Construction and evaluation of prognostic models of ECMO in elderly patients with cardiogenic shock based on BP neural network, random forest, and decision tree.

Zucong Mo, Zheng Lu, Xiaogang Tang, Xuezhen Lin, Shuangquan Wang, Yunli Zhang, Zhai Huang

Abstract read

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Article in American journal of translational research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed

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3 · Its place in the literature

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2 citing papers in PubMed.

Integrating bibliometric analysis and external validation of the SAVE score in VA-ECMO for refractory cardiogenic shock: insights from global trends and the MIMIC-IV database.Journal of thoracic disease · 2026
Article
Revolutionizing Cardiology through Artificial Intelligence-Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment-A Comprehensive Review of the Past 5 Years.Diagnostics (Basel, Switzerland) · 2024
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5 · Who and what money

Authors and funding

7 authors.

Zucong MoSector II of Intensive Care Department, The People's Hospital of Guangxi Zhuang Autonomous Region Nanning 530021, Guangxi, China.

Zheng LuSector II of Intensive Care Department, The People's Hospital of Guangxi Zhuang Autonomous Region Nanning 530021, Guangxi, China.

Xiaogang TangSector II of Intensive Care Department, The People's Hospital of Guangxi Zhuang Autonomous Region Nanning 530021, Guangxi, China.

Xuezhen LinSector II of Intensive Care Department, The People's Hospital of Guangxi Zhuang Autonomous Region Nanning 530021, Guangxi, China.

Shuangquan WangSector II of Intensive Care Department, The People's Hospital of Guangxi Zhuang Autonomous Region Nanning 530021, Guangxi, China.

Yunli ZhangSector II of Intensive Care Department, The People's Hospital of Guangxi Zhuang Autonomous Region Nanning 530021, Guangxi, China.

Zhai HuangSector II of Intensive Care Department, The People's Hospital of Guangxi Zhuang Autonomous Region Nanning 530021, Guangxi, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo analyze the predictive effect of a back propagation (BP) neural network, random forest (RF) and decision tree model on the prognosis of elderly patients with cardiogenic shock after extracorporeal membrane oxygenation (ECMO).

methodsThis is a retrospective analysis of the clinical data of elderly patients with cardiogenic shock (258 cases) who underwent ECMO in People's Hospital of Guangxi Zhuang Autonomous Region from January 2016 to January 2022. All patients were followed up for 6 months after ECMO treatment. The prognosis was evaluated, and the prognostic factors were analyzed. BP neural network, RF and decision tree were used to establish predictive models, and the predictive performance of the models was evaluated.

resultsAmong the 258 elderly patients with cardiogenic shock, 52 (20.16%) died 6 months after the ECMO treatment. Based on BP neural network, RF, and decision tree, predictive models for the prognosis and death of elderly patients with cardiogenic shock were constructed. A test set was used to predict the performance of the three models. The results showed that the predictive performances of the three models were all more than 80.00%. The accuracy, sensitivity, and specificity of the RF model were 0.987, 1.000, and 0.929 respectively, which were higher than those of the decision tree model. The area under the receiver operating characteristic curve (AUC) of the RF model was 1.000, which was higher than 0.916 for the decision tree model. DeLong test showed that there was a significant difference in the AUC of the RF model compared to the decision tree test set (D=-2.063,

conclusionThe predictive performance is good in all the three models, which have a high application value for prognosis of ECMO in elderly patients with cardiogenic shock. In clinical practice, predictive models should be selected according to the actual situation, so clinicians and patients can make decisions.

Indexed as

BP neural networkCardiogenic shockdecision tree modelextracorporeal membrane oxygenationprognosisrandom forest

Identifiers

PMID37560218

PMCPMC10408512

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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.