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. 2023 Aug 29;23(1):426.
doi: 10.1186/s12872-023-03380-y.

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

Li Wang #  1 , Yufeng Zhang #  2 , Renqi Yao #  3   4   5 , Kai Chen #  6 , Qiumeng Xu  7 , Renhong Huang  8 , Zhiguo Mao  9 , Yue Yu  10
Affiliations

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

Li Wang et al. BMC Cardiovasc Disord. .

Abstract

Background: Cardiogenic 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.

Methods: The 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.

Results: The 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).

Conclusions: ML consensus clustering analysis synthesized the pattern of clinical and laboratory data to reveal distinct CS phenotypes with different clinical outcomes.

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

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Flow diagram of patient inclusion and overview of the statistical analysis. Abbreviation: CS: cardiogenic shock; ML: machine learning; eICU: electronic Intensive Care Unit; ICD-9: International Classification of Diseases, Ninth Revision; CDF: cumulative distribution function; CM: consensus matrix; PAC: proportion of ambiguously clustered pairs; SMD, standardized mean differences; LR: logistic regression
Fig. 2
Fig. 2
(A) CDF plot; (B) Delta area plot. Abbreviation: CDF: cumulative distribution function
Fig. 3
Fig. 3
The SMD for each of baseline characteristics across clusters. Abbreviation: SMD: standardized mean differences; BMI: body mass index; CICU: cardiac cardiac intensive care unit; CSICU: cardiac surgery intensive care unit; MICU: medical intensive care unit; SICU: surgery intensive care unit; CCU-CTICU: cardiac care unit-cardiac trauma/surgical intensive care unit; NICU: neuro intensive care unit; CTICU: cardiac trauma intensive care unit; CABG: coronary artery bypass grafting; PCI: percutaneous coronary intervention; COPD: chronic obstructive pulmonary disease; AIDS: acquired immunodeficiency syndrome; SBP: systolic blood pressure; DBP: diastolic blood pressure; MBP: mean blood pressure; SpO2: oxygen saturation measured by pulse oximetry; WBC: white blood cell; RBC: red blood cell; RDW: red blood cell distribution width; BUN: blood urea nitrogen; eGFR: estimated glomerular filtration rate; SIRS: systemic inflammatory response syndrome; SOFA: Sequential Organ Failure Assessment; APS III: acute physiology score III, APACHE IV: Acute Physiology and Chronic Health Evaluation IV; IABP: intraaortic balloon pump; RRT: renal replacement treatment
Fig. 4
Fig. 4
(A) In-hospital mortality, (B) ICU mortality, and (C) Incidence of AKI after admission among different clusters. Abbreviation: AKI: acute kidney injury; ICU:intensive care unit
Fig. 5
Fig. 5
The forest plot of OR (95% CI) for (A) In-hospital mortality, (B) ICU mortality, and (C) Incidence of AKI after admission. Abbreviation: OR: odds ratio; CI: confidence interval; AKI: acute kidney injury; ICU:intensive care unit
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