Evidence map›Paper›PMID 34413363›Full record

ArticleScientific reports2021

An approach to rapidly assess sepsis through multi-biomarker host response using machine learning algorithm.

Abha Umesh Sardesai, Ambalika Sanjeev Tanak, Subramaniam Krishnan, Deborah A Striegel, Kevin L Schully, Danielle V Clark, Sriram Muthukumar, Shalini Prasad

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

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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

8 authors.

Abha Umesh SardesaiDepartment of Computer Engineering, University of Texas at Dallas, 800 W. Campbell Rd., Richardson, TX, USA.
Ambalika Sanjeev TanakDepartment of Bioengineering, University of Texas at Dallas, 800 W. Campbell Rd. BSB 11, Richardson, TX, USA.
Subramaniam KrishnanAustere Environments Consortium for Enhanced Sepsis Outcomes (ACESO), Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, 20817, USA.
Deborah A StriegelAustere Environments Consortium for Enhanced Sepsis Outcomes (ACESO), Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, 20817, USA.
Kevin L SchullyBiological Defense Research Directorate, Naval Medical Research Center-Frederick, Ft. Detrick, MD, 21702, USA.
Danielle V ClarkAustere Environments Consortium for Enhanced Sepsis Outcomes (ACESO), Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, 20817, USA.
Sriram MuthukumarDepartment of Bioengineering, University of Texas at Dallas, 800 W. Campbell Rd. BSB 11, Richardson, TX, USA. sriramm@enlisense.com.
Shalini PrasadDepartment of Bioengineering, University of Texas at Dallas, 800 W. Campbell Rd. BSB 11, Richardson, TX, USA. shalini.prasad@utdallas.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Sepsis is a life-threatening condition and understanding the disease pathophysiology through the use of host immune response biomarkers is critical for patient stratification. Lack of accurate sepsis endotyping impedes clinicians from making timely decisions alongside insufficiencies in appropriate sepsis management. This work aims to demonstrate the potential feasibility of a data-driven validation model for supporting clinical decisions to predict sepsis host-immune response. Herein, we used a machine learning approach to determine the predictive potential of identifying sepsis host immune response for patient stratification by combining multiple biomarker measurements from a single plasma sample. Results were obtained using the following cytokines and chemokines IL-6, IL-8, IL-10, IP-10 and TRAIL where the test dataset was 70%. Supervised machine learning algorithm naïve Bayes and decision tree algorithm showed good accuracy of 96.64% and 94.64%. These promising findings indicate the proposed AI approach could be a valuable testing resource for promoting clinical decision making.

Indexed as

AlgorithmsMachine LearningBayes TheoremBiomarkersCase-Control StudiesClinical Decision-MakingHumansReproducibility of ResultsSepsisBiomarkers

Identifiers

PMID34413363
PMCPMC8377018

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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.