Identification of Distinct Clinical Phenotypes of Cardiogenic Shock Using Machine Learning Consensus Clustering Approach

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.

Integration of artificial intelligence in clinical laboratory medicine: Advancements and challenges

“…The model incorporated 13 laboratory parameters (calcium, carbon dioxide, creatinine, creatine kinase-MB, HGB, glucose, mean corpuscular volume [MCV], mean corpuscular HGB concentration, platelets, potassium, RDW, sodium, and leukocytes) to forecast disease severity and mortality, revealing a nonlinear correlation between calcium and HGB and post-infarction mortality in patients. Wang et al 98 employed unsupervised ML consensus clustering techniques, primarily incorporating the following parameters: blood pressure (systolic, mean, and diastolic), renal function (creatinine, blood urea nitrogen, and estimated glomerular filtration rate), electrolytes and acid-base compounds (potassium and bicarbonate), liver function (albumin), indicators related to red blood cells (red blood cell count and red cell distribution width), as well as various scoring systems (SOFA, APS III, and APACHE IV) for the prediction and evaluation of cardiogenic shock. In exploring the best predictive model for heart failure, Li et al 50 assessed seven ML algorithms and identified XGBoost as the superior algorithm.…”