Evidence map›Paper›PMID 36061544›Full record

ArticleFrontiers in cardiovascular medicine2022

Machine learning approach identified clusters for patients with low cardiac output syndrome and outcomes after cardiac surgery.

Xu Zhao, Bowen Gu, Qiuying Li, Jiaxin Li, Weiwei Zeng, Yagang Li, Yanping Guan, Min Huang, Liming Lei, Guoping Zhong

Abstract read
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Article in Frontiers in cardiovascular medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

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4citing papers in PubMed
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3 · Its place in the literature

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4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Xu ZhaoDepartment of Pharmaceutical Sciences, Institute of Clinical Pharmacology, Sun Yat-sen University, Guangzhou, China.
Bowen GuLaboratory of South China Structural Heart Disease, Department of Intensive Care Unit of Cardiovascular Suregery, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangzhou, China.
Qiuying LiLaboratory of South China Structural Heart Disease, Department of Intensive Care Unit of Cardiovascular Suregery, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangzhou, China.
Jiaxin LiLaboratory of South China Structural Heart Disease, Department of Intensive Care Unit of Cardiovascular Suregery, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangzhou, China.
Weiwei ZengDepartment of Pharmacy, The Second People's Hospital of Longgang District, Shenzhen, China.
Yagang LiDepartment of Pharmaceutical Sciences, Institute of Clinical Pharmacology, Sun Yat-sen University, Guangzhou, China.
Yanping GuanDepartment of Pharmaceutical Sciences, Institute of Clinical Pharmacology, Sun Yat-sen University, Guangzhou, China.
Min HuangDepartment of Pharmaceutical Sciences, Institute of Clinical Pharmacology, Sun Yat-sen University, Guangzhou, China.
Liming LeiLaboratory of South China Structural Heart Disease, Department of Intensive Care Unit of Cardiovascular Suregery, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangzhou, China.
Guoping ZhongDepartment of Pharmaceutical Sciences, Institute of Clinical Pharmacology, Sun Yat-sen University, Guangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Low cardiac output syndrome (LCOS) is the most serious physiological abnormality with high mortality for patients after cardiac surgery. This study aimed to explore the multidimensional data of clinical features and outcomes to provide individualized care for patients with LCOS. Methods: The electronic medical information of the intensive care units (ICUs) was extracted from a tertiary hospital in South China. We included patients who were diagnosed with LCOS in the ICU database. We used the consensus clustering approach based on patient characteristics, laboratory data, and vital signs to identify LCOS subgroups. The consensus clustering method involves subsampling from a set of items, such as microarrays, and determines to cluster of specified cluster counts (k). The primary clinical outcome was in-hospital mortality and was compared between the clusters. Results: A total of 1,205 patients were included and divided into three clusters. Cluster 1 ( Conclusion: Patients with LCOS after cardiac surgery could be divided into three clusters and had different outcomes.

Indexed as

artificial intelligenceconsensus clusteringindividualized medicineintensive care unitlow cardiac output syndromemachine learning

Identifiers

PMID36061544
PMCPMC9434347

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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.