Mechanical Circulatory Support for Cardiogenic Shock in the Critically Ill

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

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.

Left Ventricular Unloading during Extracorporeal Life Support: Current Practice

Veno-arterial extracorporeal life support (VA-ECLS) is a powerful tool that can provide complete cardiopulmonary support for patients with refractory cardiogenic shock. However, VA-ECLS increases left ventricular afterload resulting in greater myocardial oxygen demand, which can impair myocardial recovery and worsen pulmonary edema. These complications can be ameliorated by various LV venting strategies to unload the LV. Evidence suggests that LV venting improves outcomes in VA-ECLS, but there is a paucity of randomized trials to help guide optimal strategy and the timing of venting. In this review, we discuss the available evidence regarding LV venting in VA-ECLS, explain important hemodynamic principles involved, and propose a practical approach to LV venting in VA-ECLS.