Identifying distinct clinical clusters in heart failure with mildly reduced ejection fraction

Unsupervised Cluster Analysis in Patients with Cardiorenal Syndromes: Identifying Vascular Aspects

Background/Objectives: Cardiorenal syndrome (CRS) is a disorder of the heart and kidneys, with one type of organ dysfunction affecting the other. The pathophysiology is complex, and its actual description has been questioned. We used clustering analysis to identify clinically relevant phenogroups among patients with CRS. Methods: Data for patients admitted from 1 January 2012 to 31 December 2012 were collected from the French national medico-administrative database. Patients with a diagnosis of heart failure and chronic kidney disease and at least 5 years of follow-up were included. Results: In total, 13,665 patients were included and four clusters were identified. Cluster 1 could be described as the vascular-diabetes cluster. It comprised 1930 patients (14.1%), among which 60% had diabetes, 94% had coronary artery disease (CAD), and 80% had peripheral artery disease (PAD). Cluster 2 could be described as the vascular cluster. It comprised 2487 patients (18.2%), among which 33% had diabetes, 85% had CAD, and 78% had PAD. Cluster 3 could be described as the metabolic cluster. It comprised 2163 patients (15.8%), among which 87% had diabetes, 67% dyslipidemia, and 62% obesity. Cluster 4 comprised 7085 patients (51.8%) and could be described as the low-vascular cluster. The vascular cluster was the only one associated with a higher risk of cardiovascular death (HR: 1.48 [1.32-1.66]). The metabolic cluster was associated with a higher risk of kidney replacement therapy (HR: 1.33 [1.17-1.51]). Conclusions: Our study supports a new classification of CRS based on the vascular aspect of pathophysiology differentiating microvascular or macrovascular lesions. These results could have an impact on patients' medical treatment.

Deep embedded clustering generalisability and adaptation for integrating mixed datatypes: two critical care cohorts

We validated a Deep Embedded Clustering (DEC) model and its adaptation for integrating mixed datatypes (in this study, numerical and categorical variables). Deep Embedded Clustering (DEC) is a promising technique capable of managing extensive sets of variables and non-linear relationships. Nevertheless, DEC cannot adequately handle mixed datatypes. Therefore, we adapted DEC by replacing the autoencoder with an X-shaped variational autoencoder (XVAE) and optimising hyperparameters for cluster stability. We call this model "X-DEC". We compared DEC and X-DEC by reproducing a previous study that used DEC to identify clusters in a population of intensive care patients. We assessed internal validity based on cluster stability on the development dataset. Since generalisability of clustering models has insufficiently been validated on external populations, we assessed external validity by investigating cluster generalisability onto an external validation dataset. We concluded that both DEC and X-DEC resulted in clinically recognisable and generalisable clusters, but X-DEC produced much more stable clusters.