Evidence map›Paper›PMID 38672225›Full record

ArticleBiomedicines2024

Decoding Immuno-Competence: A Novel Analysis of Complete Blood Cell Count Data in COVID-19 Outcomes.

Prakasha Kempaiah, Claudia R Libertin, Rohit A Chitale, Islam Naeyma, Vasili Pleqi, Johnathan M Sheele, Michelle J Iandiorio, Almira L Hoogesteijn, Thomas R Caulfield, Ariel L Rivas

Abstract read
In one paragraph

Article in Biomedicines, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
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.

Prakasha KempaiahDepartment of Medicine, Division of Infectious Diseases, Mayo Clinic, Jacksonville, FL 32224, USA.ORCID 0000-0002-2901-0339
Claudia R LibertinDepartment of Critical Care, Mayo Clinic, Jacksonville, FL 32224, USA.ORCID 0000-0001-9649-6802
Rohit A ChitaleMayo Clinic Alix School of Medicine, Mayo Clinic, Jacksonville, FL 32224, USA.
Islam NaeymaDepartment of Neuroscience, Division of QHS Computational Biology, Mayo Clinic, Jacksonville, FL 32224, USA.
Vasili PleqiDepartment of Medicine, Division of Infectious Diseases, Mayo Clinic, Jacksonville, FL 32224, USA.
Johnathan M SheeleDepartment of Emergency Medicine, Mayo Clinic, Jacksonville, FL 32224, USA.ORCID 0000-0003-4503-8590
Michelle J IandiorioDepartment of Internal Medicine, School of Medicine, University of New Mexico, Albuquerque, NM 87131, USA.
Almira L HoogesteijnHuman Ecology, Centro de Investigaciones Avanzadas, Merida 97310, Mexico.
Thomas R CaulfieldDepartment of Neuroscience, Division of QHS Computational Biology, Mayo Clinic, Jacksonville, FL 32224, USA.ORCID 0000-0002-1055-3069
Ariel L RivasCenter for Global Health, Department of Internal Medicine, School of Medicine, University of New Mexico, Albuquerque, NM 87131, USA.ORCID 0000-0002-2527-6906

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWhile 'immuno-competence' is a well-known term, it lacks an operational definition. To address this omission, this study explored whether the temporal and structured data of the complete blood cell count (CBC) can rapidly estimate immuno-competence. To this end, one or more ratios that included data on all monocytes, lymphocytes and neutrophils were investigated. MATERIALS AND

methodsLongitudinal CBC data collected from 101 COVID-19 patients (291 observations) were analyzed. Dynamics were estimated with several approaches, which included non-structured (the classic CBC format) and structured data. Structured data were assessed as complex ratios that capture multicellular interactions among leukocytes. In comparing survivors with non-survivors, the hypothesis that immuno-competence may exhibit feedback-like (oscillatory or cyclic) responses was tested.

resultsWhile non-structured data did not distinguish survivors from non-survivors, structured data revealed immunological and statistical differences between outcomes: while survivors exhibited oscillatory data patterns, non-survivors did not. In survivors, many variables (including IL-6, hemoglobin and several complex indicators) showed values above or below the levels observed on day 1 of the hospitalization period, displaying L-shaped data distributions (positive kurtosis). In contrast, non-survivors did not exhibit kurtosis. Three immunologically defined data subsets included only survivors. Because information was based on visual patterns generated in real time, this method can, potentially, provide information rapidly. DISCUSSION: The hypothesis that immuno-competence expresses feedback-like loops when immunological data are structured was not rejected. This function seemed to be impaired in immuno-suppressed individuals. While this method rapidly informs, it is only a guide that, to be confirmed, requires additional tests. Despite this limitation, the fact that three protective (survival-associated) immunological data subsets were observed since day 1 supports many clinical decisions, including the early and personalized prognosis and identification of targets that immunomodulatory therapies could pursue. Because it extracts more information from the same data, structured data may replace the century-old format of the CBC.

Indexed as

COVID-19immune-competenceinfectious disease

Identifiers

PMID38672225
PMCPMC11048687

What Socratic holds

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LicenceCC BY
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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 Socratic graph.