Evidence map›Paper›PMID 40949133›Full record

ArticleFrontiers in pharmacology2025

Investigating the feasibility of machine learning to guide personalized red blood cell (RBC) transfusion: analyzing the heterogeneity of RBC transfusion in septic patients with hemoglobin levels of 7-9 g/dL based on the causal forest model.

Penglei Yang, Jun Yuan, Jie He, Lina Yu, Xue Gu, Xizhen Ding, Qihong Chen

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Article in Frontiers in pharmacology, 2025. 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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7 authors.

Penglei YangDepartment of Critical Care Medicine, Jiangdu People's Hospital Affiliated to Yangzhou University, Yangzhou, China.
Jun YuanDepartment of Critical Care Medicine, Jiangdu People's Hospital Affiliated to Yangzhou University, Yangzhou, China.
Jie HeDepartment of Emergency Medicine, Yangzhou Jiangdu Traditional Chinese Medicine Hospital, Yangzhou, China.
Lina YuDepartment of Critical Care Medicine, Jiangdu People's Hospital Affiliated to Yangzhou University, Yangzhou, China.
Xue GuDepartment of Critical Care Medicine, Jiangdu People's Hospital Affiliated to Yangzhou University, Yangzhou, China.
Xizhen DingDepartment of Critical Care Medicine, Jiangdu People's Hospital Affiliated to Yangzhou University, Yangzhou, China.
Qihong ChenDepartment of Critical Care Medicine, Jiangdu People's Hospital Affiliated to Yangzhou University, Yangzhou, China.

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6 · The paper itself

Abstract

Background: This study utilized the causal forest algorithm to explore the heterogeneity of treatment effects of low-dose red blood cell (RBC) transfusion on the 90-day survival rate of sepsis patients with hemoglobin (Hb) levels of 7-9 g/dL to develop personalized transfusion strategies. Methods: The data of patients the met the Sepsis-3 criteria with a minimum Hb level of 7-9 g/dL were obtained from the MIMIC-IV and MIMIC-III databases and divided into RBC transfusion and non-transfusion groups. Patients in both groups were paired using a propensity score matching analysis (PSM) after which a causal forest model was constructed using MIMIC-IV data. The model's accuracy was analyzed using out-of-bag data. Individual treatment effects (ITE) of MIMIC-III patients were predicted and categorized into four subgroups: Quantile1 to Quantile4, based on the effect size. Kaplan-Meier survival curves were established for each Quantile to determine the survival rates. Results: The MIMIC-IV and MIMIC-III database comprised 1,652 and 868 patients, with 826 (50%) and 434 (50%) in the RBC transfusion group, respectively, after PSM. The mean prediction coefficient estimated by the causal forest was 1.00 with a standard error of 0.57, while the differential forest prediction coefficient was 1.64 with a standard error of 0.48, demonstrating the model's ability to effectively identify differences in the impact of transfusion on survival rates among individuals. There was significant heterogeneity in the ITE among patients in the MIMIC-III validation cohort. Moreover, the ITE values were divided into Quantile1: -5.4% (-8.0%, -3.9%), Quantile2: -2.1% (-2.6%, -1.7%), Quantile3: -0.5% (-0.1%, +0.1%), and Quantile 4: +3.6% (+2.0%, +6.6%). The Kaplan-Meier curves and the log-rank test demonstrated that the RBC transfusion decreased the survival of patients in Quantile1 (p < 0.001) and Quantile2 (p = 0.011) but increased the survival of patients in Quantile4 (p < 0.001). Conclusion: RBC transfusions among sepsis patients with Hb levels of 7-9 g/dL exhibit heterogenous treatment effects, which reduces the mortality of patients with high ITE. Although the causal forest model can guide personalized transfusion in these cases, randomized controlled trials are needed to validate these findings.

Indexed as

causal forestheterogeneity analysispersonalized treatmentred blood cell transfusionsepsis

Identifiers

PMID40949133
PMCPMC12423935

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