机器学习方法识别出了低心排血量综合征患者的聚类以及心脏手术后的结局。

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

作者信息

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

机构信息

Department of Pharmaceutical Sciences, Institute of Clinical Pharmacology, Sun Yat-sen University, Guangzhou, China.

Laboratory of South China Structural Heart Disease, Department of Intensive Care Unit of Cardiovascular Suregery, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangzhou, China.

出版信息

Front Cardiovasc Med. 2022 Aug 18;9:962992. doi: 10.3389/fcvm.2022.962992. eCollection 2022.

DOI:10.3389/fcvm.2022.962992

PMID:36061544

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9434347/

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 ( = 443) was defined as the low-risk group [in-hospital mortality =10.1%, odds ratio (OR) = 1]. Cluster 2 ( = 396) was defined as the medium-risk group [in-hospital mortality =25.0%, OR = 2.96 (95% CI = 1.97-4.46)]. Cluster 3 ( = 366) was defined as the high-risk group [in-hospital mortality =39.2%, OR = 5.75 (95% CI = 3.9-8.5)].

CONCLUSION

Patients with LCOS after cardiac surgery could be divided into three clusters and had different outcomes.

摘要

背景

低心排血量综合征（LCOS）是心脏手术后患者最严重的生理异常，死亡率很高。本研究旨在探索临床特征和结局的多维度数据，为LCOS患者提供个性化护理。

方法

从中国南方一家三级医院提取重症监护病房（ICU）的电子医疗信息。我们纳入了ICU数据库中诊断为LCOS的患者。我们使用基于患者特征、实验室数据和生命体征的一致性聚类方法来识别LCOS亚组。一致性聚类方法涉及从一组项目（如微阵列）中进行子采样，并确定指定聚类数（k）的聚类。主要临床结局是住院死亡率，并在各聚类之间进行比较。

结果

共纳入1205例患者，分为三个聚类。聚类1（n = 443）被定义为低风险组[住院死亡率=10.1%，比值比（OR）=1]。聚类2（n = 396）被定义为中风险组[住院死亡率=25.0%，OR = 2.96（95%CI = 1.97 - 4.46）]。聚类3（n = 366）被定义为高风险组[住院死亡率=39.2%，OR = 5.75（95%CI = 3.9 - 8.5）]。