Evidence map›Paper›PMID 42762172›Full record

ArticleTransfusion2026

How does a national blood operator detect early infectious disease signals affecting blood and cellular therapy product safety and sufficiency?

Leisa Bruneau, Carmen L Charlton, Sandra Ramirez-Arcos, Dilini Kumaran, David Allan, Olivia Norman, Matthew Seftel, Evan M Bloch, Chantale Pambrun, Sheila F O'Brien and 2 more

Abstract readCase Reports
In one paragraph

Article in Transfusion, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

12 authors.

Leisa BruneauSurveillance and Discovery Laboratory, Canadian Blood Services, Ottawa, Ontario, Canada.
Carmen L CharltonMedical Microbiology, Innovation, Canadian Blood Services, Ottawa, Ontario, Canada.ORCID https://orcid.org/0000-0002-9129-5136
Sandra Ramirez-ArcosMedical Microbiology, Innovation, Canadian Blood Services, Ottawa, Ontario, Canada.ORCID https://orcid.org/0000-0003-0705-4987
Dilini KumaranSurveillance and Discovery Laboratory, Canadian Blood Services, Ottawa, Ontario, Canada.
David AllanTransplant & Cellular Therapy, Hematology, Department of Medicine, The Ottawa Hospital, Ottawa, Ontario, Canada.ORCID https://orcid.org/0000-0003-3261-8289
Olivia NormanStem Cells, Canadian Blood Services, Ottawa, Ontario, Canada.
Matthew SeftelStem Cells, Canadian Blood Services, Vancouver, British Columbia, Canada.
Evan M BlochDivision of Transfusion Medicine, Department of Pathology, Johns Hopkins University, Baltimore, Maryland, USA.ORCID https://orcid.org/0000-0001-8181-9517
Chantale PambrunInnovation, Canadian Blood Services, Ottawa, Ontario, Canada.ORCID https://orcid.org/0000-0002-4512-7545
Sheila F O'BrienMedical Affairs, Canadian Blood Services, Ottawa, Ontario, Canada.ORCID https://orcid.org/0000-0002-5332-2789
Cynthia CranneySurveillance and Discovery Laboratory, Canadian Blood Services, Ottawa, Ontario, Canada.
Steven J DrewsMedical Microbiology, Innovation, Canadian Blood Services, Ottawa, Ontario, Canada.ORCID https://orcid.org/0000-0003-2519-1109

Funding

Canadian Blood Services
6 · The paper itself

Abstract

backgroundPublic access to up-to-date information on infectious diseases and outbreaks has grown rapidly with the advent of artificial intelligence platforms. While this information can improve patient care and support research, the sheer volume of available data coupled with the challenge of identifying misinformation and disinformation can be overwhelming. A key challenge is managing large information volumes and identifying early, subtle signals that may indicate risks or opportunities to prevent infection. The aim of this manuscript is to explain how a national blood operator identifies and evaluates subtle infectious disease signals generated by horizon scanning to proactively protect the blood supply, including biotherapy products.

methodsHorizon scanning protocols were developed to detect early signals of change (emerging/re-emerging pathogens, geopolitical/environmental changes, scientific and technological advances, new therapies/treatments for infectious diseases, and blood operator transfusion medicine changes). Communication strategies were created to alert national and international transfusion medicine decision makers of blood safety-related events.

resultsSeveral key components of a horizon scanning approach are presented to illustrate how novel information can be identified, processed, and formulated into knowledge translation products. Efficient and cost-effective approaches are presented to enable horizon scanning activities with limited resources and time commitments. DISCUSSION: Horizon scanning is an efficient and necessary activity to safeguard the blood supply. Here we present a collaborative group-based approach, which reduces operator bias and improves overall examination of multiple data sources. This approach does not require expensive technology, can focus on open-source information, and engages new scientists in learning about infectious diseases.

Indexed as

Blood SafetyBlood TransfusionCommunicable DiseasesBlood BanksHumansblood safetyemerging pathogenshorizon scanningre‐emerging pathogenstransfusion medicine

Identifiers

PMID42762172
PMCPMC13674131

What OpenQuestion holds

Textmetadata
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.