Evidence map›Paper›PMID 30283724›Full record

ArticleNature biomedical engineering2018

Diagnosis of sepsis from a drop of blood by measurement of spontaneous neutrophil motility in a microfluidic assay.

Felix Ellett, Julianne Jorgensen, Anika L Marand, Yuk Ming Liu, Myriam M Martinez, Vicki Sein, Kathryn L Butler, Jarone Lee, Daniel Irimia

Open access · greenAbstract read
In one paragraph

Article in Nature biomedical engineering, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers.

0numbers the graph read from it
0cells of the map it votes in
62citing papers in PubMed
9.9field-weighted citation impact, top 1% 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

62 citing papers in PubMed, 107 citations in OpenAlex.

  1. Leveraging Microphysiological Systems to Facilitate Neutrophil-Based Cancer Immunotherapy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Review
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  4. Observational
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  12. [Inertial label-free sorting and chemotaxis of polymorphonuclear neutrophil in sepsis patients based on microfluidic technology].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2023
    Article
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2 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors at 3 institutions in 1 country.

Felix EllettBioMEMS Resource Center, Department of Surgery, Massachusetts General Hospital, Boston, MA, USA.
Julianne JorgensenBioMEMS Resource Center, Department of Surgery, Massachusetts General Hospital, Boston, MA, USA.
Anika L MarandBioMEMS Resource Center, Department of Surgery, Massachusetts General Hospital, Boston, MA, USA.
Yuk Ming LiuShriners Burns Hospital, Boston, MA, USA.
Myriam M MartinezDepartment of Surgery, Massachusetts General Hospital, Boston, MA, USA.
Vicki SeinHarvard Medical School, Boston, MA, USA.
Kathryn L ButlerHarvard Medical School, Boston, MA, USA.
Jarone LeeHarvard Medical School, Boston, MA, USA.ORCID http://orcid.org/0000-0002-4532-8523
Daniel IrimiaBioMEMS Resource Center, Department of Surgery, Massachusetts General Hospital, Boston, MA, USA. dirimia@mgh.harvard.edu.ORCID http://orcid.org/0000-0001-7347-2082
Harvard University · USMassachusetts General Hospital · USShriners Hospitals for Children - Erie · US

Funding

Harvard Clinical and Translational Science CenterUL1TR001102 · NCATS · HARVARD MEDICAL SCHOOL · PI NADLER, LEE MARSHALL · 2013 to 2017
$106.2M
Bio-MicroElectroMechanical Systems Resource CenterP41EB002503 · NIBIB · MASSACHUSETTS GENERAL HOSPITAL · PI TONER, MEHMET · 2004 to 2018
$17.4M
Neutrophil Decision Making in Confined Environments in Health and DiseaseR01GM092804 · NIGMS · MASSACHUSETTS GENERAL HOSPITAL · PI IRIMIA, DANIEL · 2010 to 2022
$4.3M
Microfluidic Assessment of Clinical Outcomes in Preterm NewbornsR01HD089939 · NICHD · UNIVERSITY OF FLORIDA · PI IRIMIA, DANIEL, MOLDAWER, LYLE L · 2017 to 2021
$3.3M
Microfluidic tools for probing neutrophil reversed migrationR21AI113937 · NIAID · MASSACHUSETTS GENERAL HOSPITAL · PI IRIMIA, DANIEL · 2015 to 2016
$479k
NCATS NIH HHS UL1 TR001102NIAID NIH HHS R21 AI113937NIBIB NIH HHS P41 EB002503NICHD NIH HHS R01 HD089939NIGMS NIH HHS R01 GM092804
6 · The paper itself

Abstract

Current methods for the diagnosis of sepsis have insufficient precision, causing regular misdiagnoses. Microbiological tests can help diagnose sepsis but are usually too slow to have an impact on timely clinical-decision making. Neutrophils have high sensitivity to infections, yet measurements of neutrophil surface markers, genomic changes, and phenotype alterations have had only a marginal effect on sepsis diagnosis. Here, we report a microfluidic assay that measures the spontaneous motility of neutrophils in the context of plasma, in one droplet of blood. We measured the performance of the assay in two independent cohorts of critically ill patients suspected of sepsis. In the first cohort, we developed a machine-learning-based scoring system (sepsis score) that segregated patients with sepsis from those without sepsis. In the second cohort, we validated the sepsis score in a double-blinded, prospective case-control study. For the 42 patients across the two cohorts, the assay identified sepsis patients with 97% sensitivity and 98% specificity. The neutrophil assay could potentially be used to accurately diagnose and monitor sepsis in larger populations of at-risk patients.

Identifiers

PMID30283724
PMCPMC6166236
OpenAlexW2792443570

What OpenQuestion holds

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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 OpenQuestion graph.