Evidence map›Paper›PMID 32490853›Full record

ArticleLab on a chip2020

Clinical decision support tool and rapid point-of-care platform for determining disease severity in patients with COVID-19.

Michael P McRae, Glennon W Simmons, Nicolaos J Christodoulides, Zhibing Lu, Stella K Kang, David Fenyo, Timothy Alcorn, Isaac P Dapkins, Iman Sharif, Deniz Vurmaz and 5 more

Abstract read
In one paragraph

Article in Lab on a chip, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
53citing papers in PubMed, 6 pooled it
–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

53 citing papers in PubMed, 6 syntheses or guidelines pooled it.

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  12. A brief review of cytology in dentistry.British dental journal · 2024
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

15 authors.

Michael P McRaeDepartment of Biomaterials, Bioengineering Institute, New York University, 433 First Avenue, Room 820, New York, NY 10010-4086, USA. michael.mcrae@nyu.edu glennon.simmons@nyu.edu nicolaoschristo19@gmail.com sayli.modak@nyu.edu shrutiwp@gmail.com rks390@nyu.edu mcdevitt@nyu.edu.
Glennon W Simmons
Nicolaos J Christodoulides
Zhibing Lu
Stella K Kang
David Fenyo
Timothy Alcorn
Isaac P Dapkins
Iman Sharif
Deniz Vurmaz
Sayli S Modak
Kritika Srinivasan
Shruti Warhadpande
Ravi Shrivastav
John T McDevitt

Funding

Effects of Oral Inflammation on the Levels of Salivary Biomarkers of AMIU01DE017793 · NIDCR · UNIVERSITY OF TEXAS AT AUSTIN · PI MCDEVITT, JOHN T · 2006 to 2012
$13.0M
NIDCR NIH HHS 3U01DE017793-02S1NIDCR NIH HHS 5U01DE017793-2NIDCR NIH HHS U01 DE017793
6 · The paper itself

Abstract

SARS-CoV-2 is the virus that causes coronavirus disease (COVID-19) which has reached pandemic levels resulting in significant morbidity and mortality affecting every inhabited continent. The large number of patients requiring intensive care threatens to overwhelm healthcare systems globally. Likewise, there is a compelling need for a COVID-19 disease severity test to prioritize care and resources for patients at elevated risk of mortality. Here, an integrated point-of-care COVID-19 Severity Score and clinical decision support system is presented using biomarker measurements of C-reactive protein (CRP), N-terminus pro B type natriuretic peptide (NT-proBNP), myoglobin (MYO), D-dimer, procalcitonin (PCT), creatine kinase-myocardial band (CK-MB), and cardiac troponin I (cTnI). The COVID-19 Severity Score combines multiplex biomarker measurements and risk factors in a statistical learning algorithm to predict mortality. The COVID-19 Severity Score was trained and evaluated using data from 160 hospitalized COVID-19 patients from Wuhan, China. Our analysis finds that COVID-19 Severity Scores were significantly higher for the group that died versus the group that was discharged with median (interquartile range) scores of 59 (40-83) and 9 (6-17), respectively, and area under the curve of 0.94 (95% CI 0.89-0.99). Although this analysis represents patients with cardiac comorbidities (hypertension), the inclusion of biomarkers from other pathophysiologies implicated in COVID-19 (e.g., D-dimer for thrombotic events, CRP for infection or inflammation, and PCT for bacterial co-infection and sepsis) may improve future predictions for a more general population. These promising initial models pave the way for a point-of-care COVID-19 Severity Score system to impact patient care after further validation with externally collected clinical data. Clinical decision support tools for COVID-19 have strong potential to empower healthcare providers to save lives by prioritizing critical care in patients at high risk for adverse outcomes.

Indexed as

Point-of-Care SystemsAlgorithmsBiomarkersComorbidityCoronavirus InfectionsCOVID-19Critical CareDecision Support Systems, ClinicalHumansImage Processing, Computer-AssistedImmunoassayMachine LearningPandemicsPneumonia, ViralPredictive Value of TestsRisk FactorsBiomarkers

Identifiers

PMID32490853
PMCPMC7360344

What OpenQuestion holds

Textmetadata
LicenceTDM
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.