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Impact of post-transfusion hemoglobin levels on survival in critically Ill patients: a machine learning–based causal inference analysis
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  • Published: 25 April 2026

Impact of post-transfusion hemoglobin levels on survival in critically Ill patients: a machine learning–based causal inference analysis

  • Min Woo Kang1,
  • Soojeong Yun1,
  • Seung Min Song1,
  • Ji Eun Kim1,2,
  • Hyo Jin Kim1,
  • Eun Jung Cho1,
  • Young Joo Kwon1,2 &
  • …
  • Shin Young Ahn3 

Scientific Reports , Article number:  (2026) Cite this article

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Subjects

  • Biomarkers
  • Cardiology
  • Diseases
  • Medical research
  • Risk factors

Abstract

Restrictive red blood cell transfusion strategies (hemoglobin trigger 7.0–8.0 g/dL) are well established, yet existing evidence addresses primarily when to initiate transfusion. The optimal achieved post-transfusion hemoglobin level in critically ill patients with severe anemia (pre-transfusion hemoglobin ≤ 8.0 g/dL) remains unknown. We analyzed 3,710 intensive care unit admissions in the Medical Information Mart for Intensive Care IV (MIMIC-IV; 3,339 training, 371 test) and externally validated findings in 4,719 admissions from the eICU Collaborative Research Database. Eligible subjects received red blood cells within 48 h of ICU admission, had a pre-transfusion hemoglobin ≤ 8.0 g/dL, and had no additional transfusions between 48 h and seven days. Using a double machine learning causal forest, we estimated treatment effects for post-transfusion hemoglobin targets from 8.0 to 14.0 g/dL relative to 8.0 g/dL, identified the target with the lowest predicted in-hospital mortality, evaluated subgroups, and modeled predictors of targets below 10.0 g/dL. Dose–response curves were U-shaped in all datasets. The estimated optimal hemoglobin was 11.0 g/dL in the training set, 10.9 g/dL in the test set, and 11.0 g/dL in external validation. Estimated optimal levels near 11.0 g/dL were consistent across chronic kidney disease, end-stage kidney disease, myocardial infarction, congestive heart failure, hypertension, and diabetes. Lower baseline hemoglobin and lower estimated glomerular filtration rate were associated with lower individualized optimal levels. Indicators of inflammation or hemodynamic instability, including hypotension, fever, leukocytosis, hyperlactatemia, acidosis, and vasopressor requirements, were associated with estimated optimal achieved post-transfusion hemoglobin levels below 10.0 g/dL. Among ICU subjects transfused under a restrictive trigger (hemoglobin ≤ 8.0 g/dL), an achieved post-transfusion hemoglobin around 11.0 g/dL was associated with the lowest predicted in-hospital mortality.

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Acknowledgements

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No funding was received for this study.

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Authors and Affiliations

  1. Department of Internal Medicine, Korea University Guro Hospital, Seoul, Korea

    Min Woo Kang, Soojeong Yun, Seung Min Song, Ji Eun Kim, Hyo Jin Kim, Eun Jung Cho & Young Joo Kwon

  2. Department of Internal Medicine, Korea University College of Medicine, Seoul, Korea

    Ji Eun Kim & Young Joo Kwon

  3. Department of Internal Medicine Hospital Medical Center, Seoul National University Bundang Hospital, 82, Gumi-ro 173 Beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do, 13620, Korea

    Shin Young Ahn

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  1. Min Woo Kang
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Corresponding author

Correspondence to Shin Young Ahn.

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The authors declare no competing interests.

Ethical Approval and Consent to participate

This study was a secondary analysis of de-identified, publicly available data and was exempt from review by the Institutional Review Board of Korea University Guro Hospital (No. K2025-1397-001). As all data are anonymized and freely accessible, individual informed consent was not required

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Cite this article

Kang, M.W., Yun, S., Song, S.M. et al. Impact of post-transfusion hemoglobin levels on survival in critically Ill patients: a machine learning–based causal inference analysis. Sci Rep (2026). https://doi.org/10.1038/s41598-026-50363-y

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  • Received: 05 February 2026

  • Accepted: 21 April 2026

  • Published: 25 April 2026

  • DOI: https://doi.org/10.1038/s41598-026-50363-y

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Keywords

  • Red blood cell transfusion
  • Hemoglobin targets
  • Intensive care unit
  • Causal inference
  • Machine learning
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