Novel computational methods on electronic health record yields new estimates of transfusion-associated circulatory overload in populations enriched with high-risk patients

Michelle Wang; Gregory M Goldgof; Ayan Patel; Barbee Whitaker; Artur Belov; Brian Chan; Evan Phelps; Benjamin Rubin; Steven Anderson; Atul J Butte

doi:10.1111/trf.17447

Novel computational methods on electronic health record yields new estimates of transfusion-associated circulatory overload in populations enriched with high-risk patients

Transfusion. 2023 Jul;63(7):1298-1309. doi: 10.1111/trf.17447. Epub 2023 May 29.

Authors

Michelle Wang^{1

2

3}, Gregory M Goldgof^{1

4}, Ayan Patel^{1

2}, Barbee Whitaker⁵, Artur Belov⁵, Brian Chan¹, Evan Phelps¹, Benjamin Rubin^{1

2}, Steven Anderson⁵, Atul J Butte^{1

2

6}

Affiliations

¹ Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, California, USA.
² Department of Pediatrics, University of California, San Francisco, San Francisco, California, USA.
³ Graduate Program in Pharmaceutical Sciences and Pharmacogenomics, University of California, San Francisco, San Francisco, California, USA.
⁴ Department of Laboratory Medicine, University of California, San Francisco, San Francisco, California, USA.
⁵ Office of Biostatistics & Epidemiology, Center for Biologics Evaluation and Research, US Food and Drug Administration (FDA), Silver Spring, Maryland, USA.
⁶ Center for Data-driven Insights and Innovation, University of California Health, Oakland, USA.

PMID: 37248741
PMCID: PMC10449535 (available on 2024-07-01)
DOI: 10.1111/trf.17447

Abstract

Background: Transfusion-associated circulatory overload (TACO) is a severe adverse reaction (AR) contributing to the leading cause of mortality associated with transfusions. As strategies to mitigate TACO have been increasingly adopted, an update of prevalence rates and risk factors associated with TACO using the growing sources of electronic health record (EHR) data can help understand transfusion safety.

Study design and methods: This retrospective study aimed to provide a timely and reproducible assessment of prevalence rates and risk factors associated with TACO. Novel natural language processing methods, now made publicly available on GitHub, were developed to extract ARs from 3178 transfusion reaction reports. Other patient-level data were extracted computationally from UCSF EHR between 2012 and 2022. The odds ratio estimates of risk factors were calculated using a multivariate logistic regression analysis with case-to-control matched on sex and age at a ratio of 1:5.

Results: A total of 56,208 patients received transfusions (total 573,533 units) at UCSF during the study period and 102 patients developed TACO. The prevalence of TACO was estimated to be 0.2% per patient (102/total 56,208). Patients with a history of coagulopathy (OR, 1.36; 95% CI, 1.04-1.79) and transplant (OR, 1.99; 95% CI, 1.48-2.68) were associated with increased odds of TACO.

Discussion: While TACO is a serious AR, events remained rare, even in populations enriched with high-risk patients. Novel computational methods can be used to find and continually surveil for transfusion ARs. Results suggest that patients with history or presence of coagulopathy and organ transplant should be carefully monitored to mitigate potential risks of TACO.

Keywords: Health research methodology; Transfusion complications-non infectious.

Publication types

Research Support, U.S. Gov't, P.H.S.
Research Support, N.I.H., Extramural

MeSH terms

Blood Transfusion / methods
Electronic Health Records*
Humans
Retrospective Studies
Risk Factors
Transfusion Reaction* / epidemiology

Abstract

Publication types

MeSH terms

Grants and funding