Abstract
Background: Stratified medicine may enable the development of effective treatments for particular groups of patients with heart failure with preserved ejection fraction (HFpEF); however, the heterogeneity of this syndrome makes it difficult to group patients together by common disease features. The aim of the present study was to find new subgroups of HFpEF using machine learning.Methods: K-means clustering was used to stratify patients with HFpEF. We retrospectively enrolled 350 outpatients with HFpEF. Their clinical characteristics, blood sample test results and hemodynamic parameters assessed by echocardiography, electrocardiography and jugular venous pulse, and clinical outcomes were applied to k-means clustering. The optimal k was detected using Hartigan's rule.Results: HFpEF was stratified into four groups. The characteristic feature in group 1 was left ventricular relaxation abnormality. Compared with group 1, patients in groups 2, 3, and 4 had a high mean mitral E/e′ ratio. The estimated glomerular filtration rate was lower in group 2 than in group 3 (median 51 ml/min/1.73 m2 vs. 63 ml/min/1.73 m2 p < 0.05). The prevalence of less-distensible right ventricle and atrial fibrillation was higher, and the deceleration time of mitral inflow was shorter in group 3 than in group 2 (93 vs. 22% p < 0.05, 95 vs. 1% p < 0.05, and median 167 vs. 223 ms p < 0.05, respectively). Group 4 was characterized by older age (median 85 years) and had a high systolic pulmonary arterial pressure (median 37 mmHg), less-distensible right ventricle (89%) and renal dysfunction (median 54 ml/min/1.73 m2). Compared with group 1, group 4 exhibited the highest risk of the cardiac events (hazard ratio [HR]: 19; 95% confidence interval [CI] 8.9–41); group 2 and 3 demonstrated similar rates of cardiac events (group 2 HR: 5.1; 95% CI 2.2–12; group 3 HR: 3.7; 95%CI, 1.3–10). The event-free rates were the lowest in group 4 (p for trend < 0.001).Conclusions: K-means clustering divided HFpEF into 4 groups. Older patients with HFpEF may suffer from complication of RV afterload mismatch and renal dysfunction. Our study may be useful for stratified medicine for HFpEF.
Highlights
The rate of heart failure with preserved ejection fraction (HFpEF) increases with age, reaching 50% or higher in patients with heart failure [1]
Using several machine-learning algorithms, previous studies clarified the phenotypes and therapeutic strategies for HFpEF; the features of heart failure with mid-range ejection fraction may influence the features of unknown phenotypes and RV diastolic function was not taught in previous studies [9,10,11,12,13]
HFpEF was stratified into four groups by k-means clustering
Summary
The rate of heart failure with preserved ejection fraction (HFpEF) increases with age, reaching 50% or higher in patients with heart failure [1]. We reported that the rate of less-distensible right ventricle assessed by jugular venous pulse increased with age and was risk factor for cardiac events of HFpEF [17, 18]. If this feature is taught in machine learning, a new important subgroup may be found. Stratified medicine may enable the development of effective treatments for particular groups of patients with heart failure with preserved ejection fraction (HFpEF); the heterogeneity of this syndrome makes it difficult to group patients together by common disease features. The aim of the present study was to find new subgroups of HFpEF using machine learning
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