Evidence map›Paper›PMID 26954292›Full record

ArticlePloS one2016

Highly Predictive Model for a Protective Immune Response to the A(H1N1)pdm2009 Influenza Strain after Seasonal Vaccination.

Karsten Jürchott, Axel Ronald Schulz, Cecilia Bozzetti, Dominika Pohlmann, Ulrik Stervbo, Sarah Warth, Julia Nora Mälzer, Julian Waldner, Brunhilde Schweiger, Sven Olek and 4 more

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.4field-weighted citation impact, top 18% 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

6 citing papers in PubMed, 14 citations in OpenAlex.

  1. Trial
  2. Article
  3. Review
  4. Review
  5. Article
  6. 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

14 authors at 5 institutions in 3 countries.

Karsten JürchottBerlin-Brandenburg Center for Regenerative Therapies (BCRT), Charité University Hospital, Berlin, Germany.
Axel Ronald SchulzBerlin-Brandenburg Center for Regenerative Therapies (BCRT), Charité University Hospital, Berlin, Germany.
Cecilia BozzettiBerlin-Brandenburg Center for Regenerative Therapies (BCRT), Charité University Hospital, Berlin, Germany.
Dominika PohlmannBerlin-Brandenburg Center for Regenerative Therapies (BCRT), Charité University Hospital, Berlin, Germany.
Ulrik StervboBerlin-Brandenburg Center for Regenerative Therapies (BCRT), Charité University Hospital, Berlin, Germany.
Sarah WarthBerlin-Brandenburg Center for Regenerative Therapies (BCRT), Charité University Hospital, Berlin, Germany.
Julia Nora MälzerBerlin-Brandenburg Center for Regenerative Therapies (BCRT), Charité University Hospital, Berlin, Germany.
Julian WaldnerBerlin-Brandenburg Center for Regenerative Therapies (BCRT), Charité University Hospital, Berlin, Germany.
Brunhilde SchweigerRobert Koch Institute, National Reference Center for Influenza, Berlin, Germany.
Sven OlekIvana Türbachova Laboratory for Epigentics, Epiontis GmbH, Berlin, Germany.
Andreas GrützkauGerman Rheumatism Research Centre (DRFZ), Berlin, Germany.
Nina BabelBerlin-Brandenburg Center for Regenerative Therapies (BCRT), Charité University Hospital, Berlin, Germany.
Andreas ThielBerlin-Brandenburg Center for Regenerative Therapies (BCRT), Charité University Hospital, Berlin, Germany.
Avidan Uriel NeumannBerlin-Brandenburg Center for Regenerative Therapies (BCRT), Charité University Hospital, Berlin, Germany.
Berlin-Brandenburger Centrum für Regenerative Therapien · DECharité - Universitätsmedizin Berlin · DEGerman Rheumatism Research Centre · DERobert Koch Institute · DEUniversity of Zurich · CH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Understanding the immune response after vaccination against new influenza strains is highly important in case of an imminent influenza pandemic and for optimization of seasonal vaccination strategies in high risk population groups, especially the elderly. Models predicting the best sero-conversion response among the three strains in the seasonal vaccine were recently suggested. However, these models use a large number of variables and/or information post- vaccination. Here in an exploratory pilot study, we analyzed the baseline immune status in young (<31 years, N = 17) versus elderly (≥50 years, N = 20) donors sero-negative to the newly emerged A(H1N1)pdm09 influenza virus strain and correlated it with the serological response to that specific strain after seasonal influenza vaccination. Extensive multi-chromatic FACS analysis (36 lymphocyte sub-populations measured) was used to quantitatively assess the cellular immune status before vaccination. We identified CD4+ T cells, and amongst them particularly naive CD4+ T cells, as the best correlates for a successful A(H1N1)pdm09 immune response. Moreover, the number of influenza strains a donor was sero-negative to at baseline (NSSN) in addition to age, as expected, were important predictive factors. Age, NSSN and CD4+ T cell count at baseline together predicted sero-protection (HAI≥40) to A(H1N1)pdm09 with a high accuracy of 89% (p-value = 0.00002). An additional validation study (N = 43 vaccinees sero-negative to A(H1N1)pdm09) has confirmed the predictive value of age, NSSN and baseline CD4+ counts (accuracy = 85%, p-value = 0.0000004). Furthermore, the inclusion of donors at ages 31-50 had shown that the age predictive function is not linear with age but rather a sigmoid with a midpoint at about 50 years. Using these results we suggest a clinically relevant prediction model that gives the probability for non-protection to A(H1N1)pdm09 influenza strain after seasonal multi-valent vaccination as a continuous function of age, NSSN and baseline CD4 count.

Indexed as

SeasonsVaccinationAdultAgedAge FactorsComputer SimulationGermanyHumansImmunity, InnateInfluenza A Virus, H1N1 SubtypeInfluenza, HumanInfluenza VaccinesLymphocyte ActivationLymphocyte CountMiddle AgedModels, StatisticalInfluenza Vaccines

Identifiers

PMID26954292
PMCPMC4782986
OpenAlexW2296299307

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.