Evidence map›Paper›PMID 42034694›Full record

ArticleScientific reports2026

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

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

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Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Min Woo KangDepartment of Internal Medicine, Korea University Guro Hospital, Seoul, Korea.
Soojeong YunDepartment of Internal Medicine, Korea University Guro Hospital, Seoul, Korea.
Seung Min SongDepartment of Internal Medicine, Korea University Guro Hospital, Seoul, Korea.
Ji Eun KimDepartment of Internal Medicine, Korea University Guro Hospital, Seoul, Korea.
Hyo Jin KimDepartment of Internal Medicine, Korea University Guro Hospital, Seoul, Korea.
Eun Jung ChoDepartment of Internal Medicine, Korea University Guro Hospital, Seoul, Korea.
Young Joo KwonDepartment of Internal Medicine, Korea University Guro Hospital, Seoul, Korea.
Shin Young AhnDepartment 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. ahnshinyoung712@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

AnemiaCritical IllnessErythrocyte TransfusionHemoglobinsMachine LearningAgedFemaleHospital MortalityHumansIntensive Care UnitsMaleMiddle AgedHemoglobinsCausal inferenceHemoglobin targetsIntensive care unitMachine learningRed blood cell transfusion

Identifiers

PMID42034694
PMCPMC13284187

What Socratic holds

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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 Socratic graph.