Machine learning approach to stratify complex heterogeneity of chronic heart failure: A report from the CHART‐2 study

Abstract

Abstract Aims Current approaches to classify chronic heart failure (HF) subpopulations may be limited due to the diversity of pathophysiology and co‐morbidities in chronic HF. We aimed to elucidate the clusters of chronic patients with HF by data‐driven approaches with machine learning in a hospital‐based registry. Methods and results A total of 4649 patients with a broad spectrum of left ventricular ejection fraction (LVEF) in the CHART‐2 (Chronic Heart Failure Analysis and Registry in the Tohoku District‐2) study were enrolled to this study. Chronic HF patients were classified using random forest clustering with 56 multiscale clinical parameters. We assessed the influence of the clusters on cardiovascular death, non‐cardiovascular death, all‐cause death, and free from hospitalization by HF. Latent class analysis using random forest clustering identified 10 clusters with four primary components: cardiac function (LVEF, left atrial and ventricular diameters, diastolic blood pressure, and brain natriuretic peptide), renal function (glomerular filtration rate and blood urea nitrogen), anaemia (red blood cell, haematocrit, haemoglobin, and platelet count), and nutrition (albumin and body mass index). All 11 significant clinical parameters in the four primary components and two disease aetiologies (ischaemic heart disease and valvular heart disease) showed statistically significant differences among the 10 clusters (P 111.3 pg/mL, 0.9%) and lowest left atrial diameter (>42 mm, 37.4%), showed the best 5 year survival rate of 98.1% for cardiovascular death, 95.9% for non‐cardiovascular death, 92.9% for all‐cause death, and 91.7% for free from hospitalization by HF. Cluster 10 (6.0% of the total), which is co‐morbid disorders of all four primary components, showed the worst survival rate of 39.1% for cardiovascular death, 68.9% for non‐cardiovascular death, 23.9% for all‐cause death, and 28.1% for free from hospitalization by HF. Conclusions These results suggest the potential applicability of the machine leaning approach, providing useful clinical prognostic information to stratify complex heterogeneity in patients with HF.

Keywords

• Heart failure
• Cohort study
• Clustering
• Machine learning
• Prognosis