disease. Considering the broad spectrum of severity, reliable biomarkers are required for early risk stratification and prediction of clinical outcomes. Despite numerous efforts, no COVID-19-specific biomarker has been established to guide further diagnostic or even therapeutic approaches, most likely due to insufficient validation, methodical complexity, or economic factors. COVID-19-associated coagulopathy is a hallmark of the disease and is mainly attributed to dysregulated immunothrombosis. This process describes an intricate interplay of platelets, innate immune cells, the coagulation cascade, and the vascular endothelium leading to both micro- and macrothrombotic complications. In this context, increased levels of immunothrombotic components, including platelet and platelet-leukocyte aggregates, have been described and linked to COVID-19 severity.

methodsHere, we describe a label-free quantitative phase imaging approach, allowing the identification of cell-aggregates and their components at single-cell resolution within 30 min, which prospectively qualifies the method as point-of-care (POC) testing.

resultsWe find a significant association between the severity of COVID-19 and the amount of platelet and platelet-leukocyte aggregates. Additionally, we observe a linkage between severity, aggregate composition, and size distribution of platelets in aggregates.

conclusionsThis study presents a POC-compatible method for rapid quantitative analysis of blood cell aggregates in patients with COVID-19.

Identifiers

PMID37935793
PMCPMC10630365
OpenAlexW4388464572

What Socratic holds

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LicenceCC BY
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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 Socratic graph.