Evidence map›Paper›PMID 35242727›Full record

ArticleFrontiers in pediatrics2021

Bayesian Modeling Immune Reconstitution Apply to CD34+ Selected Stem Cell Transplantation for Severe Combined Immunodeficiency.

Jean-Sebastien Diana, Naïm Bouazza, Chloe Couzin, Martin Castelle, Alessandra Magnani, Elisa Magrin, Jeremie Rosain, Jean-Marc Treluyer, Capucine Picard, Despina Moshous and 3 more

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Article in Frontiers in pediatrics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

13 authors.

Jean-Sebastien DianaBiotherapy Department, Hôpital Necker Enfants Malades, Assistance Publique-Hôpitaux de Paris, Paris, France.
Naïm BouazzaUniversité de Paris, Paris, France.
Chloe CouzinBiotherapy Department, Hôpital Necker Enfants Malades, Assistance Publique-Hôpitaux de Paris, Paris, France.
Martin CastellePediatric Hematology-Immunology-Rheumatology Unit, Hôpital Necker Enfants Malades Assistance Publique-Hôpitaux de Paris, Paris, France.
Alessandra MagnaniBiotherapy Department, Hôpital Necker Enfants Malades, Assistance Publique-Hôpitaux de Paris, Paris, France.
Elisa MagrinBiotherapy Department, Hôpital Necker Enfants Malades, Assistance Publique-Hôpitaux de Paris, Paris, France.
Jeremie RosainLaboratory of Human Genetics of Infectious Diseases, Hôpital Necker Enfants Malades Assistance Publique-Hôpitaux de Paris, Paris, France.
Jean-Marc TreluyerUniversité de Paris, Paris, France.
Capucine PicardUniversité de Paris, Paris, France.
Despina MoshousUniversité de Paris, Paris, France.
Stéphane BlancheUniversité de Paris, Paris, France.
Bénédicte NevenUniversité de Paris, Paris, France.
Marina CavazzanaBiotherapy Department, Hôpital Necker Enfants Malades, Assistance Publique-Hôpitaux de Paris, Paris, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Severe combined immunodeficiencies (SCIDs) correspond to the most severe form of primary immunodeficiency. Allogeneic hematopoietic stem cell transplantation (HSCT) and gene therapy are curative treatments, depending on the donor's availability and molecular diagnostics. A partially human leukocyte antigen (HLA)-compatible donor used has been developed for this specific HSCT indication in the absence of a matched donor. However, the CD34+ selected process induces prolonged post-transplant T-cell immunodeficiency. The aim here was to investigate a modeling approach to predict the time course and the extent of CD4+ T-cell immune reconstitution after CD34+ selected transplantation. We performed a Bayesian approach based on the age-related changes in thymic output and the cell proliferation/loss model. For that purpose, we defined specific individual covariates from the data collected from 10 years of clinical practice and then evaluated the model's predicted performances and accuracy. We have shown that this Bayesian modeling approach predicted the time course and extent of CD4+ T-cell immune reconstitution after SCID transplantation.

Indexed as

Bayesian prediction algorithmCD34+ selectionhematopoietic stem cell transplantationimmune reconstitutionsevere combined immunodeficiency (SCID)

Identifiers

PMID35242727
PMCPMC8885722

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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.