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September 22, 2025Critical Care ExplorationsOpen Access

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

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

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Key Points

High predictive performance for transfusion needs was achieved with an AUROC of 0.86 using mixed clinical and HRV data.
The machine learning model demonstrated a sensitivity of 78% and specificity of 75%, indicating reliability in identifying transfusion candidates.
HRV parameters showed promise, with lower very low frequency power measures consistently predicting transfusion requirements.
This analysis included data from 269 trauma patients, emphasizing the need for informed transfusion strategies during resuscitation.

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Implication

Retrospective cohort study identifies key predictors of RBC transfusion needs in trauma, highlighting machine learning's role in decision-making.

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Cite This Study

Gopalakrishnan et al. (2025) studied this question.

synapsesocial.com/papers/68d473a631b076d99fa6c015 https://doi.org/10.1097/cce.0000000000001319

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Keep reading

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

1Is heart rate variability better than routine vital signs for prehospital identification of major hemorrhage?cohort · 2014 · 16 citations
2Utilizing machine learning to establish a predictive model for transfusion requirements in patients with severe trauma: A comprehensive analysisOriginal Research · 2026 · 2 citations
3Early prediction of transfusion requirements in trauma patients using explainable machine learningCohort Study · 2026 · 1 citations
4Heart Rate Variability Is an Independent Predictor of Morbidity and Mortality in Hemodynamically Stable Trauma Patientscohort · 2011 · 67 citations
5Predictors of Prehospital Transfusion in Paediatric Trauma: A Retrospective Analysis of 11,849 Cases from the TraumaRegister DGUⓇ2025

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