Identification of distinct clinical phenotypes of cardiogenic shock using machine learning consensus clustering approach

BMC Cardiovascular Disorders

Research

Yue Yu¹  Yufeng Zhang¹  Renhong Huang²  Li Wang³  Zhiguo Mao³  Qiumeng Xu⁴  Kai Chen⁵  Renqi Yao⁶ 

[1] Department of Cardiothoracic Surgery, Changzheng Hospital, Naval Medical University, Shanghai, China;Department of General Surgery, Comprehensive Breast Health Center, Ruijin Hospital, Jiaotong University School of Medicine, Shanghai, China;Department of Nephrology, Changzheng Hospital, Naval Medical University, Shanghai, China;Department of Orthopaedics, Changzheng Hospital, Naval Medical University, Shanghai, China;Department of Orthopedics, Changhai Hospital, Naval Medical University, Shanghai, China;Translational Medicine Research Center, Fourth Medical Center and Medical Innovation Research Division of the Chinese PLA General Hospital, Beijing, China;Department of Burn Surgery, Changhai Hospital, Naval Medical University, Shanghai, China;Research Unit of key techniques for treatment of burns and combined burns and trauma injury, Chinese Academy of Medical Sciences, Shanghai, China;

关键词: Acute kidney injury;    Artificial intelligence;    Cardiogenic shock;    Cluster;    Intensive care unit;    Machine learning;    Mortality;    Phenotype;   

DOI  :  10.1186/s12872-023-03380-y

 received in 2022-11-14, accepted in 2023-07-05,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundCardiogenic shock (CS) is a complex state with many underlying causes and associated outcomes. It is still difficult to differentiate between various CS phenotypes. We investigated if the CS phenotypes with distinctive clinical profiles and prognoses might be found using the machine learning (ML) consensus clustering approach.MethodsThe current study included patients who were diagnosed with CS at the time of admission from the electronic ICU (eICU) Collaborative Research Database. Among 21,925 patients with CS, an unsupervised ML consensus clustering analysis was conducted. The optimal number of clusters was identified by means of the consensus matrix (CM) heat map, cumulative distribution function (CDF), cluster-consensus plots, and the proportion of ambiguously clustered pairs (PAC) analysis. We calculated the standardized mean difference (SMD) of each variable and used the cutoff of ± 0.3 to identify each cluster’s key features. We examined the relationship between the phenotypes and several clinical endpoints utilizing logistic regression (LR) analysis.ResultsThe consensus cluster analysis identified two clusters (Cluster 1: n = 9,848; Cluster 2: n = 12,077). The key features of patients in Cluster 1, compared with Cluster 2, included: lower blood pressure, lower eGFR (estimated glomerular filtration rate), higher BUN (blood urea nitrogen), higher creatinine, lower albumin, higher potassium, lower bicarbonate, lower red blood cell (RBC), higher red blood cell distribution width (RDW), higher SOFA score, higher APS III score, and higher APACHE IV score on admission. The results of LR analysis showed that the Cluster 2 was associated with lower in-hospital mortality (odds ratio [OR]: 0.374; 95% confidence interval [CI]: 0.347–0.402; P < 0.001), ICU mortality (OR: 0.349; 95% CI: 0.318–0.382; P < 0.001), and the incidence of acute kidney injury (AKI) after admission (OR: 0.478; 95% CI: 0.452–0.505; P < 0.001).ConclusionsML consensus clustering analysis synthesized the pattern of clinical and laboratory data to reveal distinct CS phenotypes with different clinical outcomes.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

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