Predictors of Anemia Intolerance for Real-Time Transfusion Decision-Making During Resuscitation of Trauma Subjects: A Machine Learning Approach Using Heart Rate Variability

HRV parameters collected during the first 5-10 minutes after admission, when combined with basic clinical information that is immediately available upon emergency admission, augmented ability to predict potential for RBC transfusion, suggesting this metric may be incorporated into structured approaches to personalized transfusion decision-making.

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Publication Details

Journal
Critical Care Explorations
Published
2025-09-22
DOI
https://doi.org/10.1097/cce.0000000000001319
Primary Topic
Cardiac Arrest and Resuscitation
Type
article
Field-Weighted Citation Impact
0.00
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article

Predictors of Anemia Intolerance for Real-Time Transfusion Decision-Making During Resuscitation of Trauma Subjects: A Machine Learning Approach Using Heart Rate Variability

Ritik Goyal, Shiming Yang, Mathangi Gopalakrishnan, Chein‐I Chang et al.
Critical Care Explorations
Cardiac Arrest and Resuscitation
article

Predictors of Anemia Intolerance for Real-Time Transfusion Decision-Making During Resuscitation of Trauma Subjects: A Machine Learning Approach Using Heart Rate Variability

Ritik Goyal, Shiming Yang, Mathangi Gopalakrishnan, Chein‐I Chang, Allan Doctor, Bryon Jackson, Jie Chen, Peter Hu
article en

Abstract

HRV parameters collected during the first 5-10 minutes after admission, when combined with basic clinical information that is immediately available upon emergency admission, augmented ability to predict potential for RBC transfusion, suggesting this metric may be incorporated into structured approaches to personalized transfusion decision-making.

Critical Care ExplorationsVol. 7(10)
University of Maryland, Baltimore (US), University of Maryland Medical Center (US), University of Maryland, Baltimore County (US)
Openalex Percentile: Top 62%
Cardiac Arrest and Resuscitation
DOIOpen Access PDFOpenAlex
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