Different Pathophysiology and Outcomes of Heart Failure With Preserved Ejection Fraction Stratified by K-Means Clustering

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.

Keywords: K-means clustering; artificial intelligence; cardio renal syndrome; heart failure with preserved ejection fraction; machine learning; right ventricular distensibility; stratified medicine; systolic pulmonary arterial pressure (SPAP).

Figure 1

Study flowchart. BNP, brain natriuretic peptide; eGFR, estimated glomerular filtration rate; HFpEF, heart failure with preserved ejection fraction; Hgb, hemoglobin; LAVI, left atrial volume index; LVEF, left ventricular ejection fraction; TAPSE, tricuspid annular plane systolic excursion; TR, tricuspid regurgitant. [adapted from Figure 1 in (20)].

Figure 2

Results of Hartigan's rule.

Figure 3

Visualization of the results of k-means clustering. Proportions of variance of principle components 1 and 2 were 15.6 and 6.8%, respectively. PC, principle component; +, group 1; △, group 2; ×, group 3; ○, group 4.

Figure 4

Kaplan–Meier curves for event-free rates according to the results of k-means clustering. *, comparison between groups 1 and 2, p < 0.05; #, comparison between groups 1 and 3, p < 0.05; +, comparison between groups 1 and 4, p < 0.05; !, comparison between groups 2 and 4, p < 0.05; ¶, comparison between groups 3 and 4, p < 0.05.