Evidence map›Paper›PMID 35437749›Full record

ArticleTransfusion2022

Analyzing real world data of blood transfusion adverse events: Opportunities and challenges.

Perrin Jhaveri, Selen Bozkurt, Axel Moyal, Artur Belov, Steven Anderson, Hua Shan, Barbee Whitaker, Tina Hernandez-Boussard

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Transfusion, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 citing papers in PubMed.

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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

8 authors.

Perrin JhaveriSchool of Medicine, Stanford University, Stanford, California, USA.
Selen BozkurtDepartment of Medicine (Biomedical Informatics), Stanford University School of Medicine, Stanford, California, USA.
Axel MoyalDepartment of Medicine (Biomedical Informatics), Stanford University School of Medicine, Stanford, California, USA.
Artur BelovCenter for Biologics Evaluation and Research, Office of Biostatistics and Epidemiology, US FDA, Silver Spring, Maryland, USA.ORCID 0000-0001-6848-1224
Steven AndersonCenter for Biologics Evaluation and Research, Office of Biostatistics and Epidemiology, US FDA, Silver Spring, Maryland, USA.
Hua ShanSchool of Medicine, Stanford University, Stanford, California, USA.
Barbee WhitakerCenter for Biologics Evaluation and Research, Office of Biostatistics and Epidemiology, US FDA, Silver Spring, Maryland, USA.ORCID 0000-0002-6311-6268
Tina Hernandez-BoussardSchool of Medicine, Stanford University, Stanford, California, USA.ORCID 0000-0001-6553-3455

Funding

UCSF-Stanford Center of Excellence in Regulatory Science and InnovationU01FD005978 · FDA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI ALTMAN, RUSS BIAGIO, GIACOMINI, KATHLEEN M · 2016 to 2025
$34.5M
FDA HHS U01 FD005978FDA HHS U01FD005978
6 · The paper itself

Abstract

backgroundBlood transfusions are a vital component of modern healthcare, yet adverse reactions to blood product transfusions can cause morbidity, and rarely result in mortality. Therefore, accurate reporting of transfusion related adverse events (TRAEs) is paramount to improved transfusion practice. This study aims to investigate real-world data (RWD) on TRAEs by evaluating differences between ICD 9/10-based electronic health records (EHR) and blood bank-specific reporting. STUDY DESIGN AND

methodsTRAE data were retrospectively collected from a blood bank-specific database between Jan 2015 and June 2019 as the reference data source and compared it to ICD 9/10 diagnostic codes corresponding to various TRAEs. Seven reactions that have corresponding ICD 9/10 diagnostic codes were evaluated: Transfusion related circulatory overload (TACO), transfusion related acute lung injury (TRALI), febrile non-hemolytic reaction (FNHTR), transfusion-related anaphylactic reaction (TRA), acute hemolytic transfusion reaction (AHTR), delayed hemolytic transfusion reaction (DHTR), and delayed serologic reaction (DSTR). These accounted for 33% of the TRAEs at an academic institution during the study period.

resultsAmong 18637 adult blood transfusion recipients, there were 229 unique patients with 263 TRAE related ICD codes in the EHR, while there were 191 unique patients with 287 TRAEs identified in the blood bank database. None of the categories of reaction we investigated had perfect alignment between ICD 9/10 codes and blood bank specific diagnoses. DISCUSSION: Multiple systemic challenges were identified that hinder effective reporting of TRAEs. Identifying factors causing inconsistent reporting between blood banks and EHRs is paramount to developing effective workability between these electronic systems, as well as across clinical and laboratory teams.

Indexed as

Transfusion ReactionTransfusion-Related Acute Lung InjuryAdultBlood BanksBlood TransfusionFeverHumansRetrospective Studiesblood transfusionselectronic health recordstransfusion related adverse events

Identifiers

PMID35437749
PMCPMC9450944

What OpenQuestion holds

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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.