Evidence map›Paper›PMID 34262570›Full record

ArticleFrontiers in immunology

Immune-Based Prediction of COVID-19 Severity and Chronicity Decoded Using Machine Learning.

Bruce K Patterson, Jose Guevara-Coto, Ram Yogendra, Edgar B Francisco, Emily Long, Amruta Pise, Hallison Rodrigues, Purvi Parikh, Javier Mora, Rodrigo A Mora-Rodríguez

Open access · goldAbstract read
In one paragraph

Article in Frontiers in immunology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 109 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
109citing papers in PubMed, 4 pooled it
11.6field-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

109 citing papers in PubMed, 4 syntheses or guidelines pooled it, 203 citations in OpenAlex.

  1. Blood Biomarkers of Long COVID: A Systematic Review.Molecular diagnosis & therapy · 2024
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  11. The Central Role of Macrophages in Long COVID Pathophysiology.International journal of molecular sciences · 2025
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49 more citing papers are in PubMed but not listed here.

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

10 authors at 3 institutions in 2 countries.

Bruce K PattersonIncellDx Inc, San Carlos, CA, United States.
Jose Guevara-CotoDepartment of Computer Science and Informatics (ECCI), Universidad de Costa Rica, San Jose, Costa Rica.
Ram YogendraLab of Tumor Chemosensitivity, CIET/DC Lab, Faculty of Microbiology, Universidad de Costa Rica, San Jose, Costa Rica.
Edgar B FranciscoIncellDx Inc, San Carlos, CA, United States.
Emily LongIncellDx Inc, San Carlos, CA, United States.
Amruta PiseIncellDx Inc, San Carlos, CA, United States.
Hallison RodriguesIncellDx Inc, San Carlos, CA, United States.
Purvi ParikhDepartment of Allergy and Immunology, NYU Langone Tisch Hospital, New York, NY, United States.
Javier MoraLab of Tumor Chemosensitivity, CIET/DC Lab, Faculty of Microbiology, Universidad de Costa Rica, San Jose, Costa Rica.
Rodrigo A Mora-RodríguezLab of Tumor Chemosensitivity, CIET/DC Lab, Faculty of Microbiology, Universidad de Costa Rica, San Jose, Costa Rica.
Incell Corporation (United States) · USUniversidad de Costa Rica · CRTisch Hospital · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Expression of CCR5 and its cognate ligands have been implicated in COVID-19 pathogenesis, consequently therapeutics directed against CCR5 are being investigated. Here, we explored the role of CCR5 and its ligands across the immunologic spectrum of COVID-19. We used a bioinformatics approach to predict and model the immunologic phases of COVID so that effective treatment strategies can be devised and monitored. We investigated 224 individuals including healthy controls and patients spanning the COVID-19 disease continuum. We assessed the plasma and isolated peripheral blood mononuclear cells (PBMCs) from 29 healthy controls, 26 Mild-Moderate COVID-19 individuals, 48 Severe COVID-19 individuals, and 121 individuals with post-acute sequelae of COVID-19 (PASC) symptoms. Immune subset profiling and a 14-plex cytokine panel were run on all patients from each group. B-cells were significantly elevated compared to healthy control individuals (P<0.001) as was the CD14+, CD16+, CCR5+ monocytic subset (P<0.001). CD4 and CD8 positive T-cells expressing PD-1 as well as T-regulatory cells were significantly lower than healthy controls (P<0.001 and P=0.01 respectively). CCL5/RANTES, IL-2, IL-4, CCL3, IL-6, IL-10, IFN-γ, and VEGF were all significantly elevated compared to healthy controls (all P<0.001). Conversely GM-CSF and CCL4 were in significantly lower levels than healthy controls (P=0.01). Data were further analyzed and the classes were balanced using SMOTE. With a balanced working dataset, we constructed 3 random forest classifiers: a multi-class predictor, a Severe disease group binary classifier and a PASC binary classifier. Models were also analyzed for feature importance to identify relevant cytokines to generate a disease score. Multi-class models generated a score specific for the PASC patients and defined as S1 = (IFN-γ + IL-2)/CCL4-MIP-1β. Second, a score for the Severe COVID-19 patients was defined as S2 = (IL-6+sCD40L/1000 + VEGF/10 + 10*IL-10)/(IL-2 + IL-8). Severe COVID-19 patients are characterized by excessive inflammation and dysregulated T cell activation, recruitment, and counteracting activities. While PASC patients are characterized by a profile able to induce the activation of effector T cells with pro-inflammatory properties and the capacity of generating an effective immune response to eliminate the virus but without the proper recruitment signals to attract activated T cells.

Indexed as

Machine LearningSeverity of Illness IndexAlgorithmsAntibodies, ViralCase-Control StudiesCD8-Positive T-LymphocytesChemokine CCL5Computational BiologyCOVID-19FemaleHumansLymphocyte ActivationMalePost-Acute COVID-19 SyndromePrognosisReceptors, CCR5Antibodies, ViralCCL5 protein, humanCCR5 protein, humanChemokine CCL5Receptors, CCR5RNA, ViralCCR5chemokinesCOVID-19cytokinesPASC

Identifiers

PMID34262570
PMCPMC8273732
OpenAlexW3175290159

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.