Evidence map›Paper›PMID 39068918›Full record

ArticleCardiology2025

Evaluation of the Factors Influencing Blood Transfusion during Minimally Invasive Direct Coronary Artery Bypass Surgery.

Zhenmin Sun, Zhongqi Cui, Yan Xie, Lei Wang, Zhengqian Li, Xiaoyu Yang, Xiaoqing Zhang, Jun Wang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cardiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Zhenmin SunDepartment of Transfusion, Peking University Third Hospital, Beijing, China.
Zhongqi CuiDepartment of Cardiac Surgery, Peking University Third Hospital, Beijing, China.
Yan XieHealSci Technology Co., Ltd., Beijing, China.
Lei WangHealSci Technology Co., Ltd., Beijing, China.
Zhengqian LiDepartment of Anesthesiology, Peking University Third Hospital, Beijing, China.
Xiaoyu YangThe Information Management and Big Data Center, Peking University Third Hospital, Beijing, China.
Xiaoqing ZhangDepartment of Transfusion, Peking University Third Hospital, Beijing, China.
Jun WangDepartment of Transfusion, Peking University Third Hospital, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionThe objective of this study was to analyze the blood transfusion factors of minimally invasive direct coronary artery bypass (MIDCAB) surgery using artificial intelligence.

methodsA retrospective analysis was performed for patients undergoing MIDCAB operations and no heart-lung machine was used from January 2017 to September 2022 in our hospital. The influencing factors of blood transfusion were used to build the artificial intelligence model. Eighty percent of the database was used as the training set, and twenty percent database was used as the testing set. To predict whether to use red blood cells during operation, we compared 104 artificial intelligence models. We aimed to assess whether which factors influence allogeneic transfusion in MIDCAB operations.

resultsOf the 104 machine learning algorithms, the XGBoost model delivered the best performance, with an AUC of 0.726 in the testing set and an accuracy of 0.854 in the testing set. The artificial intelligence model showed preoperative hemoglobin less than 120 g/L, prothrombin time greater than 13.75, body mass index less than 22.7 kg/m2, coronary heart disease with additional comorbidities, a history of percutaneous coronary intervention, weight lower than 67 kg were the six major risk factors of allogeneic transfusion.

conclusionThe XGBoost model can predict transfusion or not transfusion in MIDCBA surgery with high accuracy.

introductionThe objective of this study was to analyze the blood transfusion factors of minimally invasive direct coronary artery bypass (MIDCAB) surgery using artificial intelligence.

methodsA retrospective analysis was performed for patients undergoing MIDCAB operations and no heart-lung machine was used from January 2017 to September 2022 in our hospital. The influencing factors of blood transfusion were used to build the artificial intelligence model. Eighty percent of the database was used as the training set, and twenty percent database was used as the testing set. To predict whether to use red blood cells during operation, we compared 104 artificial intelligence models. We aimed to assess whether which factors influence allogeneic transfusion in MIDCAB operations.

resultsOf the 104 machine learning algorithms, the XGBoost model delivered the best performance, with an AUC of 0.726 in the testing set and an accuracy of 0.854 in the testing set. The artificial intelligence model showed preoperative hemoglobin less than 120 g/L, prothrombin time greater than 13.75, body mass index less than 22.7 kg/m2, coronary heart disease with additional comorbidities, a history of percutaneous coronary intervention, weight lower than 67 kg were the six major risk factors of allogeneic transfusion.

conclusionThe XGBoost model can predict transfusion or not transfusion in MIDCBA surgery with high accuracy.

Indexed as

Blood TransfusionCoronary Artery BypassCoronary Artery DiseaseMachine LearningMinimally Invasive Surgical ProceduresAgedArtificial IntelligenceFemaleHumansMaleMiddle AgedRetrospective StudiesRisk FactorsAllogeneic transfusionArtificial intelligenceMinimally invasive direct coronary artery bypassPrediction model

Identifiers

PMID39068918
PMCPMC11797936

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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 OpenQuestion graph.