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

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

JOURNAL=Frontiers in Cardiovascular Medicine

VOLUME=Volume 9 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2022.962992

DOI=10.3389/fcvm.2022.962992

ISSN=2297-055X

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 multi-dimensional data of clinical features and outcomes to provide individualized care for LCOS patients.
Methods: The electronic medical information of the intensive care units (ICU) 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 clinical outcomes of these subgroups were compared.
Results: Total of 1205 patients were included and divided into three clusters. Cluster 1 (n=443) was defined as low-risk group (Hospital mortality=10.1%, OR=1). Cluster 2 (n=396) was defined as medium-risk group (Hospital mortality=25.0%, OR=2.96 (95%CI = 1.97-4.46)). Cluster 3 (n=366) was defined as high-risk group (Hospital mortality=39.2%, OR=5.75 (95%CI = 3.9-8.5)).
Conclusions: Patients with LCOS after cardiac surgery could be divided into three clusters and had different outcomes.